Understand how Gameball scores each customer on recency, frequency, and monetary value and places them into one of seven RFM segments.
Platform
Plan
Shopify
Start, Pro, & Guru
Salla
Pro, & Guru
Non-Platform
Growth & Enterprise
Every night, Gameball’s RFM Segments place each of your customers into one of seven groups, such as Best Buyers or About to Lose. This article explains how each customer’s scores are worked out and how those scores decide their segment, with examples you can follow step by step.
Each customer’s segment answers three questions. Each question gets a score from 1 (weakest) to 4 (strongest), and the three scores together decide the segment.
Recency
When did they last buy?Based on their latest completed order.
Frequency
How often do they buy?Based on their number of completed orders.
Monetary
How much do they spend?Based on their total completed spend.
Gameball calculates the scores in four steps:
1. Measure the Buying Cycle
Learn how often your returning customers usually come back.
2. Score Recency
Count how many buying cycles have passed since the last purchase.
3. Score Orders and Spend
Compare each buyer’s orders and spend with your other buyers.
4. Assign the Segment
Combine the scores to place the customer in one of seven segments.
“Recent” means different things in different businesses. A grocery customer who hasn’t ordered in a month is drifting away, while a furniture customer who hasn’t ordered in a month is perfectly normal. Before scoring anyone, Gameball measures how often your returning customers usually come back. This is your store’s buying cycle.
1
Find your returning customers
Gameball looks at every customer who bought on two or more different days in the last two years.
2
Work out each customer's average gap
For each returning customer, Gameball calculates the average number of days between their purchases.
3
Take the middle value
The gaps are sorted from shortest to longest, and the middle value becomes your store’s buying cycle.
See an example: five returning customers
Customer
First Purchase
Last Purchase
Buying Days
Average Gap
Ali
1 Jun
21 Jun
3
10 days
Omar
1 Mar
1 Apr
2
31 days
Sara
1 Jan
1 Jul
4
60 days
Mona
1 Feb
1 May
2
89 days
Lina
1 Jan
1 Dec
3
167 days
Sorted, the gaps are 10, 31, 60, 89, and 167. The middle value is 60, so this store’s buying cycle is 60 days: a typical returning customer buys about every two months.
Same-day orders count once: Two orders on the same day count as one buying day. A customer who splits a basket into two orders hasn’t really “come back”, so counting days keeps your buying cycle accurate.
A week is about the fastest a marketing team can act. Shorter windows would move large numbers of customers between segments every night.
180 days maximum
Beyond six months, “recent” stops meaning recent.
41 days for new stores
Used until your store has at least 100 returning customers and enough history to see the pattern repeat four times.
41 days is the typical buying cycle across all Gameball stores. Your buying cycle is re-measured every night, so it adjusts as your customers’ habits change.
Recency is counted in buying cycles. Using the 60-day example store:
Score
Last Purchase
With a 60-Day Cycle
In Plain Words
4
Within 1 cycle
0–60 days ago
Buying on their normal schedule
3
Within 2 cycles
61–120 days ago
Missed one visit
2
Within 4 cycles
121–240 days ago
Clearly slipping
1
Longer
241+ days ago
Gone quiet
Customers who haven’t bought yet are timed from the day they signed up. Within one buying cycle of signing up, they score 4 because they haven’t had time to buy yet. After one cycle with no purchase, they score 1.The signup date is the one your store shares with Gameball. If your store doesn’t share one, Gameball uses the day the customer was added to Gameball.
For orders and spend, there is no “normal schedule” to compare against, so each buyer is compared with your store’s other buyers. Imagine every buyer standing in a line from lowest to highest, then cutting the line into four equal parts. Orders and spend are each scored this way.
See an example: eight buyers lined up by total spend
Score
Buyers (SAR spent)
1 (bottom 25%)
Reem (40), Sami (55)
2
Omar (90), Huda (120)
3
Khaled (300), Ali (410)
4 (top 25%)
Nadia (900), Layla (2,400)
If a business account that spent 968,654 SAR is added, it joins Nadia and Layla in the top quarter. Reem and Sami still score 1, and nobody’s score changes because of it.
