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The notable advantage of data science over mainstream statistical practices is that it can extract insights and build accurate predictions from the junk of irrelevant data. That’s how sports analysts and team selectors in cricket and football could find the most accurate KPIs and build a team that can be regarded as the best suitable option against the rival teams.

To build such intelligent models, data scientists are trained and employed to produce solutions in machine learning data models. This blog post reveals the crucial steps the data science project manager and all the stakeholders need to go through to deal with complex data science projects.

Defining Data Science

To free from any confusion or misconception, it’s essential to clear your perspective about data science and its related methodologies. Data science itself is complicated to explain in an absolute frame of reference. If you ask different Data Science experts about the absolute definition of the theme, you will be bombarded with aspects and ideologies of varying degrees.

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The more straightforward way to define Data science is a combination of different fields of mathematics and computer science, specifically statistics and programming that deals with the practices of extracting insights and hidden patterns from lengthy datasets. Since the dependence of businesses on data increases at a high pace, the need to grasp the concept of data science and its related methodologies is the primary step to solve most of the data-related problems.

Data science and its related concepts

Despite being a technical methodology, managing a data science project is not a purely technical routine. It is a combination of both hard and soft skills. Like any other project, a data science project needs a roadmap that will guide you along the way of problem-solving. The Following rules will lead you to successful project completion while allowing you to save your valuable resources, time, and money.

Data Science Project Management

The Data Science Project involves a lifecycle to ensure the timely delivery of data science products or services. Like Agile and Scrum methodology, it is a non-linear, repetitive process richly inhabited with the critical queries, updates, and iterations based on further research and exposure to unseen depths of data as more information becomes available.

Let’s have a deeper look into how these steps allow you to handle your data science project efficiently.

Step 1: Frame the problem

Every data science project should be initialized to understand business workflows and their related challenges or hurdles. In this segment, all the stakeholders on board play a vital role in defining the problems and objectives. Even a negligible aspect of the business model should be taken into consideration to avoid any trouble later. The destiny of your project is determined at this level, making this step one of the toughest and crucial ones. But when carried out correctly, this phase can promise you a lot of savings in energy, cost, and time.

Ask business owners what they are trying to acquire. Being an analyst, the biggest challenge you will face here is to translate the blurry and obscure requirements into a well-explained and crystal-clear problem statement. The technical gap between stakeholders and experts can push a lot of communication-related problems in the process.Input from the business owner is essential, but in many cases, not substantial enough, or he/she may not convey his/her thoughts properly. A deeper understanding of business-related problems and technical expertise can help you in filling this gap. Don’t hesitate to learn anything from the sponsors regarding their domain if necessary.

A clear and concise storyline should be the primary outcome at this stage.

Since the framework is repetitive, rolling back to this stage will occur in later stages. Try to form a firm foundational basis of your project that everyone can understand and agrees on.

Step 2: Get the data

Now you can shift your focus to data gathering. You will find structured, unstructured, semi-structured; all types of data useful in different ways. Multiple data sources can cause a lot of confusion and provide space for creativity and alternatives. Potential data sources, including websites, social media, open data, or enterprise data, can be merged into data pools. The data has been identified but is not ready to be used. Before using it for the more significant motive, you need to pass it through several channels, from importing and cleaning to labeling and clustering. Cleaning the data ensures that data is free from all the errors and anomalies and loaded well into the machine. Make sure the data you are using is correct and authentic. Neglecting this crucial aspect can lead to long-term damages. Importing the data is the most time-consuming exercise as it could consume around 70 to 90 percent of the overall project time. If all the data sources are well organized, it can fall to 50 percent. You can also spare your time and effort by automating some processes in data preparation.

Step 3: Explore the data

After ensuring the quality of the collected data content, you can initiate the exploration drive. Observe the organizational efficiency of existing labels, segments, categories, and other distribution formats. Analyze the links and connections in various attributes and entities. Visualization tools may save you most of your time and energy while allowing you to better understand the layout of data content better understand the structure of data content and uncover essential better insights. The first purposeful glance at the data reveals you most of the gaps and misunderstanding that might be overseen in previous steps. Don't hesitate to roll back to the earlier stages, specifically the data collection, to close the gaps.

Example of data visualization tool to discover insights

The most troublesome phase in this step is to test those concepts and processes that are most likely to turn into insights. Revisit the results from the problem framing step to find some essential questions or ideas that will enable you to improve your data exploration drive. Like all the other projects, project duration, timelines, and deadlines will also restrict you from rolling back again and again. It bounds you to reconsider all the scenarios and aspects before revisiting the previous levels. Set your priorities aligned. Once you start observing clean patterns and insights, you are almost ready to model your data. Rethink your conclusions at every single step and filter your perspectives carefully. You are almost there.

Step 4: Model the data

At this critical level, you can perform the practical modeling of the data. Data scientists use a train set to train their machines to perform empirical predictive analysis and decision-making over a test set. A train set is a mighty chunk of the dataset with known output. This stage is highly repetitive. To make data align with the model, data scientists perform multiple adjustments to the parameters to best fit the data. Here fit means adjusting the parameters so that it can be used successfully with unseen data known as the test set. Once you’ve developed a model, you need to perform a validation test. This is how you ensure the accuracy and perfection of the model. If your model is either overfit or underfit, you need to rebuild it by changing the parameters and the size of the train set. Overfitting means the model gives accurate results only on the train set, and underfitting means the model delivers accurate results not even on the train set. The final product should be generalized, giving accurate results on both the training set and test set. Must ensure that your model addresses the business problem by revisiting the first stage. Keep in mind; not every model is perfect. The key to success is extracting the best out of your model in terms of accuracy and objectivity.

Step 5: Communicate the results

Last but not least, communicating the result means presenting your model to the business owner or sponsor. They certainly don’t know what to do with it and how it works. The more precise and clear the presentation, the easier for you to explain the project. Refrain from going into too many technical details; the client may not understand or may not be interested in knowing that. Document all your complex calculations and use easy wording to explain everything. Just Delivery of the model is not necessarily the last step in a data science project. It depends on the pact between you and the client. Sometimes, you and your developer team will deliver the developed model to those who will deploy it in a facility intended to perform. If you’re developing a predictive model used, for example, an e-commerce platform, you need to stay active on the server and extract insights and make predictions based on real-time customer data. If it is not part of your agreement, you must ensure that your whole work and calculations are documented to read like a user’s manual. Archive all of the methodologies that you’ve implemented. It includes evolution diagrams, graphs, and other visualizations of the data in every step, from raw form to cleaned information and the final model with relevant notes, comments, illustrations, and the programming code. By doing so, your client will be able to crack all the technical stuff you have done developing the model. If anybody needs to go back and verify the analysis, it becomes easier for them to use the guidelines and manual you provided.

Conclusion

The Data Science project demands immense transparency and collaboration. Irrespective of how much expertise you have, you cannot build an accurate model without knowing the crux of the business process. In the same way, business owners, CEOs, managers, and sales executives cannot achieve success without data science in a highly dynamic market. Refrain from working in isolation. Communicate with all the involved stakeholders and don’t hesitate to visit any previous stage if necessary. For the sake of authenticity, use authentic and information-rich data sets or results will always be the same or imperfect, while expenditure will be much higher in terms of time and money.

In this article, we will discuss some of the most popular BI tools available in the market. Data is only as useful to the experts as the learnings and insights. A positive aspect is that data visualization tools are here to assist us in driving fruitful insights from all the numbers. Organizations use different tools for data visualization to attain a better understanding of their business data. Using Business Intelligence tools, one can perform a lot of cool stuff with the data, like transform, analyze, and present it. From an absolute perspective, all BI tools make the data come alive.

There are various data visualization tools available in the market that offer a range of features and usages .to make the data more visual. All you need to do is select one that best sustains your organizational needs. With various viable options, it isn’t easy to choose one as per your requirements and feasibility. This blog post is intended to give you a clear picture regarding comparing the three most used BI tools: Power BI, Tableau, and Google Data Studio. Let’s discuss each of these tools in detail.

Microsoft Power BI

With a newly emerged sense of data as a capital, data visualization and data science are becoming the most trending areas globally. Every organization wants to turn raw data into a meaningful portrait of information. Power BI is a free or low-cost cloud-based analytical tool offered by Microsoft.

Power BI is a very outstanding analytics service in the BI domain. Like any Business Intelligence tool is intended to produce interactive and easy-to-grasp reports and dashboards to visualize your data insights.Power BI accommodates many on-premises and cloud-based services, and you can either import or export your files to perform any transformations and analysis. You can combine data coming from different sources, model it, and generate schemas as well.Power BI offers services like:

• Power Query for data transformation
• Power Pivot for tabular data modeling
• Power View for visualization
• Power Map for geospatial data
• Power Q&A for questions and answers

Why Choose Power BI?

Effective Cost

Power BI is a budget-friendly product. It is free to use if you want to build reports for yourself. A Single individual could generate datasets, dashboards, and reports in Power BI and provide these Power BI reports and files to employees for further analysis. Each individual would have their version of Power BI and would need to handle the data and reports independently. If everyone in a company or group wants to collaborate by sharing the same reports and data, Microsoft offers cloud or on-premise services against nominal service charges per user.

‍Power BI Desktop–$0.00

Power BI Cloud Service –$9.99 Monthly

Ease of Use

Power BI is easy to use as its Excel pivot tables having Excel’s data visualization tools with some additional state-of-the-art features. Using the Power BI dataset for making charts, graphs, and tables that decorate the presentation of your data is not that difficult. Any individual with average Excel expertise can learn to use it on their own. Power BI reports possess slicers for categorical reports regarding specific data according to a certain time frame or criteria. One report can filter sales by a person for a specific product line for a specific month, and then, by making few clicks, you can showcase sales per month for one customer. One can become an expert by using Power BI for 5 to 6 months.

Consistent Updates and Innovations

Microsoft makes necessary updates to Power BI frequently. Moreover, Microsoft listens to the user community and responds to their necessities. It is worthy of making suggestions or complaints about necessary improvements. People from the community can rate the suggestions as well, making it more certain that popular interests make their way into the upcoming update. Updating doesn’t require you to make any effort. Once you open Power BI, it will remind you if there is any new or pending update. Just click the link to download and install the updated version.

Data Sources

Power BI can establish rich connections with hundreds of data sources and fetch data from MS Excel and text files like XML and JSON. It can connect to SQL Server and other database platforms. It can also fetch data from Azure cloud services and other online services like Google Analytics and Facebook. Data can be downloaded from one or more sources and stored in datasets for offline analysis. Alternately, you can make queries directly to pull data in real-time.

