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How to Increase Brand Loyalty: Tactics That Work in 2026
Sam Hamer
A UK fuel retailer with 500 forecourts and a fashion chain with 80 stores are chasing the same number: more repeat visits without burning margin on blanket discounts. That is what increasing brand loyalty actually means in practice, not a feeling of affinity but a measurable shift in visit frequency, basket size and how often a customer picks you over the chain next door.
Loyalty programmes move behavioural loyalty (repeat purchase, spend) far more reliably than attitudinal loyalty, meaning how much someone actually likes you. That was the finding of a meta-analysis published in the Journal of the Academy of Marketing Science, see the JAMS meta-analysis.
That matters when you set targets. A programme that lifts visit frequency by a fraction of a point and does nothing for emotional attachment is still working.
If you want both, a points balance won't get you there. You need personalisation and game mechanics, plus a reason to open the app that isn't just "redeem your discount."
Set your first targets on behaviour. Attachment is harder to move, and it tends to follow.
The primary drivers of loyalty lift
Three levers move the needle. Personalised offers built on real purchase data come first. Next are game mechanics that reward more than spend: a visit counts, and so do a referral or a review. And then there is the friction between a purchase and the reward, which you want to cut as far as you can. According to Deloitte's 2025 Consumer Loyalty Survey, members who feel a programme understands their preferences spend measurably more and churn less than members who get generic blanket discounts, see Deloitte Insights.
Gamification without personalisation is a gimmick. Personalisation without gamification is a discount engine.
You need both running on the same data layer. That is why most loyalty projects now live or die on integration speed rather than on the creative concept.
Why personalisation beats a clever campaign concept
Personalised recommendations strengthen the link between trust, satisfaction and loyalty in e-commerce specifically, not just in loyalty programmes in general. That comes from a 2025 study in the Future Business Journal, see Springer, 2025. A 2026 systematic review of 43 studies on AI-driven personalisation looked at its effect on engagement and found that it holds across industries. It is strongest where offers are tied to recent behaviour rather than broad segments, see Frontiers in AI, 2026.
Picture a petrol retailer sending a fuel discount to someone who only ever buys coffee at the counter. That is wasted spend. NeoDay builds this segmentation into the loyalty engine itself, so offers come from actual transaction history rather than a quarterly campaign calendar.
Gamification: what it delivers, and where it backfires
A field study of nearly 19,000 users, published in the Journal of Marketing Research, found that gamified mechanics such as streaks, progress bars and tiered challenges measurably increase app engagement, see Paschmann et al., SAGE 2025.
The link to purchases holds too. A separate 2025 study tied gamification directly to repeat purchase behaviour, see ResearchGate, 2025.
But gamification is not automatically positive. A 2026 study in the Journal of Retailing and Consumer Services found that poorly framed progress mechanics can demotivate rather than engage: think of a progress bar that resets too often, or one that feels out of reach, see ScienceDirect, 2026.
The mechanic itself is rarely the problem. What separates a stamp card that works from one that gets ignored is usually the expiry window, and how big the final reward is relative to the effort.
This is the kind of design decision NeoDay works through with clients directly: how long a stamp or chili should stay valid, what the earning cap should be per visit, and where a tier should sit so it feels reachable within a normal shopping cycle. Get the expiry wrong and the whole mechanic reads as a trick rather than a reward.
Supplier and brand funding: can it offset programme cost
Yes. In food retail and QSR it is one of the most underused levers.
CPG brands routinely pay for featured placement in a loyalty app (a sampling offer, a bonus-point SKU, a sponsored challenge). They do it because the programme gives them a data-backed audience that a shelf display never can. It works best when the platform can target a specific SKU to a specific segment without anyone building lists by hand, and that is an API and segmentation question, not a creative one.
A fuel retailer or supermarket chain with reliable transaction data is sitting on an asset suppliers will fund directly, provided the targeting is precise enough to justify the spend.
Comparing the main approaches
Approach | What it rewards | Typical launch effort | Risk if poorly executed |
|---|---|---|---|
Points-for-spend only | Total spend | Low | Becomes a discount, no differentiation |
Tiered stamp or chili cards | Visit frequency | Medium | Expiry too short or too long kills motivation |
AI-personalised offers | Individual purchase patterns | Medium to high (data integration) | Weak if segmentation is shallow or batch-only |
Gamified challenges (streaks, missions) | Specific behaviours (referral, review, visit) | Medium | Can demotivate if progress feels unreachable |
Most brands that get real lift combine the second and third rows: a visible, simple mechanic (a stamp card, a tier ladder) running on top of a personalisation engine that decides which offer to surface to which customer. That combination is what /neoday-loyalty/ is built around, and it's the structure behind most of the /blog/best-retail-loyalty-program-examples we see working at scale.
