CPGmatters: Analysis of Data Underscores Importance of Best Shoppers
By
John Karolefski
Through a special arrangement, presented here for discussion
is a summary of a current article from the monthly e-zine, CPGmatters.
Analysis
of over two million grocery shoppers by Concept Shopping shows that the top
10 percent of a store’s customers visit the store more than twice a week,
spend over $39 per visit, and represent nearly 40 percent of the store’s total
sales.
The
study also found that these most valuable shoppers tend to remain very loyal
to the store, with 95 percent continuing to shop there throughout the year.
Conversely, only 34 percent of the store’s worst shoppers – those who visit
the store less than once a month and spend only $9 per visit – remain customers.
Concept
Shopping’s analysis of the two million shoppers divided them into ten equal
deciles based on their spending levels during a 12-week period. Only 11 percent
of the dollars spent by the best shoppers were on markdowns, making these heaviest
shoppers the most profitable as well. In contrast, over 35 percent of the dollars
spent by the worst shoppers were on sales items, making them unprofitable,
assuming a 33 percent profit margin.
Most retailers have some form of segmentation
or deciling, according to Michael Schiff, managing partner of Partners in Loyalty
Marketing.
“CVS does the deciles and they are probably the
savviest,” he said. “Kroger does some other segmentation. Wegmans is another
good example of a retailer that’s segmenting and trying to make stores more
about the experience than necessarily about shopping.”
“Other retailers know
about segmentation, but whether or not they really act on it is completely
different. I think most of them don’t. If you look at any of the promotions
or anything that goes out to consumers, the vast majority of it is talking
in the voice of that average shopper,” he said.
William Young, vice president
of sales and marketing for Concept Shopping, explained that it wouldn’t be
so bad if the lowest-spending customer shopped elsewhere because they tend
to “cherry pick” and largely buy items on sale. But it would be a major concern
when the best shoppers begin leaving.
“We help retailers look at their loyalty
card data, identify top shoppers who are in decline, and more importantly identify
what are the key categories that signal they are heading to the doors. What
do you need to do to promote those key categories better to those shoppers,”
he said.
Discussion
Questions: In what ways can retailers do a better job reaching their
best customers? Can vendors help?
Well for starters, they could begin researching net profitability rather than gross sales. “Best” shoppers are those who you make money on, not necessarily those who spend the most gross dollars. Obviously the industry needs much more sophisticated data gathering and analytical tools. Customers need to be managed like any other category (i.e. in terms of total contribution).
This insight shouldn’t surprise anyone in the grocery industry. Any retailer with a loyalty card and a BI package (VRMS, MicroStrategy, Oracle Retail, Teradata, etc, etc.) should be able to figure this out in their first day of looking at their data. And industry luminaries like Brian Woolf have been writing about this for 15 years (see “Customer Specific Marketing” and “Loyalty Marketing: The Second Act”).
The challenge facing retailers has always been how to take that insight and make sense of it–how should you best talk with these shoppers? What sorts of offers, programs, or information will keep them in your stores? And, how can you do this all within the razor-thin margins that most retailers face?
The most successful retailers engage their brands to provide meaningful, relevant, and compelling offers to the shoppers who matter most–the Top Shoppers. However, traditional retailer-driven segmentation isn’t really useful for this purpose. Merely being a top-decile shopper doesn’t mean someone is more inclined to buy, say, a gluten-free bread or a family-sized prepared entree. Giving brands a compelling reason to reward and target the retailer’s best shoppers means providing meaningful, ad-hoc segmentation of the retailer’s shoppers to align with the brand’s goals.
Marketers, both at chains and at brands, have moved beyond simple decile segmentation. It is time for the industry that supports these marketers to do the same.
It’s not for lack of data. Retailers need to better analyze the data they have. Many have put off the cost of this effort, and do so to their own peril. Retailers should know the shopping habits of their customers and through promotion and other incentives, be able to reward their best shoppers. This is not something they should pass to manufacturers. As Ryan says, “customers need to be managed.” As the grocery marketplace gets more crowded, having that information becomes paramount.