Excel Integration

Another notable attribute of Power BI is its tendency to save data to Excel. No matter how comprehensive the data visualization tool is, people always prefer to perform a conventional analysis by putting it in an Excel sheet. Power BI allows this practice to be performed easily.To do so, first, use the slicer tools in a report to select a specific piece of data—for instance, all sales records for the past five months. Then, performing few clicks will lead to a dialog box to save the data in Excel. You can now move to Excel and observe the actual and raw data behind the visualization.Power BI is a web-based tool that can be used on multiple devices and accessed from any browser. There also exists an offline variant that can be used offline to produce visual illustrations and analyze data. Power BI variants that run on smartphones help give Managers, CEOs, CMOs, etc., convenient and instant access to their team or business, anytime and anywhere.

Custom Visualizations

Power BI is richly supplied with state-of-the-art tools and options that allow the data to come alive. Visualization is what makes data science a driver of business decisions. Just like Excel, Power BI possesses different layouts and types of graphs and charts. Apart from that, data can be converted into information-rich content with the help of geographic maps.

Also Read: How to build a Successful B2B Lead Generation Funnel

It also has a KPI visualization tool and one called “R script visualization.” A single report can be portrayed or illustrated using a multitude of visualizations. Reports contain title boxes and cards to improve the experience. Slicers can be used to remain specific or general by selecting all reports in a dashboard or only one report. A single click on data can provide a more comprehensive view or report. All these flavors make the recipe of Power BI more interesting.

Reasons Not to Choose Power BI

The User Interface

The user interface of Power Bi is not user-friendly and showcases a very bulky look. The side panel and formula help windows can often block the view of important content.

Data Handling Capacity for Free Versions

Another downside is its tendency to work with huge amounts of data. Power BI has a limit on the amount of data it can grasp. Once the data reaches the upper limit, you must upgrade to the paid version of Power BI. Also, fetching millions of rows of data is a slow process. This can’t be put into the Power BI account, but it can be frustrating if you create datasets using big data.

Visuals Configurability

As the native visuals and, up to a large extent, the custom visuals are not configurable, the desire to optimize a visual output is limited by what can be changed.Overall, Power BI is a superb tool to perform data analysis and visualization. The advantages outweigh the disadvantages. Individuals required to work on pivot tables, charts, and simple formulas in Excel can start using Power BI to transform data into information easily.

Tableau

Tableau is another data visualization tool. It is considered a vital part of a data analyst or data scientist’s expertise, with many organizations making it mandatory as a primary skill while recruiting experts.The layout of conventional BI tools possesses some hardware boundaries. Still, as far as Tableau is concerned, it does not possess such dependencies and can perform well independently, and requires minimum hardware support. Mainstream BI tools are based on a complex set of technologies, whereas Tableau is based on Associative Search technology, which provides a fast and dynamic performance edge.Tableau allows multi-thread and multi-core computing and more advanced functionalities that traditional BI tools are failed to offer.Indeed, Tableau is the leader among all BI tools available. Let’s discuss what it has to offer and what its limitations are.

Why Choose Tableau?

Data visualization

The topmost perspective of Tableau is a data visualization tool. Therefore, its functions are rich enough to support complex computations and dashboarding for the sake of developing artistic and state-of-the-art visualizations. The output visuals showcase well-managed and beautifully represented illustrations of valuable insights that cannot simply be produced using an Excel spreadsheet. It has summited the data visualization domain due to its promising and dedicated functionality.

Quickly Create Interactive visualizations.

The drag-n-drop functionalities of Tableau allow individuals to develop a very interactive visual within a short period. The interface offers many variations while also bounding you from making charts that do not follow standard data visualization practices.

Ease of Implementation

With a variety of options available in Tableau, users can enjoy it while experiencing it. Also, Tableau is very easy to adapt compared to the mediums like Python and Domo; anyone without any prior knowledge of coding and programming language can easily get used to Tableau’s user-friendly interface.

Tableau can handle large amounts of data

Tableau can grasp millions of rows of data without any trouble. Different types of visualization can be created with a huge amount of data without the performance of the dashboards being disturbed. Also, there is an option in Tableau for users to establish real-time connections with different databases like SQLhelp give.

Use of other scripting languages in Tableau

To refrain from encountering any performance-related issues and perform complex tabular calculations in Tableau, users can implement Python or R. Using Python language. One can ease the load by performing data cleansing tasks with Python-offered functions and packages. Since Python is not a native scripting language that works side by side with Tableau, you need to import some of the visuals or packages.

Mobile Support and Responsive Dashboard

Tableau Dashboard offers an outstanding reporting feature that enables you to personalize the dashboard for a specific device such as a smartphone or PC. Tableau automatically acknowledges which type of hardware medium you are using and makes adjustments in such a way to showcase the right report to the right device.

Reasons Not to Choose Tableau

Now you know the positive aspects of this tool, let’s discuss some of the downsides.

Scheduling or notification of reports

Tableau does not allow automated refreshing of the reports due to the lack of scheduling options. Therefore, you must put effort every time you want to update the data in the back-end.

No Custom Visual Imports

Tableau is not completely open-source. Like other BI tools in the market, such as Microsoft Power BI, Tableau doesn’t support importing any new visuals. You have to recreate it.

Custom formatting in Tableau

Tableau’s formatting is limited to a 16 column table display, which frustrates the users. Also, to apply the same formatting layout to multiple fields, you cannot do that directly. Users need to perform manual formatting for each of the desired fields, making it a time-consuming practice.

Static and single value parameters

Tableau has static parameters, and only one value at a time can be selected using a parameter. Every time the data values get changed, these parameters need to be changed manually, and the user can’t automate the updating process of parameters.

Screen Resolution on Tableau Dashboards

The framing of the dashboards gets deformed if the developer’s screen resolution is different from the end user’s screen resolution. For instance, if the dashboards have a screen resolution of 1920 by 1080 pixels and the front end monitor’s resolution is set up on 2560 by 1440 pixels, it can cause the layout of the dashboards to get distorted. Also, its dashboards are not that much responsive.

Scaling and Pricing for Enterprise

Another trouble with Tableau is its cost to implement across a huge organization. Compared to other cheaper BI tools, Tableau is one of the most expensive options. The only option available for security and data sharing is Tableau Server, which costs around $180,000 for an eight-core option and around $30 per user.

Tableau Company Strategy

Tableau has done a great job making its space among the state-of-the-art data visualization tools. However, with the sudden ups surge in data science, artificial intelligence models, and machine learning, Tableau won’t survive and thrive if it doesn’t adapt to the contemporary situation quickly.

Google Data Studio

Data Studio is Google’s powered cloud-based visualization tool that allows you to create compact reports using its interactive dashboard. It firmly integrates with Google-based data sources, including Google Ads, Google BigQuery, & Google Analytics.Google Data Studio has a similar interface outlook as Google intended to provide non-technical users the opportunity to visualize data. Data Studio possesses 17 connectors powered by Google with several other connectors provided by partners but mostly as paid services. Once the connection is established, you can add various data sources to Google Data Studio, build tables and charts, and produce reports using a simple and interactive interface. The reports you build can be shared among your team and clients as well.

Why Choose Google Data Studio?

In this section, let us dive into some Google Data Studio benefits.

Cloud-based and Completely Managed

Unlike other renowned BI utilities like Power BI and Tableau, Data Studio is designed to serve as a cloud-based service. It is a fully managed service that allows users to focus on visualization tasks without managing any infrastructure or installation.

Tight Integration with Google’s Ecosystem

As discussed above, the notable advantage offered by Data Studio is its ability to integrate with Google-powered services like Google Analytics, Google BigQuery, Google Sheets, etc. Consider your ETL process is mainly built on top of Google applications, you will spare a lot of time and effort when integrating with Data Studio.

Easy to Use

Data Studio provides a very interactive and easy-to-use user interface that allows you to create reports and dashboards within a few clicks.

Access and Sharing Controls

As a google powered service, it inhabits the granular access control and sharing options like that of the Google office suite. Sharing reports and dashboards with team members and clients with access control options is a very simple task in Data Studio.

Support for Live Connections

Data Studio provides live data connection opportunities compared to other visualization tools like Power BI, Tableau, etc. It means there is no manual effort or automation tools required to make the necessary updates in data. Every time a report or dashboard is accessed or refreshed in UI, it will fetch the updated and recent data. There also exists an option to adjust the fetch cycle using cache settings.

Free of Cost

Data Studio is a free and open-source service bundled with Google cloud services. Although, the storage and processing costs are of varying degrees based on one’s requirements.

Reasons Not to Choose Google Data Studio

Now let’s see some downsides of Google Data Studio.

Lack of Real-time Updates in the Dashboard

Despite the live connection being available for different cloud and data sources, no automated mechanism exists, for now, to keep a dashboard or report view auto-updated. So if you want to showcase a real-time and auto-updated dashboard every time you present it to your team members or clients, you have to wait for google’s developer’s team to make this option available.

No Support for Excel

Being a Google-powered product, Data Studio cannot be directly integrated with commonly used data formats such as Excel and prefers Google-based services. Excel can be integrated with Data Studio after converting it into a CSV file or a Google Sheet. Lack of Comprehensive Function Support Data Studio still lacks the functions offered by Tableau and Power BI. It lacks some fundamental functions like SUMX in Power BI that allows users to calculate the sum of values in a specific column or row.

Slow Speed in Case of Live Connection

The most-reported error in Data Studio is that loading the dashboard starts getting slower with the increase in complexity of functions used. This downside comes amidst the live connection functionality.

No On-premise Deployment Option

For organizations with strict data security protocols, and on-premise security option is the biggest ever drawback. Such businesses still prefer to use BI tools like Tableau and Power BI to provide desktop installation support and the freedom to access data within the internal network.

Lack of Native Connector Support for Cloud-based Data Sources

Data Studio doesn’t have built-in connector support for some of the most frequently used cloud services like Hubspot. Although you can find third-party and community-based connectors to fill this void, these are paid utilities.

Complex Visualizations Not Possible

You can easily set up basic visualizations in Data Studio, but it lacks support for mobility and personalization offered by other BI tools like Tableau. So businesses with analytical needs and expert analysts may find Data Studio not much appealing in terms of visualizations.

Conclusion

Big data is the new commodity in this data-driven world, and its importance and uses are exponentially increasing with every passing year. By utilizing visualization and BI tools, you can start your journey towards a new peak of data understanding. Your data management will become more rational, firm, agile, and precise regarding predictions and probability. We hope you have found this article helpful in comparing the best BI tools available in the market as per your feasibility and find a perfect match for your company & requirements. Reach out to us if you are looking for a technology consulting service to help you choose and implement the right BI tool for your business.

Statistical modeling is a vital area of data analytics. By applying various statistical models to the data, data analysts can perceive and interpret the information more clearly and deeply. Rather than pondering over the raw data, this methodology allows them to discover relationships between variables, predict events based on available data patterns or trends, and use visualization tools for the smart representation of complex data models to help stakeholders understand it.[lwptoc skipHeadingLevel="h1,h4,h5,h6"]The role of data scientists is to build data models and writing machine learning codes and data analysts uses these statistical models to extract fruitful insights from data. That’s why analysts who are gearing up to shape their expertise in statistical modeling must grasp a concept of these methodologies and know where and how to apply them.