The data question: real-time events or batch, and where Snowflake fits
If Snowflake is your loyalty data warehouse, the practical split is simple.
Transactional events that trigger an immediate reward (a stamp, a flash offer, a member moving up a tier) need to move in near real time. That usually means an event stream or an API call straight into the loyalty engine.
Everything behind segmentation and reporting can run on a daily or hourly batch sync into Snowflake: lifetime value models, churn scores, cohort analysis and campaign planning. Members won't notice any difference.
The access model matters more than people expect going in. Give the loyalty engine read access to a curated event schema (purchase, product category, store ID, timestamp) rather than raw POS exports, and the integration work shrinks considerably. Defining that schema up front, before any code is written, is usually the single biggest time-saver in the whole project.
On timelines: extraction and interface development for loyalty data from an existing POS or CRM stack typically runs four to eight weeks for a chain with a reasonably standardised till estate, longer if store formats vary or data lives in multiple regional systems. This is the main reason NeoDay's go-live model targets a 10-week launch with an API-first architecture that connects to whatever stack a retailer already runs, rather than requiring a rebuild of the backend first.
The integration timeline is set by your POS and CRM, not by the loyalty platform, so that is where to look first. One short business case review against your own setup will show what a 10-week launch means for you, and it gets to the real timeline faster than a generic RFP. Do it before you commit to a longer build.
Why loyalty, not just discounting, protects margin
According to the UK's National Centre for Social Research, 97 percent of UK shoppers already belong to at least one supermarket loyalty scheme. Yet trust in how loyalty pricing actually works is lower than retailers assume, see NatCen, 2024.
That gap is an opportunity.
A programme that is open about how points and tiers are earned builds more customer retention than one that feels like a hidden discount mechanism.
A 2024 paper in the Darpan International Research Analysis journal reached the same conclusion for retail as a whole: personalisation keeps customers only when they trust how their data feeds it (Darpan, 2024). For a loyalty programme, that trust problem stays hidden at first. A scheme that is vague about data use can post a perfectly good redemption rate and still lose engagement over the long run.
FAQ
What's a realistic visit frequency uplift from a loyalty programme? Any single benchmark will mislead you, because your starting point matters more than the industry average. What holds up across the gamification studies cited above is the ranking: personalised offers combined with a visible mechanic such as stamps or tiers bring people back more often than points alone. Pilot in one region or store cluster first, measure your own baseline, and set the target from that.
Should a loyalty programme be a standalone app, an SDK, or part of our existing app? If you already have a high-traffic app with daily logins, an SDK integration keeps loyalty inside that experience and avoids asking customers to download a second app. If you don't, or your existing app is rarely opened outside a purchase moment, a standalone loyalty app or a web-based experience usually gets better enrollment. NeoDay supports all three without a different backend for each.
How do we decide stamp or chili expiry windows without guessing? Base it on your actual purchase cycle data: if your median repeat visit is every 12 days, a 30-day expiry gives room without feeling endless. Expiry that's shorter than your typical visit gap kills motivation fast, which is the failure mode the 2026 ScienceDirect study on gamification backfire describes.
Does brand funding from suppliers actually cover loyalty platform costs? It can offset a meaningful share of ongoing campaign spend in food retail and QSR specifically, where CPG brands pay for sampling or bonus-point placements, but it rarely covers the full platform cost and shouldn't be the business case on its own. Treat it as a margin improvement on top of a programme that already works on its own merits.
Do we need a new app, or can the programme run inside the app we already have? You can do either. NeoDay runs as a standalone app, as an SDK inside your existing app, or on the web, so the choice usually comes down to how many active users your current app already has. Because the frontends are ready to use and the IT effort is small, launch can take as little as 10 weeks.
If you want to see what a 10-week rollout looks like against your own store count and POS setup, ask for a short business case or a working demo. That conversation usually clarifies more than another round of research.
Sources: 40 years of loyalty programs: a meta-analysis, Deloitte Insights: Reshaping customer loyalty programs, The moderating role of personalized recommendations (Springer, 2025), AI-Driven Personalization and Customer Engagement (Frontiers in AI, 2026), Driving Mobile App User Engagement Through Gamification (SAGE, 2025), The Impact of Gamification and Customer Engagement in Loyalty Programs on Repeat Purchase Behavior (2025), Is gamification always beneficial? (ScienceDirect, 2026), Attitudes Towards Supermarket Loyalty Pricing (NatCen, 2024), The Impact of Loyalty Programs on Customer Retention in the Retail Industry (Darpan, 2024)
More questions about loyalty programs? Find the answers in our loyalty FAQ.
