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The Growth Diagnosis TreeAppendixRE-FG1 1.0

The Growth Diagnosis Tree — Printable text version

17 min read

The whole tree as text: every node with its question; at the leaves the evidence, test, diagnosis and 30-day intervention. Print it or save it as a PDF.

The identity

R = (D × c + P × r) × f × s × v

Symbols

  • R: Net sales before VAT, after discount, like-for-like branches
  • D: Demand reaching you: people new to the brand who reached you
  • c: Conversion: new customers ÷ D
  • P: Inherited base: last period's active customers
  • r: Return rate: known customers who bought this period ÷ P
  • f: Frequency: attempted transactions ÷ active customers
  • s: Service rate (capacity): served ÷ attempted transactions
  • v: Average value: net sales ÷ served transactions

Diagnosis order

  1. Is the decline real?
  2. Was demand turned away at the door?
  3. Were the item, the branch and the app available?
  4. Did price change?
  5. Did the experience let people down, so they did not return?
  6. Do those who stayed come less often?
  7. Did the ticket shrink, and why?
  8. Only now: are fewer people reaching you?

The Growth Diagnosis Tree

  1. ΔR Revenue is downRoot

    Question
    Does the company even have a marketing problem?
    Definition
    R = (D × c + P × r) × f × s × v, on like-for-like branches, against the same Hijri period.

    1. 0 Step 0: Is the decline real?Gate

      Question
      Does the decline survive once calendar, comparison, price, channel and data are corrected?
      Definition
      Six filters applied before the tree is opened. A decline that disappears after them is not a problem, and is not treated with a campaign.

      1. 0.1 Calendar: compare Hijri with HijriFilter

        Question
        Are you comparing Ramadan with Ramadan, Eid with Eid, payday week with payday week?
        Evidence
        • Weekly POS sales re-cut by Hijri weeks, with the count of Thursdays, Fridays and holidays in each period
        Test
        Re-run the comparison on matching Hijri weeks. The Hijri year is about 11 days shorter than the Gregorian year, so the same Gregorian month carries a different season each year. If the decline disappears, it was the calendar.
        Diagnosis
        A season moved; demand did not disappear.
        30-day intervention
        Make the Hijri calendar the basis of every comparison report, and redraw the capacity and campaign plan on next season's waves.
        Do not
        Do not launch a campaign to cure a week that was always going to be weaker.
        Owner
        Finance
        Verdict
        Not a marketing problem

      2. 0.2 Like-for-like: existing branches or new ones?Filter

        Question
        Is the decline in branches open more than 12 months, or does the total mix old, new and closed branches?
        Evidence
        • Monthly sales per branch, with opening and closing dates and refurbishment closure days
        • Delivery orders by customer address, to measure overlap between branch radii
        Test
        Build the bridge: group growth = new-branch sales − the like-for-like decline. Then split the like-for-like decline into what moved to the new branches (cannibalization) and what was genuinely lost.
        Diagnosis
        A group can grow while every branch in it declines, and a branch can decline because its sibling took its customers.
        30-day intervention
        A board report with three separate lines: like-for-like, new, and cannibalized. Re-measure cannibalization at months 3 and 6 after every opening.
        Do not
        Do not celebrate group growth before you see the like-for-like line, and do not advertise your way out of a sibling's cannibalization.
        Owner
        Finance + Expansion
        Verdict
        Not a marketing problem

      3. 0.3 Price: did what customers pay change, or what they take?Filter

        Question
        Did the average ticket rise because prices rose, while the real basket shrank?
        Evidence
        • A same-item price index (current ÷ previous price, weighted by last period's quantities) from the POS
        Test
        Split the average ticket: v = price index × real basket. If the nominal ticket rose and the real basket fell, you are selling less at a higher price.
        Diagnosis
        The revenue line hides two opposite movements.
        30-day intervention
        Add a price line and a volume line to the monthly revenue report, and give every price change a date the results are read against.
        Do not
        Do not read flat revenue after a price rise as flat demand.
        Owner
        Pricing + Finance
        Verdict
        Not a marketing problem

