Same engine, different questions. Here is what TamLens measures for the ten biggest local industries, and the call it helps you make.
Use case 01
Our most precise vertical. Every dentist and orthodontist is in a federal registry. So the competitor count is exact, not scraped. Demand comes from Census age data. Kids 8 to 17 drive braces. Adults drive implants and aligners.
Which city can support one more practice, and which side of town has the families?
Use case 02
Med spa demand follows income, not just population. TamLens weighs Census income by neighborhood. A small rich city can outscore a big poor one. Clients come back monthly, so lifetime value is the number to watch.
Is there enough disposable income here, and who already serves it?
Use case 03
Every home with a furnace is future demand. Census data counts the homes and their age. Older housing means more replacements. Demand spikes with the seasons, and the report shows that curve.
How many trucks can this metro feed, and when does demand peak?
Use case 04
Plumbing runs on emergencies. People search the moment something breaks. That makes search volume an honest demand signal. TamLens counts homes for the base market and searches for the reachable one. Then it shows who ranks today.
How much emergency demand goes online here, and who catches it now?
Use case 05
Big tickets, few jobs. A few thousand roofs a year can feed many crews. So the supply count matters more here than anywhere. We count owner-occupied homes and home age. Then we count the crews competing for them.
Are there already too many crews chasing this city's roofs?
Use case 06
Legal has the priciest clicks in local search. So picking the right metro matters most here. Demand differs by practice area. Search data shows which one a city needs. One good case can pay for the whole site.
Which practice area is underserved in this metro, and what is a ranked site worth there?
Use case 07
Agent count against home sales is the whole story. Fast-growing metros add buyers faster than agents. Flat ones drown in licenses. TamLens reads growth and housing counts from the Census. Then it weighs the agent supply against them.
Is this market growing faster than its agent count?
Use case 08
For restaurants, the map is the report. Foot traffic, road traffic and busy hours decide a spot's fate before the menu does. TamLens shows measured cars per day on nearby roads. It also shows when the area actually has people in it.
Does this corner have the traffic and the hours to fill seats?
Use case 09
Cars per household is public data. Commuters pass shops every day. The traffic layer earns its keep here. A bay on a 30,000 car road gets found without ads. We size the vehicle base, then map the roads worth being on.
Which commuter corridor has the cars but not the shops?
Use case 10
Gym members come from a short drive away. This is a neighborhood game, not a city game. TamLens maps adults by tract against the gyms that exist. Then it finds the dense pocket nobody serves within a ten-minute drive.
Where is the dense neighborhood with no gym inside a ten-minute drive?