Most brand loyalty statistics in circulation trace back to restatements rather than research, and the two most-quoted figures have no primary study at all. The 12 figures below show their source, sample, year, and confidence rating.
The single most quoted statistic in retention marketing was measured in a bank branch system the year Tim Berners-Lee invented the web. The paper it comes from does not contain the number people quote, and it never did.
74% of the customers who buy from you once will never buy again. That figure comes from Bluecore’s analysis of more than 100 retailers across apparel, beauty, footwear, home goods, and jewelry, and it is probably the most useful brand loyalty statistic in circulation. It is also not the one you have seen quoted.
The one you have seen quoted is that a 5 percent increase in retention lifts profits by 25 to 95 percent. It appears in vendor decks, agency pitches, LinkedIn posts, and roughly forty of the fifty pages currently ranking for brand loyalty statistics. I have quoted it myself, on this site, more than once. It is directionally useful. It is also a number that no one, even if they repeat it, can point you to in a published table, including in the paper everyone credits.
That matters more in 2026 than it did in 2023, for a reason that has nothing to do with academic tidiness. When a merchant searched Google for a statistic, they landed on a page and made their own judgment about whether it looked credible. When they ask ChatGPT, Claude, or Perplexity, the model returns a number stripped of its context, and the sourcing quality of the page it pulled from is invisible. Bad provenance now propagates silently and at scale. So this piece does something the rest of this category does not: it names the source, the sample, and the year for every number, tells you plainly which ones to stop using, and shows its work on the three that everybody gets wrong.
The “25 to 95 percent” figure comes from a 2014 Harvard Business Review article, and the 1990 research underneath it does not state a range at all. The 2014 piece is Amy Gallo’s summary of customer churn economics, which credits Frederick Reichheld of Bain and Company. Gallo’s article is where the modern phrasing enters wide circulation, and it is also the origin of the companion claim that acquiring a customer costs five to 25 times more than retaining one, which she introduces with the qualifier “depending on which study you believe.”
Trace it one step further back, and you reach Reichheld and Sasser’s 1990 paper on customer defection rates. The published findings are three named results, not a range: cutting the defection rate by 5 percent generated 85 percent more profit in one bank’s branch system, roughly 50 percent in an insurance brokerage, and 30 percent in an auto service chain. Three service businesses. Named individually. In 1990.
The ranges came later, and they do not agree with each other. Reichheld’s own 1996 book, The Loyalty Effect, states it as 25 to 100 percent. Gallo’s 2014 HBR article says 25 to 95. A large body of secondary coverage says 25 to 85. A separate lineage running through Emmett Murphy’s Leading on the Edge of Chaos puts it at 25 to 125. Four different ranges, all credited to the same underlying work, none of them accompanied by a table you can go and read.
I want to be direct about something. This site published the 25 to 95 percent version, attributed to Bain, in more than one piece. I did not check the chain, because everybody else had not checked it either and the number felt settled. Checking it is what produced this page in its current form, and it is why every row below carries a confidence rating instead of a tidy percentage.
None of this makes the underlying finding wrong. The direction has held up across thirty-five years, and the mechanism is sound, which is why it survived. But three things are worth holding onto. Nobody can show you the range. The businesses studied were financial services and auto repair, not ecommerce, which did not yet meaningfully exist. And the numbers describe what happened at three specific firms, not a law that applies to your store.
The two most repeated brand loyalty figures- that existing customers represent 65 percent of a company’s business and spend 31 percent more than new customers- both trace to secondary sources with no locatable primary study. They are worth addressing directly, because they are quoted more often than anything in the verified table below, including by publications that link here.
The 65 percent claim is usually attributed to Gartner. Some versions credit an entity called the Customer Research Institute. Neither attribution leads to a published study, a sample size, or a methodology. What exists is a chain of pages citing other pages. The claim may well be roughly true for many businesses, and Zuora’s subscription research found existing subscribers generating between 70 and 81 percent of annual recurring revenue across 891 companies, which is directionally consistent for subscription models specifically. But if you use 65 percent and someone asks where it comes from, there is no answer to give them.