Ties share a score: In most stores, many buyers have exactly one order, and they all get the same order score. This means the four order groups are rarely equal in size, and in some stores one score has nobody in it.
Scores are based on position in the line, not the amount. A business account with thousands of orders simply lands at the top and scores 4, like the rest of the top quarter. It can’t push anyone else’s score down.
Negative totals
Refunds for orders Gameball never received can leave a negative total. These are counted as 0, so a refund never ranks someone below a customer who hasn’t bought.
Only cancelled or refunded orders
Customers with no completed order are treated as never having bought: they score 1 for orders and spend, and their recency is timed from their signup date.
Impossible purchase dates
Dates such as 1 Jan 1900 are ignored. Any date before the year 2000 counts as no purchase date, so it can’t make a customer look decades overdue.
On schedule, and in the top quarter for both orders and spend.Suggested action: Reward and recognize them with early access and VIP perks.
Loyal
On schedule, with solid but not top value.Suggested action: Nudge them up with bundles, tier goals, and referral offers.
New Customers
Bought within the last cycle with few orders and low spend, or signed up within the last cycle and haven’t bought yet.Suggested action: Send a welcome offer for the first purchase, then make the second one easy.
Promising
Missed one normal visit, at any value level.Suggested action: Send a timely reminder before the habit breaks.
Can't Lose
Two to four cycles without buying, at any value level.Suggested action: Run a re-engagement campaign with a reason to return.
About to Lose
Gone quiet, but used to order and spend well.Suggested action: Make a strong win-back offer. These are the most valuable customers to recover.
Lost
Gone quiet with low value, or signed up more than one cycle ago and never bought.Suggested action: Use low-cost reactivation, or a first-purchase offer for those who never bought.
This store has a 60-day buying cycle. Expand each customer to see why they land in their segment today.
Layla → Best Buyers
Bought 10 days ago · 14 orders · 2,400 SAROn schedule (10 ≤ 60 days), and in the top quarter for both orders and spend.
Ali → Loyal
Bought 25 days ago · 3 orders · 410 SAROn schedule, with upper-middle value.
Omar → New Customers
Bought 5 days ago · 1 order · 90 SAROn schedule, but one small order so far gives him a low value level.
Yara → New Customers
Signed up 12 days ago · no order yetShe has had less than one cycle (12 ≤ 60 days) to make a first purchase, so she counts as new, not lost.
Huda → Promising
Bought 95 days ago · 2 orders · 120 SAR95 days is past one cycle but within two, so she missed one normal visit.
Khaled → Can't Lose
Bought 200 days ago · 6 orders · 300 SARBetween two and four cycles without buying. His value level doesn’t change this segment.
Nadia → About to Lose
Bought 400 days ago · 9 orders · 900 SARGone quiet (past 240 days), but she used to buy often and spend well.
Sami → Lost
Bought 400 days ago · 1 order · 55 SARGone quiet, with low value.
Segments change as time passes: If Huda buys again tomorrow, she moves back to an on-schedule segment the next night. If Layla stops buying, she moves to Promising after 60 days, even though she hasn’t done anything. If Yara still hasn’t bought after 60 days, she moves to Lost.
Each segment on the RFM Segments page shows a summary like this example from a store with a 72-day buying cycle:
Best Buyers
Bought recently and are in your top 25% for both number of orders and total spend. Last purchased 0 to 72 days ago, with an average of 7.4 orders and 1,758 SAR spent.
Days: The most and least recent purchase in the segment. For on-schedule segments, this is at most one buying cycle.
Orders and spend: The segment’s average. An average describes the typical member, while a lowest-to-highest range would mostly reflect one or two unusual accounts.
The summary numbers only count customers who have bought. New Customers and Lost also include customers who haven’t bought yet, but they are left out of these numbers so they don’t pull the averages down.
Why did a customer move segments when they didn't do anything?
Because time passed. Recency is counted from today, so a customer who stops buying moves from on schedule to Promising, then Can’t Lose, then About to Lose or Lost. Segments update every night.
Can a customer with lots of orders be in Promising or Can't Lose?
Yes. These two segments depend only on how long it has been since the last purchase. A big spender who hasn’t bought in two cycles is in Can’t Lose, which is exactly the type of customer you want to win back.
Where do customers who never bought go?
