Big Data Analytics in Retail

Retail industry collects large amount of data on sales and customer shopping history. The quantity of data collected continues to expand rapidly, especially due to the increasing ease, availability and popularity of the business conducted online. Once, data is collected, stored prepared and enriched - big data analytics can help identify customer behavior, discover customer shopping patterns and trends, improve the quality of customer service, and achieve better customer retention and satisfaction.

Following are some of the retail applications of advanced analytics in retail:

 

Some of the retail applications of data mining are in following areas:

Customer Segmentation: Customer segmentation is a vital ingredient in a retail organization's marketing recipe. It can offer insights into how different segments respond to shifts in demographics, fashions and trends. For example it can help classify customers in the following segments:
· Customers who respond to new promotions
· Customers who respond to new product launches
· Customers who respond to discounts
· Customers who show propensity to purchase specific products

 

Campaign/ Promotion Effectiveness Analysis: Once a campaign is launched, its effectiveness can be studied across different media and in terms of costs and benefits, and  this can greatly help in understanding what are the key success factors. Some of the questions that analytics can help to answer, are:
· Which media channels have been most successful in the past for various campaigns?
· Which geographic locations responded well to a particular campaign?
· What were the relative costs and benefits of this campaign?
· Which customer segments responded to the campaign?

 

Customer Lifetime Value (CLV): Not all customers are equally profitable. CLV attempts to calculate projected relative measure of value by calculating Risk Adjusted Revenue  (defined as the probability of customer owning categories/products in his portfolio that he currently doesn't have), as well as Risk Adjusted Loss (defined as the probability of customer dropping categories/products in his portfolio that he currently owns) and adding to some Net Present Value, and deducting the value of servicing the customer.

 

Customer Potential: Also, there are those customers who are not very profitable today may have the potential of being profitable in future. Hence, it is absolutely essential to identify customers with high potential before deciding what the best way to realize that potential is through the right marketing stimully.

 

Customer Loyalty Analysis: It is more economical to retain an existing customer than to acquire a new one. To develop effective customer retention programs it is vital to analyze the reasons for customer attrition. Business Intelligence helps in understanding customer attrition with respect to various factors influencing a customer and at times one can drill down to individual transactions, which might have resulted in the change of loyalty.

 

Cross Selling: Retailers use the vast amount of customer information available with them to cross sell other products at the time of purchase. This can be done through product portfolio analysis and then selling the products that are missing from typical portfolios. Also market basket analysis can be another food method for effective cross selling. Look-a-like modeling is yet another strategy where model is produce that produce some quantitative measure of affinity of the customer to a specific product.

 

Product Pricing: Pricing is one of the most crucial marketing decisions taken by retailers. Often an increase in price of a product can result in lower sales and customer adoption of replacement products. Using the analytics, retailers can develop sophisticated price models for different products, which can establish price - sales relationships for the product and how changes in prices affect the sales of other products.

 

Target Marketing/Response Modeling: Retailers can optimize the overall marketing and promotion effort by targeting campaigns to specific customers or groups of customers. Target marketing can be based on a very simple analysis of the buying habits of the customer or the customer group; but increasingly advanced analytics are being used to define specific customer segments that are likely to respond to particular types of campaigns.

 

Supply Chain Management & Procurement: Supply chain management (SCM) promises unprecedented efficiencies in inventory control and procurement to the retailers. With cash registers equipped with bar-code scanners, retailers can now automatically manage the flow of products and transmit stock replenishment orders to the vendors. The data collected for this purpose can provide deep insights into the dynamics of the supply chain.

 

Vendor Performance Analysis: Performance of each vendor can be analyzed on the basis of a number of factors like cost, delivery time, quality of products delivered, payment lead time, etc. In addition to this, the role of suppliers in specific product outages can be critically analyzed.

 

Demand Forecasting: Complex demand forecasting models can be created using a number of factors like sales figures, basic economic indicators, environmental conditions, etc. If correctly implemented, a data warehouse can significantly help in improving the retailer's relations with suppliers and can complement the existing SCM application.

 

Storefront Operations: The information needs of the store manager are no longer restricted to the day to day operations. Today's consumers are much more sophisticated and they demands a compelling shopping experience, and that is why store manager needs to have an in-depth understanding of customer's tastes and purchasing behavior and advanced analytics is perfect technology to help the manager to gain this insight.

 

Store Segmentation: This analysis takes the data that is common for different stores, and finds out which stores are similar in terms of product demand and purchases, customer characteristics, and geo-location characteristics of the area where stores are located, proximity of the schools, nearest competitors, etc.

 

Market Basket Analysis (Association analysis): This analytical method is used to study natural pull between products, where if customer purchases one products, likelihood of purchase of another product is increased. This technique is expressed in rule: "If product A, also product B". Variant of association analysis is Sequencing, which brings in time order, and it can be expressed with: "If product A, time period later - product B". Both types of analysis have various uses in the retail organization. One very common use of Market Basket Analysis is in in-store product placement. Another popular use is product bundling, products to be sold in a single package deal.

 

Category Management: It gives the retailer an insight into the right number of products to stock in a particular category. The objective is to achieve maximum profitability from a category; too few products would mean that the customer is not provided with adequate choice, and too many would mean that the products are cannibalizing each other. It goes without saying that effective category management is vital for a retailer's survival in this market.

 

Out-Of-Stock Analysis: This analysis probes into the various reasons resulting into an out of stock situation. Typically a number of variables are involved and it can get very complicated. An integral part of the analysis is calculating the lost revenue due to product stock out.

 

Channel Profitability: Data mining can help analyze channel profitability, and whether it makes sense for the retailer to continue building up expertise in that channel. The decision of continuing with a channel would also include a number of subjective factors like outlook of key enabling technologies for that channel.

 

Product - Channel Affinity: Some product categories sell particularly well on certain channels. Data mining can help identify hidden product-channel affinities and help the retailer design better promotion and marketing campaigns.

 

Finance and Fixed Asset Management: The role of financial reporting has undergone a paradigm shift during the last decade. It is no longer restricted to just financial statements required by the law; increasingly it is being used to help in strategic decision making. Also, many organizations have embraced a free information architecture, where financial information is openly available for internal use.

 

Following are some of the uses of BI in finance:

 

Budgetary Analysis: Data warehousing facilitates analysis of budgeted versus actual expenditure for various cost heads like promotion overruns can be analyzed in more detail. It can also be used to allocate budgets for the coming financial period.

 

Fixed Asset Return Analysis: This is used to analyze financial viability of the fixed assets owned or leased by the company. It would typically involve measures like profitability per sq. foot of store space, total lease cost vs. profitability, etc.

 

Financial Ratio Analysis: Various financial ratios like debt-equity, liquidity ratios, etc. can be analyzed over a period of time. The ability to drill down and join inter-related reports and analyses can make ratio analysis much more intuitive.

 

Profitability Analysis: This includes profitability of individual stores, departments within the store, product categories, brands, and individual products.

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