The 31 percent figure has a slightly clearer trail and an equally weak foundation. It travels as a pair, usually phrased as “existing customers are 50 percent more likely to try new products and spend 31 percent more,” and the trail leads to an Invesp conversion optimization infographic rather than to research. Invesp is a credible agency. An infographic is not a study.
Here is what to use instead, and it is better on every axis. Bluecore’s transaction data across more than 100 retailers found repeat buyers spending 69.2 percent more and placing 57.6 percent more orders than new buyers. That is observed purchase behavior rather than survey response or restatement, it holds across every vertical measured, and you can cite it without qualification. If you have been quoting 31 percent, the honest replacement is 69.2 percent, with the caveat about its vintage that the next section covers.
The pattern in all three of these numbers is the same. A directionally sound observation gets a specific decimal attached to it somewhere in the chain, and the decimal is what survives, because a precise number reads as more credible than an approximate one. That is exactly backwards.
Nine statistics survive a provenance check, and three widely repeated claims do not survive at all. The table below gives you the source, the year, and an honest confidence rating for each. Confidence reflects sample quality and independence, not whether the number is flattering.
That last row deserves a note. The 80/20 claim is the Pareto principle wearing a lab coat. It gets attributed to Bain, to Gartner, and to unnamed studies depending on the page, and neither Bain nor Gartner has published a dataset behind any version of it. Your own store either follows that distribution or it does not, and you can check in 10 minutes in Shopify Analytics. Use your number, not the folklore.
Across more than 100 retailers, the average repeat purchase rate is 16.5 percent, which is roughly ten points below the figure most Shopify content quotes. The Bluecore Customer Growth Benchmarks data puts health and beauty highest at 21.5 percent, followed by sporting goods and outdoor at 21.2 percent, and apparel at 20.2 percent. Those figures come from the eighth edition of the report, published in April 2024 and based on the full 2023 calendar year.
Two honest caveats before you use these. The first is vintage. This is 2023 transaction data, and it is the most recent full benchmark set Bluecore has published at this level of category detail. Bluecore’s own current benchmarks page states that over 2024 these figures increased significantly across every vertical, with home goods showing the smallest revenue improvement at a 310 percent year-over-year increase. So treat 16.5 percent as a floor rather than a current reading, and treat the new-versus-repeat spending gap as conservative.
The second caveat is definitional. The competing repeat rate figure you will see quoted is around 28 to 30 percent, usually credited to Shopify, and in most cases without a link to anything specific. Both ranges can be right, because they measure different things over different windows and different store populations. Bluecore’s set skews toward larger omnichannel retailers over a full calendar year. That definitional gap is exactly why a category average is close to useless for your decision making, and why comparing yourself to brands at your price point and purchase cycle matters far more than beating an industry number.
The figure I would actually put in front of a board is the spend gap. Across every vertical, repeat buyers placed 57.6 percent more orders and spent 69.2 percent more than new buyers. That is transaction data rather than survey data, it is consistent across categories, and it makes the argument for retention investment without needing a 1990 banking study to prop it up. If you are building the system behind that number, the retention framework that moves repeat purchase rate covers the flow architecture in detail.
Loyalty and CRM now account for 51.5 percent of the average marketing budget among program owners, meaning spend is no longer the differentiator it once was. The Antavo Global Customer Loyalty Report 2026, published in February 2026 and based on 3,000 marketers and 10,000 consumers, also reports that 92.7 percent of program owners saw a positive return, averaging 5.3 times, up from 5.2 times the prior year and rising for the third consecutive year.
Read that ROI figure carefully. The respondents are the people who selected, bought, and are now accountable for the programs, surveyed by a loyalty platform vendor. That does not make it false, and the sample is large. It does mean the number carries a structural optimism that a neutral audit would not. I have marked it moderate confidence for that reason, and I would use it as evidence that programs generally work rather than as a forecast of what yours will return. The same report found 31.3 percent of consumers saying a good loyalty program makes them more likely to keep buying from a brand, which is a considerably more modest number than the marketer-side enthusiasm suggests, and the two sitting side by side in the same study is the most useful thing in it.