For one buying cycle after signing up, they are in New Customers because they haven’t had time to buy yet. If they still haven’t bought after one cycle, they move to Lost.
Do cancelled or refunded orders count?
No. Only completed orders count toward orders and spend. A customer whose only order was cancelled counts as never having bought.
Why are Lost and About to Lose bigger than I expected?
Most stores have many customers who bought once a long time ago, plus sign-ups who never bought. Measuring against your store’s real buying rhythm shows how many customers have actually gone quiet. About to Lose is usually your best win-back opportunity.
Are test accounts included?
No. Only active customers on your live account are scored. Test, deactivated, and deleted customers have no RFM segment.
How fast does a new purchase show up?
Segments are recalculated every night, so a new purchase moves the customer at the next nightly update.
Understand the concept of RFM analysis and where to find your segments.
Using RFM Segments to Serve Your Goals
Learn how to act on each segment with campaigns and retention strategies.
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Measure the Buying Cycle`,icon:`calendar-days`,href:`#step-1-measure-the-buying-cycle`,children:_jsx(_components.p,{children:`Learn how often your returning customers usually come back.`})}),_jsx(Card,{title:`2. Score Recency`,icon:`clock`,href:`#step-2-score-recency`,children:_jsx(_components.p,{children:`Count how many buying cycles have passed since the last purchase.`})}),_jsx(Card,{title:`3. Score Orders and Spend`,icon:`ranking-star`,href:`#step-3-score-orders-and-spend`,children:_jsx(_components.p,{children:`Compare each buyer’s orders and spend with your other buyers.`})}),_jsx(Card,{title:`4. Assign the Segment`,icon:`layer-group`,href:`#step-4-assign-the-segment`,children:_jsx(_components.p,{children:`Combine the scores to place the customer in one of seven segments.`})})]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`step-1-measure-the-buying-cycle`,children:`Step 1: Measure the Buying Cycle`}),`\n`,_jsxs(_components.p,{children:[`“Recent” means different things in different businesses. A grocery customer who hasn’t ordered in a month is drifting away, while a furniture customer who hasn’t ordered in a month is perfectly normal. Before scoring anyone, Gameball measures how often your returning customers usually come back. This is your store’s `,_jsx(_components.strong,{children:`buying cycle`}),`.`]}),`\n`,_jsxs(Steps,{children:[_jsx(Step,{title:`Find your returning customers`,children:_jsx(_components.p,{children:`Gameball looks at every customer who bought on two or more different days in the last two years.`})}),_jsx(Step,{title:`Work out each customer's average gap`,children:_jsx(_components.p,{children:`For each returning customer, Gameball calculates the average number of days between their purchases.`})}),_jsx(Step,{title:`Take the middle value`,children:_jsx(_components.p,{children:`The gaps are sorted from shortest to longest, and the middle value becomes your store’s buying cycle.`})})]}),`\n`,_jsxs(Accordion,{title:`See an example: five returning customers`,icon:`calculator`,id:`see-an-example-five-returning-customers`,children:[_jsxs(Table,{children:[_jsx(_components.thead,{children:_jsxs(_components.tr,{children:[_jsx(_components.th,{children:`Customer`}),_jsx(_components.th,{children:`First Purchase`}),_jsx(_components.th,{children:`Last Purchase`}),_jsx(_components.th,{children:`Buying Days`}),_jsx(_components.th,{children:`Average Gap`})]})}),_jsxs(_components.tbody,{children:[_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Ali`})}),_jsx(_components.td,{children:`1 Jun`}),_jsx(_components.td,{children:`21 Jun`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`3`}),_jsx(_components.td,{children:`10 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Omar`})}),_jsx(_components.td,{children:`1 Mar`}),_jsx(_components.td,{children:`1 Apr`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`2`}),_jsx(_components.td,{children:`31 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Sara`})}),_jsx(_components.td,{children:`1 Jan`}),_jsx(_components.td,{children:`1 