How can a Retailer Exploit Loyalty Program Data
Ron (commenting on Larry’s post) identifies loyalty program as a key reason for the holiday season success of online channel of retailers. Apart from driving sales, loyalty programs generate valuable data which can be analyzed to determine tactical strategies like cross-promotional programs, estimating discounts on bundles etc. Here is a four step actionable process to exploit the loyalty card data, and use it to broadly target customers with effective offers, even those who are not members of the loyalty program.
Step 1
Append an off-the-shelf segmentation schema (such as Claritas Prizm NE or Acxiom Personicx) to the loyalty card customer data (matched on name and address information) to get an understanding of segments of your customers and associated demographic and lifestyle behavior
Step 2
Identify cross-promotional opportunities and determine optimal discounts by using the purchase history data of loyalty card customers to determine which products are bought together by customers (product correlations in the market basket). Use data from past promotions to identify customer’s sensitivity to discounts (paying full price vs. buying at maximum discount) and overall volume and profitability of bundles. A simple visual will show a set of product associations (at various discount levels) helping to identify products that can be bundled. Green boxes in the diagram below represent recommended bundles.
Step 3
Using the augmented loyalty data identify customers who purchased the recommended bundles and build a propensity model to score segments based on their likelihood to purchase the bundles. Rank order the segments based on their propensity to respond to particular offer bundle.
Step 4
Integrate this segment ranking information into your online and direct marketing targeting schemes.
Now, not only do you know what to bundle, but also which members to offer the bundle within your loyalty program. Since you have used an industry standard off-the-shelf segmentation schema, you can append your prospect list with the prioritized segment information and more effectively target even the non-members.
Interesting.
Just a thought. Instead of Step 2 and Step 3, why not create 6-8 business-focused segments from the Acxiom/PRIZM data and then do the Market Basket Analysis for each segment.
that way, once we have decided our focus-segments, we can tailor a marketing program directed towards them.
I think the difference of approach lies in -
You are creating new offerings and then exploring who specifically to sell to.
My approach is to identify who your customers are, how and what do they buy and then leveraging this knowledge to customize offering towards them.
Comment by Anshuman Acharya — March 20, 2007 @ 12:12 pm
Hi Anshuman,
By identifying cross promotional opportunities in Step 1 we will identify customers as loyalty program members. Market basket analysis can be done on member purchase data.
In instances, where a certain co-branding option is provided by the manufacturer, the retailer can still try and force fit the product bundling on a customer segment that has a higher propensity to purchase this product bundle.
Hence, as you rightly pointed out, the approach can be used by starting to identify the customers and analyzing their purchase history or by having a product bundling which needs to be marketed to the relevant customer segment.
Comment by Harish — March 21, 2007 @ 1:02 pm
How would you embed this analysis in the micro-decisions that impact customer treatment every day? It seems to me that knowing is important but so is doing.
JT
Comment by James Taylor — March 22, 2007 @ 11:24 am
Hi,
This approach is for marketing campaigns. To make micro decisions for customer along all interaction points - segmentation analytics is a starting point but additional tools will be needed (probably from companies like yours) to operatalize it.
Thanks.
Comment by Harish — March 26, 2007 @ 11:26 am
[…] However, the key here is to understand which customer prefers which B2C and which C2B channel. Right-Targeting Customers is as important as Targeting Right Customers! Someone who spends 14 hours a day in front of his computer and has no time to go to a store 5 days a week may prefer a home delivery channel. On the other hand, a student in a college is only interested in the bargain channel, irrespective of the inconveniences, maybe. This report also mentions how retailers need to manage their investments across channel against the scale and timing of their expected return. I would go beyond Ron’s Right Channeling [read post] to include all aspects of targeting under the concept of Right-Targeting. Having said that, I agree that today’s world is about multi-channel customers, and the need of the hour is to optimize channel returns, rather than just channel re-alignment/phase-out. Targeting Right Customers- Its equally important is to understand how channel profitability gets affected if you are not targeting the right customer. For instance, Wal-Mart, even with its Everyday Low Prices (EDLP), must be making money on some products/ some SKUs and these would drive the overall positive profitability. However, what if your customers are not buying your profitable SKUs? What if the draw that brings them there is not luring them to buy more? What if there is no up sell/cross-sell/bundling that happens there? And suddenly the business realizes that channel profitability is coming under immense pressure! (One of our earlier posts tries to answer the question of market basket analysis and product bundling). […]
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