7 Proven Strategies to Boost Your Loyalty Program's AOV

Average order value (AOV) is total revenue divided by the number of orders in a period. In a loyalty program, the useful measure is member AOV compared with a holdout, read alongside order frequency, because a higher AOV with fewer orders may not mean more revenue.

Loyalty programs are often built to increase how often members buy. Raising how much they spend per order is a separate design problem with separate levers. This guide covers seven program design strategies that can lift AOV in consumer and B2B programs, how each one works and how to tell whether a rise in AOV is real growth or members simply consolidating purchases they would have made anyway. Each strategy rests on a clear mechanism; how much it lifts AOV in a given program has to be tested. For the behavioral background, see the guide to loyalty program psychology.

Key Takeaways

Is low AOV a design problem or a member mix problem?

Split member AOV by segment, such as tenure, channel or category, not by visit frequency. Low AOV in a few segments points to mix; low AOV everywhere points to design.

A design problem means the earn structure rewards frequency without rewarding basket size, so members have no reason to buy more per visit, and, where earning is per visit or per transaction, some may split orders to earn more often. A member mix problem means engaged members already spend well, but a large group of lightly engaged members pulls the average down; the fix is a segment-specific plan, not a program-wide change. Avoid splitting members by visit count alone: frequent buyers may place smaller orders, so a gap between heavy and light visitors can reflect frequency rather than a design flaw. Low AOV across every segment can also come from pricing, assortment or shipping thresholds that no loyalty mechanic will fix. Look at how AOV and order frequency move together before choosing which strategies below to start with.

1. How should the earn structure reward basket size?

A flat points-per-dollar rate earns the same for four small orders as for one large one. Reward larger baskets directly with spend multipliers, category earn rates or bundle bonuses.

Spend $10 four times and spend $40 once, and a flat rate pays the same points, so it gives members no reason to combine them into one order. Three adjustments reward basket size: a higher earn rate on the portion of an order above a set amount, higher earn rates on higher-margin categories to steer basket composition, and bonuses for buying a defined combination of products in one order. Check the margin effect of each before launch, because a multiplier that mostly rewards orders that were already large adds cost without adding revenue.

2. How should spend thresholds be set?

Set thresholds above what members typically spend per order so that reaching them takes a slightly larger basket, then test several levels to find the one that changes behavior.

Thresholds, such as a bonus or reward unlocked at a minimum order value, act directly on order size. Research on goal progress found that members buy more often as they near a reward: Kivetz, Urminsky and Zheng found that members of a café reward program "purchase coffee more frequently the closer they are to earning a free coffee" (Journal of Marketing Research, 2006). That finding is about frequency, so applying it to basket size is a hypothesis to test. Start from the median basket for the target segment, not the mean, which large orders pull upward. A threshold at the median pays a bonus on about half of orders without changing them, and any threshold pays members whose orders already clear it, so count that cost. Set a rule for returns, so a member who adds items to cross a threshold and then returns them loses the bonus. Test a few levels above the median against a holdout. Telling members how close they are, at the moment they can act, is part of the mechanic. BLOYL™, Brandmovers' loyalty platform, configures earning rules by product, channel, segment, time window or behavioral action without engineering work and runs A/B tests against a control group, so several threshold levels can be tested side by side.

3. How can tier structures reward spend rather than visits?

Qualify tiers on annual spend rather than visit count, so members who make fewer, larger purchases advance as fast as frequent small buyers, and attach some benefits to order value.

Tiers based on visit count reward more transactions, not larger ones, and can encourage members to split orders. Qualifying on annual spend removes that incentive, though on its own it rewards total spend rather than order size; the AOV effect comes from removing the reason to split orders and from benefits tied to order value. Tier benefits that require a minimum order value to use, such as a tier-only reward on orders above a set amount, add an incentive inside each visit as well as across the year. Clear re-qualification messages, showing what spend is needed and by when, give tier members a concrete target; watch for spend that jumps just before the qualification deadline and drops after it. The guide to designing a tiered rewards program covers tier design.

4. How do cross-category incentives grow baskets?

Bonuses for buying across several categories can widen what members buy and introduce products they have not tried; tie the bonus to a single order when the goal is AOV.

