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Database Marketing for Retailers:
A Six-Step Program Using Point-of-Sale Purchase Information
By Jim Wheaton
Principal, Wheaton Group
Original version of an article that appeared in The
DMA's "Retail Council Newsletter," March 1998
This article outlines a six-step database marketing program for driving
incremental revenues and profits via purchase information captured at the
point-of-sale. The article will conclude with a case study of how a $1.5
million investment in a point-of-sale transaction database generated
incremental revenue of $207 million and incremental contribution to profit and
overhead of $23 million.
Customer Profiling - The First Step
Generally,
it makes sense to begin by developing demographic and psychographic
profiles of a retailer's customers. The idea is to feed this
information to creative and marketing personnel so that they can
begin to fine-tune promotional and merchandising strategies.
Overlay profiles offer marketers the unique opportunity to develop precise
customer "portraits" using hundreds of individual, household and
geographic-level data elements. Such detail is impossible with survey
research. This is because the number of questions that would have to be
asked would be so large that response rates would be
drastically-depressed. Overlay profiles offer the additional advantage of
quicker turnaround.
Two techniques are used:
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Univariate analysis offers variable-by-variable customer portraits for "age,"
"income," "marital status," "presence of children," and the like.
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Multivariate analysis often is more insightful. Here, statistical
procedures are used to identify the combinations of variables that correspond
to multiple audiences. An example will clarify the process:
Assume that the following characteristics are over-represented on an extract of
diamond ring buyers: "young," "married," "affluent," and "male." It
would be hazardous to conclude that the target audience is "young, married,
affluent males." There just as likely could exist multiple audiences,
such as:
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"Young (single) males (of various income levels)" who purchased an engagement
ring.
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"Affluent couples (of various ages)" who bought a ring to commemorate an
important wedding anniversary.
Customer Modeling - The Beginnings of Sophisticated Database
Marketing
In the absence of a sophisticated database
marketing program, retailers invariably waste fifty percent or more
of their promotional dollars on contacts to unprofitable customers.
Generally, segmentation is limited to crude Recency/Frequency/Monetary
("RFM") Cells. Often, the replacement of RFM Cells with sophisticated,
statistics-based predictive models frees up sufficient promotional
dollars to self-fund a one-to-one differential marketing program.
Generally, two predictive models are created to rank-order customers by their
predicted overall future purchase volume. One model is reserved for those
customers with just one existing purchase ("single-buyers") and a second for
those with two or more existing purchases ("multi-buyers"). Among other
reasons, this is done to economize on royalty charges for the use of
supplemental demographic and psychographic overlay data. Here's
why:
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Single-buyers, by definition, have limited purchase information. All that
exists is the recency, the order size and the product category of the solitary
order, as well as miscellaneous secondary characteristics such as
cash-versus-charge, and returns and exchange information. Therefore,
overlay data often provide cost-effective incremental segmentation power.
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Multi-buyers, on the other hand, possess a wealth of purchase
information. Therefore, overlay data provide very little incremental
segmentation power; meaning, in turn, that they often are not cost-effective
additions to the modeling process.
Product Affinity Analysis - Initial Progress Towards Tailored
Messages
The profiling of customers and their subsequent rank-ordering by
predicted overall future purchase volume is a solid start.
It allows promotional and merchandising strategies to be fine-tuned.
And, massive savings are achieved in promotional costs by
eliminating contacts to unprofitable customers.
Nevertheless, all of this does not qualify as state-of-the-art database
marketing. In order to attain such a level of sophistication, strategies
must be developed to tailor the promotional message to each customer's
transaction and demographic profile. Often, this process begins with a
statistical technique known as product affinity analysis.
Most retailers offer a large number of SKU's, often with a broad range of price
points and margins. In order to evolve towards matching the optimal
promotional message to the needs of each individual customer, it is critical to
understand which SKU's tend to be purchased together - either simultaneously or
over time.
With this in mind, the transaction patterns of a large sample of multi-buyers
are analyzed to determine the logical (positive and negative) relationships
between SKU's. (Because of the inherent volatility of SKU's, it often is
helpful to perform this analysis at the merchandise category level.)
Using a statistical technique known as factor analysis, categories (or SKU's)
are aggregated into a small but manageable number of tightly-clustered product
affinity groups. These groups have the following characteristics:
-
Within each, a purchase from a "member" category translates into a high
probability of future purchase from that, or another, "member" category.
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Across each, purchase patterns are less consistent. In other words, many
purchasers from a given affinity group will not have an inordinately-high
probability of making a future purchase from any one of the remaining
groups. Product affinity analysis suggests that many customers view a
retail outlet as being analogous to multiple specialty stores within a
mini-mall. Customers often have strong loyalty to just a single
"specialty store." Although they periodically "visit" the remaining stores, the
emotional attachment is not strong.