Statistical Modeling in a Nut Shell

A statistical model collects probability distributions on a set of all plausible conclusions of an experiment.

It is the mathematical approach that involves the process of applying statistical modeling and analysis to datasets. Building a statistical model means discovering or establishing a mathematical proportionality between one or more random variables and other non-random variables. By applying statistical modeling to raw data, data scientists use data analysis as a strategic method and provide intuitive visualizations that help discover latent linkage between variables and make predictions.To make statistical models, data comes from a variety of public sector sources, including:

• Internet of Things (IoT) sensors
• Survey data
• Public health data
• Social media data
• Imagery data
• Satellite Data
• Passengers Data
• Consumers Data
• Student Record, etc.

Statistical Modeling Techniques

The initial step involved in building a statistical model is data gathering, which may be driven using various sources like spreadsheets, databases, data lakes, or the cloud. The second most crucial part involves data analytics consisting of supervised learning or unsupervised machine learning methodologies. Some renowned statistical algorithms and approaches include logistic regression, time series, clustering, and decision trees.

Supervised Learning

Regression models and Classification models fall into the category of Supervised learning methodology.

Regression model

This predictive model is used to analyze the relationship between a dependent and an independent variable. Common regression models include logistic, polynomial, and linear regression methodologies. Practical uses include forecasting, time series algorithm, and discovering the causality between variables.

Classification model

It is the type of machine learning methodology by which an algorithm analyzes an available, huge and complex set of known data points to acknowledge and appropriately classify the data in different classes based on its nature. The most common approaches include decision trees, Naive Bayes, Nearest Neighbor, Random Forests, and Neural networks. All these methodologies contribute to modern Artificial Intelligence (AI) approaches.

Unsupervised learning

This domain includes methodologies of clustering algorithms and association trees, discussed below:

K-means clustering

It piles a certain number of data points into a specific number of groupings based on similarities.

Reinforcement learning

It is the area of deep learning that deals with the models iterating over several cycles, assigning weights to the steps that produce favorable outcomes, and abandoning moves that produce false results, therefore training the algorithm to learn optimized attributes and data points.There are three main types of statistical models: parametric, nonparametric, and semiparametric:

• Parametric: defined as a group of probability distributions that has a finite number of parameters.
• Nonparametric: defined as a domain in which the number and attribute of the parameters are flexible and not predetermined.
• Semiparametric: It shares both a finite-dimensional component (parametric) and an infinite-dimensional component (nonparametric).

How to Build Statistical Models

The initial and most crucial step in building a statistical model is acknowledging how to choose a statistical model depending on several variables. The sole purpose of the analysis is to address a very certain question or problem or to make accurate predictions from a set of variables. Here are few questions that need to be kept in mind while choosing a model.

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• How many descriptive and dependent variables are there?
• What is the nature of the relationships and proportionality between dependent and descriptive variables?
• How many parameters must be added to the model?

Once these questions are answered, the appropriate model can be selected.

• Once you select a statistical model, the next step is to build the model. This process of building a model involves the following steps.For the sake of simplicity, start by pondering over the univariate data attributes. Visualize the data to identify errors and understand the nature and behavior of variables you’re using.
• Build a prediction table using visible and readily available data attributes to observe how related variables work together and then evaluate the outcomes.
• Move one step forward by building bivariate descriptives and graphs to identify the strength and nature of relationships that the potential predictor creates with every other predictor and then evaluate the outcome.
• Frequently outline and evaluate the results from models.
• Discard non-significant relations first and ensure that any variable that possesses a significant association with other variables must be included in the model by itself.
• While studying and discovering the overwhelming relationships between variables and categorizing and testing every fruitful predictor, refrain from deviating from the research question.

Applications of Statistical Models

Here are some of the most common applications of statistical models.

Spatial Models

Spatial analysis is a kind of geographical analysis intended to demonstrate patterns of human behavior and its spatial expression in terms of mathematics and geometry. Some practical examples include nearest neighbor analysis and Thiessen polygons. The co-variation of attributes within geographic space is known as Spatial dependency; properties at proximal locations appear to be correlated, either positively or negatively. Spatial Modeling is done by dividing an area into many similar units, normally grid squares or polygons.

The resultant model may be connected to a GIS for data input and visualization. This approach finds location-oriented insights and patterns by overlapping geographic and business data layers onto geographical maps. It allows us to visualize, analyze, and get a different perspective and a 360-degree view of existing data to solve complex location-based problems.

Time Series

Time series is defined as an ordered sequence of values of a variable at equally spaced time intervals. The time-series analyses in categorized into two approaches:

1. Frequency-domain methods - include spectral analysis and recent wavelet analysis.
2. Time-domain methods - include auto-correlation and cross-correlation analysis.

The usage of time series models is twofold:

• Acquire an understanding of the impactful events and structure that produced the observed data
• Adjust a model and start forecasting, monitoring or feedback, and feedforward control.

Time Series Analysis is different areas such as:

• Economic Forecasting
• Sales Forecasting
• Budgetary Analysis
• Stock Market Analysis
• Process and Quality Control
• Inventory Control
• Utility Studies
• Survey Analysis

Survival Analysis

Survival analysis is an area of statistics that analyzes the expected time frame taken by one or more events to happen, such as the death of biological organisms and failure in mechanical or electrical equipment. This area is termed reliability theory or reliability analysis in various fields, such as engineering, duration modeling in economics, analysis of causes and consequences of events in history, and human behavior analysis in sociology.Experts use Survival analysis to answer questions like:

• What is the percentage of a population which will survive past a certain time or certain catastrophic event?
• At what rate will entities likely die or fail in case of any catastrophic event?
• Can multiple causes of death or failure be taken into consideration?
• How do certain factors or parameters increase or decrease the probability of survival?

Actuarial science experts and statisticians use survival models, and marketers are designing customer engagement models.Survival models are also used to predict time-to-event, such as time taken by a virus to start spreading to turning into a pandemic or modeling and predicting decay.

Market Segmentation

Market segmentation, also known as customer profiling, is a marketing strategy that revolves around dividing a huge target market into subsets market segments based on demographics, consumers, businesses, or psychology that have, or perceived to have common demands, requirements, hobbies, and priorities, and then designing and stationing strategies to meet them.

Market segmentation strategies are generally used to outline and define the target market and provide insights from data to develop smart marketing plans. The four types of market segments are represented in a visual below:

Recommendation Engines

Recommendation engines are defined as a subclass of information filtering methodology that tends to predict the ‘rating’ or ‘preference’ that a user is more likely to give to a certain product or service based on data analysis.

Recommendation engines or systems are becoming a new norm for users to get exposure to the whole digital world using their experiences, behaviors, priorities, and interests. For instance, consider the case of Netflix. Instead of browsing through thousands of categories and movie titles, Netflix allows you to navigate a much narrower selection of items that you are most likely to enjoy. This feature allows you to save both time and effort by delivering a better user experience. With this feature, Netflix reduced cancellation rates, allowing the company to save around a billion dollars a year.

Association Rule Learning

Association rule learning is a method for uncovering useful relations among variables in huge databases. For instance, the rule {Milk, Bread, Eggs} ==> {Yoghurt} found in the sales data of a superstore would show that if customers buy Milk, Bread and Eggs together, they are likely to buy Yoghurt.

In fraud detection, association rules are used to extract patterns linked with fraudulent activities. Association analysis is carried out to outline additional fraud cases. For example, suppose a credit card transaction made by user A was used to make a fraudulent purchase at store B by analyzing all shopping and transaction activities related to store B. In that case, we might identify fraudulent activities linked with another user, C.

Attribution Modeling

An attribution model refers to a principle or set of principles that dictate how credit for sales and conversions is dedicated to touchpoints in conversion paths. By analyzing each attribution model, you can get a better idea of the ROI for each marketing channel.A model comparison tool enables you to analyze how each model distributes the value of a conversion. There are six common attribution models:

• First Interaction
• Last Interaction
• Last Non-Direct Click
• Linear
• Time-Decay
• Position-Based

Google Analytics uses last interaction attribution by default. However, you can compare different attribution models in your account.

Scoring

Predictive models can predict defaulting on loan payments, risk of accident, client, or chance of buying a product or services. The scoring model is a special kind of predictive model that typically uses a logarithmic scale. Each additional 50 points in your score reduce the risk of defaulting by 50%. The foundational basis of these models relies on logistic regression and decision trees, etc. Scoring technology is mainly used in transactional data and real-time scenarios like credit card fraud detection and click fraud.

Predictive Modeling

Predictive modeling utilizes statistics to predict outcomes. The event one wants to predict is in the future, but predictive modeling can be applied to any unknown event, regardless of its time frame. Predictive modeling is the foundational base of some applications above and can be used for weather forecasting, predicting stock market trends, predicting sales, etc. Military strategists can also use it to predict the enemy’s next move based on the previous records.

Neural networks, linear regression, decision trees, and naive Bayes are some of the methodologies used for predictive modeling. It is carried out by maintaining a machine learning model and improved using the cross-entropy technique. It doesn’t necessarily need to be a statistical approach but also a data-driven methodology.

Clustering

Clustering is the methodology linked with grouping a set of attributes or data points so that data points in the same segment known as clusters are more similar to each other than those in separate segments or clusters. It is the primary technique used in data mining and a common practice for statistical data analysis, used in various areas, including machine learning, image recognition, speech recognition, pattern recognition, and bioinformatics.

Unlike supervised classification, which is discussed below, clustering does not require training sets, but some hybrid methodologies are known as semi-supervised learning.

Supervised Learning

Supervised learning is the machine learning methodology of developing a function by enabling a model to learn from a labeled training set ( A part of the data set with which the machine is unaware). The training data consist of a set of training samples. In supervised learning, each sample is a pair based on an input object which is usually a vector, and the desired output value is also known as label, class, or category. A supervised learning algorithm feeds over the training data, analyzes patterns and associations, and finally produces an optimized function to map new and unseen data samples known as a test set.

An ideal scenario requires the algorithm to determine the class labels for an unseen data set accurately. Like predictive modeling, the training phase is improved using the cross-entropy technique to produce a highly optimistic function for accurately recognizing the test set.

Extreme Value Theory

These characteristic values are the smallest (minimum value) or largest (maximum value) and are known as extreme values. Extreme value theory or extreme value analysis (EVA) is an area of statistical modeling that deals with the extreme deviations from the median of probability distributions. It is intended to analyze random variables in a sample and the probability of more extreme events than any previously thought or experienced, both minimum and maximum. Consider an example of catastrophic events that occur once every 1000 or 500 years. Mathematically, it states that if a function f(x) is continuous on a closed interval [a, b], then f(x) has both a maximum and minimum value on [a, b].

