Every store your product should be at,and what each one is worth.
Network Expander 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 grows the number of stores you serve. The other grows what each store is worth to you.
A plan drawn on a partial map is already wrong
Your commercial strategy fails twice in the traditional channel, and this is the first failure. Plans get built on the stores you already sell to, because that is the only list that exists. It shows up in two ways: the stores you cannot see, and the stores you cannot rank. Network Expander is the half that closes both, with one model for each.
The stores nobody serves do not appear anywhere
A customer master lists who buys from you. It cannot list who would, so the white space stays invisible and never enters a plan. Independent stores do not report sell-out either, so there is no outside source that fills the gap for you. And when a campaign finally goes out on that list, 30 to 50% of the stores on it have closed or already belong to a competitor.
The costGrowth that was always available, never planned for.
The stores you do serve are not ranked
Two stores order the same amount from you this month. One is already at its ceiling. The other could buy three times more, and nothing tells you which is which, because your records show what a store bought and never what it is capable of buying. So visit frequency follows convenience and habit, and the store with room to grow gets the same attention as the one that is already maxed out.
The costRevenue sitting inside the customers you already have, invisible.
It is not about seeing more stores. It is about knowing which ones are worth more, which are about to fall, and where the next dollar is.
A report tells you what happened. A model tells you what is there.
A report can only show you what is already in your records. Every store in it is a store you already sell to, and every number in it is a sale that already happened. That is the ceiling on a report, and it is exactly why the stores you are missing never appear in one.
A model works the other way round. We take what has actually happened across tens of thousands of stores in this channel, find the patterns that hold, and use them to answer a question about a store nobody has records on. A store that size, on that corner, with that foot traffic, those neighbours and that income around it, buys about this much of this category. That number exists nowhere. It is calculated.
Proprietary means we built them. These are not a configuration of somebody else’s platform, and not a generic template pointed at your market. They were built inside the channel structures Latin America actually runs on, and then calibrated to your business, your categories and your commercial priorities.
And only at the stores you already have.
And leaves the thinking to you, usually on a Friday.
And hands your team the answer: this store, this much, this week.
Nobody on your team has to understand any of it.
What arrives is a list, in order, with a number next to every store.
Two models: more stores, and more from each store
Both run on the same picture of the market. One grows your customer base horizontally, by finding the stores you are not serving. The other grows it vertically, by working each store for what it is actually worth. Between them they answer the questions most consumer goods companies cannot answer today: which stores are at risk of churning, which existing customers are buying below their potential, and which new stores in which zones will move the most volume.
Phigital Prospecting
Grow your customer base, profitably.
We cross your portfolio against external sources to identify every active store in the territory, and the white space nobody is serving yet.
The complete universeEvery prospect is scored by estimated billing potential, so the size of the opportunity is known before anyone drives to it.
Qualified leadsUnderserved zones and channels are surfaced as lists, heat maps and routes that are ready to hand to the field.
Immediate executionHow it worksUniverse mapping → Entity resolution → Potential estimate → Tactical activation
Results · Phigital ProspectingA carbonated beverage company. The universe was built in four weeks, prioritised by potential and activated in the field.
Store Growth Navigator
Serve every store for what it is worth, not for what it sells today.
The DNA of each store: geographic and socioeconomic profile, consumption patterns, foot traffic, dwell time, shopper preferences and sales potential.
A segmented universeWe maximise the probability of purchase by category or strategic focus, and the return on where you place people, investment and point-of-sale material.
Directed investmentVisit frequency and dedicated rep assignment follow the real value of the store. Customers at risk raise an alert, with the retention action to take.
Efficient coverageHow it worksData collection → Store segmentation → Potential estimate → Commercial actions
Results · Store Growth NavigatorThe same model across Latin America, pulled through three different commercial levers: brand, price and coverage.
Versus prior year, and +1.2pp against the previous month.
