A finished MKT-834 Topic 7 program counterfactual evaluation example that replaces a loyalty program's reported member gap with a store-level comparison and nets coupon costs against the estimated gain. Searches like "mkt 834 topic 7 assignment example", "mkt834 topic 7 sample" and "mkt-834 topic 7 example" land here.
What a finished MKT-834 Topic 7 program counterfactual evaluation looks like
Two figures from the program's annual report open the finished evaluation, each followed by the reason it cannot answer the question. Comparing member and non-member spending sets people who chose to join against people who did not, and the rising member share of sales measures card adoption, since a sale counts as a member's only once a card is scanned. The design exploits a staggered launch across three store regions. Total store sales, covering every shopper, are compared with regions not yet launched, and a synthetic control of the kind Abadie and colleagues developed weights untreated stores into a comparison for the first region. The estimated lift, illustrative, is a small fraction of the reported gap. Net of discounts, the verdict is plain: a modest gain, with much coupon spending going to purchases members would have made anyway.
How an MKT-834 Topic 7 example is structured
The evaluation begins with the question it will answer, what the program added to the chain's sales net of its costs, and states why the annual report answers a different one. A section on the reported figures examines each in turn, the member spending gap and the member share of sales, and names the process that produced it. The design section describes the staggered launch, the choice of store-level total sales as the outcome and the synthetic comparison for the first region. Results follow with illustrative estimates, reported as ranges. A cost section nets discount spending, card administration and the analytics contract against the estimated gain, including redemption on items members bought regularly before joining. The strongest objection, that the program's customer data has value beyond sales, is answered by pricing that value separately. The closing section recommends redesigning the coupons and keeping a holdout of stores.
A spending gap built by selection
Members spend more because frequent shoppers joined first, so comparing members with everyone else measures who enrolled rather than what enrollment changed.
A share that measures scanning
Member share of sales can only rise as more shoppers present cards at checkout, so the headline trend records adoption of the card rather than any change in buying.
Every shopper counted at store level
Total store sales include members and non-members alike, which removes the selection problem and lets launched regions be compared with those still waiting.
A synthetic region for comparison
Following Abadie and colleagues, untreated stores are weighted to track the first region's sales before launch, supplying a comparison where no single region matched.
Coupons on purchases already habitual
Much redemption fell on items members bought routinely before joining, and the evaluation counts those discounts as cost with no matching gain in sales.
Data value priced on its own
The program's customer records may be worth something beyond sales, the objection holds, and the evaluation prices that value separately instead of folding it into lift.
Where marks go in MKT-834 Topic 7
The heaviest loss on this evaluation goes to papers that accept the member spending gap as the program's effect, a comparison that self-selection alone could produce. Close behind is the rising member share of sales, which many drafts cite as growth without noticing that the program's own card defines who counts as a member. Evaluations that estimate a lift but never net discount costs against it report revenue rather than value. Synthetic control and difference-in-differences are sometimes named without showing that the comparison tracked the launched region before launch, the evidence either design needs. Many papers treat customer data as a benefit that settles the question, instead of pricing it. A doctoral reader also expects the recommendation to include a way of continuing to measure, since a program judged once on evidence reverts to its dashboard the following year.
Get an MKT-834 Topic 7 example written to your instructions
Send the MKT-834 Topic 7 instructions and the rubric your classroom posts, with the program or data your section provided. A custom example is written to those instructions, with reported figures examined for how they arose, a counterfactual built at the right level, costs netted against the estimated gain and the data-value objection priced, in 24 to 48 hours. The first one is free.
MKT-834 Topic 7 questions, answered
Why is the member versus non-member comparison misleading?
Because membership is chosen, and the people who choose to join a grocery loyalty program are disproportionately those who already shop at the chain often. Their higher spending existed before they joined. Comparing them with non-members therefore measures the difference between two kinds of shoppers. The example avoids the problem by measuring total store sales, where every shopper counts regardless of membership.
What is a synthetic control?
A method developed by Abadie and colleagues for evaluating an intervention in one unit, such as a region, when no single untreated unit is a good match. It builds a weighted combination of untreated units whose past outcomes closely track the treated unit's before the intervention, then uses that combination to estimate what would have happened afterward. The example applies it to the program's first region.
Is a loyalty program worth keeping if its sales lift is small?
Possibly, if its other benefits justify the cost, but those benefits have to be priced rather than assumed. The program's data may improve assortment or pricing decisions, and the example estimates that value separately. It also finds that redesigning coupons to target items members did not already buy could raise the lift. The recommendation keeps the program on those conditions and keeps a store holdout to verify it.