Jul`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`4`}),_jsx(_components.td,{children:`60 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Mona`})}),_jsx(_components.td,{children:`1 Feb`}),_jsx(_components.td,{children:`1 May`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`2`}),_jsx(_components.td,{children:`89 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Lina`})}),_jsx(_components.td,{children:`1 Jan`}),_jsx(_components.td,{children:`1 Dec`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`3`}),_jsx(_components.td,{children:`167 days`})]})]})]}),_jsxs(_components.p,{children:[`Sorted, the gaps are 10, 31, `,_jsx(_components.strong,{children:`60`}),`, 89, and 167. The middle value is 60, so this store’s buying cycle is `,_jsx(_components.strong,{children:`60 days`}),`: a typical returning customer buys about every two months.`]})]}),`\n`,_jsx(Info,{children:_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Same-day orders count once`}),`: Two orders on the same day count as one buying day. A customer who splits a basket into two orders hasn’t really “come back”, so counting days keeps your buying cycle accurate.`]})}),`\n`,_jsx(Heading,{level:`3`,id:`buying-cycle-limits`,children:`Buying Cycle Limits`}),`\n`,_jsxs(CardGroup,{cols:3,children:[_jsx(Card,{title:`7 days minimum`,icon:`arrow-down`,children:_jsx(_components.p,{children:`A week is about the fastest a marketing team can act. Shorter windows would move large numbers of customers between segments every night.`})}),_jsx(Card,{title:`180 days maximum`,icon:`arrow-up`,children:_jsx(_components.p,{children:`Beyond six months, “recent” stops meaning recent.`})}),_jsx(Card,{title:`41 days for new stores`,icon:`seedling`,children:_jsx(_components.p,{children:`Used until your store has at least 100 returning customers and enough history to see the pattern repeat four times.`})})]}),`\n`,_jsx(Note,{children:_jsx(_components.p,{children:`41 days is the typical buying cycle across all Gameball stores. Your buying cycle is re-measured every night, so it adjusts as your customers’ habits change.`})}),`\n`,_jsx(Heading,{level:`3`,id:`typical-buying-cycles-by-industry`,children:`Typical Buying Cycles by Industry`}),`\n`,_jsxs(Table,{children:[_jsx(_components.thead,{children:_jsxs(_components.tr,{children:[_jsx(_components.th,{children:`Type of Store`}),_jsx(_components.th,{children:`Buying Cycle`})]})}),_jsxs(_components.tbody,{children:[_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Payments app`})}),_jsx(_components.td,{children:`7 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Weekly grocery`})}),_jsx(_components.td,{children:`7 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Grocery delivery`})}),_jsx(_components.td,{children:`24 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Fashion and lifestyle`})}),_jsx(_components.td,{children:`54–70 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Pharmacy`})}),_jsx(_components.td,{children:`72 days`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Specialty retail`})}),_jsx(_components.td,{children:`92–119 days`})]})]})]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`step-2-score-recency`,children:`Step 2: Score Recency`}),`\n`,_jsx(_components.p,{children:`Recency is counted in buying cycles. Using the 60-day example store:`}),`\n`,_jsxs(Table,{children:[_jsx(_components.thead,{children:_jsxs(_components.tr,{children:[_jsx(_components.th,{children:`Score`}),_jsx(_components.th,{children:`Last Purchase`}),_jsx(_components.th,{children:`With a 60-Day Cycle`}),_jsx(_components.th,{children:`In Plain Words`})]})}),_jsxs(_components.tbody,{children:[_jsxs(_components.tr,{children:[_jsx(_components.td,{\"data-numeric\":`true`,children:_jsx(_components.strong,{children:`4`})}),_jsx(_components.td,{children:`Within 1 cycle`}),_jsx(_components.td,{children:`0–60 days ago`}),_jsx(_components.td,{children:`Buying on their normal schedule`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{\"data-numeric\":`true`,children:_jsx(_components.strong,{children:`3`})}),_jsx(_components.td,{children:`Within 2 cycles`}),_jsx(_components.td,{children:`61–120 days ago`}),_jsx(_components.td,{children:`Missed one