Conclusion

Statistical modeling and analysis are used extensively in IT, science, social sciences, and businesses. As well as testing hypotheses, statistics can provide a probability for an unknown outcome or event that is difficult or sometimes impossible to measure or even assume.There are still some limitations just like in some social science areas, such as the study of human consciousness and rational, irrational, or random human choices and decisions, are practically impossible to measure, but statistical modeling and analysis can shed light on what would be the most likely or the least likely to happen based on existing data, information, knowledge, cases or wisdom.Despite having some limitations and downsides, statistical modeling is still in use and producing extraordinary results in various fields, but there is still much space for improvement and advancement in this respect.

While we are all preparing worldwide to deal with the coronavirus epidemic in numerous respects, businesses worldwide have been affected. Many startups are struggling to sell their products hence nearing a downfall.

We have our approach, vision, and strategy while working on a product. The process can sometimes encounter a crisis. It may be a positive crisis like a vast potential client who might need new features to be introduced quite rapidly to attract them, or a poor crisis, for example, a crucial issue in the manufacturing system or a global pandemic.

How do you manage a product when your company is facing a time of crisis? We will be sharing some useful tips in this article that you may pick to gain stability and sales for your product(s).

How to Manage a Product in a Time of Crisis

Find your Product’s Strengths

While the crisis triggers a great deal of damage, it also provides good opportunities as the goals and requirements change. Those fluctuations, as during the COVID-19 crisis, are immense as the behaviors of many people change suddenly, and massive sums of money and resources are refocused on different needs. You can use this time to use your strengths. Some qualities are specific to your product or service, and you need to work harder to figure out how your product does well and how it would solve the new problems or be productive for people in crisis management.

This change helps businesses adjust, switch direction, or even recreate themselves if needed to resolve this crisis much more flexibly. New technology can also be utilized to make your product more secure and can offer a tremendous advantage.

Understand your Customer

You need to focus on how a crisis has affected your customer; you should develop product-based strategies to tackle those problems and help your customers. Thinking about your customer's needs and reshaping your approach to match their requirements will ultimately increase sales and give you more potential clients. Your database and previous records will provide useful knowledge regarding your consumers that will let you understand their needs. Evaluate your customers' records, as it holds a lot of useful information. Check for trends to see how the consumers usually place orders. You can also evaluate your product's performance using that information. You may conduct a customer satisfaction survey to give you useful insights and make your customer feel valued. Inform your customers when you make any changes or improvements based on their feedback. In addition to requesting reviews, create a consumer contact program to ensure you stay in communication with your consumers.

Pay Attention

The number of queries and demands bursts during a crisis. You would potentially face budget, supply, advertising, and many more queries. Consider what you and your organization do to remain focused on the primary goals. Your priorities will change during a crisis. For your product to perform best, study your competitors, focus on your strengths and productivity. Make use of your team's efforts and strengths. Besides seeking new recruitment opportunities, boost the distribution and marketing activities. This will allow you to satisfy changing customer demands. You will benefit from reduced promotional rates when other brands reduce their promotions in the crisis.

Product Planning

Product planning is an element of the development process of each product. It encourages the stable production of goods because it can measure potential risks and challenges. Project managers use product planning principles in their job to produce better strategies and results. Better product planning in a crisis will lead to better results. A good stock forecasting and management framework allows better handling of inventory and thus decreases investments. Product planning also tends to manage raw materials efficiently and helps to enable more profitable purchases. Product Planning makes the usage of existing capital and inputs into the manufacturing process effective. One of the advantages of product planning is eliminating waste of usable money and the efficient allocation of necessary resources.

Build Trust

People look for transparent products in times of crisis to help them in fulfilling their requirements. The pandemic has made people conscious of what they are buying and if it satisfies their needs or not. You may be able to sell a few copies of your product by false advertisements, but you can't get away with it. A few bad reviews can destroy your product's image in the market. You can follow the below practices to gain your customers' trust:

• Demonstrate results
• Ask for Feedback
• Good Customer Service
• Content Marketing
• Share Best Practices
• Loyalty Programs
• Be Transparent
• Pur Your Customers First
• Build Social Proof

Wrapping it Up

The right product in a time of crisis will ultimately be the one that would readily solve your customer's issues. You need to focus on your planning and processing stages and make any changes, whenever appropriate, to suit the current needs. The tips mentioned in this article can help you explore a new perspective and help you manage product in crisis.

For businesses to operate rationally, you need to have customers who are up for purchasing your products or services. But, how can you engage these potential buyers? The answer is, you need to acquire leads that can be turned into prospects, which you can finally convert into customers. Most businesses invest a lot of time grooming their lead generation strategies so that the leads keep showing up. To do so, you need to improve the lead funnel.

A proper lead generation funnel allows you to sustain a sleek conversion process. You will determine how many leads establish a connection with your business and how many have turned into customers. This is how you will figure out why leads are converting and even why they are not.This information enables you to tighten up your sales funnel, increase your conversion rates and boost your revenue notably.Building your first lead generation funnel can be difficult for any newbie. But optimizing your first lead generation funnel doesn’t involve that much rocket science. It’s all about having the proper ingredients to make up the right dish.This blog post will discuss what a lead generation funnel is and how to build one in five simple steps. Let’s get started.

Lead Generation Funnel - In a Nutshell

A lead generation funnel, also known as a lead funnel, is a systematic methodology to generate leads that are potential customers. It involves the process of funneling your target audience through different stages until they decide to purchase with high probability. A lead generation funnel can range in complexity from very simple to very hard in terms of understanding. A simple lead generation funnel appears like this:

Understanding lead funnel phases (Operations-based Division)

Lead funnels possess three phases in general. Let discuss each of them.

Phase 1. Top of the Funnel - TOFU

The first phase, the top of the funnel, is related to awareness. The top of the funnel is your company’s first contact with a potential lead, and you meet with the opportunity to introduce your purpose of presence to a future customer. Content during this phase involves articles, images, videos, etc.

Phase 2. Middle of the Funnel - MOFU

Once prospects filter to the middle of the funnel, they start to interact with your company. It requires you to continue nurturing and harvesting them. You can feed them via various specialized content such as ebooks, whitepapers, and case studies. The best way to do so is to run email campaigns for free trial users.

Phase 3. Bottom of the Funnel - BOFU

Finally, there is the bottom of the funnel. You’ve turned random website visitors into leads until this phase and started to establish a trusted relationship with them. They believe in your company and need a final thrust to become active customers and start buying from you. Optimal BOFU content involves a series of demos, consultations, and discount offers. This three-stage funnel is intended to turn random visitors into paying customers. But can you develop such a funnel? Let’s embark on an answer.

How to Build a Lead Generation Funnel

You will find these five steps to build a lead generation funnel very beginner-friendly.

Step 1. Mapping your customer experience

The first step to create your first lead generation is to acknowledge and understand your customer’s journey. It is important to sense that if you don’t know them, their likes and dislikes, their problems, and what they are looking for, etc. Won’t you be able to find and engage them? Using Google Analytics, start to extract data such as bounce rates, traffic stats, and conversion rates.

These reports showcase the actual entities that need to be improved on your site. But, you can’t expect a complete revolution just by adjusting numbers as they fail to give you more human and personal insights into your customers.

Knowing your customers by conducting interviews and carrying out surveys. You will be able to know their trigger points.

Mapping your customer journey is what initiates the stream from the first touchpoint to purchase. If you want to understand your customer’s experience in the more rational way possible, communication is the key.

Step 2. Creating a state of the art content

Once you’ve mapped out your customer journey, your next responsibility is to create surprising and pleasing content to drive this process further while holding the first lead. The content can be anything from blog posts, podcasts, videos, ebooks that connects with your customer. You don’t need a huge team and unlimited funds to create amazing content to engage your audience. Here’s how you can craft exceptional content. Beat the competition. Take a look at your rivals and the sort of content they’re creating. Then create better content. It’s all about quality, not quantity, as more words alone can’t make a difference, but more value will. If you don’t know what to write, try to outperform an article on the same topic from your competitor.

Optimize for SEO. Ensure that your content is optimized for SEO. This is how your potential audience will find you naturally as they surf the web to solve their problems. This goal can be achieved by enriching structured data, header tags, and internal linking on all your online content. Create content for each lead funnel stage. Produce content for each stage of your lead generation funnel. Articles, social media posts, and YouTube videos work well for the top of the funnel. Downloadable and directed content such as ebooks, survey reports, statistical reports, and case studies serves a purpose for the middle of the funnel. At the same time, free trials and discounts offer help at the bottom of the lead funnel.

Step 3. Driving traffic

For now, you’ve created different pieces of state-of-the-art content, and it’s time to drive traffic to it. This milestone can be achieved in several ways:

Search Engine Optimization - SEO

A relevant and right SEO strategy is a fruitful way to drive traffic to your content in the long run. SEO is the only way your target audience reaches you by making different searches as per their interests. In search of their answers, they start pouring into your funnel.

Email marketing

Almost 50-60 percent of marketers say email marketing is their most effective strategy for boosting ROI. It’s a perfect practice to promote your company’s content. If your organization has an active and likely email list to engage the audience, you can utilize it to inform your new content subscribers and push them to check it out.

Social Media Platforms

Social media has transformed the lives of both individuals and the collective over the past 15 years. It’s a way for businesses to engage their target audience. So refrain from hesitating to inform your social media followers whenever you publish new articles, videos, or podcasts. If you’ve been using your social media platforms in the right way, you’ll be surprised at how much traffic a single post or a story can drive. It’s crystal clear that social media is worth the time, effort, and investment.

Paid Advertisement

Last but not least, if you have a sufficient budget, don’t hesitate to invest in advertising your content. Google Ads and Facebook Ads are examples of popular mediums for B2Bs.Similarly, other search engines and social media platforms have advertising options as well. The practice is intended to explore the right platform for your unique audience as per your feasibility. By doing so, you can start utilizing them to target your market and start engaging customers for your esteemed content.

Step 4. Collecting prospect information

Now, you know about your audience, crafted content, and driving traffic to it. What’s the next step? Keep in mind that all the world traffic won’t create an opportunity for your business if you don’t have a way to collect visitor information. The mainstream way to collect the relevant information of visitors is to create a lead magnet that you give away for free in exchange for an email address. Your lead magnet could be anything, but it must sustain your target market and audience’s interest and relate to your business.

Ref: The Network of Women in Business

At this stage, must ensure a rich difference in mainstream content and that of a lead magnet as it must be valuable for that very unique and selected audience. When your lead magnet is ready, take a flight, make it active and available via a landing page. Once website visitors fill out the simple form on your landing page, they’ll gain access to the very unique and valuable gated content.