Product Affinity Analysis - Impetus to Multiple Specialized
Predictive Models
Having identified the moral equivalent
of multiple specialty stores within a mini-mall, the next step is
to build corresponding specialized statistics-based predictive models.
For every "store" customer, the appropriate model predicts the merchandise-specific
future purchase volume.
Inevitably, this revolutionizes a retailer's promotional strategy. The
single-buyer and multi-buyer overall-purchase models determine whom to promote,
and the specialized "affinity" models what to promote. These affinity
models drive the following types of initiatives:
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"General" sale flyers are customized for the first time. Ink-jet messages
point to the specific merchandise that a given customer is most likely to
purchase. Also, selective binding tailors the merchandise mix to the
tastes of each customer.
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Specialty flyers are developed and targeted to the most loyal customers within
a given affinity group.
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Special in-store events or standalone offers are created for the heaviest
spenders within a given group. For large retailers, such a promotion can
generate several million dollars in a single day off a promotional investment
of less than one hundred thousand dollars.
Product Affinity Analysis - Impetus to Cross-Sell Modeling
Often,
because of favorable price points and/or margins, one or two affinity
groups are disproportionately lucrative to a retailer. This
logically leads to the building of cross-sell predictive models
to "mine" the balance of the customer database for likely-converters.
The challenge, of course, is to identify that portion of customers
with the highest conversion propensity. Often, supplementing
historical purchase data with overlay demographics and psychographics
provides a cost-effective improvement in discrimination.
Lifetime Value - Driving Promote/Do Not Promote Cross-Sell
Decisions
A monetary investment must be made to convert
a given customer to a highly-lucrative product affinity group.
In order to determine the maximum level of investment, a lifetime
(or "long-term") value analysis must be performed on the affinity
group. The results of the lifetime value analysis are then
"matrixed" with the cross-sell model to determine whom to promote,
as follows:
-
The cross-sell model assigns a conversion probability to each customer.
-
This conversion probability is multiplied by the lifetime value that will
result if the conversion is successful.
-
The result is the "Expected Lifetime Value" (i.e., "Cross-Sell Probability X
Lifetime Value"). A cross-sell contact is sent to every customer whose
Expected Lifetime Value is higher than the cost of the contact.
The Six-Point Program - Return on Investment
One
multi-billion dollar national retailer, with hundreds of outlets
across the United States, pursued an aggressive program of point-of-sale
transaction data to build a robust database of over eleven million
active customers:
-
Multiple point-of-sale data capture strategies were employed:
computerized reverse telephone number lookup, credit card processing
(proprietary as well as bank card), and check scanning.
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Over time, transaction-capture techniques advanced to the point that the
database reflected over 90% of the retailer's multi-billion dollar annual sales
volume.
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With an average order size of about $80, detailed information on about 17
million orders and 25 million items were applied to the database each
year.
Throughout an entire Fiscal Year, four test-panel promotional treatments were
tracked versus a "holdout" panel that received no database-driven
promotions. The holdout, of course, was exposed to all mass media such as
FSI's, ROP and broadcast. These longitudinal tests were performed to
determine Incremental Revenue as well as Contribution to Overhead and Profit
from the database marketing program. The test panel results were then
extrapolated to the full database.
For each of the four promotional treatments, the database generated estimated
Incremental Revenue of between $139.5 and 206.9 million. Corresponding
Incremental Contribution ranged from $12.9 to $23.2 million. The cost of
maintaining the database, including data hygiene, overlay data royalties,
analytical research and strategic consulting, was only $1.5 million.
Interestingly, these Incremental Revenue and Contribution numbers are
understated:
-
Because of the nature of the test structure, the contact strategy was
suboptimal. All customers within the test panels were promoted regardless
of qualification. Many customers who began the test period in marginal -
but mailable - Predictive Model Segments subsequently migrated to unmailable
Segments. Under normal circumstances, they would not have been promoted.
-
On both the Revenue and Contribution side, the cost/benefit analysis did not
take into consideration other uses of the database. Specifically, the
database helped optimize FSI and ROP distribution, and assisted in store-site
and merchandise placement decisions.
According to the client's intuitive estimate, the database's total annual
contribution to the corporate bottom line using the best of the four
promotional strategies was about $30 million a year.
Jim Wheaton is a Principal at Wheaton Group, and can be reached at 919-969-8859
or jim.wheaton@wheatongroup.com. The firm specializes in direct marketing
consulting and data mining, data quality assessment and assurance, and the
delivery of cost-effective data warehouses and marts. Jim is also a
Co-Founder of Data University www.datauniversity.org.
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