September 24, 2007
CPGmatters: Integrate Demand Indexing with Loyalty Card Data
By Jack Grant
Through a special arrangement, what follows is an excerpt of a current article from CPGmatters, a monthly e-zine, presented here for discussion.
Integrating loyalty card data with demand indexing is not a widespread practice in mass market retailing. But that doesn’t mean it won’t become commonplace in the future.
“Every retailer thinks it’s a good idea,” said Eric Togneri, a principal with CPG CatNet, the association for category development professionals. “It
will take education and time. Suppliers will be all over it, and it will be
good for consumers.”
Demand indexing is looking at what the demand is on average for a set of key characteristics, indexing that against a finite piece of the overall pie, and determining whether or not it positively or negatively skews against the average. For example, Caucasian men aged 45 and over buy a certain category at a certain level across the entire market. Does the same hold true for the consumer with that same profile living west of Boston? The demand index indicates whether they buy more or they buy less.
Mr. Togneri learned about the potential of integrating loyalty card data and demand indexing a few years ago while serving as senior category development manager for Wyeth Consumer Healthcare. While at Wyeth, he worked with the Extra Care card program operated by CVS and found the chain was focusing most of loyalty efforts on gaining incremental purchases from “price sensitive” customer segments.
“These are the folks who fall into two categories: Loyal Pantry Loaders and Non-Loyal Price Chasers,” said Mr. Togneri. “Far too often our marketing efforts fell into these two groups that both had notable issues in targeting.”
Pantry Loaders are attractive because of their loyalty, but loyalty cards often led to a discount that isn’t always necessary.
“Essentially,” he said, “we were taking care of our loyal consumers who would have purchased us any way and traded them down on a price per equivalized unit. Not a great way to run a business.”
“Price Chasers are the fair-weather friends of the CPG industry,” Mr. Togneri went on to say. “You can keep them through heavy promotions, but they are just as easily swayed to purchase alternatives. That is a high investment for those who will not be with the brand for the long haul.”
Mr. Togneri said there are data sources that can provide a targeted group with a great deal of appeal to marketers. It includes those consumers who look like a brand’s consumer demographically, live a lifestyle that is consistent with a brand’s consumer profile, and are frequenting the retailers that sell the brand.
“So why are they — the under-indexing consumer — not purchasing the brand?’ he asks. “There is no way to know for certain. But the more these individuals can receive brand communications directly — whether they are price or education or some other combination — the increased chance that incentives will lead to purchases and ultimately loyalty.”
Discussion Questions: What do you think of the potential of demand indexing to reach the under-indexed consumer segments within loyalty programs? What challenges do see in properly executing such a program?
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Indexing demand for given shopper segments may yield useful insights if the segments are well chosen. In general, I favor this kind of systematic approach, where possible.
Where robust frequent shopper card databases exist, viable segments may be derived using data-mining techniques that rely on brute force mathematics. This approach has the advantage of being unbiased, but also could yield resultant segments that are primarily mathematical constructs and are difficult to work with in practice.
An alternative approach, segmenting shoppers based on a few observable differences in demographics or behavior–like age, gender, basket size, shopping frequency, or propensity to purchase certain items or categories–may be more intuitive, but also more subjective. Using data mining tools to test these segments is a good idea.
Comparing a segment’s local demand index for a category or a brand with a national index (as described near the beginning of the article) should reveal a relative measure of performance. This may be useful if there is also some insight into the reasons for the variation.
One of the challenges of this approach is finding the right targeting level. Setting up very large, broad segments allows for relatively simple analyses and planning, but this carries with it the risk of making broad, crude judgments that don’t optimize performance very well. Splitting the targets down too narrowly would result in an excess of planning and evaluation effort, while potentially sending conflicting and confusing messages to shoppers.
An area of potential advantage here may come if a marketer identifies certain segments with special interest in a brand or category. Here is where targeted communications and offers may be very productive and revealing. I believe demand indexing may be especially useful within and across such groups.
