CommercialIntelligence
Adapted to your business strategy.
The traditional channel carries 40 to 70% of your revenue, and it is the one place you cannot see. That is not a reporting gap. It is where your growth has been hiding. Our models map the whole universe, including the stores nobody serves, and score what each one is worth, so your team starts the week with an answer: where the next dollar is, and exactly what to execute tomorrow, aligned with the goals you have already set.
Your strategy fails twice
Most consumer goods companies know where they want to grow. What they cannot see is the channel that carries most of it, so the strategy gets built on a picture that was already out of date when it arrived.
You cannot see or predict most of your market
Your reps work a partial map, with no read on which stores are worth more, which are about to go dark, or where demand is building unserved. A meaningful share of your highest-potential stores are on no route at all, and nothing in your reporting would tell you. And when a campaign finally goes out on that map, 30 to 50% of the stores on the list have closed or already belong to a competitor.
The costGrowth you never see, so you never plan for it, and coverage you lose without ever seeing it go.
The strategy fails in translation
Even the decisions you do make do not execute as intended at the shelf. Direction gets diluted between planning and the route, and what actually happened comes back as a photo in a chat thread, weeks after it could have been fixed.
The costNo signal on whether your decisions are working at all.
Both are decisions you cannot make with what you have today.
Commercial intelligence
Two engines. One closed loop.
Built for consumer goods companies whose growth lives in the traditional channel, and whose commercial decisions only count once they become a stop on a rep’s route.
Network Expander
Every store your product should be at, and what each one is worth.
Builds the most complete picture of the market from internal and alternative data: store sales, store census, mobility data, geographic profile and socioeconomic factors. Two proprietary models run on top of it, one that grows the number of stores you serve and one that grows what each store is worth to you.
SpotOn
Know exactly what to do next.
Knowing where the opportunity is does not move product. SpotOn turns the coverage picture into specific direction at every level of the commercial structure, all reading one shared database, each with the metrics that level is measured on.
One solution: Commercial Intelligence. What the field reports comes back to sharpen the intelligence.
Every visit your team makes feeds the models. What each store actually ordered, what was on the shelf, whether it is still open and still buying: all of it goes back into the potential estimate, the churn risk and the visit frequency for that store. So the ranking your team works from next week is built on what happened this week. Intelligence with no route into the field is a report. A route with no intelligence behind it is habit. Run together they close, and every cycle leaves the models sharper than the last.
Real time data. Always current, never a snapshot that was true last quarter.
The store scoring, the routing logic and the task architecture are calibrated to the priorities you have already set, not a default configuration you have to bend to.
Want to see this run on your territory?
Talk with an expert A 30 minute call. We will map a sample of your universe, including the stores you do not serve yet, and score what it is worth.Universe first. Value second. Mission last.
Data platforms produce reports. Analytics tools produce dashboards. Neither of them produces a route plan for a sales rep in Monterrey on a Tuesday morning. That gap is the whole problem, because the most valuable insight is worthless if it does not survive the journey from the director’s screen to the shelf. It is also why the two halves cannot be bought separately. Build the universe first, since a plan drawn on a partial map is already wrong before anyone executes it. Score potential second, since a complete map with no sense of value just spreads your team thinner across more stops. Only then hand the field a mission, since a rep with a route and no reason behind it is back to working on habit. Three steps, each built on the output of the last. Two separate tools cannot do that, because neither of them owns the handover between them.
Four things have to be true
Beverages, snacks and confectionery, dairy and bakery, personal care, home cleaning. The category changes what is in the box. It does not change the problem, and what decides whether this fits is not what you sell. It is how your business reaches its market.
Your market is thousands of independent outlets, not a handful of chain accounts
Modern trade is a few negotiations a year with buyers who report their own numbers. This is the opposite: a long tail of small stores, each one a separate decision, and no two of them worth the same.
You have your own field force working them
Reps, promoters and supervisors covering more ground than anyone can hold in their head. The route was drawn once and has been worked from habit ever since, and nobody has had the time to ask whether it is still the right one.