The pattern underneath all of this is one I saw constantly during my years at Shopify working with DTC brands: merchants at $500K to $2M install a loyalty app expecting it to fix retention, when the actual problem is that nothing happens between the first order and silence. A program layered on top of a broken post-purchase experience adds a discount liability without adding a relationship. If your repeat rate is under 20 percent and you have no post purchase sequence, build the sequence first. When you are ready, the Shopify referral and loyalty apps worth evaluating in 2026 breaks down the options by stage, and if you would rather not build it in house, there are retention marketing agencies working with Shopify brands at the $5M and above tier.
Fifty-seven percent of consumers say they would let an AI shopping agent switch brands on their behalf if it found better value. That figure comes from Checkout.com’s Agentic Commerce 2026 report, published June 9, 2026, and it is the single number in this piece most likely to matter in eighteen months. The same report found merchants saying AI agents account for only 3 percent of transactions today while 89 percent are actively preparing for agentic commerce, which is the gap worth watching.
The supporting evidence is consistent, though thinner than the headlines suggest. NIQ found that 42 percent of consumers used at least one AI tool to shop in the past month, from its Quick Question research with a monthly sample of roughly 500 US consumers, so read it as a directional US signal rather than a global benchmark. Bain’s analysis of autonomous shopping frames the consequence in one line: AI shifts loyalty from brands to outcomes.
Here is the part that connects loyalty to visibility. McKinsey’s State of the Consumer 2026 reports that only 1 percent of the sources large language models cite when answering questions about a brand come from that brand’s own website. Even among the ten most cited sites, which together account for roughly 22 percent of all citations, brand owned properties make up to 10 percent. That citation finding comes from XEO360 data covering October 2025 to May 2026, not from McKinsey’s consumer survey, and it is scoped to consumer goods.
Worth knowing that McKinsey has published a competing figure. Their 2025 analysis of AI powered search says a brand’s own sites in many cases comprise 5 to 10 percent of the sources AI search references. Same firm, different method, an order of magnitude apart. I would not treat either number as precise. What both support is the direction: your product pages, your about page, your carefully written brand story are a small minority of what the model reads when a shopper asks which brand to buy.
Apply the 18 month test. Points and tiers still work, and I am not telling anyone to dismantle a program that is returning. But a loyalty program that only exists inside your checkout is invisible to an agent comparing options across a category. The brands that hold position are the ones making their offer, inventory, and reasons to choose them machine readable, which is what Shopify MCP exposes about your catalog to AI agents is for. Structured data is becoming a retention channel, which is a genuinely strange sentence to write and I think it is correct.
Name the source, the sample, and the year, or do not use the number at all. That test takes under two minutes and it disqualifies a startling share of what circulates in this category. If a page cites a statistic without a link, treat the statistic as unverified until you find the original. If the link goes to another blog post rather than to a report, you have found a restatement, not a source.
Four failure modes account for almost everything that goes wrong. Range invention, where three named results become a range that no published table contains. Attribution drift, where a number credited to a 2014 HBR summary gets recredited to Bain, then to Gartner, then to nobody. Context loss, where a finding from three service firms in 1990 gets presented as an ecommerce benchmark. And vendor framing, where a survey of people who bought a product reports how well that product works.
For your own store, the honest move is to stop benchmarking against category averages entirely. Pull your repeat purchase rate, your time to second purchase, and your revenue split between new and returning customers. Those three numbers tell you more than every statistic on this page combined, because they describe your business rather than an aggregate of businesses that are not yours. The connection between email, loyalty programs, and community and your retention numbers is where most of the movement comes from once you know your baseline.
I ran an online contact lens business before this, in a category where replenishment is the entire model. What I learned there has stuck with me through several hundred merchant conversations since: what looked like brand loyalty was mostly being the default reorder, and the moment a competitor made reordering marginally easier, the loyalty evaporated. That is worth remembering as agents get better at comparison. Convenience has always been doing more work than affinity, and we are about to find out exactly how much.
