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Growth intelligence

Open it on Monday.
It tells you what to do.

Attribution answers what happened. This answers what to do about it: which campaign to scale, which to cut, what the store already has and is not serving, all priced on your own costs and margins, with the arithmetic shown.

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Questions this answers

  • What moved this week, and what is it worth?
  • Where does the next rupee go, and where does it come from?
  • What does my store already have that I am not serving?
  • Which conclusion am I about to reach that is wrong?

Command Center

This week, beside last week’s counted figure.

Revenue, orders, new-customer share and contribution, each with the prior period as a counted row, never a projection through a window average. Target against trajectory leads the page; the two day-by-day stories the tiles compress are drawn underneath.

  • Prior periods counted; a missing comparison is said, not substituted
  • Both series drawn as lines on their own scales
  • Every card carries its own 14-day trend
  • What moved between the last two weeks, and what it is worth
Sales · actionsMeasured spend

brand search

₹33,480 spent · ₹1,44,720 back

4.32×
Scale

cheapest growth

retargeting · DPA

₹37,350 spent · ₹1,43,460 back

3.84×
Scale

well past the line

magnesium search

₹55,890 spent · ₹1,37,430 back

2.46×
Hold

profitable

pmax · bestsellers

₹1,13,130 spent · ₹2,33,280 back

2.06×
Hold

just above it

prospecting · advantage

₹1,62,180 spent · ₹1,57,320 back

0.97×
Rebalance

under break-even

generic supplements

₹1,46,610 spent · ₹39,420 back

0.27×
Cut

loses every order

The line inside every bar is your break-even at 1.82×. Past it, a campaign buys revenue at a profit.

Today’s Actions

Scale, cut, hold, rebalance. Priced on your costs.

Every campaign is judged on measured spend, on contribution after product, gateway, shipping and packaging costs, and on who it brought. Retargeting at 4.8x whose buyers had already bought gets cut back; prospecting at 1.6x whose buyers are 91% new gets scaled. When tagging is too thin to judge on, the verdict is withheld and it says so.

  • Cost per new customer and the days until it paid back
  • A coverage gate that withholds half the verdicts rather than guess
  • Evidence sentences with metric moves in colour
  • A PDF plan document, the same verdicts, for the review
Insights · how buying happens

Time from first touch to order

Under 1 hour41%
1-24 hours1%
1-7 days19%
Over 7 days39%

Visits before buying

First visit41%
2-3 visits37%
4-9 visits22%
10+ visits1%

Set retargeting to at least 15.3 days.

A shorter window stops paying before nine in ten buyers arrive; everything past it is spend on people who already bought.

Every panel ends in something to do, not a chart to interpret.

What We Found

Sixteen findings. Each names the wrong conclusion first.

Structural facts about your store that a dashboard would let you misread: a single-touch share that looks like efficiency and is harvesting, a sub-one-day repeat gap that is a split basket, not a repeat order. Every card shows the customers behind the share and what to do with them.

  • The same sixteen cards in every workspace
  • A real weekly chart on every card, that refuses to draw a lie
  • Don’t conclude, then Do this, on every finding
  • See who: the audience behind the finding, without building a segment
Journey · browse vs buy
1 session·5 products viewed·2 bought

Omega-3 1000 mg

₹1,499BOUGHT

Magnesium + B6

₹1,299NOT BOUGHT

D3 + K2

₹1,899BOUGHT

Ashwagandha KSM-66

₹1,699NOT BOUGHT

Collagen + hyaluronic

₹2,249NOT BOUGHT
Three of these were considered and not bought. That is the retargeting list nobody else can build.

Value, cost, and who is next

Customer Value, Profit & Economics, and who buys next.

Lifetime value by the ad that first found them, LTV to CAC, payback and cohorts aged from their own first order. POAS beside ROAS once costs are complete. A propensity score calibrated nightly on your own store, published honestly: only the reachable share is ever promised.

  • Break-even on contribution at the store’s gross margin
  • Retargeting window from how fast buyers actually decide
  • What each campaign sells, revenue split by product
  • Never a ranking published as a rate
Customers · identity

Mobile visit

Instagram in-app browser

mm_id_fbp

Desktop visit

Google Ads click

mm_idgclid_ga

Email click

newsletter, 6 days later

mm_idemail
resolved to one customer

Customer profile

3 devices · 4 orders · first touch: Meta Ads

Lifetime value

₹0

Repeat buyerMineralsBuys on discountReachable by WhatsApp
One person, not three visitors. That is what makes a coupon land and a conversion match.

Inside the module

The growth layer, page by page

Ten pages under Growth, each a report that ends in a decision.

Command Center

The front door: scorecards, target vs trajectory, what moved

Today’s Actions

Verdicts per campaign, priced, with evidence

What We Found

Sixteen structural findings

Customer Value

LTV, LTV to CAC, payback, cohorts

Who Bought

Order distributions per campaign, movement in points

Audience performance

Platforms, campaigns, audiences: cost per add-to-cart

Channel Roles

First, middle, last, assisted revenue, overlap

Profit & Economics

Contribution, POAS beside ROAS

Funnels

The store funnel, the price of each leak, by campaign, by product

Open a real action report from the homepage. Four verdicts, the evidence under each, and the budget that follows.

The other modules

Attribution

Every order credited to the ad that earned it, with the receipt and a match score.

Open

Analytics & Insights

Twenty reports on one date window, every one ending in a decision.

Open

Audiences

Segments from behaviour the store records, sized against what Meta can match before they sync.

Open

Recovery

The checkout they abandoned, won back on WhatsApp or email. Counted, never claimed.

Open

Decide Monday in ten minutes

Know what is working.Know what is not. Then spend.

Connect the store and the ad accounts, and the first Command Center draws itself from your own orders.

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