      4. 0.4 Mix and channel: did the customer move from dine-in to the app?Filter

        Question
        Did revenue fall in the books because the same demand moved to a door that is booked at net settlement after commission?
        Evidence
        • Revenue and orders by door: dine-in, takeaway, aggregators, direct delivery, B2B
        • Platform settlement statements, and how they are booked (gross or net)
        Test
        Compare total orders across all doors before revenue. If orders held and revenue fell, look at booking method and door mix before anything else.
        Diagnosis
        The customer did not leave. They changed doors.
        30-day intervention
        Standardise booking: gross revenue before VAT for every door, with commission as a cost line. Then read contribution by channel.
        Do not
        Do not punish the door your customer moved to, or reward the one they left.
        Owner
        Finance
        Verdict
        Not a marketing problem

      5. 0.5 Data: is the number itself right?Filter

        Question
        Does the POS reconcile with e-invoices, bank settlements and platform statements?
        Evidence
        • Count and value of simplified e-invoices from the e-invoicing system (Fatoora, ZATCA)
        • Card and mada settlements, platform statements, voids and refunds log, and POS offline days
        Test
        Reconcile three sources per branch per week: POS, e-invoices and settlements. Any gap larger than usual is explained before it is discussed.
        Diagnosis
        Some declines are recording errors: menu codes changed, a branch moved between regions, or VAT counted inside the number one year and outside it the next.
        30-day intervention
        One source of truth and one revenue definition: net sales before VAT and after discount.
        Do not
        Do not call marketing into a meeting whose number has not been reconciled.
        Owner
        Finance
        Verdict
        Not a marketing problem

      6. 0.6 One-offs: was last year the odd one?Filter

        Question
        Did the comparison period hold a one-off: a large catering order, a launch spike, a competitor's closure, or road works outside the branch this year?
        Evidence
        • An events log per branch, large B2B orders, and days when access to the branch was disrupted
        Test
        Remove one-offs from both periods, then compare again.
        Diagnosis
        The base was inflated, or the road was blocked.
        30-day intervention
        A standing events log per branch, updated weekly and attached to every comparison report.
        Do not
        Do not judge a branch against a number made by an order that will not repeat.
        Owner
        Finance + Operations
        Verdict
        Not a marketing problem

    2. D Demand reaching you is downBranch

      Question
      Are fewer new people putting you among their options?
      Definition
      D: people new to the brand who reached you in the period: opened your app page, walked into a branch, or searched for you.

      1. D1 The market itself shrankLeaf

        Question
        Did the whole category fall in your city, not just you?
        Evidence
        • SAMA's weekly point-of-sale data for restaurants and cafés by city (card-terminal payments only; not cash or in-app payments)
        Test
        Compare your like-for-like growth with the sector's growth in your city for the same Hijri weeks. If you fell as much as the market, your share held.
        Diagnosis
        The tide went out; the boat held its place.
        30-day intervention
        Reset costs, rosters and stock to the new market size, protect margin, and send the next riyal to the occasions still growing.
        Do not
        Do not buy share with discounts in a shrinking market; you pay the discount on every order that was coming anyway.
        Owner
        Finance + Operations
        Verdict
        Not a marketing problem

      2. D2 You lost share of mindLeaf

        Question
        When the family group asks 'where shall we go?', does your name come up less often?
        Evidence
        • A small monthly sample: which brand they name first for each buying moment (after isha, lunch, unexpected guests)
        • Branded search and Google profile views, against the market's trend
        • Competitor openings in your trade area
        Test
        Three conditions together: the market is flat or growing, your share is falling, and conversion and capacity are sound. If any condition fails, the problem is elsewhere.
        Diagnosis
        People did not reject you. They forgot you.
        30-day intervention
        Name the three biggest buying moments in your category, tie your name to them with consistent distinctive assets, and aim reach at those moments, not at follower counts.
        Do not
        Do not measure success in followers, and do not manage average CAC; watch the marginal one.
        Owner
        Marketing
        Verdict
        A marketing problem

