What Prescreen Campaign Data Actually Tells Credit Unions About Where to Compete
The most valuable thing prescreen campaign analytics can do is tell you where you’re winning and where you’re wasting money. A review of aggregated weighted campaign performance data across offer types and campaign audiences reveals a clear pattern — one that holds up under a consistent evidence standard, and contains one finding that should change how credit unions think about member acquisition.
Why Market Share, Not Conversion Rate, Is the Right Metric
The Measurement Problem: When Conversion Rate Gives the Wrong Signal
Market share is defined as your institution’s closed loans in a given product category divided by the total loans closed in that category across all competing institutions serving the same population that received your firm offer of credit. Conversion rate measures what your campaign did in isolation. The two can move in opposite directions — and when they do, conversion rate is wrong.
| Campaign 1 | Campaign 2 | |
|---|---|---|
| Offers Sent | 1,000 | 1,000 |
| Your Loans Closed | 20 | 25 |
| Total Market Closings | 200 | 500 |
| Campaign 1 | Campaign 2 | |
|---|---|---|
| Offers Sent | 800 | 600 |
| Your Loans Closed | 24 | 15 |
| Total Market Closings | 120 | 50 |
Most institutions evaluate campaign performance with conversion rate: the number of members or prospects who responded to an offer divided by the number of offers sent. It’s intuitive, easy to compute, and almost entirely useless as a measure of competitive improvement.
Conversion rate measures what your campaign did. Market share — defined here as your institution’s closed loans in a given product category divided by the total loans closed in that category across all competing institutions serving the same population that received your firm offer of credit — measures how you performed relative to everyone else competing for the same borrowers at the same time. Institutional competitiveness is a relative concept, not an absolute one. A credit union’s goal is not to convert more offers; it is to win a larger share of the lending market it operates in. Market share is the only metric that directly measures that.
The distinction matters because conversion rate and market share can move in opposite directions — and when they do, conversion rate produces a false signal as described in the illustration above.
All findings in this analysis are expressed in market share terms. Conversion rate appears only where it is arithmetically necessary to derive market share from reported data.
The Evidence Standard
To separate reliable signal from noise, findings here require a minimum of 24 total sales — target and other product combined — on both the existing and prospective side before any comparison is treated as actionable. That threshold eliminates most of the data. Five bilateral comparisons survive it. A separate disclosure applies to mortgage new purchase, explained below.
The Gap That Sets the Frame
The Performance Gap: Existing Members vs. Prospective Campaigns
Aggregated weighted campaign data across participating credit unions and community banks. Minimum evidence threshold: 24 total sales (target + other) on both existing and prospective sides. Five bilateral comparisons survive. Market share = institution’s closed loans ÷ all loans closed among the same prescreened population.
Across all campaigns in the dataset, existing member campaigns capture 25.7% weighted market share versus 1.6% for prospective campaigns — a 16.5x gap supported by more than 3,200 total sales in existing campaigns and more than 2,200 in prospective ones. Every product-level finding that follows sits within this frame.
Four Products Where the Evidence Is Unambiguous
Personal and consumer loans are the strongest finding in the dataset. Existing member PCL campaigns capture 56.1% market share — nearly 1,000 total sales — while prospective PCL campaigns capture 2.2% on more than 200 total sales. More than half of existing members who enter the personal loan market chose their credit union. Fewer than 1 in 40 prospects did. No other bilateral comparison is this well-supported.
Auto loan refinance is the highest-volume comparison available. With 563 total existing sales and 1,225 prospective, the 32.0% versus 2.0% gap is the most statistically reliable finding in the data. ALR is also the only prospective product with a large enough sample to model cost-per-acquisition with genuine confidence.
Auto loan extensions show the same structural pattern at 19.3% existing versus 1.1% prospective across 424 and 662 total sales respectively. The relationship advantage is real, reproducible, and not dependent on small-sample luck.
Home equity products confirm the same direction — 22.0% existing versus 1.9% prospective — though both sample sizes just clear the minimum at 33 and 42 total sales. The direction is credible; the specific figures warrant more caution than the loan products above.
Mortgage New Purchase: The Outlier — With a Disclosure
Mortgage new purchase is the most strategically significant finding in the dataset, and the one that requires the most careful reading. Existing member MNP campaigns show 4.3% market share; prospective MNP campaigns show 3.2%. The gap is 1.3x — compared to 12–25x for every other validated product.
Full disclosure on classification: The MNP existing data has an unusual composition: 12 target product sales (mortgages) and 144 other product sales, for a total of 156. That 12:144 ratio of target-to-other is far outside the norm for other offer types and raises a legitimate question about whether some of those 144 sales involve mortgages that were attributed to a different product category. If any meaningful number of the 144 other-product sales were actually mortgages coded differently, the true existing market share could be materially higher than 4.3% — which would widen the gap and weaken the narrow-gap finding. The prospective side is less exposed to this concern, with 19 target sales and only 5 other product sales. This classification question is under separate review. The narrow gap is included here as a directional finding, not a settled conclusion.
