August 27, 2019

Top 10 Use Cases For Predictive Analytics in Retail

Predictive analytics is now the go-to proactive approach by retailers and decision-makers to make the best use of data. Retailers face a constant barrage of data, the majority of this crucial data goes to waste in the absence of any concrete process or tool to gain valuable insights into the mind of the customer.

 

Predictive analytics amalgamates this huge inflow of data with historical records to forecast activity, behavior, and trends in the future.

Once heavily criticized as a magic trick based on make-believe, Predictive Analytics has proved to be an important asset in the arsenal of retailers and is now being widely used throughout the world to maintain an edge over the competition and gain considerable market share. Predictive Analytics is a purely data-driven science that commands a multi-billion dollar market today.

The reach of predictive analytics is unlimited, here are 10 use cases for Predictive Analytics in retail:

#1
CASE 1: PRICING
Pricing is one of the core areas of functionality of predictive analytics where its real-time machine learning and data science comes to the fore. The wonders of predictive analytics make it possible for retailers to get answers to questions like:
What is the correct price point to maximize sales?
How often to run price-based promotional activities?
What is a customer’s optimal attainable price?
What would be the impact of competitive pricing on sales?
Besides this, Predictive Analytics also takes factors like weather forecasting and real-time sales data into consideration to alter and induce the best pricing.
#2
CASE 2: INVENTORY MANAGEMENT
A poorly maintained inventory is every retailer’s worst nightmare. Not only does it lead to a loss in sales over time, but also represents a poor indicator of inadequate demand for a product. Questions like what to store, what to discard and when to do so can all be answered. No one wants to hold on to products that are not yielding any sales and every retailer wants to keep replenished stocks of items that are popular with consumers. Predictive analytics removes the need to buy and remove stocks of products on a hunch.
#3
CASE 3: RECOMMENDATION ENGINES
You will have come across recommendations that magically pop up on a feed promoting items that you had been looking for in online stores. Instead of recommending the customers products based on their purchase history, predictive analytics makes use of cumulative data to forecast what the buyer is likely to buy next and will generate product recommendations to match.
#4
CASE 4: SMART REVENUE FORECASTING
Counting chickens before they hatch is what every retailer loves to do. With Predictive Analytics, this becomes a viable reality. Instead of banking on just the historical data of old clients and customers to predict revenue in the uncertain arena of retail where trends, tactics, and promotions are constantly changing from one year to the next, Predictive Analytics makes use of accurate forecasts that combine deep analysis of new buyers and their probable buying habits.
#5
CASE 5: CHANNEL INVESTMENT
Social media marketing and advertising is the new hot property in retail marketing. But it comes at a higher upfront cost when compared to regular modes of advertisement and traditional paid search. Predictive Analytics helps retailers maintain a strong social media marketing presence without the repercussions of low returns and delivers the accurate forecast of social customers acquired.
#6
CASE 6: PROMOTIONS
Promotions act as the biggest customer magnets and are cleverly targeted towards specific individuals to generate greater sales. While offline activities were enough to give retailers adequate information about their customers through their purchases, eCommerce allows the study of individuals to a vastly elevated level. Predictive Analytics monitor the actions of consumers minutely to dish out a corresponding promotion by aligning them with the buying needs of the consumer.
#7
CASE 7: CAMPAIGN MANAGEMENT
Marketing campaigns are an important part of the retail business. However, there have been many cases where inefficiency in the management of marketing campaigns led to poor ROI due to budget constraints and inaccurate calculations. Predictive Analytics is useful to implement actions for individual campaigns that target a specific sector of consumers, hence making the marketing more budget efficient.
#8
CASE 8: BEHAVIOUR ANALYTICS
Understanding consumer behavior to further work on communications with them to increase revenue and decrease the acquisition cost are some of the main challenges faced by retailers. Predictive analysis is a data-driven technique that can gain useful insights about consumers in a short period through data accumulated over time. Predictive Analytics filters this data to gather useful information and understand the consumers better.
#9
CASE 9: SHOPPER TARGETING
Once the retailers are proficient in understanding the behavior patterns of their consumers, understanding the consumer demographics is the next natural step in the process of consumer satisfaction. Predictive Analytics acts as a beacon to target retailers to give focused and customized offers that are targeted specifically at shoppers.
#10
CASE 10: FRAUD DETECTION
Retailers have been in a constant struggle of fraud detection and prevention since time immemorial. Not only does fraud lead to huge financial losses but also damages the reputation of firms and retailers when consumers suffer. Predictive Analytics acts as a defender against fraudsters and fraud rings by constantly monitoring for suspicious activities.