Record-low CPL. A flood of leads that never walked into a store.
A premium electronics retail chain running region-specific lead-gen to drive store visits and high-value in-store purchases. Meta and Google hit their lowest-ever CPL and the agency celebrated. Meanwhile 70% of those leads were bots, out-of-region, or people who never set foot in a store — and the in-store data that held the truth never reached the ad platforms.
The story in 10 seconds
A record-low ₹340 CPL was hiding the real problem: 7 of every 10 leads never walked into a store. We connected the in-store and CRM data to Meta & Google and flipped the optimization signal from form-fills to real store visits and purchases — and on the same ad budget, the cost to acquire a paying in-store customer dropped 31% in 90 days.
01 — The Setup
The agency hit CPL targets. The sales floor couldn’t close.
A premium electronics retail chain running geo-targeted campaigns across the regions its stores actually serve. Meta and Google were set to optimize for form fills — the cheapest available signal. Both platforms delivered record-low CPLs. The agency invoiced for the win.
The store teams turning walk-ins into sales were working from in-store and CRM data neither platform had ever seen. Which keyword drove an actual store visit? Which ad produced a real buyer in the right city? Impossible to answer.
A cheap lead isn’t a good lead. It’s a cheap problem that lands on the store team’s floor.
02 — What Was Broken
Same signal. Same junk. Platform couldn’t learn otherwise.
What 100 leads actually became at each stage
Cost at each funnel stage in ₹
03 — What Flable AI Did
Connected the in-store data. Flipped the optimization signal to store visits and purchases.
Bidirectional CRM & Store Sync
Meta + Google + in-store / CRM data synced both ways. Every store visit and in-store purchase flowed back to the ad platforms in real time, so the platforms finally had the right signal.
Signal Flip: Lead → Store Visit → Purchase
Optimization moved from form-fill to qualified lead to actual store visit and purchase, with the full purchase value passed back. Platforms learned to find buyers, not form-fillers.
Revenue & Region Budgeting
Budget allocation shifted to revenue-per-source and per-region, not CPL. Channels and regions that produced real purchases got more spend. Sources generating junk leads got cut regardless of CPL.
Store-Catchment Scoring
Custom logic: leads outside a store's catchment were down-weighted and proximity-to-store signals fed into targeting — so spend concentrated where a walk-in was actually possible.
04 — Outcome by Month 3
Better pipeline quality. Lower cost to close. First full-funnel visibility ever.
Same ad budget, 2.5x the qualified pipeline. Quality over cheap volume.
CPL rose from ₹340 to ₹510 — the junk filter working. Cost per actual SQL dropped 47%.
Cost to acquire a paying in-store customer — the only number that matters.
Ad click → store visit → in-store purchase. Visible end to end for the first time.
Lead-to-SQL rate and store-visit rate improving month on month
Bottom line
A cheap lead is not a good lead. Once ads optimized for actual closes, the entire funnel cleaned up.
Frequently Asked Questions
If the CPL was at a record low, why was that a problem?
Because cost per lead prices a form fill, not a customer. With 70% of leads bot, out-of-region, or people who never set foot in a store, a cheaper lead just meant cheaper junk arriving faster — and burning more of the store team's time. The number that actually moves the business is the cost to acquire a paying in-store customer, and that was climbing even as CPL fell.
What does connecting your store and CRM data to the ad platforms change?
Once store visits and purchases flow back to Meta and Google in real time, the platforms optimise toward real buyers in the right regions instead of form-fillers. The algorithms finally learn from the outcome that matters — a paying customer who walked into a store — rather than the cheapest signal available to them.
Did Flable replace the agency?
No. Flable changed what the campaigns optimise for and gave everyone full-funnel visibility from ad click to in-store purchase. The media kept running — the difference was that spend could finally be judged on revenue, not on dashboard CPL.
How quickly did results show up?
Lead-to-SQL quality started improving within the first month as the platforms re-learned on store-visit and purchase signal. The headline 31% reduction in purchase CAC landed by month 3.
What data do you need to get started?
A connection to your ad accounts (Meta, Google), your CRM, and your store-visit / purchase (POS) data. From there Flable reconciles the full funnel automatically — no manual spreadsheet stitching, no agency intermediary.
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