visit`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{\"data-numeric\":`true`,children:_jsx(_components.strong,{children:`2`})}),_jsx(_components.td,{children:`Within 4 cycles`}),_jsx(_components.td,{children:`121–240 days ago`}),_jsx(_components.td,{children:`Clearly slipping`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{\"data-numeric\":`true`,children:_jsx(_components.strong,{children:`1`})}),_jsx(_components.td,{children:`Longer`}),_jsx(_components.td,{children:`241+ days ago`}),_jsx(_components.td,{children:`Gone quiet`})]})]})]}),`\n`,_jsxs(Note,{children:[_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Customers who haven’t bought yet`}),` are timed from the day they signed up. Within one buying cycle of signing up, they score `,_jsx(_components.strong,{children:`4`}),` because they haven’t had time to buy yet. After one cycle with no purchase, they score `,_jsx(_components.strong,{children:`1`}),`.`]}),_jsx(_components.p,{children:`The signup date is the one your store shares with Gameball. If your store doesn’t share one, Gameball uses the day the customer was added to Gameball.`})]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`step-3-score-orders-and-spend`,children:`Step 3: Score Orders and Spend`}),`\n`,_jsx(_components.p,{children:`For orders and spend, there is no “normal schedule” to compare against, so each buyer is compared with your store’s other buyers. Imagine every buyer standing in a line from lowest to highest, then cutting the line into four equal parts. Orders and spend are each scored this way.`}),`\n`,_jsxs(Accordion,{title:`See an example: eight buyers lined up by total spend`,icon:`calculator`,id:`see-an-example-eight-buyers-lined-up-by-total-spend`,children:[_jsxs(Table,{children:[_jsx(_components.thead,{children:_jsxs(_components.tr,{children:[_jsx(_components.th,{children:`Score`}),_jsx(_components.th,{children:`Buyers (SAR spent)`})]})}),_jsxs(_components.tbody,{children:[_jsxs(_components.tr,{children:[_jsxs(_components.td,{children:[_jsx(_components.strong,{children:`1`}),` (bottom 25%)`]}),_jsx(_components.td,{children:`Reem (40), Sami (55)`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{\"data-numeric\":`true`,children:_jsx(_components.strong,{children:`2`})}),_jsx(_components.td,{children:`Omar (90), Huda (120)`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{\"data-numeric\":`true`,children:_jsx(_components.strong,{children:`3`})}),_jsx(_components.td,{children:`Khaled (300), Ali (410)`})]}),_jsxs(_components.tr,{children:[_jsxs(_components.td,{children:[_jsx(_components.strong,{children:`4`}),` (top 25%)`]}),_jsx(_components.td,{children:`Nadia (900), Layla (2,400)`})]})]})]}),_jsx(_components.p,{children:`If a business account that spent 968,654 SAR is added, it joins Nadia and Layla in the top quarter. Reem and Sami still score 1, and nobody’s score changes because of it.`})]}),`\n`,_jsx(Info,{children:_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Ties share a score`}),`: In most stores, many buyers have exactly one order, and they all get the same order score. This means the four order groups are rarely equal in size, and in some stores one score has nobody in it.`]})}),`\n`,_jsx(Heading,{level:`3`,id:`how-unusual-customers-are-handled`,children:`How Unusual Customers Are Handled`}),`\n`,_jsxs(AccordionGroup,{children:[_jsx(Accordion,{title:`Very large accounts`,icon:`building`,id:`very-large-accounts`,children:_jsx(_components.p,{children:`Scores are based on position in the line, not the amount. A business account with thousands of orders simply lands at the top and scores 4, like the rest of the top quarter. It can’t push anyone else’s score down.`})}),_jsx(Accordion,{title:`Negative totals`,icon:`minus`,id:`negative-totals`,children:_jsx(_components.p,{children:`Refunds for orders Gameball never received can leave a negative total. These are counted as 0, so a refund never ranks someone below a customer who hasn’t bought.`})}),_jsx(Accordion,{title:`Only cancelled or refunded orders`,icon:`ban`,id:`only-cancelled-or-refunded-orders`,children:_jsx(_components.p,{children:`Customers with no completed order are treated as never having bought: they score 1 for orders and spend, and their recency is timed from their signup date.