Diwo
CatalystFinancial ServicesRetail

Campaign Optimization Use Case

The marketing lead at a mid-size card issuer just got the monthly life-events refresh — thousands of cardholders who relocated, married, or changed jobs in the last 30 days.

Start free 15-day trialHow Diwo solves it
Ranked in dollars
·
What-if validation
·
Push to ops
01The Problem

Today’s workflow is the bottleneck.

The marketing lead at a mid-size card issuer just got the monthly life-events refresh — thousands of cardholders who relocated, married, or changed jobs in the last 30 days. Each one is a behavior change waiting to happen, and a window she needs to act inside. Her current playbook sends the same three offers to everyone and measures success by gross redemption. She knows the Gen-Z post-relocation cohort doesn't want what the newly-married cohort wants, but scaling real insight across over one thousand card issuers' portfolios isn't something a quarterly campaign review can do.
The status quo· typical decision cycle
signal decays
  1. Signal captured
    Models score. Data is fresh.
    Day 1
  2. Dashboard built
    Analyst pulls CSVs, joins sources
    Day 2–3
  3. Review meeting
    Stakeholders ask for context, re-pull
    Day 4
  4. Window has closed
    Signal stale, action wasted
    Day 5+
By the time the action is ready, the window has closed.
5 days between signal and action. The data science team did their job. The operator is still waiting.
02The Approach

How Catalyst handles it.

Catalyst doesn't push another batch campaign. It analyzes cardholder preferences and transaction behavior across sectors, then uses its Recommendation First System to generate segment-level offers ranked by maximum expected lift. After launch, it measures target customer behavior post-event against a control group — so campaign effectiveness isn't guessed, it's observed. Structured transaction data joins unstructured signal through a Semantic Knowledge Graph, and continuous learning means next month's offers are sharper than this month's. The marketing lead ships a tailored offer to the Gen-Z post-relocation segment, not a blast to everyone who moved.
Catalyst · Conversation
A
Which campaign should we run for the Gen-Z cardholder segment post-relocation?
Catalyst · 320ms · sources: 4
Here’s what I found — three drivers explain most of the signal, and I’ve ranked them by impact.
RECOMMENDATION
Take action on the top-ranked driver first.
Expected lift: +15.3% · next sprint.
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03Ask

Questions you can ask.

Every case ships with a set of high-leverage prompts — the shortlist operators reach for every week. Here are the ones we see working for campaign optimization use case.
Anchor question
01 · start here
Which campaign should we run for the Gen-Z cardholder segment post-relocation?
02
How does spending behavior shift after marriage, a new job, or a move?
03
What's the expected lift from offering X to segment Y this month?
04
How did the target group perform against control after last quarter's campaign?
05
Which cardholder segments have the most customer lifetime value upside left?
06
Which card issuers in our network should receive which recommended campaign next?
Proof Points

What this looks like in production.

Claim 01

Insights delivered to "over one thousand card issuers."

Claim 02

No other explicit numeric claims on source page.

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