Step 5. Closing the sale

It’s a big-time now as you have the contact information of a website visitor, they termed as a make sure we’re both a good fit. Closing the sale is different for every company. For some, it will begin with your marketing team harvesting your leads to sign-up. But, for others, it will begin with sending qualified leads to a sales team to trigger the conversation and close the deal. Determine as per your feasibility and circumstances what works well for you and start turning those leads into potential customers.

How to Optimize your Lead Generation Funnel

Congratulations! You’ve now built your first lead funnel, but you’ve not finished it yet. The climax must possess completeness. If you want to cast more leads, you need to optimize your lead generation funnel. Let’s discuss how.

1. Employ the right tools

Several software tools help you generate more leads. You’ll be able to learn what company your visitor work for, how much time they spend on each of your web pages, and get detailed content information for employees at that company. By deploying this data, you just have to adjust your already existing lead funnel process appropriately and strategically. If you have extra pennies in your budget, don’t hesitate to acquire tools to make lead generation more effective. Remember to sync said tools in all the five-step of the processes mentioned above.

2. Qualify your leads

Not all leads are going to produce benefits. It’s in your best interest to qualify leads and spend your time and effort on the ones that are the most likely to score a purchase. To rationally qualify a lead, first, evaluate how they have interacted with your content. Those who download anything directly linked to your business, such as a case study, are plausibly higher quality leads than a visitor who just opened your careers page. You can also qualify leads based on the nature of interactions they make with your company once they’ve given you their contact information.

• Do they consistently check your emails?
• Are they up for taking surveys?
• Do they answer your sales calls?

The answers to these questions will tell you a lot about the quality and nature of lead.

3. Never abandon experimentation

Finally, refrain from abandoning experimentation. Nothing is 10 out of 10, and they’re always a space to improve your lead generation funnel as what worked in the past might not work today. Your audience is continuously evolving with the changing market trends and global events, so your lead funnel needs to reform and evolve with them. Don’t hesitate to create different kinds of content to check what your ideal customers connect with best. Also, adjust your advertising strategies, landing pages, and sales efforts whenever required.

Closing Note

Now you have a complete roadmap to build and optimize your first lead generation funnel. Suppose you follow these simple yet important five-step processes and optimize your funnel accordingly. In that case, you’ll embark yourself on the journey to build a sustainable lead generation funnel and drive more ROI.

With plenty of channels and platforms available for digital marketing in the global digital village, it is getting tougher to pick the right strategy among the variety of digital marketing strategies and tactics you can deploy in your ecommerce marketing to maximize engagement and ROI. For instance, consider eCommerce marketing — How it involves platforms like social media, content, SEO, and email marketing?

eCommerce marketing and digital marketing are not entirely the same areas. But eCommerce stores can integrate all digital mediums to promote a product online and boost RoI.

eCommerce Marketing Techniques for eCommerce Store

This eCommerce marketing blog post will allow you to dive deep into the ocean of the digital world to extract mediums and strategies that enable you to reach new summits of the eCommerce domain. Now, let's discuss the platforms and channels that allow you to grow your online business, attract more traffic, and boost engagement.

‍Social Media If you think handling your social media effectively is as easy as posting content once a day, you underestimate the liabilities and responsibilities. When it comes to social media, your ecommerce marketing strategy needs you to develop a proper strategy.

Every social media platform has a certain target audience. For instance, if you’re selling cosmetic products, you certainly won’t be able to drive your sales from LinkedIn. Instead, you have your attention and efforts on visual platforms like Instagram, Facebook, and Pinterest. Instagram will be more likely to improve your engagement. For best-performing ads, Facebook is the answer. And you might attract a lot of customers from Pinterest, which you can re-engage with Facebook ads later. After analyzing and evaluating which platform best serves your purpose as per your market segment and target audience, it’s now time to gear up for your marketing strategies. Such as the type of content you post, the tactics you can execute to accelerate your growth game, and how frequently you should post.

Search Engine Optimization (SEO)

Is there anything else that can prove to be more powerful at driving traffic to a website than social media? You must not underestimate the power of Search Engine Optimization (SEO). It won’t bring you desired results in a single click. Keep in mind; marketing strategies aren’t about short-term goals. All these techniques demand utter patience, a long-term mindset, and consistency. Most immature retailers focus on immediate gratification when it comes to setting up their eCommerce websites. But if you want to keep your budget as low as possible and have fruitful outcomes, in the long run, SEO is the key. The fact about SEO is that it’s not necessarily meant for selecting keywords for a specific niche but expanding your process and impact a little bit. For instance, if you have a store related to fitness products, you wouldn’t only focus on keywords related to fitness and training. You’d also focus on keywords related to a balanced diet, keto diet, health, etc.

The reason is SEO is all about attracting new customers, not just sticking to the existing ones. By generating content like weight loss programs, diet plans, food supplements, you can still introduce the concept of fitness as a combined result of exercise, diet, and a healthy routine. It allows you to expand your domain and attract a diverse audience who could still be interested in buying from you. Add SEO tools like Plug-In SEO or SEO Manager so you can regularly perform quality and plagiarism checks on your online store’s SEO. These tools reveal the performance of your content and notify you of any SEO blunders you make unconsciously. For instance, duplicate content can result in penalization by Google, which results in your elimination from visibility in search engines and hence reducing your traffic.

Content Marketing

One of the most renowned marketing strategies used for ecommerce marketing in today’s time is content marketing. Your content can be of any type, from blog posts to ebooks, videos, short clips, surveys, and podcasts, etc. Content marketing allows you to engage more audiences while keeping your budget low. But it serves as a trigger factor for a much broader chain reaction; no sudden serving will be offered here. It means that not everyone will be convinced to purchase from your store the first time they visit your website. However, your content can serve as a relationship builder. The more content someone comes into contact with, the more attached the likely customer becomes with your brand and online presence.

In the long run, that familiarity will serve as a law of attraction. Like SEO, it’s like test cricket, not a one-day inning. Content marketing has more to offer rather than just acquisition. It can also educate your customers so that they can adjust their mindset and presence within the niche. For example, if your store is related to cosmetic products, you can create makeup tutorials and beauty hacks kind of videos. This will help your customers look more beautiful using your products and keep them on board to use your product. In the future, whenever customers want to buy a similar product, they’ll be more likely to purchase from you as content marketing boosts customer retention too. Stick to the bottom-to-top approach as the most successful blogs and websites started for a very specific market and became broader as their audience grew. Doing the opposite won’t produce promising results.

Boost Product Visualization

Consumers want to acquire as much information as they can before making a purchase. Not only that, but they like to use a visual approach. Using some unique product visualization techniques can effectively and easily show off your products to the consumers to look at the details without reading descriptions and specifications. Leverage tools that enable customers to zoom in get a 360° spin view of your product, and add roll-over, pop-up info tools are very helpful in improving user experience. Keep in mind the customer’s perspective; give as many utilities and interaction ease as possible to boost engagement and enhance the shopping experience.

Artificial Intelligence (AI)

Artificial Intelligence (AI) can cast a notable shadow on your eCommerce business plan. It can collect data, extract useful patterns from it, and precisely predict customer behavior based on past experiences. Statistical approaches in AI such as Predictive analysis and association rules modeling provide valuable insight and intelligent solutions to manage your eCommerce business effectively and improve customer’s experience. You can collect data to predict unforeseen future events and hidden insights to divert your attention and operations as per the plausible challenges and meet customer’s demands. The better the experience for your customers, the more likely they will be to buy from you in the future, recommend your products, and provide positive reviews. You can also use the AI model to improve your logistics and supply chain process to satisfy customer experience from purchase to delivery. Competitive-edge Product Filtering With more products comes more management. You require advanced product filtering tools with increasing product items and categories in your store. No one has time to search your entire website based on a world full of hustle and alternatives. To provide ease, you have to provide your customers with the options to shop on their terms and find what they need in as few clicks as possible.

A fact is 42% of e-commerce sites are not using advanced filtering tools, and you can gain a competitive edge over the rivals by having a desirable site for customers to find what they need without wasting much of their time and effort. The more categorized and filtered store layout you offer, the easier it is for them to shop and improve customer engagement.

Automate using Chatbots

Again, this is AI in action, but it needs to be discussed as a separate methodology. Chatbots may be the first point of contact that can improve the customer experience. Being readily available to answer queries brings so much ease on the customer's end. You can readily resolve issues and clear your way to the shopping cart. Chatbots make it easy to interact with customers in a very human manner without being involved all the time and, in turn, offer a customized experience that can notably cast an impact on sales. Chatbots not only serve as excellent customer service providers but provide up-selling options on the spot while notifying customers about a discount or deal if available.

Reduce Cart Abandonment

You will most likely encounter cart abandonment if a customer gets to check out and finds the delivery options do not suit their needs. More than 70% of the time, it happens due to additional shipping fees. Other reasons for abandonment involve bitter user experience due to complex checkout processes, a requirement to sign up, and substandard website performance such as slow loading and crashes. One of the most effective ways to address this issue is to introduce an email recovery strategy. When someone abandons a cart, you can send emails to encourage customers to complete their orders. According to Sales Cycle, almost 50 percent of recovery emails are opened, and around 30 to 40 percent result in a completed purchase.

Personalization

Predicting what customers are likely to buy is a step towards success. Understanding your customers’ behavior and buying habits can predict what they are more likely to buy. All of this information improves their shopping experience. A big part of personalization involves local approaches that make people feel connected. This is also a strategy that can help you improve delivery services based on the demographics of your customers and their proximity to your warehouses. Personalization or customization can also help you sell items as per the climatic conditions. You can show customers in the south a different set of products than those in the colder north as per their current climatic conditions. Moreover, you can showcase different products to different customers based on their culture and traditions in cross-border sales.

Collaborations and Partnerships

Brand collaborations or partnerships can allow you to lift your business to new summits. Marketing strategies like collaboration enable you to lift your business by stepping into the domain of other brands. Most perceive brands in similar market segments as rivals, but they can be considered the necessary alliances. For instance, if two brands target the same audience but offer different product categories, they grow together by co-creating content or launching a joint product. By targeting another brand’s existing customers, you can expand the domain of your market campaign; you can target a bigger audience and turn this opportunity into success. Another aspect of collaboration is Influencer marketing, as it allows you to capitalize by making a deal with influencers to grow your business. This strategy serves well if you have a small audience and want a sudden surge in engagement. Choosing an influencer is not about choosing a random attractive person with a maximum number of followers. Before choosing an influencer, dig deeper into the data and keep in mind that:

• They have the right audience
• They are uncontroversial
• They are politically unbiased
• They represent your brand positively

Conclusion

You can find various eCommerce marketing strategies out there, but the methodologies mentioned above and strategies effectively boost RoI, improve customer experience, and improve engagement. anging from budget-friendly and instantaneous to those that will let you outperform in months and require thousands of dollars to implement, these all strategies are effective in their way. Keep in mind; your eCommerce marketing strategy should not rely on a single source but involve various methodologies that allow you to capitalize by targeting the right market and audience effectively.