There are good reasons for why data management and integration problems have been a challenge for years. First, the data sets involved present their own unique integration challenges. There are few common definitions and standards governing retailer POS data structures across markets, categories and between individual retailers. Because of this, it has historically proven difficult and costly to integrate POS data at an SKU or attribute level with internal data from a company’s master data system.
I know of a global CPG company that seeks to leverage daily retailer POS data to generate a 5% revenue increase and 1 million dollar profit by reducing out-of-stocks and improving promotional compliance and better in-store implementation of new products. Additionally, retailer loyalty is expected to be a beneficiary of this effort. The company’s key account managers and supply chain planners increased visibility of their brand performance at the retail level via a solution built on a specific loyalty data mining platform. The pilot is currently underway in one country, where POS data and retailer regional DC inventory data is analyzed daily. There, at the retailer, data is integrated with internal master data and analyzed using a proven shopper and consumer insights tool. The resulting KPIs, alerts, loyalty statistics, segmentations and reports are available daily on the Web for key account managers, category managers and supply chain planners. Already a success, the pilot is being expanded to cover 10 to 15 retailers across Europe.
Bottom line, the technology exists to address these challenges. But the first step is to define the PROCESS to utilize that technology. That, indeed, is where most companies fail.
Segmentation by measures of shopper visit recency, frequency of visits, and monetary expenditure, aka RFM analysis, is a tried and true activity of Customer Relationship Management. The premise is that customers who spend more, more often and more recently, are more valuable compared with others who shop less often and buy less. This works pretty well for B2B CRM, but is somewhat iffy for more numerous and variable shopper relationships.
Retailers can certainly gain some useful insights from applying RFM to their customer base, but there are several caveats to consider in formulating actions.
To begin with, RFM it is not the only relevant kind of segmentation. Separating shoppers into spending level deciles is one type of “first cut.” They may also be divided by share-of-wallet, promotional responsiveness, trip missions, and their demographic similarity to other high-value shoppers.
Even if we rank shoppers by total net profit dollars, as Ryan wisely counsels, we must consider what relevant shopper and environmental traits may be masked by this metric, as well as how we can identify shoppers with the potential to be increased in value.
And finally, we must decide whether to apply these analyses globally across the entire chain, by store cluster, or on a store-by-store basis. This presents a trade-off between useful insights and operational complexity at store level.
So in short: Shopper segmentation = good. Simplistic mechanical methods = not so great. Putting insights to work in stores = pretty darned hard. Which is why ranking shoppers into sales deciles is a practical initial step on a long journey to shopper-centric retailing.
Loyalty programs have proved to be a superb way of capturing a stronger understanding of “what” consumers do within a retailer’s own store. It’s critical that they have a perspective of “what” consumers are doing in other competitors’ stores.
Grocers, with the help of CPG/manufacturers, have focused attention on ‘ticket’, ‘trips’, and ‘share of stomach’. All good monitors. If they are to fully exploit, and grow their business from there various decile levels, they need to keep another formula that they developed, in mind: CRAFT
Convert
Recency
Acquire
Frequency
Ticket
Grocers, and other retailer consumer insight pros, need to understand the consumers “What,” “Where,” “Why,” and “When” they are shopping in the total competitive universe on each of these CRAFT scores, if they are to maximize their revenues from the ‘Best Customers’, and many other ‘Customers’ who they might be underserving now.
That will take some data integration. It’s available within the industry.
Ironically, Ray Jones (fellow BrainTrust Panelist) and I were discussing the upcoming third installment in the RETAIL:NEXT survey series yesterday, which will focus on the shopper experience and which attempts to influence it provide the greatest ROI for retailers. We were discussing digital devices and their ability to capture even more data, and Ray made the assertion that “it almost doesn’t matter if devices deliver more data. Retailers have terabytes of data now. They just need to DO SOMETHING WITH IT!”
Identifying top shoppers (sales, profitability, pick your measure) and doing something to keep them happy would seem a logical choice for “doing something.” As others engaged in generating and analyzing data have pointed out, the tools are readily available today to find these shoppers and know what they buy. So what’s stopping us?
One plausible possibility is that our ability to execute complex strategies at retail has not evolved on pace with our ability to develop them. Even if my local Jewel can ID me, determine that I’m a very profitable shopper and that I tend to eat a Mediterranean diet, what can they do about that at the South Milwaukee Avenue store?
