Retailers Own the Best Data in the Industry. So Why Are Their Campaign Proposals Still Built on Guesses?
Brands signing in-store campaigns all want to know the same thing: will this actually sell more product?
It's a fair question, and for most retail media teams, it's still an uncomfortable one. Not because the answer is no, but because the proposal sitting on the table wasn't built to answer it. It was built to show reach, impressions, and a CPM. Clean numbers. Wrong question.
The data that would make a genuinely defensible proposal? Retailers already own it. They just haven't had a planning layer that uses it.
The proposal problem nobody talks about
Retailers have spent years building the infrastructure to run in-store media. The screens, the audio, the network, the rate card. What hasn't kept pace is the planning layer.
Most in-store campaign proposals are still built the same way they were a decade ago. Take a store count, apply a foot traffic model, multiply it out to an impression number, attach a CPM, and call it a media plan. Whether the buyer is a brand partner or the retailer's own trading team, the deck looks the same. So does the problem underneath it.
Reach matters. But it's half the answer. Anyone buying in-store media wants to know how many shoppers they'll reach and whether that campaign will actually shift product. The standard proposal answers the first question and leaves the second to chance.
So campaigns get signed. They run. Results come back. Somewhere between the projected reach and the actual sales outcome, trust takes a hit.
This mismatch shows up at the channel level too. Roughly 80% of consumer spending still happens in physical stores, yet around 90% of retail media ad budgets are spent online (EMARKETER, November 2024). The opportunity in-store is enormous. The planning rigour to capture it has lagged behind.
Source: EMARKETER, November 2024
POS data is the answer the planning process is missing
What it actually takes to plan a campaign with confidence is proof that the stores in the plan are the right ones, at the right times, for the outcome being chased: whether that's growing a brand partner's category share or driving sales of a retailer's own-label product.
Retailers have that proof. It's in the POS system.
Which stores over-index for a category. Which dayparts drive velocity. Which SKUs are growing and where. Which stores will move product if a campaign activates there versus which ones won't. No third-party data provider, no foot traffic model, and no competitor network has what a retailer has sitting in their own transaction data.
A proposal built on real purchase data is worth more than one built on modelled estimates. Brand partners know it. Retail trading teams know it. The tools to build it just haven't existed until now.
What a POS-backed proposal actually changes
Swap out the impression estimate for a store-level demand profile and a few things shift immediately.
The conversation moves from "how many people will hear this" to "how much will this sell."
That's the question on both sides of the table when anyone is deciding whether to commit budget. Answer it directly and half the friction comes out of the deal.
The proposal becomes something that holds up internally. When a brand partner's procurement team asks how the numbers were calculated, the buyer can say the projection is built on the retailer's own purchase data, store by store. When a retail trading team is justifying spend on their own campaign, the same rigour applies. That answer holds up. "We used a foot traffic model" does not.
The post-campaign debrief gets easier. When the projection is built on real demand and the campaign delivers close to what was planned, that's the foundation of a renewal conversation. When it's built on assumptions and delivery misses, everyone's on the back foot before the deck is even open.
The competitive reality
Digital media has been running goal-based planning for years. A brand buying programmatic can specify the outcome they're optimising for, and the platform models expected delivery against that goal before the campaign goes live. They get a projection they can pressure-test.
That rigour is exactly why digital retail media keeps pulling budget. Closed-loop attribution and deterministic measurement, tying ad exposure directly to purchase, are widely cited as the reason retail media is outgrowing other digital channels. Brands are following the data, not the format.
In-store has been asking everyone to accept a lower standard, and it shows in the numbers. U.S. in-store retail media spend was estimated at around $370 million in 2024, projected to reach roughly $1.06 billion by 2028 (Statista). That's real growth off a small base, but it's a fraction of what digital retail media commands, even though far more shopping happens in the aisle than on the screen. The gap isn't a lack of demand. It's a lack of planning infrastructure that gives brands the confidence digital already has.
Source: Statista
That gap is closing, and retailers who close it first will be the ones brands treat as serious media partners rather than audio vendors they're testing a budget with.
The relationships worth building, whether with brand partners or internal trading teams, are built on proposals that hold up. That's what a POS-backed planning layer delivers.
The question worth asking
How long does it currently take a retail media team to build a campaign proposal? How much of that time is spent tracking down the right data, massaging it into a format the buyer can understand, and hoping the numbers hold up when questions get asked?
If the answer is "too long" and "not very confident," the problem isn't the team. It's that the planning layer hasn't been connected to the data that would make it work properly.
Retailers own the most valuable planning data in the industry. The in-store data advantage is real. It just hasn't been made usable for planning yet.
That's the gap worth closing. Not for the sake of a better-looking deck, but because the retailer that can walk any buyer through a store-level demand profile, show where category velocity is already moving, and project what a campaign will deliver against that, is the one that sells more campaigns, at higher value, with fewer post-campaign conversations nobody wants to have.
QSIC built Playbook to close exactly that gap.
Playbook by QSIC is the planning layer of QSIC Intelligence, projecting incremental sales lift against real retailer POS data before a campaign launches.
How much more do you think it could help you sell?
See how Playbook works
Sources: Retail Media Growth, Statistics, and Trends for 2026, Fugo.ai (EMARKETER, November 2024); Digital Advertising Statistics 2026, DigitalApplied; U.S. in-store retail media ad spend, Statista.