Message to Mr. Togneri–you can’t sell a customer unless he is in your store. Then entire purpose of the “loss leader” is to attract customers SO you can sell them something else. If your marketing is so supplier focused that it only passes on discounts without a plan to generate additional sales then you might be a grocery retailer. If your “integrated demand indexing” can actually be used to make sure that inventory is sufficient to eliminate OOS then I am all for it. The current problem isn’t a lack of information. The problem is not acting on available information.
The best thing about loyalty card data: it’s individualized, so marketing tests can easily be run on individual subsets and the results measured within days. The easiest way to run the tests: coupons custom-printed for each shopper. This has been done for a couple of decades. But many shoppers ignore coupons. It would be neat to combine RFID loyalty cards with RFID shelf pricing, so that as the shopper went by the shelf, the loyalty card (still in the shopper’s handbag or pocket) signal could make the shelf price signs turn colors with custom price promotions. (“Hey Mary! Buy me and get $1 off!”)
The core competency of every successful merchant is to marry customer attributes with product attributes. If you don’t stock what your customers want, you will have a hard time.
Yet the retail industry doesn’t have a universally accepted methodology to assign attributes to customers and merchandise. These are widely regarded as two separate tasks done by different groups. The direct marketers assign customer attributes; the buyers assign product attributes. They never match typically.
Yet if you ask a buyer from whom they are buying a product, they will describe the intended customer. I think it is time merchants got to meet the marketing folks. They are in a position, if they have Business Intelligence software, to realize incredible insights.
With a common methodology and approach, merchants will be able to analyze inventories and orders, location by location, to ensure the attribute mix marries to attribute mix of the customer shopping there. This will forestall incredible waste in overstocks and needless markdowns.
Marketers will see when inventories mixes demand more intensive recruitment programs to find a particular type of customer.
Duane Reade in New York City got it right. For every $100 spent (cumulatively over how ever many trips to the store it takes), the customer receives $5 off their next purchase of whatever they want to buy. Everyone has a Duane Reade card. It’s a win win situation for the retailer and the customer. The retailer gains insight on who’s buying what, and the customer gets $5 off and saves on other specials throughout the store. More retailers should implement this practice.
Why are so many assumptions being made?–we can’t know why customers who look like they should be loyal aren’t or our loyal customers will buy anyway without the price incentive.
How do you know that’s true? Why not do some research to answer those questions? Yes, the research is expensive but it is also expensive to just keep assuming you know the answers to important questions and blindly try new strategies that may or may not work.
Consumer buying patterns in today’s world are somewhat not as good as our parent’s or grandparent’s were!
One of the keys to loyalty marketing is knowing your target consumer and then your converted shopper. In some retail businesses, indexing may be possible, if the index can incorporate the differences in market share of the retailer; its image, and importance of and reasons for a premium loyal buyer.
Better indexing processes may be a vehicle for customer service call-ins, and associate service levels at outlet. Hmmmmmm
I think all of us applaud CPG channels getting smarter about to whom, where and when they apply discounts–targeting those offers vs. simply subsidizing everyone’s purchases makes a lot of sense. The interesting question in loyalty is always–do you give your best price to your best customers–who probably would have bought anyhow–or do you use it to entice potential customers? While using data modeling to identify likely customers who aren’t is a core best practice, retailers and brands need to make sure that they don’t spend so much time chasing new customers that they forget to show proper appreciation to their current customers. Good loyalty programs should always balance these two. Want to make it more robust? Do market research in the non-buying segments to get more rich data about the true reasons why they don’t buy–that could save a lot of time and money in the end.
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Indexing demand for given shopper segments may yield useful insights if the segments are well chosen. In general, I favor this kind of systematic approach, where possible.
Where robust frequent shopper card databases exist, viable segments may be derived using data-mining techniques that rely on brute force mathematics. This approach has the advantage of being unbiased, but also could yield resultant segments that are primarily mathematical constructs and are difficult to work with in practice.
An alternative approach, segmenting shoppers based on a few observable differences in demographics or behavior–like age, gender, basket size, shopping frequency, or propensity to purchase certain items or categories–may be more intuitive, but also more subjective. Using data mining tools to test these segments is a good idea.
Comparing a segment’s local demand index for a category or a brand with a national index (as described near the beginning of the article) should reveal a relative measure of performance. This may be useful if there is also some insight into the reasons for the variation.
