How to Use Retail Data Analytics for Customer Retention?
There used to be a time not so long ago when shopping was a flat, unidimensional experience. There was the retailer and there was the customer.
The first had something to offer; the latter could either take it or leave it. Somewhere in this equation, there used to be some sops on offer like price cuts or freebies as customer bait. Computing combined with the Internet changed all that.
Digital Transformation of Retail and the Need for Data Analytics
Digitization has ushered in a radical makeover of the retail business. The behavior and habits of both, the retailer as well as the consumer, are today a more inter-active, 3D experience, transmogrified, if one may be allowed to say, into a living, breathing creature, devouring and spewing out information almost simultaneously, leading to the generation of copious amounts of real-time data.
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Your average John or Jane Doe is no longer just a “customer”. A buyer dons many hats – he/she is a marketer, an advertiser; a content creator, even.
Here’s how: Ask yourself this even as you read – what did you do the last time you purchased something online? Most of you would have written, posted, or commented about either the experience or the product on –
- A social network
- Chat service
- Comments or the ‘Product Review’ section
But wait, that’s not all. Back up a wee bit. Think of what you did before you bought the stuff? You searched online, looked for similar products, compared prices to find the cheapest deal, read the reviews, checked out the specs, counted the ‘Likes’, and compared delivery dates.
There’s a pattern somewhere in there, and astute retailers are scrambling to catch those signals. Not merely grasp but to decipher them, too. Why? The answer’s the same as before, my friend – to sell and earn money.
Only, the action has got extremely fiery, ‘cause retail is no longer the purview of a few with inventory, staff, and money power.
That’s the world we live in – almost anyone can now sell almost anything online – grocery, food, clothes, gadgets, locations, paintings, even sex.
We sell what we don’t own or even store. Instead, we sell data, that’s the nature of the retail beast.
Take cars, for example. A car buyer’s journey today has changed. Study after study has shown that 70-80 % of the potential car, new and old, customers today begin their buying process online.
Yes, the actual sale of a car may still be done offline but 70 times out of 100, the pre-sales journey starts on the Web.
No longer is only the retailer calling the shots. The most important output of digitization is that the consumer is empowered like never before. In the battle of the wallet, the weapon of choice for retailers and consumers is the same – data.
Here’s an example: A potential consumer (for this exercise let’s presume she is 30+ and prefers to touch her goods before purchasing them) has walked into a store intending to buy a pair of jeans.
Standing in the shop’s aisle itself she logs in on the Net from her smartphone to check out the designs and prices of similar pants to understand if she was going to get the best here.
The retailer, too, has equipped himself with information to sell to that customer. His first salvo was fired much before Jane Doe walked into the store.
In fact, it may have been because of his online marketing efforts that she picked his store in the first place.
If she is a returning customer, the store owner has already analyzed the details collected from the previous Point of Sale (PoS) for a peek into her psyche.
Using multi-marketing channels, the retailer has skillfully enticed her.
His tactics would include one or all of these direct and indirect communication channels:
- emails, digital newsletters
- interactive store website
- email order catalogs
The storekeeper’s arsenal to help make up Jane Doe’s mind into buying the jeans today may include beacons or Augmented Reality.
If Jane Doe is in her teens, she would probably have brought the store into her house, thanks to technology, and purchased the jeans online.
Unlike yesteryears, retailers today have the luxury of multichannel marketing to interact and retain customers.
Location-based services such as Radio-Frequency Identification tags, Customer Relationship Management (CRM) software, and data analytics in retail are just a few of the tools available.
An astute retailer will use them to:
- Collect data on customer behavior, location
- Create interactive experience
- Push out tailor-made offers in real-time
- Cultivate customer loyalty
The transformation of retail today is not 100 % complete, though. It continues to straddle the two worlds – real as well as virtual. For retailers coming to grips as it is with increasing digitization, this poses a real challenge.
Again, retail marketing, like the actual process of shopping, has undergone a sea change.
Done the right way, multiple channels of communication like online advertising, social and traditional media, and emails, gather data to build a 360-degree view of the average shopper.
The advent of mobile computing devices such as smartphones and tablets has made this exercise not only quicker but even more focused. Basically, what a retailer is looking for at a macro level is a customer’s attributes and preferences.
But it is not only enough to gather voluminous data related to customers. Someone or something has to make sense of all of it and then act upon the analysis.
In addition to improving customer experience, such Big Data can be leveraged for other purposes, too. Improving the supply and distribution chains is one of them. But that’s part of another blog post.
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Information that a retailer gathers can help in two ways:
- Acquire new customers
- Retain the loyalties of the old ones
The easiest form of data that can be collected is demographic details such as age, gender, geographical location, ethnicity, among others.
There used to be an era when old-world marketing campaigns were devised based on these parameters. That’s no longer enough.
Moving beyond mere demographics, insights can be used to:
- Recognize those customers with the predisposition to overspend
- Identify those most likely to switch loyalties
- Check past behaviors and consumption habits
- Understand a shopper’s Emotional Quotient
All this data can then be used, in turn, to determine the types of products to be profiled, in real-time; pricing – a powerful marketing/sales lever, and pushing out “offers” that entice customers to buy, and take any other corrective action as deemed fit to retain customers.
That’s customer segregation for you. Based on the data bank and its analysis. Clearly, an up-to-date data bank is just the start of customer segmentation. Data Analytics clubs individual consumers with common traits into silos.
Data analytics in retail is the mechanism by which synergy is established between the goals of a retailer and those of the shoppers.
Value of Analytics in Customer Segmentation
Advanced data analytics in retail by highly trained data scientists is required to provide a holistic picture of your customer base.
At the very core, advanced data analytics in answers a very simple question – who is your customer?
On a higher plane, since consumers are individuals, each complete with his/her own likes and dislikes, needs, and histrionics (and no two are ever similar), you cannot fuse them into one homogenous mix, and apply a standardized approach to address them.
Armed with insights from customer segmentation research, retailers can:
- Have targeted engagement of various categories of customers
- Create advertising and promotional plans tailored to each
- Support product development and pricing initiatives
- Develop tailored customer contact practices that will improve a customers’ experience and result in greater sales from each category
- Optimize CRM strategy
- Allow for an effective allocation of marketing resources and the maximization of cross-selling opportunities.
An Engine That Drives Customer Intelligence
Oyster is not just a customer data platform (CDP). It is the world’s first customer insights platform (CIP). Why? At its core is your customer. Oyster is a “data unifying software.”
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