      3. D3 You became harder to reachLeaf

        Question
        Did you become harder to find or reach: a narrower delivery radius, shorter hours, a lower app ranking, a blocked road?
        Evidence
        • Actual visible hours on each platform × delivery radius in km
        • Published branch data (hours, location, menu) on Google, the apps and AI assistants
        • In FMCG: numeric and weighted distribution from the retail audit
        Test
        Match the date demand fell with the date of any change in visibility, radius, hours or distribution.
        Diagnosis
        People look for you and cannot find the way.
        30-day intervention
        Restore hours and radius where capacity allows, correct the branch data on every platform, and give it one owner.
        Do not
        Do not compensate with advertising for a door you closed yourself.
        Owner
        Operations + Expansion
        Verdict
        Not a marketing problem

      4. D4 The occasion shiftedLeaf

        Question
        After the calendar is corrected, did demand move from one occasion to another: late night to lunch, the dining room to the home?
        Evidence
        • Sales by daypart and door, on matching Hijri weeks
        Test
        If one daypart fell and another grew by about the same, it is an occasion migration, not a demand loss.
        Diagnosis
        The same customer, with a different job to do.
        30-day intervention
        Redesign menu, rosters and format around the rising occasion.
        Do not
        Do not push an occasion whose time has passed with advertising.
        Owner
        Product & menu + Operations
        Verdict
        Not a marketing problem

    3. c Conversion is down: they saw you and didn't buyBranch

      Question
      Did they reach you and then choose someone else at the last moment?
      Definition
      c = new customers ÷ demand reaching you. On the app: opened the page, then ordered. In retail: walked in, then paid.

      1. c1 Price and value at the moment of choiceLeaf

        Question
        Did the customer see your app price, hold it against the menu board they remember, and walk away?
        Evidence
        • Conversion by item and door before and after the price-change date
        • The app-to-branch price gap on 'ruler' items, and direct competitors' prices
        Test
        Branches or platforms that changed price against those that did not, in the same weeks. If conversion fell only where price changed, it is price.
        Diagnosis
        Customers do not read your price. They compare it.
        30-day intervention
        Protect the ruler items, move channel cost into items and boxes designed for delivery, and name the gap where terms allow.
        Do not
        Do not fix a price problem with a temporary offer; you teach the customer to wait for the discount.
        Owner
        Pricing
        Verdict
        Not a marketing problem

      2. c2 Not available at the moment of choiceLeaf

        Question
        Did the customer reach you and find the item sold out, the branch closed against its published hours, or the app offline for a reason unrelated to demand?
        Evidence
        • A sold-out log by hour, published vs actual hours, and pause reasons on each platform
        Test
        The tie-break rule: a pause because of order pressure goes to Capacity; every other pause or stock-out stays here. Then compare conversion in clean hours with disrupted hours.
        Diagnosis
        The door is shut, and the advertising is sending people to it.
        30-day intervention
        Measure lost orders ÷ attempted orders for two weeks at the five highest-volume branches, and fix the top three stock-out causes.
        Do not
        Do not raise spend to bring people to an item that is not there.
        Owner
        Operations
        Verdict
        Not a marketing problem

      3. c3 Ordering is hardLeaf

        Question
        Did the path from wanting to a paid order get longer: a long menu, failing payments, missing photos?
        Evidence
        • Steps and minutes from page open to payment, payment failure rate, abandoned baskets
        Test
        If conversion fell after an app, menu or payment update, and recovered where the update was rolled back, it is friction.
        Diagnosis
        The customer wanted it; the system wore them out.
        30-day intervention
        Remove one step from the highest-volume path this month, and fix the top cause of payment failure.
        Do not
        Do not drive more visits to a page people fall out of.
        Owner
        Product & menu + Operations
        Verdict
        Not a marketing problem

      4. c4 Reviews and what people sayLeaf

        Question
        Did your rating fall, or complaints go unanswered, so people see you and hesitate?
        Evidence
        • Rating trend over the last 4 weeks on Google and each platform, complaints open more than 24 hours, and the most repeated reason
        Test
        Check whether the rating fell before conversion did. Then ask: is the most repeated complaint operational (lateness, wrong orders, cold food) or about communication (expectations, photos)?
        Diagnosis
        The rating is a symptom. The cause is usually in the kitchen; the reply is in marketing's hands.
        30-day intervention
        Operations fixes the most repeated cause; marketing answers every complaint within 24 hours.
        Do not
        Do not buy reviews or bury complaints; the credibility of the reply matters more than the count of negatives.
        Owner
        Operations + Marketing
        Verdict
        Not a marketing problem

    4. v Average value is downBranch

      Question
      Is each visit worth less?
      Definition
      v = net sales before VAT and after discount ÷ served transactions. It splits into price index × real basket.