One of the challenges of this approach is finding the right targeting level. Setting up very large, broad segments allows for relatively simple analyses and planning, but this carries with it the risk of making broad, crude judgments that don’t optimize performance very well. Splitting the targets down too narrowly would result in an excess of planning and evaluation effort, while potentially sending conflicting and confusing messages to shoppers.
An area of potential advantage here may come if a marketer identifies certain segments with special interest in a brand or category. Here is where targeted communications and offers may be very productive and revealing. I believe demand indexing may be especially useful within and across such groups.
Message to Mr. Togneri–you can’t sell a customer unless he is in your store. Then entire purpose of the “loss leader” is to attract customers SO you can sell them something else. If your marketing is so supplier focused that it only passes on discounts without a plan to generate additional sales then you might be a grocery retailer. If your “integrated demand indexing” can actually be used to make sure that inventory is sufficient to eliminate OOS then I am all for it. The current problem isn’t a lack of information. The problem is not acting on available information.
The best thing about loyalty card data: it’s individualized, so marketing tests can easily be run on individual subsets and the results measured within days. The easiest way to run the tests: coupons custom-printed for each shopper. This has been done for a couple of decades. But many shoppers ignore coupons. It would be neat to combine RFID loyalty cards with RFID shelf pricing, so that as the shopper went by the shelf, the loyalty card (still in the shopper’s handbag or pocket) signal could make the shelf price signs turn colors with custom price promotions. (“Hey Mary! Buy me and get $1 off!”)
The core competency of every successful merchant is to marry customer attributes with product attributes. If you don’t stock what your customers want, you will have a hard time.
Yet the retail industry doesn’t have a universally accepted methodology to assign attributes to customers and merchandise. These are widely regarded as two separate tasks done by different groups. The direct marketers assign customer attributes; the buyers assign product attributes. They never match typically.
Yet if you ask a buyer from whom they are buying a product, they will describe the intended customer. I think it is time merchants got to meet the marketing folks. They are in a position, if they have Business Intelligence software, to realize incredible insights.
With a common methodology and approach, merchants will be able to analyze inventories and orders, location by location, to ensure the attribute mix marries to attribute mix of the customer shopping there. This will forestall incredible waste in overstocks and needless markdowns.
Marketers will see when inventories mixes demand more intensive recruitment programs to find a particular type of customer.
Duane Reade in New York City got it right. For every $100 spent (cumulatively over how ever many trips to the store it takes), the customer receives $5 off their next purchase of whatever they want to buy. Everyone has a Duane Reade card. It’s a win win situation for the retailer and the customer. The retailer gains insight on who’s buying what, and the customer gets $5 off and saves on other specials throughout the store. More retailers should implement this practice.
Why are so many assumptions being made?–we can’t know why customers who look like they should be loyal aren’t or our loyal customers will buy anyway without the price incentive.
How do you know that’s true? Why not do some research to answer those questions? Yes, the research is expensive but it is also expensive to just keep assuming you know the answers to important questions and blindly try new strategies that may or may not work.
Consumer buying patterns in today’s world are somewhat not as good as our parent’s or grandparent’s were!
One of the keys to loyalty marketing is knowing your target consumer and then your converted shopper. In some retail businesses, indexing may be possible, if the index can incorporate the differences in market share of the retailer; its image, and importance of and reasons for a premium loyal buyer.
Better indexing processes may be a vehicle for customer service call-ins, and associate service levels at outlet. Hmmmmmm
I think all of us applaud CPG channels getting smarter about to whom, where and when they apply discounts–targeting those offers vs. simply subsidizing everyone’s purchases makes a lot of sense. The interesting question in loyalty is always–do you give your best price to your best customers–who probably would have bought anyhow–or do you use it to entice potential customers? While using data modeling to identify likely customers who aren’t is a core best practice, retailers and brands need to make sure that they don’t spend so much time chasing new customers that they forget to show proper appreciation to their current customers. Good loyalty programs should always balance these two. Want to make it more robust? Do market research in the non-buying segments to get more rich data about the true reasons why they don’t buy–that could save a lot of time and money in the end.