Rather than rewarding spend in general, cross-category bonuses direct it: a bonus for buying from three defined categories in one order, for example, gives members a specific basket goal rather than "spend more." A bonus earned across a month can widen what members buy without making any single order larger, so match the window to the goal. In B2B programs, the equivalent is a bonus for buying across more of a manufacturer's product lines.

5. When do time-limited events help raise AOV?

Time-limited bonuses can prompt members to consolidate purchases into one larger order, especially when tied to a spend threshold or category, but frequent events teach members to wait.

A double-points event that ends on Sunday can pull a purchase forward and make it larger, particularly if the bonus applies only above a spend level. Limited availability adds urgency only when it is credible: if almost every active member can claim the event, there is no scarcity. The risk is habituation. When bonus events are frequent and predictable, members learn to save purchases for them, so measure purchases between events as well as during them.

6. How should bonus events be personalized?

Target bonuses at categories a member is close to buying, such as one they tried once or one next to their usual purchases, and test whether the offer changes behavior.

A double-points event on a category a member always buys mostly pays for purchases they would make anyway. Categories the member has tried but not returned to, or that sit next to their usual purchases, are reasonable targets to test, and requiring the bonus category in the same order as a usual purchase keeps the effect on basket size rather than on a separate order. Proximity messages can be personalized too: telling a member exactly how much more they need and suggesting a specific product that would get them there is more actionable than a generic progress update. Test both against a control group. The guide to hyper-personalization covers where personalization becomes intrusive.

7. How does redemption support larger baskets?

Easy redemption keeps members in the earn-and-redeem cycle. A first reward reachable within a few purchases, a cash-plus-points option and a prompt toward the next reward all help.

Members who never redeem never experience the payoff, so points are less likely to motivate a larger basket. This is the least direct of the seven levers: easy redemption mainly supports engagement, and its effect on order size works through the other mechanics. Orders paid partly with points also lower cash revenue per order, so report AOV with and without redeemed value. Three design choices help: set the lowest reward low enough to reach within a few typical purchases, offer a cash-plus-points option so members can reach a higher-value reward, and show the next reward and how to reach it right after redemption. The guide to redemption rate optimization goes further.

How does AOV work in B2B loyalty programs?

In B2B programs, purchase volume per account per period and the breadth of product lines an account buys are often more useful than spend per order.

Per-period volume thresholds carry a specific risk: accounts may load up just before a period closes and order less afterward, or return stock, so read volume across the following period as well.

BENGAGED™, Brandmovers' B2B channel incentives platform, supports points and rebates by product or sales type and bonus rules for tiers, velocity and stretch goals.

How do you know AOV gains are real?

Track revenue and margin per member, not AOV alone, and compare members who receive an AOV mechanic with a random holdout over at least one full purchase cycle.

AOV can rise for reasons that add no revenue: members consolidate two orders into one, time purchases around bonus events, or low-spending members stop ordering, which lifts the average while revenue falls. Calculate revenue per member across everyone assigned to each group, including members who stopped buying, alongside order frequency and margin after reward cost. Define AOV consistently, net of returns and of any value paid with points. Consolidation is not always worthless: fewer, larger orders can lower shipping and handling cost per order, so include cost to serve in margin. Compare a random holdout with members who receive the change, and measure across a full purchase cycle so that pulled-forward purchases are counted. Watch purchases between bonus events for signs that members are waiting. The loyalty KPI dashboard sets out the formulas.

This is general information, not legal advice.

Conclusion

The most direct AOV levers reward basket size itself: earn rates and thresholds that pay for larger orders, order-minimum tier benefits and bonuses for buying categories together. Events, personalization and easy redemption support those levers rather than replace them. Diagnose the problem first, test each change against a holdout and judge success by revenue and margin per member, not AOV alone.

Sources

• Ran Kivetz, Oleg Urminsky and Yuhuang Zheng, "The Goal-Gradient Hypothesis Resurrected," Journal of Marketing Research 43(1), 2006
• Brandmovers, Aquatrols B2B loyalty program case study
• Brandmovers, Babybel Lunchbox Design Studio case study
• Brandmovers, Signia Aspire B2B loyalty program case study
• Brandmovers, Canadian regional distributor B2B loyalty program case study