      1. v1 The mix traded downLeaf

        Question
        Did customers move to cheaper items or sizes, or to a daypart with a smaller ticket?
        Evidence
        • A price–volume–mix analysis from the POS, by item and daypart
        Test
        Hold prices at last period's level and recompute the ticket with today's mix. If it falls, the mix is the cause.
        Diagnosis
        The customer stayed, but picks cheaper.
        30-day intervention
        Re-engineer the menu: put high-contribution items where the eye lands, and design a middle meal between the cheapest and the dearest.
        Do not
        Do not raise the cheap items' prices to compensate; you will speed up the exit.
        Owner
        Product & menu + Pricing
        Verdict
        Not a marketing problem

      2. v2 The discount became the priceLeaf

        Question
        Has the share of discounted orders risen until full price is the exception?
        Evidence
        • The discounted share of each door's orders and its trend, and brand-funded discount from platform statements
        • The increment ratio in the latest holdout test
        Test
        If the discounted share rises while the increment ratio falls, you are subsidising a habit, not building demand. Switch the offer off in half the pairs and measure what remains.
        Diagnosis
        Here marketing is not the cure; it is the cause.
        30-day intervention
        No offer without a pre-set increment test, and an exit test on the recurring offers this month.
        Do not
        Do not read an offer on before-and-after, and do not stop every offer at once.
        Owner
        Marketing + Finance
        Verdict
        A marketing problem

      3. v3 The basket shrankLeaf

        Question
        Are there fewer items per ticket: fewer drinks, fewer sides, broken combos?
        Evidence
        • Items per ticket and drink and side attach rates, by branch and shift
        Test
        If the drop is concentrated in certain shifts or branches, it is counter behaviour or combo design, not a customer decision.
        Diagnosis
        It was not offered, so it was not bought.
        30-day intervention
        One suggestion line at the counter and in the app, a combo designed for attachment, and a daily attach-rate check.
        Do not
        Do not turn the suggestion into pressure; one suggestion is enough.
        Owner
        Operations + Product & menu
        Verdict
        Not a marketing problem

      4. v4 The door changed the ticketLeaf

        Question
        Did orders move to a door with a smaller ticket or less left over, with the same customer?
        Evidence
        • Average ticket and contribution per order by door
        Test
        Compute the ticket for each door separately. If it held within every door and the total average fell, it is the mix between doors.
        Diagnosis
        The ticket did not shrink; the door changed.
        30-day intervention
        Price each door to its cost within the terms, and design delivery boxes and baskets that carry its cost.
        Do not
        Do not grow a door whose contribution you do not know.
        Owner
        Finance + Pricing
        Verdict
        Not a marketing problem

    5. f Frequency is down: your customers come less oftenBranch

      Question
      Is the same active customer visiting fewer times?
      Definition
      f = attempted transactions ÷ active customers. It measures how intensely those who bought in the period bought, not who stayed away.

      1. f1 You lost one of their occasionsLeaf

        Question
        Do they still come to you for dinner, but no longer for lunch?
        Evidence
        • Visits per identified customer by daypart and occasion
        Test
        If frequency fell in one occasion and held in the rest, the problem is that occasion: its hours, its menu, or its speed.
        Diagnosis
        The customer did not leave you. They stopped hiring you for one job.
        30-day intervention
        Product fixes the offer for the lost occasion; then marketing ties it to its buying moment.
        Do not
        Do not advertise an occasion whose offer you have not fixed.
        Owner
        Product & menu + Marketing
        Verdict
        Not a marketing problem

      2. f2 They now split their visits with someone elseLeaf

        Question
        Is the same customer now giving a new competitor some of their visits?
        Evidence
        • Frequency decline by trade area, against competitor opening dates
        • A short question to a customer sample: where else did you eat this week?
        Test
        Remember double jeopardy: smaller brands have fewer buyers who buy less often. If frequency fell alone while penetration held, that is a strong signal of a specific competitor in a specific trade area.
        Diagnosis
        They did not drop you. They started sharing you.
        30-day intervention
        In the affected trade areas only: remind people of the buying moments you win, and protect what makes your product different.
        Do not
        Do not start a citywide price war with the new competitor.
        Owner
        Marketing + Product & menu
        Verdict
        A marketing problem

