Use cases of data analytics in retail

“Knowledge is power in business, and data is the fuel that creates this power “

Data is very beneficial for every companies decision making, a thorough analysis of huge amount of data allows to manipulate or influence the customer’s decisions. Data is useless until some meaningful insights are gain from it so to extract such useful insights data scientist perform data analysis. Data scientist uses scientific methods, algorithms and systems to extract knowledge or meaningful insights from data and take the maximum advantage of this data to take major decisions is a key strategic practice for any data.

When we talk about retail in business , it is developing rapidly every single day. The retailers have to analyze data and get meaningful insights to understand his/ her customers. Thereby, it can help to easily trick the customer needs to get maximum benefits from them.

Retail analytics helps to translate real- world business activities into qualified and quantified data to help in better in decision making. The data can help to get insights like consumer behaviour patterns, inventory updates, supply chain information like:

1.      Recommendation engines

2.      Market basket analytics

3.      Warranty analytics

4.      Merchandising

5.      Lifetime value prediction 

Let’s discuss every type in detail.

·        Recommendation engines:  It is a system which helps to analyze data and predict preferences of user’s while they are browsing the internet. This has helped retailers a lot in understanding customer needs and patterns . Thereby, providing customer’s what they need and helping retailers to analyze trends to increase their sales and revenue. There are three main type of recommendation systems:

1.      Collaborative filtering

2.      Content-based filtering

3.      Hybrid recommendation system

·        Market basket analysis: It is a traditional tool for data analysis in retail. This process includes organizing huge amount of data collected via customers transactions. Future trends and decisions can be predicted on a large scale with the help of this tool. Knowledge of present items in basket along with customers likes, dislikes and previews are very beneficial for retailers for prices making and content placement. It is mostly conducted via rule mining algorithm.

·        Warranty analytics:  Warranty analytics in retail is used as a tool for warranty claims monitoring, detection of fraudulent activities, increasing quality and reducing costs. For identification of claim patterns and problem areas data and text mining is involved.The data further  is transformed into actionable real-time plans, insight, and recommendations via segmentation analysis. The main concentration is on detecting anomalies in the warranty claims.

·        Merchandising: Merchandising is a very essential part of retail business. It covers a vast range of activities and strategies which is aimed to increase sales and promotion of product. The implementation of merchandising tricks help to influence the customer’s decision making. Rotation merchandise helps the retail businesses to keep the assortment renewed and always fresh. Branding and attractive packaging retains customers. The merchandising helps in picking up the insights from data and forming priority sets for customers.

·       Lifetime value prediction:  It is the total value of customers profit over the entire customer business relationship. Main attention is paid on revenues, as far as they are not so predictable as costs. All forecasts are made on past data which leads to recent transactions. Generally the CLV models ( Customers lifetime value prediction models) collects , cleans and classifies data according to customers preference, expenses , their most recent purchase and behavior that structures them into input. This application helps to identify the customer’s buying patterns until he/she stops purchasing.