Every month in the United States, around 550,000 businesses start, and about 80% survive the 1st year, 70% for two or more. Approximately 50% of companies survive for the 1st five years, and 1/3rd stay in business for ten years.What is the reason behind this notable failure of start-ups? The answer lies at the start of their lifecycle. Many businesses failed to start with a concrete strategy. Whereas successful businesses possess one common attribute — A brilliant business plan.[lwptoc skipHeadingLevel="h1,h4,h5,h6"]This blog post is a practical guide to rational business planning. At the end of this article, you’ll be able to determine how to draft and customize an eCommerce business plan and how to utilize it for promising success.

Ecommerce Business Plan

Intelligent and fruitful business plans help businesses stay focused on a predetermined goal and free from distractions. They also assist business owners in showcasing their businesses to investors and other patrons. If you're doing an online business, your eCommerce business plan is your great asset to rely on. Start with defining your business, your journey, and then set your goal - how to achieve it.

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The human mind is filled with the clutter of information, including useful and garbage, so it’s best to visualize or paper down this information. The ideal business plans appear like mainstream travel plans. They include drafts for growth, projected achievements, and financial summits. Like real-world travel planners, they outline plausible roadblocks, contingency backups, and preventive measures. They clarify vital segments like cash flow, expenditures, support tools, and distribution mediums in detail. In simple words, your eCommerce business plan or strategy serves as the foundational base of your enterprise.

Why do You Need an Ecommerce Business Plan?

Your eCommerce business plan or strategy serves as the lifeline of your company. Writing this vast content and information helps you filter out your priorities and mark the apparent and latent problems before they start to happen. While creating a business plan, you have to:

1. Know your Business

Most of the ideas we got randomly while walking, reading, or taking a bath, and sometimes it took years for someone to formulate an idea. Either way, writing a business plan can help a lot in filtering facts from fiction.Many entrepreneurs start by listing down the critical point effortlessly, like bullets or mind maps, and expand this listing further. You can’t learn to swim by directly diving into the ocean in stormy weather, so it's better to leave complex questions related to tax, supply chain blockages, warehousing, employee satisfaction, etc., for later.

eCommerce Solution: Cloud-based Omnichannel Commerce Platform

At this stage, what you’re searching for is a precis document of your final business plan. To ensure the completeness of your plan, ask yourself these crucial questions and answer them:

• What are you going to sell - tangible products, software products, or services?
• To whom are you going to target – Customer and Market?
• Is a dedicated social media page or a website is required?

2. Identify the resources needed to run your business

You have now prioritized and filtered your ideas in a managed form. Now, you need to collect the resources for practical implementation. Whether small or huge, every business requires some investment in terms of money, but the list of supplies cannot be generalized. You have to categorize your monetary resources and consumption into three segments:

• Financial Resources
• Physical Resources
• Human Resources

Let's discuss this in detail.

Financial Resources

Most entrepreneurs start with a limited budget and capitalize on it as they start progressing. I can buy tools from your savings for the greater good; that’s perfectly fine. If you can’t, there are four alternates to meet this challenge:

• Apply for a business loan
• Financial partnership
• A Patron in the form of investor
• A Private or Government-owned crowdfunding facility

Physical Resources

Filter out the most essential; that’s a technical utility you’ll need for your eCommerce business at the least: one fast PC or a laptop, budget-friendly camera equipment for product photography, and a printer. If you are planning to manufacture your goods, you are required to buy some raw materials. And don’t forget to invest in other essential and apparent items, like the workplace, communication medium (digital and physical), and machinery.

Human Resources

Some entrepreneurs prefer to work in isolation, while others in the form of a clan. It is better to involve some like-minded partners while going for a hike. You can share ideas, financial resources, and more than everyone can learn from the skills and experiences of others.Human resources also include employees, outsourcing vendors, supply chain staff. You need skilled and dedicated people on your side as it’s your company against the world. Money is the biggest ever motivation, so you’ll have to pay them fairly.

3. Build a road map

Where do you assume yourself in the next five or ten years? Are you planning to station an international-level eCommerce platform? Do you want to create job opportunities for your locals or help people grow in underdeveloped nations– No one knows your priorities and end goals better than yourself?Your business plan draft is your success strategy to list your growth strategies, sales targets, and motivation. You have to set the milestones and outline key performance indicators (KPIs).

4. Know your competitors

Many confused entrepreneurs spend most of their time pondering over the doubts like whether or not their business plans are unique or trustworthy? It’s okay if your plan doesn't sound like that of other successful ideas you ever heard of. The only thing that matters is how smartly you gear up against your competition.Take some time to evaluate the strengths and weaknesses of your rivals. Try to learn from their success as well as failures. Focus on producing the goods or unique services with lower cost and higher quality than those offered by your competitors. By doing so, you’ll be able to make your place in the eCommerce marketplace.

5. Seek the opportunities

Opportunities are scattered everywhere in the Universe; you just have to use the appropriate lens and perspective to see. You can easily team up with the already existing companies whose products can align perfectly that with yours. For instance, if you make a charger, they might be making data cables.

eCommerce Solution: Loyalty Management System

Don’t underestimate the power of influencers and social media figures. Partnerships with popular Instagram, YouTube, and Facebook influencers can give you the utmost benefits in the form of word-of-mouth marketing up to the next and smart level. As their followers inspire, product recommendations and suggestions made by influencers can rapidly lead to fame, trend and success.

Comprehensive Business Plan

Efficient and comprehensive business plans are very complex documents. Before you start writing, just go through some business plan templates or designs. Read a couple of sample business plans. After doing so, ask yourself the following three questions and answer these questions with your strategic roadmap.

What are you selling?

As discussed earlier, eCommerce companies sell three different products: goods, services, and digital products. You need to spoon-feed your audience what you are trying to sell and why. You can explain in a general way if you are selling a large variety of items or services, but if you’re a single-item retailer or have limited stuff to sell, don’t hesitate to go into detail.If you sell digital equipment, be clear about its specifications and how customers can get their hands on them. Will consumers download software, music, tutorials, or educational documentaries from your site? What about licensing and other regulatory requirements?If you’re a service provider, explain to your audience precisely what kind of services you will provide and where? Are you bound to local services? How far can you travel or tend to provide services? Do you plan to expand your service-based company for cross-border functionality? It is advisable to maintain FAQ sessions on your site.

Who is your target customer?

Many eCommerce businesses directly trade with consumers. Data is a new commodity. If you’re a business-to-consumer (B2C) facility, maintain a consumer database to use for an online marketing strategy for your product.Some online retailers and service providers operate a business-to-business (B2B) facility. Such as, Manufacturers or vendors who sell raw ingredients to food companies or components to electronics manufacturers fall into this segment.Whether you’re B2C or B2B, you need to figure out why your consumer will fall for you. How helpful for them to visit your site? Why should they opt for you over other available options? Maintain some level of uniqueness in this matter.

How will you acquire necessities?

Some eCommerce companies have in-house manufacturing facilities. But let's say you make tripods or silicon mobile cases; maybe you work on android app development or store management systems. You have to buy raw material or deploy a downloading service in such cases, and you’re ready to take off.If you plan to outsource via a third-party manufacturer, try to maintain a healthy and trustworthy relationship with your vendor before the launch date, and don’t forget to complete any legal or personal documentation, if necessary.Other product-sourcing alternatives are Dropshipping or Wholesale.

Ecommerce Business Plan Outline

All business plans are unique, but most contain these seven primary and essential elements; one cannot miss out. You might decide to add additional topics as per your feasibility and choices, but if you start with the following key issues, you’ll be able to stay on the path.

• Executive summary
• Company overview
• Market analysis.
• Competitive Analysis
• Products and services
• Marketing plan
• Logistics and operations plan
• Financial plan

Executive Summary

An executive summary refines your business idea into a clear perspective and a very simple one-page outliner in a busy routine. Your executive summary joins the plan with the beginning of your plan-making journey, but you might want to reshape and reform it once you have done planning because you will get a new perspective after understanding your venture, customers, and stakeholders. Read it in the end, and don’t hesitate to make any updates and changes. Let's discuss how the executive summary will look like and what should be added to it.

Purpose of your business do?

Provide a brief account of what your facility does. Make it attractive and easy to digest. Must ensure completeness by answering these three questions:

• Are you a B2B business or a B2C business?
• Do you sell products or services?
• What industry is your business in?
• What goals does your business want to achieve?
• Write about your objective and mission. What do you want to attain? Why are you better than the rivals? Why should one choose you?
• What products do you sell?

Provide a brief but information-rich note of the products you sell. If you sell a lot of products, it’s okay to provide a point summary and description. If you believe that your goods or services are better than your rivals', notify what makes you unique.

Who is your audience?

Give a briefing about your target audience here. If you plan to sell your products to a variety of different demographics, tell your audience who they are.

External Resource: eCommerce Business Plan Templates (Link)

Where are you going to sell your products?

If you have a physical outlet as well as an online platform, mention it here. If you’re strictly an eCommerce facility, you might have different online sales mediums such as a brand website, eBay, Amazon, etc.

What is your monetization strategy?

Offer a brief and complete summary of your monetization plan here. Are you going to work with wholesale vendors? Are you collaborating with social media influencers, or will you opt for SMS or email marketing?

Company Overview

Now you have to provide the audience with a detailed and in-depth overview of your business. For now, they’ve done overviewing your company description, mission, and vision; reviewers will know what type of facility you run, what you are selling, and what makes your presence unique.Start with a catchy but straightforward opening statement, and then discuss the following attributes:

• Brand Name
• Domain Name
• Mission
• Vision
• Background Statement
• You and your team

Market Analysis

To win in the dynamic and continuously evolving eCommerce Universe, you have to sharply filter out your target market. While writing this segment of your business plan, you’ll get to know your target sector. You’ll learn about your rivals, your company’s survivability skills in a market, your strengths and weaknesses, and your consumer demographic categorization. Run a SWOT analysis routine. Some key areas to keep in mind here are:

• Market Opportunities
• Industry Trends
• Customer Behaviour

Competitive analysis

To know your rival is the first step towards success. If you fail to understand your competitors, you can’t supersede them. Conducting a competitive analysis can help you overtake the crowd. First, try to filter out the key monopoly giants and potential rivals in your industry; then, list your direct and indirect competitors and act accordingly after observing their strengths and weaknesses. Once you know what your competitors have to offer, you can strategically station your company as a better alternative.