`})}),_jsx(Accordion,{title:`Impossible purchase dates`,icon:`calendar-xmark`,id:`impossible-purchase-dates`,children:_jsx(_components.p,{children:`Dates such as 1 Jan 1900 are ignored. Any date before the year 2000 counts as no purchase date, so it can’t make a customer look decades overdue.`})})]}),`\n`,_jsx(Heading,{level:`3`,id:`combining-orders-and-spend-into-a-value-level`,children:`Combining Orders and Spend Into a Value Level`}),`\n`,_jsxs(_components.p,{children:[`The order score and spend score are averaged (rounded down) into one `,_jsx(_components.strong,{children:`value level`}),`:`]}),`\n`,_jsxs(Table,{children:[_jsx(_components.thead,{children:_jsxs(_components.tr,{children:[_jsx(_components.th,{children:`Value Level`}),_jsx(_components.th,{children:`Who`}),_jsx(_components.th,{children:`Example Order + Spend Scores`})]})}),_jsxs(_components.tbody,{children:[_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Top`})}),_jsx(_components.td,{children:`Top 25% for both orders and spend`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`4 + 4`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Upper middle`})}),_jsx(_components.td,{children:`Strong on both, or top on one`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`3 + 3, 4 + 2`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Lower middle`})}),_jsx(_components.td,{children:`Average, or strong on one and weak on the other`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`2 + 2, 1 + 4`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`Low`})}),_jsx(_components.td,{children:`Few orders and little spend`}),_jsx(_components.td,{\"data-numeric\":`true`,children:`1 + 1, 1 + 2`})]})]})]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`step-4-assign-the-segment`,children:`Step 4: Assign the Segment`}),`\n`,_jsx(_components.p,{children:`Recency decides the row. For customers who are on schedule or have gone quiet, the value level decides the segment as well.`}),`\n`,_jsxs(Table,{children:[_jsx(_components.thead,{children:_jsxs(_components.tr,{children:[_jsx(_components.th,{children:`Recency`}),_jsx(_components.th,{children:`Low`}),_jsx(_components.th,{children:`Lower Middle`}),_jsx(_components.th,{children:`Upper Middle`}),_jsx(_components.th,{children:`Top`})]})}),_jsxs(_components.tbody,{children:[_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`4 – On schedule`})}),_jsx(_components.td,{children:`New Customers`}),_jsx(_components.td,{children:`Loyal`}),_jsx(_components.td,{children:`Loyal`}),_jsx(_components.td,{children:`Best Buyers`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`3 – Missed one visit`})}),_jsx(_components.td,{children:`Promising`}),_jsx(_components.td,{children:`Promising`}),_jsx(_components.td,{children:`Promising`}),_jsx(_components.td,{children:`Promising`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`2 – Slipping`})}),_jsx(_components.td,{children:`Can’t Lose`}),_jsx(_components.td,{children:`Can’t Lose`}),_jsx(_components.td,{children:`Can’t Lose`}),_jsx(_components.td,{children:`Can’t Lose`})]}),_jsxs(_components.tr,{children:[_jsx(_components.td,{children:_jsx(_components.strong,{children:`1 – Gone quiet`})}),_jsx(_components.td,{children:`Lost`}),_jsx(_components.td,{children:`Lost`}),_jsx(_components.td,{children:`About to Lose`}),_jsx(_components.td,{children:`About to Lose`})]})]})]}),`\n`,_jsx(Tip,{children:_jsxs(_components.p,{children:[`A customer who has gone quiet but used to buy and spend well is worth winning back, so they go to `,_jsx(_components.strong,{children:`About to Lose`}),` instead of `,_jsx(_components.strong,{children:`Lost`}),`.`]})}),`\n`,_jsx(Heading,{level:`3`,id:`what-each-segment-means`,children:`What Each Segment Means`}),`\n`,_jsxs(CardGroup,{cols:2,children:[_jsxs(Card,{title:`Best Buyers`,icon:`trophy`,children:[_jsx(_components.p,{children:`On schedule, and in the top quarter for both orders and spend.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Reward and recognize them with early access and VIP perks.`]})]}),_jsxs(Card,{title:`Loyal`,icon:`heart`,children:[_jsx(_components.p,{children:`On schedule, with solid but not top value.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Nudge them up with bundles, tier goals, and referral offers.