With that disclosure in view, the structural argument for MNP as an acquisition vehicle remains: home purchase is a life-event decision driven by rate and timing rather than relationship depth. Buyers comparison-shop regardless of where they bank, which should reduce the existing-member advantage relative to products where trust and convenience dominate. Whether the advantage narrows to 1.3x or somewhat more will be clearer as classification questions are resolved and as more campaign data accumulates.
What a Narrow MNP Gap Suggests Strategically
Even with the classification caveat, the mortgage new purchase finding is worth acting on directionally. But the downstream logic deserves careful framing.
Oliver Wyman’s widely-cited analysis “Mortgage Cross-Sell: The Elusive Opportunity” found that for large banks, the mortgage-to-primary-relationship conversion is more assumed than achieved: primary banking relationships are sticky, most mortgage borrowers don’t consolidate their checking account with their lender, and only 10–15% of customers switch primary banks in any 18-month window. That finding applies to large commercial banks operating at arm’s length from their customers.
Credit unions are structurally different in three ways that change the arithmetic. First, credit unions predominantly portfolio their mortgage loans rather than selling them to the secondary market, which means the servicing relationship — monthly statements, escrow management, rate conversations — stays in-house for the life of the loan. Second, membership itself is a continuing engagement mechanism: once someone becomes a member to close a mortgage, they are already inside the cross-sell environment. Third, credit unions tend to service smaller geographic footprints where branch proximity and community identity reinforce the relationship in ways a national lender cannot replicate.
The hypothesis, then — supported directionally by this data but not yet proven within it — is that a prospective MNP campaign that wins a home purchase loan at or near existing-member conversion rates delivers downstream value no other prospective offer type can match. The new borrower becomes a member; the member encounters the same PCL, ALR, and HCC campaigns where existing members convert at 20–56% market share. Whether that flywheel actually closes, and at what rate, is a question this dataset cannot fully answer — but it is the right question for credit unions running MNP campaigns to measure going forward.
The rate environment reinforces the timing argument. High rates have suppressed purchase volume and reduced origination competition. Mortgages originated now become existing members for the next rate cycle — where this data consistently shows market share in the 20–56% range across personal loans, auto refinance, and home equity products.
The Practical Allocation Framework
For existing member campaigns, PCL, ALR, and ALE are the highest-confidence investments, supported by hundreds to thousands of total sales each. All three should run continuously at maximum eligible coverage. HCC belongs in the mix with appropriate precision caveats.
For prospective campaigns, MNP is the only offer type where the data — subject to the classification disclosure — suggests near-parity with existing member performance. PCL and ALR are the only other prospective products with sufficient volume to evaluate meaningfully; both convert at roughly 1/15th the existing rate, and the economics should be modeled explicitly before committing budget.
The Conclusion the Data Supports
Three Strategic Conclusions
Conversion rate measures campaign activity in isolation. Market share — your closed loans divided by all loans closed in the same prescreened population — measures whether you are winning or losing relative to competitors. Only market share tells you if your competitive position is improving. Track it every campaign, every product, every segment.
Existing member campaigns outperform prospective campaigns by 12–25× across every well-evidenced product type — Personal Consolidation Loans, Auto Loan Refinance, Auto Loan Term Extensions, and HELOC Consolidation. Your members are in the market right now, and they are choosing other institutions when you don’t make an offer. Continuous existing-member coverage is the highest-return activity available to a credit union or community bank.
Prospective campaigns convert at 1/12th to 1/25th the rate of existing member campaigns — except for Mortgage New Purchase, where the gap narrows to approximately 1.3×. That exception is the acquisition entry point: win the mortgage, gain a member, then deploy the existing-member campaigns where your institution already dominates. Not every prospective offer type earns the economics; choose the ones where the data says you can compete.
Volume, conversion rate, and cost-per-acquisition are useful inputs. But the only question that answers “are we getting more competitive?” is: did our share of the prescreened market go up? Campaigns that increase market share are working. Campaigns that don’t — regardless of what conversion rate shows — are not.
Four products tell a consistent story: the existing member relationship produces 12–25x the market share of prospective campaigns. One product — mortgage new purchase — appears to be an exception, with a gap of approximately 1.3x, subject to pending classification review. That exception, if it holds, is the acquisition strategy: maximize existing member coverage on proven products, and use MNP prospective campaigns to bring in new members who will encounter those same high-converting campaigns for years — a flywheel that the Oliver Wyman research suggests is more accessible to credit unions, with their portfolio-and-service model, than to the large banks where the mortgage cross-sell thesis has historically underdelivered.
Micronotes helps credit unions and community banks run smarter prescreen campaigns — combining FCRA-compliant targeting, behavioral analytics, and campaign performance measurement to maximize market share across both existing and prospective segments.
