Footfall and receipts sit apart
A busy store does not show which recorded member visits became purchases.
DataGlue for Retail & shopping-centre operators
I see visits and sales. I need to see where they meet.
Tills, loyalty, websites and visit systems each hold a piece. DataGlue joins the records that share a trusted key, while store totals stay store totals.
A dedicated team builds it with you. Start with one decision.
POS / tillsLoyaltyWebsiteGuest Wi-Fi
DataGlue
Show the formula.
Which events. In what order.
Example tagMember visit became a purchase
To your CRM, ads, BI, email or chosen LLM.
Connections and destinations agreed in the build.
Sound familiar?
You could count the visits. You just can’t connect every purchase.
Online and store teams see halves
A member browses online and buys at a till, in separate reports.
Loyalty history misses visits
Check-ins and purchases are not always joined to the same member.
Counts get mistaken for people
A camera count or vehicle visit cannot identify a customer on its own.
A joined picture
Join known records. Keep unknowns clear.
Use member IDs to join the steps you can confirm. DataGlue shows the event formula and the limits of the sources. Footfall and vehicle records keep their own meaning.
Systems usually joined
- POS / tills
- Loyalty
- Website
- Guest Wi-Fi
- Camera footfall
- Number-plate readers
Example 01
- Website: member signed in
- Loyalty: member purchase recorded
In this order, within 7 days, on the same member ID.
Events → tagMember visit became a purchase
Example 02
- Loyalty: reward issued
- POS: reward used on a purchase
In this order, within 14 days, with the same recorded member and reward IDs.
Events → tagReward used in store
Example 03
- Number-plate reader: vehicle arrived
- Number-plate reader: vehicle departed
In this order, within the same visit, at the same car park. This tags a vehicle visit, not a shopper or a purchase.
Events → tagRecorded car park visit completed
Example rules, not customer results. We agree source access, match keys, time windows and tag destinations in your build.
How we work
One question. Five steps with your team.
The same build process, shaped around your decision and its sources.
Book a call
Bring one question.
Talk it through with the people who would build it. We find where its data lives today.
Working session
A working session to map your data.
We map every source behind the decision, then show you what it looks like glued together. You get the scope and plan before work begins.
Build together
We connect the pieces.
A dedicated team finds the signals, glues them into one living model and matches records on evidence. Your systems stay where they are.
Go live
Your first result, ready to use.
We take the first result live in your Glue Console. Ask in plain words, see what each step read, and open the counts to their rows.
Keep building
Your team keeps the Console.
Then the next outcome builds on the same model. The rollout runs in phases, each measured against the outcome.
What you get
Useful context. In the tools you use.
Member journeys joined
Follow confirmed member IDs from visit to purchase.
Store and web context
Bring both sources into the same recorded path.
Reward use visible
Link issued rewards to their till use.
Honest footfall limits
Keep aggregate counts separate from customer identity.
Useful member tags
Send agreed tags to CRM, email, ads or BI.
Your existing systems
Join the till and loyalty tools you already run.
See the working
Check the approach before the call.
Read the published outcome examples, source details and comparisons behind this approach. They explain the method; they are not measured results for your role.
Trusted by
Fair questions
What you might be wondering.
Couldn’t I just compare footfall with sales?
Yes, for store-level trends. Linking a particular visit to a purchase needs a trusted shared key, such as a recorded member ID. DataGlue keeps those linked journeys distinct from aggregate footfall.
Can a camera or number plate identify a shopper?
Not on its own. Camera footfall stays an aggregate count; number-plate records describe vehicles. Any link to a member needs its own permitted collection and confirmed evidence, agreed during the build.
Do we need a data team or clean data first?
No. Bring one question and the names of the systems behind it. A dedicated team maps the sources, joins the records and builds the result with you. Your team sets ownership, privacy and quality rules.
What does it cost, and what happens on the call?
The first call checks the outcome, the sources and the fit. No system access is needed for that conversation. If it fits, we map the data in a working session. The build is a separate engagement, scoped and priced before work begins; there is no public rate card.
Your next step
Bring the question you keep coming back to.
We’ll talk through the outcome, where its data lives and whether a build fits. No system access needed for the first call.
Book a Call
Free first call. Scope and price agreed before a build begins.
Is it a fit?
An honest fit check.
A good fit
This may suit you if…
- You need a question answered across till, loyalty and visit systems.
- Your member journeys have trusted keys and permitted uses.
- You want aggregate and person records kept distinct.
Probably not a fit
This may not suit you if…
- You only need a store footfall total.
- You expect anonymous cameras to reveal who bought what.
- You want to treat every vehicle as a known shopper.