The most defensible brand loyalty statistic is that repeat buyers spend 69.2 percent more and place 57.6 percent more orders than new buyers, from Bluecore’s analysis of more than 100 retailers. It is transaction data rather than survey data, it holds across every vertical measured, and the sample is large enough to be meaningful. The companion figure from the same source, that 74 percent of customers buy once and never return, is equally well grounded. Both reflect 2023 calendar year data published in 2024, and Bluecore reports the new-versus-repeat gap widened over 2024, so treat them as conservative. Both are more useful than the widely quoted claim that a 5 percent retention lift raises profits 25 to 95 percent, which cannot be traced to any published table.
The 25 to 95 percent range appears in a 2014 Harvard Business Review article but not in the 1990 research it cites. The original study by Frederick Reichheld and W. Earl Sasser reported three named results rather than a range: an 85 percent profit gain in one bank’s branch system, roughly 50 percent in an insurance brokerage, and 30 percent in an auto service chain. At least four different ranges circulate under the same attribution, including 25 to 85, 25 to 95, 25 to 100 in Reichheld’s own 1996 book, and 25 to 125 from a separate lineage. None comes with a table you can check. The direction of the finding has held up for thirty-five years, and the mechanism is sound. The specific range is not citable.
The 65 percent figure is usually attributed to Gartner, sometimes to an entity called the Customer Research Institute, and neither attribution leads to a published study, sample size, or methodology. It circulates as a chain of pages that cite one another. The claim may be roughly right for many businesses, and Zuora’s research across 891 subscription companies found that existing subscribers generate 70 to 81 percent of annual recurring revenue, which is directionally consistent with subscription models. But there is no primary source to point at. If you need a defensible figure for the value of existing customers, use Bluecore’s finding that repeat buyers spend 69.2 percent more and place 57.6 percent more orders than new buyers.
The 31 percent figure traces back to an Invesp conversion optimization infographic rather than to primary research, and it usually accompanies a claim that existing customers are 50 percent more likely to try new products. Invesp is a credible agency, but an infographic is not a study, and no underlying dataset has been published. The better-sourced replacement is Bluecore’s transaction data from more than 100 retailers, which found that repeat buyers spent 69.2 percent more than new buyers. That figure is observed purchase behavior rather than survey response, it holds across every vertical measured, and it is roughly twice as large as the number it replaces.
A good repeat purchase rate depends far more on your category and purchase cycle than on any cross-industry average. Bluecore’s benchmark data puts health and beauty at 21.5 percent, sporting goods and outdoor at 21.2 percent, apparel at 20.2 percent, and the blended average across all verticals at 16.5 percent. Other widely cited sources put the ecommerce average closer to 28 to 30 percent, and both can be correct because they measure different store populations over different time windows. A supplements brand at 20 percent has a retention problem. A furniture brand at 20 percent is performing well. Benchmark against brands with similar price points and natural repurchase intervals, not against a category average.
AI is shifting loyalty from brands toward outcomes, and 57 percent of consumers now say they would let an AI shopping agent switch brands on their behalf if it found better value. Checkout.com’s June 2026 research found that, alongside evidence that adoption is moving fast, NIQ reports 42 percent of US consumers used an AI tool to shop within the past month. The structural problem for brands is visibility. McKinsey found that only 1 percent of the sources language models cite when answering questions about a brand come from that brand’s own website, though a separate McKinsey analysis puts the figure at 5 to 10 percent, so treat the direction as reliable and the precision as not. Machine-readable product and offer data is becoming a retention requirement.
Loyalty programs are worth building, but spend is no longer a differentiator because program owners now allocate 51.5 percent of the total marketing budget to loyalty and CRM. Antavo’s 2026 report, based on 3,000 marketers and 10,000 consumers, found that 92.7 percent of program owners saw a positive return, averaging 5.3 times, though that figure comes from a vendor survey of the people who purchased the programs and should be read with that in mind. The same report found only 31.3 percent of consumers saying a good program makes them more likely to keep buying, which is a useful reality check. The sequencing matters most: if your repeat purchase rate is under 20 percent and you have no post-purchase email flow, build the flow before the program.