`]})]}),_jsxs(Card,{title:`New Customers`,icon:`seedling`,children:[_jsx(_components.p,{children:`Bought within the last cycle with few orders and low spend, or signed up within the last cycle and haven’t bought yet.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Send a welcome offer for the first purchase, then make the second one easy.`]})]}),_jsxs(Card,{title:`Promising`,icon:`lightbulb`,children:[_jsx(_components.p,{children:`Missed one normal visit, at any value level.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Send a timely reminder before the habit breaks.`]})]}),_jsxs(Card,{title:`Can't Lose`,icon:`hourglass-half`,children:[_jsx(_components.p,{children:`Two to four cycles without buying, at any value level.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Run a re-engagement campaign with a reason to return.`]})]}),_jsxs(Card,{title:`About to Lose`,icon:`triangle-exclamation`,children:[_jsx(_components.p,{children:`Gone quiet, but used to order and spend well.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Make a strong win-back offer. These are the most valuable customers to recover.`]})]}),_jsxs(Card,{title:`Lost`,icon:`user-slash`,children:[_jsx(_components.p,{children:`Gone quiet with low value, or signed up more than one cycle ago and never bought.`}),_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Suggested action:`}),` Use low-cost reactivation, or a first-purchase offer for those who never bought.`]})]})]}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`worked-example-eight-customers-in-one-store`,children:`Worked Example: Eight Customers in One Store`}),`\n`,_jsxs(_components.p,{children:[`This store has a `,_jsx(_components.strong,{children:`60-day buying cycle`}),`. Expand each customer to see why they land in their segment today.`]}),`\n`,_jsxs(AccordionGroup,{children:[_jsxs(Accordion,{title:`Layla → Best Buyers`,icon:`trophy`,id:`layla-best-buyers`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 10 days ago · 14 orders · 2,400 SAR`})}),_jsx(_components.p,{children:`On schedule (10 ≤ 60 days), and in the top quarter for both orders and spend.`})]}),_jsxs(Accordion,{title:`Ali → Loyal`,icon:`heart`,id:`ali-loyal`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 25 days ago · 3 orders · 410 SAR`})}),_jsx(_components.p,{children:`On schedule, with upper-middle value.`})]}),_jsxs(Accordion,{title:`Omar → New Customers`,icon:`seedling`,id:`omar-new-customers`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 5 days ago · 1 order · 90 SAR`})}),_jsx(_components.p,{children:`On schedule, but one small order so far gives him a low value level.`})]}),_jsxs(Accordion,{title:`Yara → New Customers`,icon:`seedling`,id:`yara-new-customers`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Signed up 12 days ago · no order yet`})}),_jsx(_components.p,{children:`She has had less than one cycle (12 ≤ 60 days) to make a first purchase, so she counts as new, not lost.`})]}),_jsxs(Accordion,{title:`Huda → Promising`,icon:`lightbulb`,id:`huda-promising`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 95 days ago · 2 orders · 120 SAR`})}),_jsx(_components.p,{children:`95 days is past one cycle but within two, so she missed one normal visit.`})]}),_jsxs(Accordion,{title:`Khaled → Can't Lose`,icon:`hourglass-half`,id:`khaled-cant-lose`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 200 days ago · 6 orders · 300 SAR`})}),_jsx(_components.p,{children:`Between two and four cycles without buying. His value level doesn’t change this segment.`})]}),_jsxs(Accordion,{title:`Nadia → About to Lose`,icon:`triangle-exclamation`,id:`nadia-about-to-lose`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 400 days ago · 9 orders · 900 SAR`})}),_jsx(_components.p,{children:`Gone quiet (past 240 days), but she used to buy often and spend well.`})]}),_jsxs(Accordion,{title:`Sami → Lost`,icon:`user-slash`,id:`sami-lost`,children:[_jsx(_components.p,{children:_jsx(_components.strong,{children:`Bought 400 days ago · 1 order · 55 SAR`})}),_jsx(_components.p,{children:`Gone quiet, with low value.