Products and Services

You gave a brief account of your products or services in your executive summary session. Now it’s time to expand that brief section. As we have discussed earlier, make your products and services section attractive and easy to comprehend. To do so, keep paragraphs short, better to use bullets, use non-technical vocabulary.

Marketing Strategy

Your business plan is almost ready, and you’re geared up completely. Now you just have to attract traffic and influx of audience to your eCommerce platform and convince visitors not to leave your online site without buying any product. Here engagement is the key. In simple words, you need a smart marketing strategy.The marketing budget should be kept in mind. You need to find a way to put your product in front of the most accurate and targeted customers without spending irrationally.Keep in mind these 4 Ps to develop an effective and complete plan:

• Product: How will your product sustain consumer between demand?
• Price: how strong your price competition edge is?
• Place: which product placement strategy can drive more attention?
• Promotion: Which marketing medium will you utilize to showcase your product?

Paid Marketing Channels

• SMS marketing
• Social media ads
• Influencer marketing
• Organic Marketing Channels:
• Search engine optimization (SEO)
• Social media pages
• Content marketing

Logistics & Operational Planning

What are your physical and objective requirements to operate your eCommerce business? Your business plan’s logistics and operations segment involves all essential aspects from technical utilities such as PCs, printers, cameras to inventory or warehousing. Your logistics and operations plan should cover Suppliers, Production, Shipping & fulfillment, and Inventory details.

Financial Plan

Nearly all organizations, either small or big, need a certain amount of money to start or support progress. Some entrepreneurs put their own money in this effort, while others build alliances with stable partners and patrons, seek crowdfunding campaigns, or opt for loans.Most financial plans possess the following three economic analyses:

1. Income statement

Your income statement provides a breakdown of your revenue sources and expenses over a specific period. The difference between these variables calculates your deficit or surplus in the form of loss or profit.

2. Cash-flow statement

Cash-flow statements are like real-time income statements. These statements involve the record of both cash inflows and cash outflows over some time, hence helps a lot in managing budget and control expenses.

3. Balance sheet

Entrepreneurs use balance sheets to determine how much equity they have in their facility. The difference of liabilities and assets is calculated as the business’s shareholder equity.

Conclusion

An efficient and smart eCommerce business plan possesses a concrete and promising foundation for success. As you research and paper down your strategy, you can explore more essential topics and insights of market dynamics, trends, challenges, and financial hurdles, as well as solutions.Let us know in the comments section below if the guide was helpful or you have suggestions to make the guide better. Also, feel free to share your e-commerce business experience.

Artificial intelligence (AI) is the future of the technology-driven world. With every day passing, AI is revealing its potential bright side in different domains. Meanwhile, all tech giants such as Google, Microsoft, and Amazon deploy and implement intelligent methodologies across all the segments in their tech framework. AI model for business can help businesses streamline their processes and drive growth.

This advancement in AI and Machine Learning (ML) methodologies, along with the support of rich computational power, is transforming our tech world like never before. By unleashing the latent potential of all the data that organizations collect, AI and ML are indistinguishable from magic, as C. Clarke said quoted for sufficiently advanced technologies. How is this happening so fast? Because recent breakthroughs in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) put the cup of AI on everyone’s table.

Using AI for Businesses Process

For businesses, AI can be deployed in different organizational processes such as business intelligence (BI) to deliver actionable information, data mining to improve customer engagement, and other automation tools to optimizing supply chain functionality.

Step 1: Understand the difference between AI and ML

People get confused when it comes to choosing between AI and ML. If you’re troubling with the same situation, let’s begin by exploring the differences between Artificial Intelligence and Machine Learning.

Also Read: Data Science Vs Data Analytics Vs Machine Learning: Know the Difference

The two terms are often used interchangeably, but they have some notable differences that impact their manifestation. If you can differentiate between them, you certainly know which technology to implement. Let’s dive into further details.

What is Artificial Intelligence?

Artificial Intelligence (AI) is an area of computer science that deals with developing intelligent machines that can think, respond, and solve problems just like humans can do. To make those machines act intelligently, they must be richly provided with enough information about the surrounding world.Artificial Intelligence was coined in 1956 when experts started finding ways to improve the problem-solving capability of computers. In the early 2000s, the development in this field started to acquire notable speed. Until now, AI has become a crucial element of every modern technology.

Some renowned examples of AI include autonomous vehicles, speech-recognition personal assistants, face-recognition systems, etc. Several other AI-based modern solutions have primarily used various business processes to automate and streamline repetitive tasks, improve customer engagement, targeted marketing campaigns, and increase operational capacity with less human resources.

What is Machine Learning?

Machine Learning is the methodology in Artificial Intelligence that uses computer algorithms and statistical models to train machines over massive datasets. By learning and extracting insights from data to conclude, make predictions & rational decisions for business-oriented purposes, ML serves as a state-of-the-art aspect of technology.ML models don’t require human assistance to learn through data patterns. Instead, it’s enough to give some necessary parameters and direction to analyze and compare, and they can figure out how to automatically utilize this information.In an actual sense, Machine Learning works just like the cognitive process of the human brain to acquire knowledge. Consider a toddler that is learning how to speak.

It gathers information from the surrounding, processes this information, and starts making meaningful sounds of its own after some time. This ability and skill began to grow with time. ML works in the same way. A machine gathers data inputs, breaks them down, builds connections, and translates them to deliver an intelligent solution.Now you can differentiate between AI and ML; let’s answer two essential questions about the ongoing thread before discussing AI implementation.

How can AI improve business effectiveness?

There is no absolute answer to this question as it depends on your specific needs and expectations. But some evident and promising main advantages include:

• Increase Employee Productivity
• Improve Marketing Strategies
• Spare Time and Energy
• Reduce Human Error Probability
• Get the best of Businesses Output
• Maximize Sales
• Open Opputininity Corridors
• Boost Revenue

Where is AI ineffective?

Despite being a superpower in the technology domain, AI also has some limitations in certain circumstances. To avoid any trouble and loss of investment, you must acknowledge what you should not expect from AI.

Awareness Limitation

Machines cannot program themselves as they lack awareness regarding our complex real-world problems. Thus, you cannot expect AI to understand our reality and do everything on its own.

Generate creative content

AI can create content using data but cannot maintain creativity in writing content or a blog. You cannot expect an AI to deliver a piece of art.

Ethical decision-making

Machines lack feelings and emotional intelligence because they don’t have consciousness. So, we can’t let them crack the decisive actions and make moral judgments for people that enjoy varying degrees of opinions and mindsets, and even humans fail to do so.

Dependence or Independence

AI can only help humans but cannot replace them. Several job roles are going to end due to machines taking over, but it comes amidst the introduction of several other job roles like data scientists, data analysts, and business analysts, etc. We cannot blindly trust AI to make decisions on our behalf, and we must appreciate this performance gap.

Innovation and invention

AI can extract insights and learn from data, but its ability to build conclusions is limited to some extent as it cannot be creative and comes up with innovative ideas or out-of-the-box solutions.

Step 2: Define your business needs

After acknowledging differences between Artificial Intelligence &Machine Learning, exploring the limitations and capabilities of AI, and filtering fact from fiction. Now you need to consider what you’re looking for and how these two technologies can help you get where you want?First of all, outline the problems you want AI to solve by trying to answer these five questions:

• What outcome(s) are you expecting?
• What are the primary roadblocks in achieving these outcomes?
• How can AI add value to your business that other measures fail to do?
• How can you define success?
• How much data you have readily available, and what additional data do you need to employ?

The answers to these questions effectively define your business goals clearly, then head towards the optimistic solution.

Step 3: Prioritize the main driver(s) of value

Once you’ve defined your business goal, you need to rectify your prioritizations regarding your AI project’s potential business and financial benefits. You should consider all the plausible AI solutions and try to relate each option with dedicated returns. To do so, focus on short-term objectives and encapsulate either the financial or business value that best suits you as per your circumstances and conditions.While outlining your objectives, refrain from ignoring the value drivers such as increased value for consumers or enhanced employee productivity and feasibility.

Must consider the aspects and domain where machines can outperform the human factor, especially regarding iterative or cyclic tasks.Beware of not implement solutions based on fiction or impractical optimism. Popular opinion is sometimes not a sane idea.Instead, consider if you can efficiently augment a solution into your work routine, analyze how it sustains your business model, and evaluate whether integrating an AI-based solution to your existing business model would increase your operational capability over the long run.

Step 4: Evaluate your internal capabilities

There’s often a void between what you want to do and what you can acquire within a specific period. Therefore, after aligning and sorting your priorities, it’s required to decide which approach best suits your condition. It can help in:

• Developing a new solution using internal resources
• Employing a ready-made product
• Collaborating with a partner to build an AI-based solution
• Outsourcing the resources or a team for AI development and integration

Again, choose the option that best suits your objective and available resources. Keep in mind to do in-depth research on existing solutions before jumping into the lengthy process of development. Try to find a product that meets your criteria and integrate it into the current business model. This is one of the most cost-effective practices.

Step 5: Consider consulting a domain specialist

If you already have a highly skilled developer team, they can just maybe build your AI project off their own back. Regardless, it could help to consult with domain specialists before they start.Developing AI is not the same as building typical software. AI is a hyper-specific specialism that’s difficult to learn. It requires lots of experience and a particular combination of skills to create algorithms that can teach machines to think, improve, and optimize your business workflows.

If you have any doubts, you may simply choose to outsource your AI development to an agency specialized in big data, AI, and machine learning. AI agencies not only have the knowledge and experience to maximize your chance for success, but they also have a process that could help avoid any mistakes, both in planning and production.

Step 6: Prepare your data

AI is a kind of superpower. To make it perform at its best, you need high-quality and clean data. But what does clean mean in this scenario?The clean dataset is the one that is:

• Free from inconsistent information
• Accurate up to the highest degree possible
• Organized in such a way that it contains all the necessary data points required by an algorithm

Data is the key. Even the most advanced algorithms cannot return you the desired outcomes if you lack high quality and clean data set. The prerequisite to AI is to organize, filter, and enlarge your dataset as much as you can.Indeed, investing in the quality of data is something you'll never regret. Integrating an AI-based solution is not the liner or one-time task like mainstream software deployments.

The process involves a series of scalable solutions and updates, but you must build their foundations on pure data to adapt to this development and integration environment. Similarly, the more data you have, the better and accurate outcomes your AI solution can provide you.After getting your data prepared and refined, don’t forget to make it secure and protected. Mainstream security measures like encryption, anti-malware apps, or a VPN are enough to protect your data asset. Don’t hesitate to invest in modern security infrastructure.

Step 7: You’re ready to start - but start small

Approaching this step, you’re almost prepared to start. It’s good to stay a little insecure. Stay attentive and selective while feeding your data to the AI. Refrain from installing all your data in the machine and waiting for ripened fruits.Start by feeding the algorithm with a sample dataset and use AI to evaluate the results. If the odds turned out to be in favor, launch the solution carefully with necessary preventive measures.

