>Business >ML in the retail industry – Use cases, applications, and the future Part 3

ML in the retail industry – Use cases, applications, and the future Part 3


ML and Predictive Analytics are two connected spheres that are not dependent on each other, while Predictive Analytics can be introduced with or without ML. The variations between these two options are noteworthy in what they can provide to confer advantages on enterprises in Retail industry, specifically speaking.  

What is the meaning of Predictive Analytics? 

Predictive analysis is the procedure of exploring massive amounts of data to search for info and patterns, which are converted into numbers and insight worthwhile for humans to comprehend the upcoming or possible outcomes of something – like real-time predictions of staff satisfaction in the business space. 

What is the meaning of Cognitive Analytics? 

Cognitive Analytics or Cognitive Computing is terminology that is really connected to tasks that fall under Artificial Intelligence. AI emulates the manner in which a human brain functions via learning from current historical data and inferring patterns from that data. This variant of analytics has the implication that the algorithms reaches certain conclusions from current knowledge bases and then puts them into the knowledge base in a type of self-learning loop.  

This is accomplished with ML algorithms, so Cognitive Analytics is the umbrella term for all the things connected to machines processing prior unstructured data and extraction of meaning from it. Therefore, that’s what ML accomplishes. Cognitive Analytics seems to take the dominant role owing to its efficiency. Ultimately, we can detect 3 manners in which processing of massive amounts of data can be done, in addition to drawing insights from it. 

  • People: They can generate patterns inferred from new, unstructured data – even though, for massive amounts of data, this procedure will be really slow and monotonous. 
  • Big Data Analytics: It can swiftly draw insights from the massive amount of structured data, even though it cannot instantaneously generate it from unstructured data 
  • Cognitive Analytics: Lastly, Cognitive Analytics brings together two benefits of the manner in which a human brain functions to efficiently process unstructured data and simultaneously swiftly manage massive amounts of data. 

Predictive Analytics utilities and Predictive Technologies 

Predictive technologies, to begin with, point towards a grouping of means leveraged to predict numbers/patterns based on prior data or records. It has been leveraged primarily for marketing and business perspectives to forecast and supply and demand patterns. Tasks like stock exchanges, weather forecasts, and socioeconomic activities were performed, to start with, with Predictive Analytics tools.  

Amazon is one of the forerunners to harness predictive tech. Its website displays lists of merchandise that clients have demonstrated interest in and displays a listing of products purchased by clients with similar interests.  

TIA or Total Information Awareness is another Predictive Analytics utility, the objective of which is to gather massive amounts of data on people (i.e., an individual’s formation signature) from all available sources, process this information, and identify potential terrorist activity.  

Presently, there are tons of predictive analytics utilities – a majority of which are available to leverage on the internet but they are not free tools. These utilities can reduce the time required to collect a huge dataset from several sources, clean up this information on the basis of custom parameters, and make an analysis with differing tech and algorithms. A research article by Deloitte on the Analytics Advantage had the revelation that utilizing analytics prior to beginning any business or strategy made any connected decision more successful in half of the cases researched. 

The Power of Predictive Analytics 

Equipped with the sophisticated ML strategies like more complex regressions, classifications, and neural networks, ML engineers can leverage a computer’s capacities to forecast strategically crucial outcomes within e-commerce, retail, and marketing. Companies are required to have the capacity to deploy AI in operational functions in several departments to carry out several processes and make forecasts to improve their business agenda – particularly within financial planning, where Predictive Analytics can unveil the value obfuscated in the gathered data. This enables enterprises to function with lesser expensive mishaps. 

ML and Predictive Analytics 

While ML is leveraged to scale models for automatization and optimization tasks in an array of disparate domains, it is additionally leveraged for making more consistent and precise risk evaluations, making suggestions for business intelligence reasons, and carrying out other predictive tasks that can be accomplished with the power of Predictive Analytics. 

Advantages of Predictive Analytics driven by ML 

Predictive analytics can manage assumptions with regards to the outlook with no assistance from ML models; although, this manner of functioning has its drawbacks.  


Meanwhile, leveraging ML for the purpose of predictive analytics has its advantages:                                                                                                                                                                                                                                                                     


If a person broadly considers the aspects above, the advantages of Predictive Analytics with Machine Learning are obvert. Forecasts with ML frameworks are our future and sophisticated enterprises should be reliant on them, instead of mere simplistic Predictive Analytics tools and tech leveraged by statisticians.  

How is Predictive Analytics Leveraged in Business? 

Predictive Analytics is leveraged in businesses to avert business loss, forecast client behaviour over a protracted duration, improve the share of a business segment, detect targeted markets on the basis of actual data and indicators, obtain insights on the ideal manner to approach individual clients, and undertake analysis of everything from purchase patterns to client behaviour and social media activity. 

Predictive analytics confers advantages upon businesses in these spheres:  

Customer Segmentation 

Each enterprise develops its proprietary way of undertaking research which market to delve into, considering what would bring the most value addition to their domain, services, and products. Actual data and indicators assist in detection of targeted markets with predictive Machine Learning strategies. The next stage is to identify the most relevant market segments for the goods or services your company provides. 

Churn Prevention 

It is a lot simpler for an enterprise to keep their current clients than to make expenditures on marketing campaigns to obtain new ones. Predictive analytics can assist in prevention of client churn, averting the requirement to substitute a loss of revenue. If you can swiftly detect the characteristics of dissatisfaction amongst the current clientele in your database, you can not just avoid losing those clients, but additionally, detect the client segments that are at risk of going elsewhere to carry out their transactions. 

Predictive Maintenance 

The budgeting for maintenance can be 11/2 times bigger if an enterprise does not possess any downtime forecasting and prevention protocol. ML utilities can undertake analysis of unstructured data and metrics connected to tech equipment lifecycle administration. Probable upkeep events and capital expenditure requirements can be forecasted to avert expenditure of monetary resources on repairs instead of making investments in infrastructure and equipment.  

Risk Modelling 

A humongous amount of historical information gathered through the enterprise’s existence is an origin for worthwhile data to derive risk regions and trends to assist in determination of the situations that can negatively impact enterprises. Predictive Analytics can capture and quantify risk problems, examine them, and suggest actions to reduce the factors behind it. 

Quality Assurance 

Inadequate QC might crucially impact client’s satisfaction levels and their purchase trends, eventually influencing an organization’s revenue and market share. Therefore, a smartly applied Predictive Analytics strategy can furnish insights into potential problems and trends prior to them starting to impact the organization. 

Implementing ML in retail 




Most widespread methods of implementing predictive analytics within marketing 

There are some activities carried out with predictive analytics that can be categorized as connected to AI within marketing and therefore enhancing a business’s marketing techniques.  



This brings us to the end of the third part of our blog series. Presently, a majority of progressive enterprises have started to be reliant on the electricity of our time – AI solutions, which provide the older Predictive Analytics strategies fresh life and convert it into a very effective instrument to obtain valuable business-related insights and forecasts. Predictive analytics has advantageous applications in various domains like Retail, e-commerce, and Marketing. It turns into the fuel to drive an enterprise’s business decisions and forecast its success in the future. 

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