Who should get the loan offer?
Your last campaign went to everyone, and 85% of the budget bought silence. A logistic regression on the pilot data scores each customer's chance of accepting, and ranking by that score changes the math: most of your acceptors are hiding in a small slice of the list. This template fits the model, reads the odds ratios, and turns the ranking into a mail-or-skip decision with real campaign economics.
Marketing Advanced Logistic Regression free
After you install, this is the model to open.
Who Should the Campaign Target?
- In your spreadsheet, click the Sortia icon in the strip of icons down the right-hand edge. No strip? Click the arrow at the bottom-right to open it. You can also use Extensions, then Sortia, then Open Sortia.
- Click Start from a template and put that name in the search box.
- Pick the card with that name and click Load this template. It arrives on a new tab with real numbers already in it.
The answer
Fit the logistic model, rank all 60 customers by predicted probability, and hold the pilot's response rates at a 10,000-contact rollout:
- Top-decile response
- 83% vs 15% overall, a 5.6x lift
- Acceptors reached
- 8 of 9 contacting only the top 30%
- Extra profit
- +$22,667 top 30% vs mailing all 10,000
- Budget saved
- $56,000 7,000 fewer packages at $8 each
Ranked by the model, the top 10% of customers respond at 83%, a 5.6x lift over the 15% base rate, and the top 30% holds 8 of the 9 acceptors. At rollout scale that top-30% list earns $242,667 in profit versus $220,000 for mailing everyone, while spending $56,000 less. The bottom half of the ranking contains zero acceptors, so every dollar spent there is pure waste.
The model
Sixty pilot-campaign records from a regional credit union: acceptance (1/0), income in $000, and CD-account status, with the offer economics layered on top.
| Customers scored | 60 pilot records |
| Base acceptance rate | 15% (9 of 60) |
| Predictors | Income ($000), CD account (1/0) |
| Income odds ratio | 1.04 per $1K (1.5 per $10K) |
| CD account odds ratio | about 16x |
| Cost per offer package | $8 per contact |
| Profit per accepted loan | $200 |
Once it is in your sheet
- The model arrives with real numbers in it and runs as it stands, so you can press the button first and understand it second.
- Change the numbers to yours. The sheet marks which cells are inputs and which hold formulas, and most labels carry a note explaining the row.
- Press the run button at the bottom of the panel. It is labeled for the tool you are in, and the result lands on its own tab, with a written reading of it beside the figures.
Never used Google Sheets? Start here goes the whole way, in seven steps, and assumes nothing.
Next question
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- Is that jump in your conversion rate real, or just luck?Is That Rate Real, or Just Luck?
- How big is your serviceable market, really?Serviceable Market Size (SAM) Range
- What are your customer segments, actually?Find Your Real Customer Segments
Every model like this one, and the method behind them: Machine learning in Google Sheets.