You can track the performance of your newly installed AI against the train set and then gradually start supplying the system with your test set that the machine has never read before.The strategies above serve as preventive measures and strategic steps to successfully build a dedicated AI solution without any plausible loss or damages.

AI Solutions You Can Implement Today

Here are some practical examples of AI applications that businesses are already using nowadays. Modern companies implement these solutions to automate and enhance business processes, gain a competitive edge over rivals, and boost their investment (ROI).

AI-based Recommendation Engines

AI-based product research tools are among the most trending artificial intelligence applications in the retail and e-commerce industry. They assist companies and business owners in predicting consumer behavior and buying patterns to offer personalized advertisements, enhanced engagement, and uplift revenue via upselling and cross-selling.

According to research, recommendation engines drive around 25% more site traffic and 25% more revenue and boost the probability of confirmed orders by over 10% as an average.

These solutions utilize algorithms gathering historical data such as past purchases, product search, customer demographics, and build recommendations to suggest each client “things they may also like” and propel higher sales. They also make use of content-based filtering and collaborative filtering. The first method considers keywords typed by customers when searching for products online; the second makes shopping predictions based on customer behavior and preferences.

Chatbots

Today, it’s becoming ridiculous to be surfing any site without ever falling prey to the chatbot. Bots have penetrated the abyss of the enterprise domain. Many businesses rely on utilities like Google Dialog flow or Motion.ai to develop personalized chatbots and integrate the interactive medium on their sites to engage more customers intelligently and smartly.

Since AI-based bots are generally linked with a communication medium, they can also prove helpful in handling routine tasks. Intelligent bots are becoming human counterparts in scheduling appointments, sending emails, and public dealing up to a great extent in domains like travel bookings, order confirmation, etc.

Business Process AI Automation

Another notable aspect of the deployment of artificial intelligence in business models is business process automation (BPA). This methodology addresses the problems associated with automating iterative business processes and routines that enable an organization to save effort, time, and human resources for core business processes and areas. It allows companies to utilize the full potential and skills of employees for more significant problems and tasks. Irrespective of the nature of the iterative process or routine, we can streamline and automate it by integrating AI and ML solutions.

For instance, consider extending the influence and role of Artificial Intelligence and Machine Learning in airline ticketing services. BPA model can manage all functions related to ticket availabilities, perform text recognition to recognize customer’s interests, prioritize seat availability options by filtering all the data points that reveal customer’s mindsets, and finally inform public service agents about complaints and issues addressed immediately.

Existing Artificial Intelligence solutions also help businesses to streamline human resource departments in the hiring domain. For instance, an AI-driven system can handle the delivery and receipt of recommended documents. It can also notify new employees about the company policies, rules, and regulations and recommend necessary procedures to practice as they come onboard. ML and AI-based solutions can also respond to some FAQs that new employees are likely to ask.

Customer’s Behaviour Prediction

Imagine if you know with high certainty what your customers will most likely buy and use that insight to boost sales by offering packages. AI gives a magical wand that can do it for you. Predicting customer behavior with predictive analysis allows businesses to engage more customers effectively and increase sales by offering relevant products and services.

By mining the data from eCommerce sites and social media, predictive AI algorithms capitalize on the deep insights they extract from every customer’s data and develop an offer with a high probability that customers are more likely to buy. This capability provides retail and eCommerce businesses with the gateway to engage favorable customers at a favorable time, prioritize those who are more likely to purchase products or services resources, and attract them to move forward with a purchase by sending notifications and reminders, social media advertisements, and personalized emails with promotional offers.

AI-based Anomaly Detection

Anomaly detection deals with the capability of detecting abnormal and dubious behavior or patterns within the gathered data pool. This service can be used explicitly in extensive datasets that would be more complex to handle and unlabelled data that are more difficult to analyze for mainstream analytical solutions.Historically, businesses used to detect problems, errors, and damages after their occurrence. They would indeed have some preventive measures, but those supplements are based on predetermined situations, making them outdated for today’s rapidly evolving, increasingly complex business environment.

AI provides a solution with predictive analytics and intelligent Machine Learning algorithms, uplifting businesses while filtering anomalies and gaps in various business processes. Supervised anomaly detection is the methodology that processes data to separate normal” from “abnormal” based on the categorical variables and labels they were supplied with. The unsupervised approach involves its methods to detect data that appears to be somehow distinct from the rest of the mass. These approaches and methodologies can be used to detect fraud, network intrusions, and any other segment where anomalies can damage efficiency and put stress on cost.

The examples above are a glimpse of what AI can do. Other real-life examples include order predictions, speech and image recognition, and much more.

Conclusion

We have discussed what AI and Machine Learning are capable of doing and how they can assist businesses to enjoy the various benefits and independence, and they will continue to expand up to new horizons. AI-driven solutions possess some unique features, making them more complex to deploy. Below are few golden rules to keep in mind while going through this extensive exercise. Firstly, the development depends on the quality and quantity of data available at the moment. If data is corrupted and lacks organization, the project will surely fail, no matter how intelligent your AI model is.Secondly, the best AI experts are very hard to find and demand high salaries in return for their services. It makes the in-house development process very expensive and complex.

Outsourcing is the key here.Finally, the development of AI-based solutions requires extensive knowledge and expertise to align with the problem you’re trying to curb accurately. If this alignment fails, it may either turn out to be a chaotic scenario or a complete loss of resources, time, and money.The development of an AI-based solution capable of handling several business processes proceeds in almost similar phases as other software development life cycles but needed to be dealt with extra care. The whole idea is to perform information and resource gathering, followed by research and validation, data cleansing, then move to the development phase and testing with necessary data security protocols and preventive measures that lead to the final product creation, finally release the product and make regular reforms to add more value.Let us know about your experience or queries in the comments section below about building and using an AI model for business.

Nearshore outsourcing is a mechanism for outsourcing such duties to firms in neighboring countries to improve operating costs. It provides better control and can promote collaboration between the outsourcing company and the active outsourcing partner.[lwptoc title="Contents" skipHeadingLevel="h1,h4,h5,h6"]More cultural and linguistic continuity is therefore available, which decreases the risk of confusion and promotes teamwork. It can optimize business efficiencies and minimize the conventional offshoring barriers.

Image Ref: Cleveroad

Why Nearshore Outsourcing is the Key to Grow Your Business?

We will dig in deeper to unveil the key advantages of nearshore outsourcing in this article and will discuss how it can boost your business in multiple ways:

Less Cultural and Language Differences

Language and cultural background are essential for good communication between both parties. When your in-house staff and your nearshore outsourcing company's employee speak or understand the same language, it cuts down the communication barriers and makes coordination much more manageable.Cultural Background also plays its part as the teams better understand one another and schedule plans quite well due to similar working routines, holidays, etc. You can discuss your projects and get input more efficiently thanks to the similar culture and eventually engage your nearshore Outsourcing Partner more and invest in your goals.

Proximity

With Nearshore Outsourcing, you get a team with cultural similarities, a common language, and technological know-how, allowing the external unit to connect with the existing team easily.Improved teamwork and coordination enhance the speed at which internal and external resources help you achieve your goal. This makes it easy for the external staff to understand and implement the methods and quickly understand the proposal's specifics.By speeding up the integration phase and exchanging information, the external team will add value quite soon, boost the ROI significantly and ensure that the projects meet their deadlines.

Improved Infrastructure

Nearshore outsourcing also contributes to better infrastructure. You will feel content about your company's required infrastructure as with the number of competitive nearshore destinations, and your business will have the infrastructure that it needs to work efficiently.

Also Read: How Staff Augmentation Helps with Software QA

Picking the right collaborator in a region with stable Internet and communication networks would prevent unforeseen events from maligning the project.

Urgent Workforce

Nearshore developers are the perfect way to extend the workforce in losing staff since engineers are accustomed to project-based operations and are agile enough to plunge into a new project with quality expectations. For the nearshore externalizations, this phase would naturally be swift due to the language and cultural similarities.

Cost-Effective

Cost reduction is also the key goal in an outsourcing process. Nearshore outsourcing projects prove to be way more affordable than offshore outsourcing. The starting costs and risk-related costs are exponentially reduced. Furthermore, nearshore outsourcing removes quality issues and results in a beneficial outcome.

How to choose the Best Nearshore Outsourcing Company?

Always keep in mind the following factors when deciding to collaborate with a Nearshore Outsourcing Company:

Experience

The main thing is to pick a software vendor that has solid core principles like experience. An experienced vendor can grasp and understand the unique requirements more efficiently. For web development, always go for an experienced software development agency to save your time and get the best outcomes.Your selected company should have at least four to five years of experience in the field. Always ask questions about their previous projects, accomplishments, and samples.

Reputation

It is essential to do proper research about the company and its history before hiring a nearshore partner. Visit their website, read reviews and then decide if you want to approach them or not.You may ask them about their portfolios as successful companies with positive results will not shy from sharing their achievements; on the contrary, if a company hides its information, you can consider it a significant red flag.

Security

Although companies are eager to give their cybersecurity services, you should know that most companies lack adequate security. You should always enquire about the security measures and their intensity before hiring a nearshore security service.A successful outsourcing company will give you an outline of its strategy to show its approach to cybersecurity. They should demonstrate how they will protect your sensitive data and manage proper backups and storage. A reliable security approach starts in the planning stage of a project and continues throughout the project.

In-house Staff Compatibility

Companies in near-shore outsourcing projects make your in-house staff's job easier by offering skilled experts a valued compliment to the team. Therefore, it is necessary to make the in-house staff satisfied with the Nearshore outsourcing team to deter them from moving to other companies.Sometimes it is believed to substitute workers with inexpensive workers through outsourced partners. But it all involves growing and maintaining established teams. As businesses embrace the external unit as an integral element of the core team, they build more possibilities for mutual development and creativity.If you are searching for a partner, make sure your dealer has employees compliant with your own, and reassure your in-house staff that their jobs are secure. Otherwise, the partnership tends to deteriorate.

Nearshore vs. Offshore vs. Onshore: What's the difference between them?

Nearshore, onshore, and offshore outsourcing are based upon collaborating or temporarily hiring employees for your project(s) and expanding your team. However; their difference is as follows:

• Nearshore Outsourcing refers to taking your outsourcing partner's services in a neighboring country or a nearby region.
• Onshore Outsourcing refers to taking your outsourcing partner's services in the same region or country as yours.
• Offshore Outsourcing refers to taking your outsourcing partner's services in a different country far from your location and following a different time zone.

In Conclusion

Nearshore Outsourcing has numerous benefits as it minimizes costs, improves your company's infrastructure, and builds effective teams, ultimately leading to your project and business' success. The best practice is to conduct proper research before finalizing a nearshore outsourcing company for a collaboration.