Digital Retail is coming – a true revolution that offers retailers many new opportunities. But at the same time seriously endangers the competitiveness of all those who ignore or do not understand this trend.
Various data-driven methods enable retailers to understand their customers better than ever before. This means that merchants can address them in a more personalized way across all sales channels.
And last but not least, also that they recognize in advance if they risk losing customers – in order to be able to take countermeasures at an early stage. This article shows which effective and simple methods are available to prevent customer churn.
Note: This article is part of our #DigitalRetail series – click here for an overview
Looking back to anticipate the future
In order to understand where Digital Retail is heading – let’s first briefly look back at the early days when everything started.
Fifteen years ago, US consumer electronics retailer Best Buy noticed an interesting development: Finding themselves in stiff competition with the retail giant Walmart, they saw that their online sales were thriving appreciably. They decided to use the momentum – and to systematically put the customer in the center. As retail researcher and innovator Nikki Baird remembers it:
“Omnichannel didn’t begin as omnichannel, it began as Customer Centricity.”
This is how the first customer-centric cross-channel strategy was born. The assumption for Best Buy back in 2003 was that customers would be willing to pay a little more for better service – and that paid off.
Clearly, omnichannel customer engagement, service and sales channels are some of the key factors for positive customer experience. Considering that it costs five times more to gain a new customer than to keep an existing one, it becomes clear that businesses should spend a lot more of their efforts on improving their customer’s retention: A study by Price Intelligently showed that an increase in acquisition of 1% affects the bottom line by only about 3%. However, increasing the retention by 1% results in a nearly 7% higher bottom line.
An increased customer retention is much more effective in comparison to customer acquisition, a recent study by PriceIntel showed
So the question is: How can I as a retailer improve the retention for my customers, across all channels?
US politician Adlai Stevenson, Junior unknowingly gave the answer, all the way back in 1952, by uttering the wise words:
“Understanding human needs is half the job of meeting them.”
But even today, companies have a hard time keeping track of their customers’ needs, inquiries and interaction across channel boundaries: More often than not, e-commerce was originally added as an afterthought to the main brick-and-mortar business.
And this is understandable: Even today, 90% of all retail sales in the US still happen through brick-and-mortar channels, US census data from 2017 shows. However, building an omnichannel, customer-centric database is one of the key pillars of a successful Digital Retail strategy.
Building a Customer-Centric Database
Many customers don’t feel like creating a customer account or using a loyalty card. This is why one of the primary options to identify returning customers, the basis for successful customer-centric analyses, is the payment instrument. Through their payment service provider, retailers now have the perfect identifier for opted-in returning customers: It became the “cookie” of the Digital Retail world.
From there on and with the help of their Payment Service Provider (PSP), retailers can build a customer-centric database and enrich it with new pieces of information with every customer interaction.
#DigitalRetail: Understanding how to use business analytic tools will determine how competitive retailers will be in the future.
Here are 3 effective data analytic methods
Once retailers have built their customer-centric database, suddenly a range of business analytics methods become available. Listing all of them would certainly go beyond the scope of this blog article, but here are a three of them that have proven to be very effective in the past:
1. Customer Segmentation and Customer Lifetime Value (CLV), to understand who the customers are
Nowadays, customer segmentation by demographic factors is rather pointless: Purchasing habits vary wildly across demographic clusters. Modern family concepts make demographic concepts obsolete: You will find 60-year-old dads of little children as well as grandfathers of the same age, 20 year old technologically illiterates as well as seniors planning their life with their iPads.
Complex concepts like emotion-based Limbic profiles require very deep integration into the merchant ecosystem, but luckily, a simpler solution is available: Segmenting customers according to their purchasing behavior.
By clustering similar customers together according to all available attributes (i.e., in an n-dimensional space, n being the number of attributes available), retailers reach a much better understanding of who their customers are.
76% of companies think CLV is an important concept for their organization – but only 42% are able to measure their CLV. With the segmentation tool, Wirecard is now able to identify precisely the customer lifetime revenue and thus the customer lifetime value of every aggregated customer segment: Instead of wild guesswork, I now know exactly what I am willing to invest in a certain campaign and what revenue potential I can activate.
Customer Lifetime Value (CLV): Most know of its importance – but less than half can actually measure it (Source: invesp infographic)
To make this more concrete, think of a chain of car repair shops: Their services are designed to get the customer in the door with low price offers on specific offerings that might even have a negative contribution margin, but to upsell with other products, both online and in-store. These retailers know exactly how much to invest in an engagement campaign targeted at their churned customers last seen in the previous year because they know the expected return through their CLV analysis.
2. RFM Analysis gives retailers a new angle
A “customer value analysis” based on Recency, Frequency and Monetary value (RFM) can give retailers a new angle on their customer base: They can now cluster my customers according to typical spending types. For example, the telecommunications corporation Orange ranked their customers from “Best” to “Churned”, whereas other adopters of the RFM analysis used other creative types.
Rita Rising: Not many purchases, but highest basket size, good upselling potential
Dean Double-Minded: Switching between my business and the competition depending on offers
Rosanna Risky: Former star customer, high potential if reactivated
Lars Lazy: Has never been a regular, but has high-value baskets
Olivia One-Time: Seen only once and never again
The RFM matrix of the different “personas”, showing the recency and frequency of their shopping, combined with the sums they are spending (from Martin Clark’s RFM Analysis Article)
A prime example for successful utilization of these types of analyses is Orange Cash, the value-added mobile payment system in France and Spain, with integrated Wirecard-provided business analytics solutions.
Using the RFM tool, Orange was able to more specifically target their customers, in one example leading to a 7.9% activation rate of churning and lost users of Orange Cash over just 30 days.
3. Churn Analysis – winning customers back before it’s too late
If for certain customers all else failed and I as a retailer have not been able to satisfy them, or they buy suddenly from my competitor for other reasons, a customer will be churning or even be lost.
Keep in mind that the real cost of customer churn is the lost revenue plus the lost potential plus what it takes to replace the lost revenue with new customers that you will have to acquire and develop. All the more important it is for retailers to talk to their customers before they have churned, i.e., enter churn analysis.
The better you know the purchasing patterns of your customers, the better you can identify unusual or changed behavior. To give an example, this is the difference between “John Jogger” and “Rita Rising”: While you know that there can be long periods of time between purchases by Rita, you should be very concerned when John misses a sales period or two. And this should instantly raise red flags in your marketing department, a prime case for a sudden high risk of churn.
In an analysis we did with a large retailer in France, we were able to reactivate 11% of customers in high and very high risk of churning in favor of a competitor. This is a number 22x higher than the success rate of the campaign targeting new customers with the same investment (0.5%).
Former CIO of Dell Technologies Jerry Gregoire was perfectly right when he said:
“The customer experience is the next competitive battleground.”
Customer retention through an improved digital customer experiencethrough all sales channels is one of the big megatrends in Digital Retail today, and the FinTech and payment industry are playing a key role in driving digitization, omnichannel convergence and thus customer experience. Payment itself has always been an “omnichannel topic”. As mentioned above, customer retention has always been a lot easier than customer acquisition – and is closely related to Customer Centricity.
And with the simple-to-use business analytics tools we have available today, customer retention is easier and more effective than ever before. Understanding how to use them will determine how competitive retailers will be in the future.
Great post. Articles that have meaningful and insightful comments are more enjoyable, at least to me. It’s interesting to read what other people thought and how it relates to them or their clients, as their perspective could possibly help you in the future.
There are having the 3 simple methods to run the business analytics which is needed for the digital analytics where the retailers improve the number of machine learning that is going to be the valuable part.