Certainly they can isolate me with tailored offers of coupons by mail or web, hit me with a mobile marketing campaign or even send me Tweets–but these are all capabilities provided by third parties. What can they do at the most critical level–the point of purchase?
Amen, Ben!
Data insight needs to be part of the culture. Once it is, a commitment to treating customer segments differently is a huge opportunity. Why don’t “gold” supermarket customers have their own lane, with to-the-car delivery (like Publix does for everyone)? Retailers can deliver better service without relying on discounts alone, and customers will respond.
This is not a CPG issue, it’s a retailer issue. Retailers control the shopper relationship. And retailers should strongly promote their products where and when buying decisions are made with a strong focus on Retailer Brands (as well as selected better margin CPG brands.)
Spending time doing sophisticated segmentation based upon sales, products, demographics, satisfaction, or loyalty is a great exercise and has little to do with running the business. Unless, as Ryan said, retailers can identify which consumers are more valuable (profitable, connectors, or however retailers define which consumers help them make money), AND develop good insights about those consumers, AND develop good tools for interaction (not just one-way communication) AND listen to those consumers, this process is just another exercise.
So 10% produce 40% of sales? Sounds about right to me. There is almost nothing of significance at retail that exhibits a “normal” bell curve distribution. And yet, human beings seem to inherently think about averages.
Obviously, I agree with all of the panelists but need to clarify some of the technology comments as to why the analysis or “data mining” is not used to its fullest.
First off, vendors such as Teradata, Microstrategy, Oracle, Neteeza etc, use antiquated, non-columnar computing platforms requiring them to roll up summary level data or to build summarized data tables in order to get a query to run in a reasonable period of time. As a result, the detailed level analysis that can be fully automated to indicate individual shopper profitability, lifetime value, RFM, loyalty indices, best/worst decile etc, is stale at best or difficult to operationalize across the enterprise.
There are newer technologies available from smaller vendors that allow the processing of billions of rows of data, anytime, from anywhere, instantly in the cloud at a fraction of the cost these companies are paying. This would allow for other data elements from third parties to be added to one very large database at the lowest level of detail imaginable so that a true data mining capability would exist light years past where most companies are today.
The roadblock I see is that the adoption curve for advanced technologies is pretty steep in retail as most executives are fat, dumb, and happy in their current roles and with their current vendor relationships. Not looking to upset the apple cart or to take any risks, they are happy with marginal improvements from their existing core vendors.
My advice is to engage these smaller, more nimble vendors in a series of pilot programs to see for yourself the power of “insight on demand” so that your analyses are up to date, current and representative of what happened 15 minutes ago, not 15 weeks ago.
Top shoppers already know, understand, and reward retailers with their loyalty. Also, as pointed out, many–even the lowest percentage customers–remain customers. Do the top customers know something that the lower percentage customers don’t? Maybe, but not always.
Focusing solely on your highest percentage customers has it own dangers. They already have chosen the retailer. They’ve committed. Moreover, rather than rewarding them further for what they already have given you, finding out why would be a better effort, from my view.
All too often we talk of loyalty programs as a silver-bullet answer towards identifying and rewarding our ‘best’ customers. Loyalty programs don’t do that. The customer grants loyalty. You don’t manage them. They manage you. Every day that they continue to shop, they are being rewarded by the retailer’s offer and rewarding the retailer by returned visits and a larger than average basket size.
A far greater effort should be made to determine why and look for ways to apply and enhance that effort towards shoppers visiting less frequently and spending less. Those shoppers, in many cases, remain loyal, yet don’t offer the same perceived return. They like you–just not so much. Why not? Disregarding them is not the answer. Turning them into your best shoppers is the answer. It’s the only answer for growth and increased sales.
Doing something with data doesn’t create loyalty. Doing something with data is the opportunity to grow shoppers into becoming best shoppers. All customers deserve that effort. A loyalty card or program doesn’t do that. Finding the answers from your best customers and combining them with the answers from your lowest percentage customers does that. The problem with card-based data is that it might tell you what they buy, but it sure doesn’t tell you much more than that. Incentives to continue the same habits provide the same results.
