How AI Is Rewriting the Rules of C-Store Loyalty

Researcher Angela Duckworth spent years studying what separates high performers from everyone else. She found it wasn't IQ or circumstance; it was grit. This is the willingness to keep working when others stop.

That framing opened Paytronix's session at Outlook Leadership in August 2026, and it wasn't accidental. The point: the operators who will win the next five years of fuel retail aren't necessarily the ones with the biggest budgets. They're the ones who pick up AI tools and do something with them before anyone asks them to.

To put the pace of change in context: the transition from horse to car took centuries. Coal to oil has taken more than a hundred years, and we still burn coal. AI tools have been developed over the course of years and become mainstream adoptions in months.

What Paytronix can demonstrate today in a 30-minute campaign setup would have required a full analytics team and weeks of work just 12 months ago. In six months, today's methods will likely be outdated again.

That's the lens; now for the specifics.

The Loyalty Math That Makes This Urgent

Before getting into tools and tactics, it's worth grounding the opportunity in data shared at the session. This data reframes how operators should think about their customer base.

8% of your visitors are driving 40% of your revenue. That mirrors a broader economic pattern: the top 10% of US earners account for roughly half of all consumer spending. Loyalty programs exist, in part, to identify and protect that 8% and to grow it.

The challenge is that most operators think of loyalty as a retention tool, not a recruitment tool. That's a missed opportunity.

When a program reaches maturity, roughly half of a brand's customers are enrolled and half aren't. The unenrolled half who are already shopping at the store are one problem.

But the people who have never set foot on the forecourt at all... those are the real acquisition target, and they're reachable in ways that weren't practical before.

Acquire: A 4-Step Playbook for Winning Net-New Members

Step 1: Suppress Your Existing Members

If the goal of a paid advertising campaign is to recruit new loyalty members, spending impressions on people who are already enrolled is pure waste. The first move is to suppress your loyalty list from the ad audience entirely. As the campaign runs and new members join, they're automatically removed from the served audience, compounding efficiency over time.

Step 2: Target the Right Strangers

The targeting logic here is straightforward but powerful: go after people who match your brand's target demographic but have never visited your stores. One concrete method: target devices that appear on a competitor's forecourt but never on yours. These are already fuel buyers, already in your trade area, and already choosing someone else.

The creative principle, borrowed from legendary adman David Ogilvy: lead with the fire, not the extinguisher. Get to the value proposition immediately. In a world of streaming skip buttons and infinite scroll, you have seconds.

Step 3: Remove Every Friction Point at Enrolment

Getting someone to click an ad is only half the battle. The enrolment experience itself is where most programs leak.

Paytronix's data shows that removing the app-download requirement and replacing it with a simple phone-number entry, at the pump or at the counter, can at least double enrolment rates, with some markets seeing up to 6× the enrolment volume compared to traditional app-first flows.

The mechanism: a customer enters their phone number, receives a one-time passcode to validate, and they're in. No password to remember. No form to fill out. No app required at that moment.

Step 4: Nurture From Opt-In to Full Profile

This is where most operators stop and where the real work starts. A phone-number opt-in gives you a text thread and double opt-in consent.

That's a lead, not a member. Paytronix's TextFlow tool uses that thread to progressively close the gap: inventivizing app download, encouraging full profile completion, and building the data richness that makes future segmentation possible.

• Current benchmarks: 40–50% of phone-number opt-ins complete full profile registration in the initial flow. The remaining 50–60% can be recovered through nurturing: weekly SMS prompts, POS screen messages, or additional incentives tied to registration milestones.

• On incentive sizing: Mapco runs a staggered registration reward of 15, 25, and 35 cents off the first three visits; 75 cents cumulative. Chevron offers a dollar off the first five fill-ups. Fuel Rewards Network staggers rewards with 30 cents on the third fill-up.

The principle: look at your competitive set and make sure your carrot is at least comparable. And factor registration rewards into your true acquisition cost; a $6.25 media cost per new member can become $30–40 all-in once incentives are included.

The Campaign That Proved It

Paytronix ran a real acquisition campaign across two regional markets in a single flight. The figures were adjusted for confidentiality, but the performance curve is real and has been replicated.

The control group's half-point gain is the baseline: what happens if you do nothing. The 11-point swing in the campaign group is what AI-enabled, suppressed, closed-loop advertising delivers on top of that.

One notable finding: the two markets performed very differently. The gap came down to asset class: newer, better-maintained stores outperformed older ones. A hypothesis going in; a fact coming out.

Industry benchmark for cost per new loyalty member across QSR and c-store: $10. The $6.25 result in this campaign beat that benchmark by 37.5%.

