Dynamic Customer Management and the Value of One-to-One Marketing
The concept of one-to-one marketing is intuitively appealing, but there is little research that investigates the value of individual-level marketing relative to segment-level or mass marketing. In this paper, the authors investigate the financial benefits of and computational challenges involved in one-to-one marketing. They investigate the impact of customizing promotions on the two most important consumer decisions: the decision to buy from the store and expenditure level. The modeling approach accounts for two sources of consumers’ responsiveness to various marketing mix elements: cross-sectional differences across consumers and temporal differences within consumers based on the purchase cycle.
A series of policy simulations show that for an online retail business, customizing promotions leads to a significant increase in profits relative to current practice of uniform promotions to all customers.
Specifically, they find for this online retail business:
- Customizing offers based on purchase cycle (Recency or weeks since last purchase) contributes more to profitability than exploiting variations across consumers using previous transactional content (segmenting by purchase category, basket size, demographics, etc.). This is important because the computational burden of implementing the dynamic optimization to account for variations across consumers is far greater than accounting for purchase cycle.
- A substantial number of customers purchase without a promotion of any kind. Offering any promotion to these customers substantially reduces the profitability of a campaign, and targeting by purchase cycle is key to avoiding this problem.
- Free shipping tends to be the most profitable promotion for re-acquiring lapsed customers, whereas discounts are the most effective tool for managing active customers. Offering the “wrong” promotion (e.g. free shipping to active customers) substantially reduces the profitability of a campaign.
- Customizing offers by previous transactional content in addition to purchase cycle increases profitability further, with customizing at the individual level outperforming customizing at the segment. However, gains in profit using individual level targeting when accounting for costs might not exceed the gains relative to cost by segment targeting; outcomes need to be tested.
This is an incredibly rich study and I highly recommend a personal review for WAA members involved with online commerce. There is a ton of detail on how the different promotions affect response and order size, in addition to how these parameters interact with purchase cycle to variously contribute to profit.
For those not used to discussing purchase cycle as a segmentation variable, I offer this chart on purchase rate (not response rate) from the paper:
What you are looking at is a model constructed from actual test results. The model maps probability of purchase by 4 groups of online customers, by weeks since last purchase. Three of these groups are being offered promotions – Coupons, Free Shipping, and a Reward program. The Baseline group is offered no promotions.
Example: Looking at the Baseline (lowest) curve, with week = 0 being the last purchase date, and remembering these customers receive no promotions: about 3.3% of customers will make their next purchase 1 week later; about 5% of customers will make their next purchase 2 weeks later; about 5.5% of customers will make their next purchase 3 weeks later, and so on.
Please recognize that there is a “Natural” purchase rate, as represented by this “Baseline” group – those offered no promotion. This natural purchase rate peaks at about 4 weeks, and after 4 weeks of no purchases, the likelihood to purchase again begins to fall each week that no purchase is made.
Your business model has a chart that looks similar to this one. The peak may be different, the slope may be different, but the general characteristics will be the same. The Baseline group is often called the “control” group, and is simply a sample of the population that receives no promotion, which allows you to measure the natural purchase rate and revenue generated from these buyers.
The chart above shows what Marketers mean when they talk about “Lift”, as opposed to response. Let’s say the response to a campaign may be 8% from buyers 4 weeks into the cycle. If the natural purchase rate for people receiving a campaign is 4% at that same 4 week point in the purchase cycle, then the campaign is only responsible for generating 4% of behavior – literally 50% of the “response” to the campaign. The Baseline or control group tells you the natural buying rate and revenue generated from natural buyers in each point of the purchase cycle, which starts at last purchase date (week = 0 in chart).
This also means that when you do a financial analysis of your campaigns, you should only be taking credit for the Lift caused by the campaign. Said another way, the full cost of the campaign should be applied against only those sales the campaign is responsible for generating, in the above example, the 4% rather than the 8%. As you might expect, this cost allocation against the true performance of the campaign can dramatically affect profitability.
And this is why segmenting customers by purchase cycle contributes more profitability to a campaign than segmenting by transactional content like category, basket size, demographics, and so forth. The timing of the offer is a more powerful determinant of profitability than the content of the offer.
Why is this important to you?
Because if you believe in the power of interactive to “pull” customers in, if you believe that usability and customer centricity really matter, then it follows you should be thrilled to have a high natural purchase rate. In fact, increases in natural purchase rate can be used to prove that customer centricity drives increased profitability.
Logically, if you accept the above premise, “push” campaigns will encounter higher levels of natural demand as a business becomes more customer-centric. Which means that as your business becomes more customer-centric, you should rely on more and more on purchase cycle targeting to drive higher profitability.
Impact of Different Promotions
An example of how to take action on purchase cycles is represented in the study, where free shipping tends to be the most profitable approach for re-acquiring lapsed customers, and discounts are the most effective tool for managing active customers. Look at the graph above to see how this works.
On the left side of the graph, when weeks since last purchase are low, you can see purchase incidence is higher in the “Coupon” group than the “Freeship” group; the Coupon line is higher than the Freeship group so the delta versus the Natural buying rate is greater for Coupons than for Free Shipping.
If you follow the Coupon line down to the right, you can see it drops below the Freeship line at 6 weeks with no purchase and in the out weeks, closely approaches the purchase incidence of Natural buyers. This is happening while the Freeship group maintains a significant delta to the purchase incidence of Natural buyers. If the purchase cycle analysis for your business looked exactly like this one, what should this data mean to you? Primarily two things:
- In order to maximize purchase rate, customers who are offered Coupons and are non-responsive after 6 weeks should then be offered Free Shipping.
- Offering a Coupon after 20 weeks of non-response generates very little lift in purchase rate; virtually all the responders are Natural buyers who would have purchased anyway. This means you are probably generating negative profit after campaign and discount cost on these efforts.
I think it’s worth repeating again that purchase cycle (or more broadly, LifeCycle, to include analysis of any action including visits, log-ins, downloads, etc.) curves will not look exactly like this one for your business, and the optimal timing of switching offers by purchase cycle likely won’t be the same.
However, having seen these same types of curves many times over my 15+ years working with online businesses, I can tell you this kind of work is worthy of your attention and effort – and especially so if your company is actively working on becoming more customer-centric. The more successful you are in pulling customers back to you, the more attention you should pay to purchase cycle segmentation to drive company profitability.
If you believe a fundamental part of your business model is to be “interactive”, time since last interaction – perhaps you’d prefer the term “dis-engagement” – is one of those most powerful segmentation approaches you can use.
Measuring Engagement Series/ contains examples of measuring and acting on days since last action as a segmentation tool for Campaigns, Visitors, and Customers.