Actually talking to your customers and getting real answers helps. Doing something with the answer helps far more than giving a customer a reward for doing what they already are doing.
I don’t know how a card program becomes a conversation without a huge loss and expense and fuzzy answers. Real conversation might be a novel idea. But, that would take a people effort. Most think cards and technology does that for them, yet fail to execute on even those results. Maybe treating every customer that walks through the door as your best customer might just make them so. It certainly could even be considered a ‘green’ initiative–in more ways than saving the useless plastic card and key fob. Green might hit the tills too.
Scanner must be a retailer, and I would suspect a darn good one. He is right. Before “granular data” and store level planograms, we had store management that engaged customers and were able to influence product mix and pricing. (Not establish it necessarily, but communicate to headquarters things that customers wanted and a competitive price if necessary.) This also established store level planograms as we accommodated mix to the demands of our neighborhood.
Well we have come full circle again and are doing it with massive amounts of data interpreted by an array of analysts who may or may not understand what they are looking at. This is the science. The art is feedback from the troops, the store managers, grocery managers, District managers that are on the front lines. My observations are that in many of the major retailers this information feed is discouraged. Category management tends to go with consensus analysis, leading to a predetermined decision. Typical of this sort of thinking is reducing assortment to improve the bottom line. Store managers are screaming that they are losing their “Best” customers as a result. I have difficulty understanding how converting a loyal, profitable customer into a less frequent shopper wins the day.
You are already doing a great job reaching your best customers–they shop more, spend more, and make you more. What more do you expect?
I would focus my energy on getting more best customers without alienating existing ones.
Ryan makes a good point about tracking your most profitable customers. Sending offers and deals to cherry pickers is not a great way to grow your business.
It’s not an end-all solution, but merchants can certainly better leverage their frequent shopper card programs by creating member levels. Chains can offer their best customers more rewards, while allowing their worst customers to still participate–and perhaps incite that latter group to spend more to increase their access to reward niceties. And keep in mind that “rewards” needn’t be solely monetary (e.g., access to nutritionists or special checkout-lanes). By creating a buffet of monetary and experiential rewards–and even partnering with vendors and non-competitors–retailers can reward the best while showing the worst what they’re missing.
It’s amazing! You read the comments and you can spot the contributors who have actually engaged in serving the public vs serving the retailer. Scanner has it right, your best customers are your best customers for a reason. Get to know them, then concentrate your efforts on turning second tier customers into top tier customers. You won’t grow your business keeping your best customers happy, but you will if you increase sales to other customers.
There are a number of great companies that are members or soon to be members of Loyalty 360 that area doing a lot of interesting work in this space.
I would suggest Group Aeroplan, dunnhumby, EYC, Clear Cell, Netezza for great segmentation tools.
Consumer insight is the key. Making sure to make the data actionable and insightful and creating sustainable behavior change is the challenge.
For starters retailers need to identify their customers. Yes, most biggies have a loyalty program in place but there are many who don’t. Next step would be to analyze the data and create meaningful segments. It could be deciles as in CVS, RFM, or a little more sophisticated like fact score in Safeway. Demographic data could be overlaid to derive further insights into the segment.
Start capturing customer purchase history. Then strategies for each segment need to created. Offer targeted promotions based on their propensity to respond to a particular promotion type and channel.
As transparency emerges as the key theme, retailers also need to focus on seamless integration of the channels as one view of the customer. Based on the segments that shop a certain category the most, it could be merchandised based on the segment’s need and preference, to ingrain customer centricity into the organization. The customer segments could be incorporated right from the financial planning stage where targets could be set at a category store level for customer segments either to achieve an increase in the segment basket size in the category, or to increase number of baskets; and this view could flow down to assortment promotions, pricing, and even inventory.
This is and remains the Holy Grail of shopper insight: how to attract and retain the very best (most profitable) shoppers. The crush of data has plagued the distillation of meaning and insight for at least 30 years. It’s thrilling to see some meaningful improvement in understand the shoppers and meeting their unique needs. Mobile marketing (e.g., smartphone couponing, online comparison shopping while in-aisle) will help drive retailers to identify, quantify and satisfy their best customers. Or they will not prosper.