`})]})]}),`\n`,_jsx(Info,{children:_jsxs(_components.p,{children:[_jsx(_components.strong,{children:`Segments change as time passes`}),`: If Huda buys again tomorrow, she moves back to an on-schedule segment the next night. If Layla stops buying, she moves to `,_jsx(_components.strong,{children:`Promising`}),` after 60 days, even though she hasn’t done anything. If Yara still hasn’t bought after 60 days, she moves to `,_jsx(_components.strong,{children:`Lost`}),`.`]})}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`reading-the-segment-summary-on-your-dashboard`,children:`Reading the Segment Summary on Your Dashboard`}),`\n`,_jsxs(_components.p,{children:[`Each segment on the `,_jsx(_components.strong,{children:`RFM Segments`}),` page shows a summary like this example from a store with a 72-day buying cycle:`]}),`\n`,_jsx(Card,{title:`Best Buyers`,icon:`trophy`,children:_jsx(_components.p,{children:`Bought recently and are in your top 25% for both number of orders and total spend. Last purchased 0 to 72 days ago, with an average of 7.4 orders and 1,758 SAR spent.`})}),`\n`,_jsxs(_components.ul,{children:[`\n`,_jsxs(_components.li,{children:[_jsx(_components.strong,{children:`Days`}),`: The most and least recent purchase in the segment. For on-schedule segments, this is at most one buying cycle.`]}),`\n`,_jsxs(_components.li,{children:[_jsx(_components.strong,{children:`Orders and spend`}),`: The segment’s average. An average describes the typical member, while a lowest-to-highest range would mostly reflect one or two unusual accounts.`]}),`\n`]}),`\n`,_jsx(Note,{children:_jsxs(_components.p,{children:[`The summary numbers only count customers who have bought. `,_jsx(_components.strong,{children:`New Customers`}),` and `,_jsx(_components.strong,{children:`Lost`}),` also include customers who haven’t bought yet, but they are left out of these numbers so they don’t pull the averages down.`]})}),`\n`,_jsx(_components.hr,{}),`\n`,_jsx(Heading,{level:`2`,id:`common-questions`,children:`Common Questions`}),`\n`,_jsxs(AccordionGroup,{children:[_jsx(Accordion,{title:`Why did a customer move segments when they didn't do anything?`,id:`why-did-a-customer-move-segments-when-they-didnt-do-anything`,children:_jsxs(_components.p,{children:[`Because time passed. Recency is counted from today, so a customer who stops buying moves from on schedule to `,_jsx(_components.strong,{children:`Promising`}),`, then `,_jsx(_components.strong,{children:`Can’t Lose`}),`, then `,_jsx(_components.strong,{children:`About to Lose`}),` or `,_jsx(_components.strong,{children:`Lost`}),`. Segments update every night.`]})}),_jsx(Accordion,{title:`Can a customer with lots of orders be in Promising or Can't Lose?`,id:`can-a-customer-with-lots-of-orders-be-in-promising-or-cant-lose`,children:_jsxs(_components.p,{children:[`Yes. These two segments depend only on how long it has been since the last purchase. A big spender who hasn’t bought in two cycles is in `,_jsx(_components.strong,{children:`Can’t Lose`}),`, which is exactly the type of customer you want to win back.`]})}),_jsx(Accordion,{title:`Where do customers who never bought go?`,id:`where-do-customers-who-never-bought-go`,children:_jsxs(_components.p,{children:[`For one buying cycle after signing up, they are in `,_jsx(_components.strong,{children:`New Customers`}),` because they haven’t had time to buy yet. If they still haven’t bought after one cycle, they move to `,_jsx(_components.strong,{children:`Lost`}),`.`]})}),_jsx(Accordion,{title:`Do cancelled or refunded orders count?`,id:`do-cancelled-or-refunded-orders-count`,children:_jsx(_components.p,{children:`No. Only completed orders count toward orders and spend. A customer whose only order was cancelled counts as never having bought.`})}),_jsx(Accordion,{title:`Why are Lost and About to Lose bigger than I expected?`,id:`why-are-lost-and-about-to-lose-bigger-than-i-expected`,children:_jsxs(_components.p,{children:[`Most stores have many customers who bought once a long time ago, plus sign-ups who never bought. Measuring against your store’s real buying rhythm shows how many customers have actually gone quiet. `,_jsx(_components.strong,{children:`About to Lose`}),` is usually your best win-back opportunity.`]})}),_jsx(Accordion,{title:`Are test accounts included?`,id:`are-test-accounts-included`,children:_jsx(_components.p,{children:`No. Only active customers on your live account are scored. 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