Nobody reports sell-out, so you plan on shipments and habit
Independent stores do not tell you what left the shelf. What you have is what you dispatched, a census from last year and a panel that reports the category. None of it comes from the stores where the revenue actually moves.
Growth depends on coverage and how well each outlet is worked
Not on one big account renewing. On how many stores you reach, which of them are worth reaching, and what actually happens once a rep is standing in the doorway.
That is the operation Commercial Intelligence was built for.
And the timeline is one you can absorb. An enterprise rollout is scoped in quarters and still needs your own analysts to run it once it lands. This is scoped to a commercial cycle: a complete store universe in about four weeks, with our team operating it alongside yours from there.
Which stores get served, how often, and by whom. Visit frequency follows what a store is worth rather than how convenient it is to reach.
Where to launch and where to push. Zones chosen on live demand signal instead of last year’s territory map.
Price, pack and promotion decided against the potential of the individual store, not the average of the region.
Where the margin actually sits once cost to serve is counted, so growth is not bought at a loss one store at a time.
Forward deployed
Most of the companies we work with do not have an analytics team to operationalise a tool on their own. So we do not hand one over and leave you to it. Plug-and-play works for software. It does not work for commercial field operations. Most implementations end at go-live. Kin starts there.
We join the team.
We onboard like a new hire. System access, a seat in the commercial planning conversations, and our hands on the real process. That is how we learn the day to day of each role: what a director needs to see to set direction, what a supervisor needs to catch the week before it is lost, and what a rep needs in a shop doorway with a phone in one hand. We build for each of them, not for an analyst with time to interpret a dashboard.
Deep business understanding.
We do not build anything until we understand how your commercial operation runs and where it is trying to go. That knowledge goes directly into the store scoring, the routing logic and the task architecture, so the intelligence supports the strategy you have already set instead of pulling against it. If this year’s push is a category, a pack format or a region, the model prioritises for it. It is the difference between intelligence that changes what happens on a route and software that just gets installed.
We show up.
Remote calls and video are convenient, and we use them. But nothing replaces being in the market, so we ride routes with your reps and sit with your supervisors rather than only dialling in. That proximity is what builds trust, surfaces the reasons a plan bends in the field, and makes the work stick.
We’re accountable for the outcome.
A project does not end when the solution is delivered. We define the success metrics with you up front, coverage gained, effective visit rate, volume against plan, the numbers your business lives by, then track measurable impact against them over time. Efficiency and profitability read against your business objectives, not against ours.
Why the tools you already have stop short
None of these are wrong. They just answer a different question than the one your commercial team is actually asking.
Your last census is already wrong.
A foot census is built street by street over months, and it goes out of date with every store that closes, opens or changes hands while it is being collected. It is a snapshot, and it is stale on delivery. Kin is continuous, so the ranking your team works from is current that morning.
We go where panel data cannot.
Panels and macro reports stop at the distributor. They tell you what happened to a category across a market, which confirms the problem without locating it. They cannot tell you which individual store is underperforming its potential. Panels see the category. We see the shelf.
Built for your scale, not theirs.
Enterprise revenue growth platforms are built to be configured and then run by your own analysts, which assumes a BI team to operate them and a rollout measured in quarters. Commercial Intelligence is not a lighter version of that. It is a different arrangement: our team operates it alongside your commercial team instead of handing it over, so it starts producing decisions in weeks.
They track. We decide.
Your SFA confirms a rep was at a store. That is a record of activity, not a decision about where the activity should have gone. Kin sits above the app you already run as the intelligence layer, choosing which stores matter and what to do at each one, and it can feed those decisions straight into the SFA or CRM you already run.
The status quo has a price. You just cannot see the invoice.
Manual reports and coordination over chat feel free because the cost never appears as a line item. It shows up instead as coverage you never gained, execution drift nobody caught in time, and decisions made on numbers that were already weeks old.
A decade turning data into commercial decisions
These models were not adapted for Latin America. They were built in it, inside the channel structures the region actually runs on.
