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Moving Towards Real Insights: Translating Big Data for Your Business and Customers
  • Moving Towards Real Insights: Translating Big Data for Your Business and Customers

Moving Towards Real Insights: Translating Big Data for Your Business and Customers

These days, the term “Big Data” seems to be synonymous with an opinion – everybody has it. But it’s more than just a buzzword or urban myth – it actually has the potential to make or break your business. And with the U.S. economy (finally) back on its feet and retailers battling for fatter consumer wallets and more elusive loyalty, the biggest challenge isn’t collecting shopper data, but analyzing the data that’s in place to make sharper business decisions.

Our experience has shown that leveraging shopper data for consumer insights can drive more targeted approaches to marketing. It can even increase consumer retention rates as much as 28% higher versus mass promotions and untargeted discounts.

Yet, with only 3% of useful data actually being tagged or analyzed (IDC), retailers are missing out on important context around purchases – and that amplifies the risk of making bad business decisions.

If retailers are going to be successful in today’s market, they’ll need to know how to tie the right, relevant data together to meet business needs and drive desired consumer behavior. The following are a few things to consider when it comes to translating this data into meaningful insights:

Think Strategically

Before any data analysis begins, it’s important that retailers think about what they want to achieve. Your goals should be customized according to your business model and priorities. For example, if a retailer is looking to increase growth in convenience products, it may be best to focus on data rendered around assortment optimization, whereas another retailer hoping to raise the level of online sales might take a closer look at data surrounding the effectiveness of direct marketing.

The Data Exists – Put it to Work!

Getting the most out of existing data, calls for going beyond the traditional one lens view to get the biggest return. Retailers must look across multiple facets of consumer data before understanding “what” consumers want and “how” they want it – however, it doesn’t always require going outside of your business’s walls.

Taking advantage of existing data is so much more than relying on a large scale program. Although it can be a great tool, it’s really about tying valuable information on consumer behaviors from in-store EPOS systems, online and mobile purchases and loyalty programs to adjust store layout and product offerings  to drive incremental growth – what do they love, what are they rejecting, what influences them to buy more, who are your most loyal?

Don’t Fall Back on Loyalty Programs – Go Beyond Purchase Rewards

Loyalty programs continue to proliferate the market place, with Whole Foods and Kohl’s recently joining the ranks of major food retailers who’ve started their own formal programs in hopes of generating more data and insights from consumers.  While loyalty programs frequently reward both the retailer (data) and consumers (in-store discounts, other offers), it’s about translating this data to understand “individual” purchase behaviors and behaviors outside of the purchase to gain true loyalty.

A study in 2013 found that U.S. loyalty programs grew 26.7%, but there was also a 4.3% decline in active membership, (Colloquy). This consumer decline can be attributed to the fact that shoppers are slowly becoming desensitized to these programs. They know they are being watched – the long list of coupons attached to the check-out receipt attests to that – yet do they actually feel more loyal because of the rewards they’ve received (or perceived) to date? 

Apart from simply recommending products based on consumer purchases, loyalty program data should be used by retailers to improve the overall shopping experience and to respond to trends and demands in real time. For example, what brand of soup does a loyal customer prefer and when do they like to buy it? Develop specific offers that reward their loyalty for that brand and surprise and delight the most loyal consumers with offers or experiences they wouldn’t expect.

Data Deserves a Seat at the Decision-making Table

Consumer data has the power to go beyond marketing promotions to provide key insights for retailer operational and merchandising decisions, but retailers must be willing to implement these insights into their businesses. With 94% of retail sales occurring in physical stores, this data determines whether large chains fly or flop and can even show retailers where potential customers are and where they’ll be over the next 10 – 25 years. 

Many retailers have mistaken floor sales as a key deciding factor when determining whether to keep or delist a product. However, delisting a product due to sales volume runs the risk of losing your most loyal customers to competitors.  These customers might come to your store for this brand, but they also make large purchases of other items, essentially rewarding you for keeping that brand on your shelves.

Before making any product  decisions, take a closer look at your data to gather these insights and determine which products  matter most to loyal customers. With sharper analysis, retailers can provide an immediate return on data currently in place and fuel better marketing, merchandising and operational decisions.

Now that You’ve Got the Insights, Run with Them!

A wise man by the name of Albert Einstein once defined insanity as “doing the same thing over and over again and expecting different results.” If Big Data is truly going to live up to its promise, then some of your existing business and decision making processes must change to be more aligned around opportunities identified in the insight. Purchasing decisions shouldn’t continue to be swayed by manufacturer discounts, but instead by consumer needs. If your data says a particular product will lift overall sales value, be sure to listen and make the changes necessary for your business!

Although retailers would like to think that Big Data insights can be rendered by simply rubbing a crystal ball, it takes a great deal of patience and investment to meet business needs and drive desired consumer behavior. Luckily much of the data you’re looking for is already in place; it’s just a matter of going beyond spreadsheets to find it.

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