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Top-5 uses of Predictive Analytics for supermarkets and retail grocers

 

Predictive Analytics, the science of forecasting future trends based on the study of present-day and past data, was once dismissed as some kind of mumbo jumbo or crystal ball gazing. Today, inexorably, it’s making inroads into the retail sector. For example, more and more grocery retailers and supermarkets, whose profit margins, more often than not, skate on thin ice, have started turning to this data-driven science to help them get a leg up over competition.

Worldwide, the retail grocery sector has become a free-for-all battleground, with even non-grocery players (Amazon.com, instacart.com,netgrocer.com, peapod.com to name a few) in the fray, vying for the food dollar. So retailers are using every available weapon to counter the onslaught, and that includes Predictive Analytics. This was inevitable, for we live in a world where increasingly, data-based marketing is taking precedence over other forms.

A Transparency Market Research report has forecast that the overall market for Predictive Analytics software to touch US $6.5 billion globally by 2019.

Increasingly, Predictive Analytics is becoming one of the key drivers of profit. Retailers in the US, for example, are leveraging on predictive technology tools to unleash the power of data for customer-facing and operational functions. To twist a popular movie title, this form of analytics not only tells the retailer of what you did last summer (that’s data mining, the source of all Predictive Analytics) but can tell, to a vast degree of accuracy, what you will be doing this summer.

But where exactly can this method of analytics be used by this business segment? What are the priority areas, for example? Based on research, this is what we at Express Analytics #ExprAnalytics found:

  1. Promotions
  2. Shopper targeting
  3. Marketing Campaign Management
  4. Pricing
  5. Inventory management

Gone are the days when a grocer’s marketing effort was relegated to a weekly mass mailer, these are no longer effective. The customer has moved on from coupon clipping to mobile coupons on their smartphone. The marketing is now a two way street, the consumer tells you their preferences, location, price sensitivity, basket sizes, via their smartphones and browsing behavior. The marketer has to make sense of this digital stream and respond with near realtime promotions, targeting, replenishments and dynamic pricing. It takes two to tango so you better sense where your dance partner is going to be next and what her move is going to be. That needs practice. A database marketing practice, I mean.

 The assistance obtained from Predictive Analytics can help players in this segment to integrate it into their marketing and sales operations.

  1. Promotions (including Coupons)

Without being really conscious about it, shoppers give away a lot of details about themselves to retailers through their offline, and now online, activities. Every time they download some online information or cut out a newspaper coupon, or pick up that flyer from the checkout counter, they send out signals to the retailer. Even the method of payment says something about them. Predictive Analytics uses information gathered from these acts, matches them with the actual purchases, and aids retailers anticipate the customers’ needs. Thus, it helps the former to build cleverer and well-targeted promotions and loyalty programs, and also helps build a marketing message that includes an array of products for those customers who have, in the past, demonstrated their inclination to spend. So what happened here: Thanks to Predictive Analytics, a retail grocer now has an idea of who his true customers are, their spending power, their behavior, all of which he can align with his promotional campaigns.

  1. Shopper Targeting

We have just seen how Predictive Analytics helps grocers form a better picture of their consumers. But shopping as an activity, with the customer at its center, also forms a pattern over time, with the inclusion of granular data such as a customer’s demographics. This can then be used to the retailer’s advantage, for example, to create focused, customized offers, targeted specifically at a particular shopper (single or in small groups).

  1. Campaign Management

Marketing campaigns of grocers is another area where Predictive Analytics can help. This form of analytics now helps marketers optimize individual campaigns – armed with a specific purpose for a particular segment of the consumers. The same marketing budget is now suddenly more effective, and fetches better results, thanks to Predictive Analytics and its tools, or helps reduce marketing budgets to a fraction of what they were. Either way, more for less is what it is all about.

  1. Pricing

Predictive Analytics can give considered answers to several questions that are in the minds of the retailers who often use pricing as a tool to pull in customers:

  • What is the correct price point to maximize sales?
  • How often to launch price-based promotional activities?
  • What is a customer’s optimal attainable price?
  • What would be the impact of competitive pricing on sales?

In fact, some experts believe that Pricing is one area where the use of Predictive Analytics starts to show results in about six months, helping a retailer achieve about 5% increase in margins. A grocer can make revenue gains in this manner, since he now has the help of this form of analytics to “price it just right”.

  1. Inventory Management

What to store, what to discard, when to store, when to discard? Some of the questions that every retailer finds asking himself, very frequently. Inventory management is a process of allocation and replacement. Admittedly, almost all retailers have a plan for this, based on the macro picture of footfalls, sales and revenue, to name a few. When a particular stock starts to run low, replenish it. But, well, that’s no longer effective, even advisable.

Predictive Analytics now helps grocers remove the uncertainty factor from Inventory Management. No longer is a particular brand of shampoo bought and stored on the basis of a mere hunch or past sales. Ask any retailer the painful costs that comes with holding stuff that nobody or very few ultimately buy. Predictive Analytics accurately predicts demand, and suggests better replenishment strategies. It does not end there. Deploying it, shoppers often find that a particular product was missing from their stores, and add it to find customers ringing up amazing sales. Inventory imbalances are removed because of Predictive Analytics. The overall result – decrease in inventory costs and an increase in sales.

Retail grocers can utilize Predictive Analytics to many more areas of their operations, both customer facing and at the back-end. One of the world’s largest retailers Kroger has been using a combination of this form of analysis with technical solutions, for example, to ensure that there’s never more than one person in front of you at check out time. We have all heard of the famous Target case where it used Predictive Analytics to tell accurately who among their teenage shoppers were in the early stages of pregnancy, even before being officially intimated about it. The above-five areas mentioned in this post are where Express Analytics found Predictive Analytics to be deployed the most. The sky, or in this case, the store, is the limit for its potential uses.

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