      3. f3 Coming back became tiringLeaf

        Question
        Did repeat visits get harder: less parking, longer peak waits, a clumsy reorder in the app, a loyalty programme that ended?
        Evidence
        • Average peak wait, reorder steps, and the date of any loyalty programme change
        Test
        Compare identified customers' frequency before and after the change in affected branches against unaffected ones.
        Diagnosis
        The customer likes you, but the way to you got longer.
        30-day intervention
        Remove the single biggest obstacle to the second visit this month.
        Do not
        Do not give a come-back discount to a customer bothered by the journey, not the price.
        Owner
        Operations + Product & menu
        Verdict
        Not a marketing problem

      4. f4 Price cut the visitsLeaf

        Question
        After the price rise, does the same customer now come twice instead of three times?
        Evidence
        • Identified customers' frequency before and after the price-change date
        Test
        If frequency fell only where price changed, and the nominal ticket rose, the customer is spending their usual budget in fewer visits.
        Diagnosis
        The customer did not change their budget. They changed their number of visits.
        30-day intervention
        Judge the price rise on contribution, not on ticket, and revisit the ruler items first.
        Do not
        Do not celebrate a higher ticket before you see frequency.
        Owner
        Pricing
        Verdict
        Not a marketing problem

    6. r Retention is down: they didn't come back at allBranch

      Question
      How many of last period's customers did not buy from you even once this period?
      Definition
      r = known customers who bought this period ÷ last period's active customers. A yes-or-no question per customer, not how often.

      1. r1 The service let them downLeaf

        Question
        Are the customers who came on a bad night (lateness, wrong order, refund) the ones who did not return?
        Evidence
        • 90-day repeat by first-visit conditions: utilization that hour, wait time, and any error or refund
        Test
        Compare the return rate of two cohorts: first visit clean, versus first visit at a choked peak or with an error. The gap is the price of service.
        Diagnosis
        The campaign chose the night the customer met you, and it was your worst night.
        30-day intervention
        Fix the top cause of errors and refunds, and contact every let-down customer within 24 hours.
        Do not
        Do not buy new customers to replace the ones service drove away.
        Owner
        Operations
        Verdict
        Not a marketing problem

      2. r2 A new competitor took themLeaf

        Question
        Is the non-return concentrated in a trade area where a competitor opened?
        Evidence
        • Return rate by trade area, against a map of competitor openings and dates
        Test
        If return fell only in the competitor's trade area and held elsewhere, it is the competitor. If it fell everywhere, look at service or value.
        Diagnosis
        People tried the new option, and some stayed there.
        30-day intervention
        Identify what the competitor offers that you do not, and close the gap in the product before the message.
        Do not
        Do not answer a competitor's opening with a citywide discount.
        Owner
        Product & menu + Marketing
        Verdict
        Not a marketing problem

      3. r3 They came for the discount and left with itLeaf

        Question
        Do customers acquired with a deep discount fail to return at full price?
        Evidence
        • 90-day return by first-visit discount depth and by door
        Test
        If deep-discount customers return clearly less than full-price customers through the same door, the discount bought a visit, not a customer.
        Diagnosis
        The value that attracted them was not yours. It was the discount's.
        30-day intervention
        Redesign the acquisition offer to reward the second visit, not the first, and compute CAC payback on the cohort.
        Do not
        Do not count discount customers as acquired before you see them return.
        Owner
        Pricing + Marketing
        Verdict
        Not a marketing problem

      4. r4 The product changed from branch to branchLeaf

        Question
        Do new branches fail to repeat the reference branches' experience, so customers try once and do not return?
        Evidence
        • Return rate by branch age against the reference cohort at the same age, and the Geographic Replication Index
        Test
        If weak return is concentrated in the latest opening wave, the system is not travelling.
        Diagnosis
        Branch 30 does not taste like branch 3.
        30-day intervention
        Find what is not travelling (recipe, training, supplier) and fix it before the next wave.
        Do not
        Do not open branch 31 before branch 30 performs like branch 3.
        Owner
        Operations
        Verdict
        Not a marketing problem

    7. s Capacity: demand came and was turned awayBranch

      Question
      Did demand arrive that you could not serve?
      Definition
      s = served ÷ attempted transactions. What is not served because of throughput limits: the app paused 'due to high demand', rejected orders, cars that left the lane.