Optimize: How AI Segmentation Doubled Fuel Volume at Half the Cost

Getting members in is only the beginning. The bigger margin opportunity is in what you do with them once they're enrolled.

Paytronix's optimization approach starts with a segmented baseline: seven customer segments, each receiving a variable fuel offer. The setup takes 20–30 minutes. The model then runs, learns, and adjusts.

The key inputs fed into the model:

• Lapse status and days since last visit
• Weighted average margin by site (sourced from tools like Calibrate or Opus)
• Merchandise purchase behavior linked to loyalty transactions within a 30-minute window
• Food service category data (cross-category targeting)

The result over a measured test period:

Starting point: 67,000 gallons at a cost of 10 cents per gallon.

After the model ran and iterated: volume roughly doubled, cost per gallon roughly halved.

Then the team layered in merchandise. Because loyalty transactions capture in-store purchases within 30 minutes of a fuel transaction, the model can identify which members are already buying high-margin items (and shouldn't be steered away from that behavior) versus which members are light on, say, mobile food ordering (and should be targeted with a food service offer).

Food service example: Rather than sending a generic pizza offer to all members, which gives away margin on customers who would have bought anyway, the model targets the specific segment where the offer changes behavior. Over 12 weeks, this compounding segmentation approach delivered:

• +4% growth in gallons above baseline
• +2.25% growth in sales above baseline

And when the team tried to override the model during periods of high fuel margin volatility, giving away more to capture more, the model was right. The lesson: test your floor, but trust the data.

The Tool Stack Making This Possible

Two components sit behind the optimization work:

• Journey Builder (Advanced Messaging): A drag-and-drop campaign builder that lets operators set up 6–8 segmented campaigns, including reminder sequences, without exports, imports, or external configuration. The 20–30 minute setup time referenced above is enabled by this tool.
• Agentic AI / Campaign Copilot (launching later in 2026): Built in partnership with The Access Group and Paytronix, this tool allows operators to query campaign performance in natural language ("How is my lapsed-member campaign performing?"), receive dashboard-linked answers, and action AI-recommended optimizations in one or two clicks, without rebuilding campaigns from scratch. The ambition: close the loop in hours or minutes instead of months.

Measure: The 6 KPIs That Prove Your Program Is Working

There's always a gap between a program that feels like it's working and one that's proven to be. These are the six measures Paytronix uses with clients to close that gap:

The principle underlying all six: demand closed-loop proof. Served → walked in → transacted. Not just a platform-reported cost-per-acquisition that flatters itself.

Three Things to Do Now

1. Suppress first. Before your next paid acquisition campaign goes live, pull your loyalty member list and exclude them from the audience. Stop paying to reach people who are already yours.
2. Demand closed-loop proof. Ask your media partner or platform for served-to-visited-to-transacted attribution. If they can't provide it, that's a gap worth fixing.
3. Give every segment its own offer. Members who visit twice a week don't need a 30-cent fuel discount. Members who haven't visited in 60 days might. Segment your active, latent, and lapsed guests and test different offers against each. Let the model learn.

The mindset shift underneath all three: Move one recap hour a week to an hour spent on testing. The gap between "looks like it's working" and "proven to be working" closes on its own, if you let the data do the work.

As the session closed: That's grit meeting AI.

Frequently Asked Questions

What is the average cost to acquire a new loyalty member in c-store?

The industry benchmark across QSR and c-store is approximately $10 per acquired member (defined as a new member who completes at least one transaction). Paytronix's suppressed, closed-loop campaign approach has achieved $6.25 in tested markets, though the right target varies significantly by brand, offer, and market.

Does removing the app download requirement really increase enrollment?

Yes. Paytronix data shows that friction-free phone-number enrollment at the pump or counter produces at least double the enrolment rate of app-first flows, with some markets seeing up to 6× the volume. The trade-off, a thinner initial profile, is addressed through SMS nurturing via TextFlow.

What conversion rate should I expect from phone-number opt-ins to full profile?

Paytronix clients are currently seeing 40–50% complete full registration in the initial flow. Nurturing campaigns (SMS, POS prompts, and additional incentives) can recover a significant portion of the remaining 50–60%.

How long does it take to set up an AI-optimized segmented campaign?

With Paytronix's Journey Builder, the baseline setup across seven segments takes approximately 20–30 minutes. The model then runs and iterates automatically, with human review of recommendations.

How do I measure whether my loyalty program is actually driving incremental revenue?

Use a control group methodology: run your campaign to a test segment and compare fuel loyalty share, gallon volume, and in-store spend against a matched control group that received no campaign. The difference, not the absolute number, is your true incremental lift.