Year to date versus prior year, across the top 9,900 high-potential stores.
Versus prior year, and +0.6 share year to date.
Sold under the growth plan the model set.
Versus baseline, with key packs up 22%.
Against the previous month.
Against the rest of the traditional channel: +20% versus −3.7%.
Same model, different levers. Brand, price and coverage all run on it, which is what makes it a growth engine rather than a single tactic.
Results from Kin engagements across Latin America. Figures are per market and per lever, and what any one operation can expect depends on its own baseline, category and coverage.
Not every store is worth the stop.
Coverage and profitable coverage are not the same number. The score that tells you which stores are worth more than they buy today also tells you which stops cost more to serve than they return. Visit frequency follows what a store is worth rather than how convenient it is to reach, so your team holds coverage without adding a rep, and growth is not bought at a loss one store at a time.
The universe, not a sample
A census samples what a team can physically walk. Network Expander builds and scores the whole universe from data, then keeps it current from what your own field team confirms.
Your sales history and customer master are crossed against external and alternative sources: census records, mobility, geographic and socioeconomic data. Entity resolution matches them so one store is one record, not three near-duplicates with different spellings.
The list stops being your customers and becomes your market. Every active store in the territory, including the ones on no route today, with the white space visible for the first time as something you can plan against.
Each store gets a value, a risk and a stage, so the universe arrives ranked rather than as a longer list. That ranking is what a route plan can actually be built on.
Why the alternatives stop short
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. This 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 a problem without locating it. They cannot tell you which individual store is underperforming its potential. Panels see the category. We see the shelf.
It is the starting point, not the answer.
Your own records are the most valuable input here and the reason the scoring reflects your business rather than a generic market. What they cannot contain is the part you have never sold to. That is the half the external layers add.
A map decays. A model improves.
A study is at its best the day it is delivered and worth less every week after. Because every visit your team logs confirms or corrects a record, this runs the other way: the universe gets more precise the longer it is in use.
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. And these models were not adapted for Latin America. They were built in it, inside the channel structures the region actually runs on.
That closes the first failure. The second one is still open.
See SpotOn A ranked universe does not move product on its own. Engine 02 turns it into what each rep does tomorrow, and reports back what actually happened.Questions commercial teams ask
How do you build the store universe without a foot census?
By reconciling sources rather than walking streets. Your sales history and customer master are crossed against census records, mobility, geographic and socioeconomic data, and entity resolution matches them into one record per store. That produces a complete, ranked universe in about four weeks, where a census takes months and is out of date before it is finished.
What is the difference between the two models?
Phigital Prospecting grows your customer base horizontally: it finds the stores you do not serve yet, scores them by potential and hands the field a prioritised list. Store Growth Navigator grows it vertically: it profiles the stores you already serve and works out what each one could be buying, so investment, visit frequency and offer follow real value rather than habit. Most teams need both, because coverage and depth are different kinds of growth.
How current does the universe stay?
It updates continuously. External layers refresh, and every visit your field team logs confirms or corrects a record, so the scoring sharpens with use instead of decaying. That is the difference between a model and a map.
What does potential scoring actually tell me?
What each store could buy by product category, against what it buys today. The gap between those two numbers is the revenue available without adding a single new store, and it is usually the fastest thing to act on. It also tells you which stops cost more to serve than they return.
Does this work with the sales app my team already uses?
Yes, and nothing gets replaced. Your app records what happened on the visit. Our models decide which stores are worth visiting and in what order, and the ranked universe can feed straight into the SFA or CRM your team already opens every morning.
How soon can my team act on it?
The complete universe is typically ready in about four weeks, ranked and ready for the field. 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 report against them.
Do we need an analytics team to run this?
No. Kin is forward deployed. Our analysts and engineers work inside your commercial operation through onboarding, pilot and the full results cycle, so the universe is operated with your team rather than handed over as a file.
Start the week
with an answer
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 which territory you are least sure about.
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.