Phone orders, offline sales, delayed purchases, repeat revenue — none of it reaches Google or Meta. So smart bidding chases the wrong customers while your CAC rises and ROAS stagnates. We capture the full customer journey and feed it back into your ad platforms. The algorithms get smarter. Your spend works harder.
Results from retail brands using Pragmatik
GA360 tracks pageviews. Your CRM tracks customers. Your ad platforms track clicks. But nothing connects them — and the signals that matter most never reach the algorithms doing your bidding.
Phone orders, in-store purchases, catalogue sales, delayed B2B deals — none of it makes it back to the ad platform. Smart bidding optimises on partial data, wasting budget on customers who never convert and ignoring the ones who do.
"We know our tracking is incomplete. We just don't know how to fix it."
A £30 impulse purchase and a £3,000 high-LTV customer are both "1 conversion" to Google. You're running "value-based bidding" with no actual values. The algorithm chases volume, not profit — and your margins suffer.
"Conversions are up but revenue is flat. Something isn't right."
Last-click says paid search is your best channel. First-click says display. Neither is true. Without a real attribution model, you're making million-pound budget decisions on guesswork — and the wrong channels get the credit.
"Every platform claims credit for the same sale."
A proper marketing data warehouse with attribution, LTV modelling, and ad platform integration requires a data engineering team, ML expertise, and 12-18 months of build time. Most retail brands don't have that runway — or that budget.
"We've been talking about this for two years. Nothing's happened."
The retailers winning right now have complete data flowing into their ad platforms — not just what the pixel catches. They're optimising on profit, not just conversions. You can keep running on partial data, or you can close the gap.
What changes when your ad algorithms see the full picture — every conversion, every revenue value, every customer's predicted lifetime worth?
"We're getting more conversions but margins are shrinking. Our tracking must be missing something."
"ROAS is up 30% since we started feeding LTV data into bidding. The algorithm is finally finding our best customers."
Everything you need to feed complete, accurate signals into your ad platforms — without building it yourself.
Online orders, phone sales, catalogue purchases, in-store transactions, delayed conversions — all captured and piped back to Google, Meta, and Bing as first-party events. The algorithm finally sees reality.
Actual revenue, predicted LTV, and margin data flowing into your ad platforms. Not "conversions" — profit. The algorithm stops chasing £30 orders and starts finding £3,000 customers.
Markov chain attribution model showing true channel contribution — not last-click guesswork. Understand which touchpoints actually drive revenue and allocate budget accordingly.
ML models that predict customer lifetime value at acquisition. Feed predicted value into bidding so algorithms pursue high-value customers from day one, before repeat purchases prove them out.
A managed data warehouse you own. Full SQL access. Export anytime. No lock-in, no surprise cloud bills. Your data science team can build on top of it. It's an asset, not a rental.
No 18-month build. No hiring a data engineering team. First conversions flowing back to ad platforms within weeks. This quarter, not next year.
Real outcomes from retail brands that closed the signal gap.
We started feeding real revenue values and LTV predictions into bidding instead of treating every conversion as equal. ROAS jumped 30% on the same ad spend. The algorithm was always capable — it just needed the right data.
£100M+ eCommerce retailer
Phone orders, offline sales, delayed purchases — none of it was reaching Google. After plugging in the pipeline, smart bidding had 20% more signals to work with. Performance improved because the machine finally saw reality.
Multi-brand retail group
For the first time, we could see which acquisition channels produced customers who came back versus one-time buyers. We shifted budget to the channels that mattered. CAC went down. LTV went up.
European eCommerce portfolio
Our data team had scoped a marketing data warehouse project at 12-18 months and two full-time hires. With Pragmatik, we had conversions flowing back to Google in three weeks. The team can focus on analysis instead of infrastructure.
UK retail brand
A clear path to closing the signal gap.
We understand your current tracking setup, data sources, and ad spend. Identify the gaps and the opportunity.
Week 1
Lightweight tracking script installed. Connections to your CRM, eCommerce platform, and ad accounts. Data starts flowing.
Week 2-3
Offline conversions, revenue values, and LTV predictions begin feeding into Google, Meta, and Bing. Smart bidding gets smarter.
Week 4-6
Attribution models, customer segmentation, and predictive intelligence layer on. Continuous improvement as data accumulates.
Ongoing
Your competitors are already feeding complete signals into their ad platforms. Every month you wait, the gap widens. Let's talk about what closing that gap would look like for your business.
Book a Discovery Call →30 minutes. No commitment. Just a conversation about whether this fits.
Prefer email? mitch@pragmatik.ai — I'm the founder and I read every message.