All studies below are anonymized. Client names and brand identifiers have been removed. The numbers are real.
A major beer brand was managing a complex media landscape with limited clarity on what was working. By measuring 70+ campaigns across 14+ retail banners, Pathformance replaced reactive, volume-anchored planning with a POS-anchored framework.
+4.02% flagship UPC lift · $5.01 incremental ROAS · +3.6% average sales lift across the partnership (vs. 2.8% benchmark).
Confirm viability before committing budget: store count, retailer, and flight length recommendations returned within 48 hours.
Market quadrant analysis turns gut-feel store selection into a data-driven framework: defend share, capture share, grow category, or deprioritize.
Digital, in-store, and shopper programs measured on the same POS baseline, no siloed reads.
Each campaign feeds the next. 70+ campaigns means the benchmark gets sharper with every flight.
A major CPG beverage brand was growing 8–12% annually in the convenience & gas channel, but had no store-level measurement to prove which SKUs or activations drove it across 5,000+ stores heading into a Q4 '26 planogram reset.
Pathformance deployed DiD on retailer POS, measured at the individual store and SKU level within the convenience channel across 5,000+ locations.
A leading food brand wanted to prove a national cured-meat campaign moved product, not just drove engagement. Pathformance measured across 24,000+ test stores using test vs. control on retailer POS.
Topline results confirmed the campaign drove real incremental volume, not just awareness. Hero items and halo items both contributed, with the top three items accounting for the majority of incremental sales.
A $250K, 6-week lower-funnel retail-media campaign. Pre-launch store selection and mid-flight reallocation compounded the result at every stage.
Store-level brand and category performance data narrowed the target pool from 2,000 to 1,600 stores before a dollar was spent. Lift estimate entering launch: ~2% unoptimized baseline.
After applying the pre-campaign store selection framework, lift improved to ~5%. Same flight, better targeting.
The bottom ~10% of test stores were identified and cut mid-flight; budget reallocated to higher-performing stores. Final lift: ~8%, a ~4x improvement from the unoptimized baseline.
Tell us the campaign, the retailer, and the meeting you're walking into. We'll show you what a PathX study design looks like for your exact situation.