      1. s1 The peak is full (80% or more)Leaf

        Question
        Does the branch run at 80% or more of its capacity in the peak hour, delivery included?
        Evidence
        • U = peak orders from all doors ÷ (orders the kitchen serves per hour × peak hours)
        Test
        Compute U for each branch and each peak window, and for the two Ramadan windows separately. At 80% or more, any fall in s, r or the rating is read first as capacity.
        Diagnosis
        The hall is full. The invitations worked; the chairs did not multiply.
        30-day intervention
        Redirect demand to quieter hours, a nearby branch or pickup; narrow the peak delivery radius when a sister branch has room.
        Do not
        At 80%, no new campaign before capacity is added or demand redirected. Above 100%, no campaign for that branch at all.
        Owner
        Operations
        Verdict
        Not a marketing problem

      2. s2 The slowest station runs the branchLeaf

        Question
        Where do orders pile up: the grill, the payment window, or the riders' shelf?
        Evidence
        • Per-station timestamps from the kitchen screen and POS, and a manual count every fifteen minutes on Thursday night
        Test
        The station with the longest queue in front and the shortest behind is the constraint. By Little's law, the queue = arrival rate × time in the system; a campaign raises the first, the constraint sets the second.
        Diagnosis
        The branch moves at the speed of its slowest station.
        30-day intervention
        The five focusing steps: identify the constraint, exploit it fully, subordinate the other stations to it, elevate it, then look for the next one.
        Do not
        Do not add staff or equipment at a station that is not the constraint.
        Owner
        Operations
        Verdict
        Not a marketing problem

      3. s3 The app is paused at peakLeaf

        Question
        How many minutes was the app paused 'due to high demand' over the last two Thursday nights?
        Evidence
        • Busy-mode and pause log on each platform, by minute and reason, with rejected orders
        Test
        Multiply paused minutes by the usual order rate in those minutes: that is the demand turned away. Compare it with what was spent to bring demand in the same week.
        Diagnosis
        The advertising knocks, and the app says: busy.
        30-day intervention
        Set an order cap per window instead of a full pause, a separate riders' lane, and a visible expected time for the customer.
        Do not
        Do not run paid visibility on a platform you are pausing in the same hour.
        Owner
        Operations
        Verdict
        Not a marketing problem

      4. s4 Capacity on paper, not in the hourLeaf

        Question
        Is the capacity there across the day, but not in the hour people come?
        Evidence
        • The shift roster against the hourly demand curve, with the two Ramadan windows (before iftar, around suhoor) separately
        Test
        If peak U is above 80% while the daily average is low, the problem is how hours are spread, not how big the team is.
        Diagnosis
        The team is big enough, but in the wrong hour.
        30-day intervention
        Rebuild rosters on the Hijri demand curve, and decide automation versus hiring with one written rule.
        Do not
        Do not staff for the daily average; the peak is what governs.
        Owner
        Operations
        Verdict
        Not a marketing problem

    8. P The inherited baseBase

      Question
      How many active customers did you inherit from last period?
      Definition
      P is not a branch to diagnose this period; it is the result of last period's tree. Part of today's decline was decided yesterday.
      Evidence
      • Identified active customers in the previous period

Signatures: patterns that move together

  • A capacity crunch: s falls at peak, then r falls for cohorts met on crowded nights, then the rating, then c. s1 r1 c4
  • A price rise: Nominal v rises, the real basket falls, and c and f fall only where price changed. c1 f4 v1
  • Discount addiction: The discounted share rises, v falls, f rises during the offer, then r falls after it. v2 r3
  • A new competitor: In one trade area: D falls against the market, f falls, r falls. Outside it, everything holds. D2 f2 r2
  • A brand being forgotten: D falls against the market in every trade area, while c, s and f hold. D2
  • A system that does not travel: r and v are weaker in the new branches only; the reference branches are sound. r4 v3

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