These models read the channel, not the company. A traditional channel has the same structure whether you ship a million cases a month or a fraction of that, so the same models run for the largest bottlers in the region and for teams a tenth their size. Nothing here is an enterprise product scaled down.
A complete, prioritised store universe in four weeks, and a customer base growing 22% a year instead of 10%.
A foot census takes months, is built street by street, and goes out of date with every store that closes or changes hands while it is being collected. Network Expander built the same picture in four weeks, ranked by potential and ready for the field, and the growth attributable to the model took the customer base from roughly 10% organic annual growth to 22%, more than double the rate the business was growing on its own. The universe only turned into growth because it reached the field as routes and daily direction, which is the half SpotOn does. And every role involved in the strategy sees execution from their own angle, with the metrics that role is measured on, so the rep, the supervisor and the director read the same week instead of three different reports.
Trusted & certified
now, they know
Working with Kin has enabled us to carry out high-impact AI-based projects in over 11 Latin American countries, achieving completely disruptive results.”
The idea of processing technical tests using AI became a reality thanks to the expertise of Kin Analytics, paving the way for us to innovate the evaluation industry. For years, they have been an important ally in driving disruption.”
Questions commercial teams ask
What is the traditional channel, and why is it so hard to measure?
The traditional channel, also called traditional trade, is the network of independent stores that sit outside modern retail chains. It carries 40 to 70% of consumer goods revenue in most emerging markets, and almost none of it reports sell-out data. Companies end up planning against distributor shipments and panel estimates, which describe the category but never the individual store. Kin builds the complete store universe and scores each store, which panel data cannot do.
How do I find the stores my reps are not visiting?
By building the universe rather than sampling it. Network Expander crosses your existing portfolio against external sources and resolves them into one list of every active store in your territory, then flags the gap between where your team goes and where the volume actually is. A complete, actionable universe takes about four weeks. A foot census takes months and is out of date before it is finished.
How is this different from a store census or panel data?
A census is a snapshot and it is stale on delivery. Panel data stops at the distributor and reports categories, not stores. Kin is continuous and store-level, and it updates from what your own field team confirms on every visit. Panels see the category. We see the shelf.
We already use a field sales app. Where does Kin fit?
A field sales app records that a rep was there. It does not decide where they should have been. Kin sits above it as the intelligence layer, deciding which stores matter and what to do at each one, and it can feed those decisions into the SFA or CRM you already run. They track. We decide.
What data do you need from us to start?
Less than most teams expect. Your sales history by point of sale and your current customer master are enough to begin. We bring the external and alternative layers ourselves, census records, mobility, geographic and socioeconomic data, along with the entity resolution that reconciles them against your records. If your data is messy or incomplete, that is normal, and sorting it out is part of the work rather than something you have to finish before we start.
Will our reps actually use it?
That is the right question to ask, and it is usually why this kind of project fails. SpotOn is built so the rep gets something back rather than simply being monitored: the stores worth their time ranked first, a route that earns more in the same day, and exceptions reported in one or two taps instead of a form filled in at night. We also do not hand it over and leave. Our team rides routes during rollout, because adoption is won or lost with the supervisors and reps, not in the launch meeting.
How long before we see results?
The complete store universe is typically ready in about four weeks. Commercial results follow the first execution cycle, because the model only pays once the field acts on it. We agree the success metrics with you before we start and we report against them.
Do we need an analytics team to run this?
No, and most of the companies we work with do not have one. Kin is forward deployed. Our analysts and engineers work inside your commercial operation through onboarding, pilot and the full results cycle. You are not handed a login and left to interpret it.
See your territory
as it actually is
A 30 minute call, then an audit of one territory, so you can put a number on the opportunity you are not working yet.
Talk with an expert
Tell us where your commercial decisions are running blind, and which territory hurts most.
We will build a sample of your full store universe, including the stores no rep visits, and rank it by potential.
- 1A 30 minute call. You describe the territory and where the decisions are running blind.
- 2We map and score a sample of your universe, and rank the stores you are not covering.
- 3You get the audit findings, and we scope the work from there.