Each template is a real flow on live data. Open it, change the city and the kind of place, test it and deploy it. Grouped by what the team wants to do.
Market data and research 21 templates
See them in the template gallery
Every coffee shop in five cities, counted and summarised every month.
Every month: every coffee shop across five cities, counted per city with chains and independents, a market note from xMap AI, saved to a sheet and emailed. Market sizing usually takes days of desk research. This flow does it every month with the same method, so trends are real.
- Good for
- An investor looking at a category · A consultant sizing a market · A brand planning entry
- Each run gives
- A market note by email · A data sheet
- Time saved
- About 8 hours a month (an estimate)
New openings saved to your own database every week, classified.
Every week: places that opened in the last six months across your cities, only the new ones, classified, saved to your own database for your models. Analysts want raw data in their own models. This flow feeds your database with new openings on a schedule.
- Good for
- A data science team · A fund building signals · A research firm
- Each run gives
- Rows in your database · An email
- Time saved
- About 8 hours a month (an estimate)
The branches of a chain you are studying, the strongest and the weakest marked, in the best order to visit them.
Every Monday: the branches of the chain you are studying in the city you visit, the strongest and the weakest marked by xMap AI, put in the best order to visit them by car from your hotel, with directions, and a visit plan with what to ask at each, emailed with a map. Site visits are the slowest part of commercial due diligence. This flow picks which branches to see and plans the day around them, with directions and what to ask at each.
- Good for
- A private equity deal team · A franchise buyer · A consultant on a market study
- Each run gives
- A visit plan by email · A map of the branches
- Time saved
- About 6 hours a month (an estimate)
A monthly hiring index for a sector across cities.
Every month: job postings in a sector across your cities, one per company, counted and summarised by xMap AI as a hiring index, saved to a sheet and emailed. Hiring is a leading indicator of growth. A monthly index shows where a sector is expanding.
- Good for
- An economist · An investor tracking a sector · A government analyst
- Each run gives
- A note by email · A data sheet
- Time saved
- About 4 hours a month (an estimate)
A daily snapshot of port activity: ships by type, moving or waiting.
Every day: the ships in a port area (live AIS), grouped by type and whether they are moving, a short activity note from xMap AI, saved to a sheet. Port activity tracks trade before official figures. A daily snapshot builds a series you can use.
- Good for
- A commodity analyst · A shipping investor · A trade economist
- Each run gives
- A note by email · A data sheet
- Time saved
- About 10 hours a month (an estimate)
The demographic profile of each city, compared, every quarter.
Every quarter: the people living in each of your cities (men and women, age groups, density), saved to a sheet and summarised by xMap AI. Comparing cities by who lives there is the first step of any market entry. The profiles land in one sheet.
- Good for
- A consultant comparing markets · A retailer choosing a city · A fund analyst
- Each run gives
- Profiles in a sheet · A comparison by email
- Time saved
- About 4 hours a month (an estimate)
Rating changes and closures at your portfolio companies' branches, weekly.
Every week: each branch of your portfolio companies on the map, rating changes and closures, what each change means from xMap AI, saved to your database and emailed. Portfolio monitoring should not wait for quarterly reports. This flow flags problems as they happen.
- Good for
- A private equity firm · A lender · A franchise investor
- Each run gives
- Rows in your database · An email of changes
- Time saved
- About 8 hours a month (an estimate)
A market entry study refreshed every month: size, chains, demand, the best areas and the risks.
Every month: every place of a category across four cities, grouped by chain and independent, the people around them, an AI agent profiling the strongest areas, a market entry memo from xMap AI, saved to a sheet and your database, and emailed. A market entry study is usually a one-off that is stale in a quarter. This one refreshes itself monthly with the same method, so you see the market move.
- Good for
- A brand entering the Gulf · A consultant running an entry study · An investor screening a category
- Each run gives
- A market entry memo · A data sheet and your database · An email
- Time saved
- About 10 hours a month (an estimate)
Chains against independents in several cities.
Every quarter: cafes in several cities, split into chains and independents with their average rating, in a report by xMap AI for a consumer research brief. The share of chains tells how mature a market is. The comparison is ready each quarter.
- Good for
- A consumer research firm · A coffee brand entering a market · A franchise consultant
- Each run gives
- A report in Google Drive
- Time saved
- About 4 hours a month (an estimate)
Tracked shops that closed, as a monthly index.
Every month: the shops in your watch table that closed since last month, counted by city and kind, in a short index note by xMap AI for the research newsletter. Closures are a lead signal for a retail economy. The index counts them each month.
- Good for
- A retail research firm · A business journalist · A credit analyst
- Each run gives
- An email
- Time saved
- About 3 hours a month (an estimate)
EV chargers per city, ranked.
Every quarter: electric vehicle charging stations in each city, with people per charger, in a ranked table by xMap AI for an energy transition report. Charging density shows how fast cities electrify. The index compares them.
- Good for
- An energy research firm · A charging investor · A city sustainability office
- Each run gives
- An email with the list · A map of the results
- Time saved
- About 4 hours a month (an estimate)
Hospitals ranked by the people within 5 km.
Every year: hospitals in your country’s cities with how many people live within 5 km of each, ranked from most stretched, for a healthcare investment memo. Stretched hospitals point to where investment is needed. The memo ranks them.
- Good for
- A healthcare investor · A development bank · A health ministry adviser
- Each run gives
- An email
- Time saved
- About 4 hours a month (an estimate)
Coworking spaces across a region, new and rated.
Every quarter: coworking spaces in a region’s main cities, their ratings and how many opened in the last year, in a report by xMap AI for a flexible office investor. Flexible offices grow city by city. The report shows where the market is open.
- Good for
- A flexible office investor · A coworking operator · A real estate consultant
- Each run gives
- A report in Google Drive
- Time saved
- About 4 hours a month (an estimate)
The skills employers ask for most this month.
Every month: job postings for your sector in several cities, with the skills each asks for pulled out by xMap AI, and a trend report on the most wanted. Skills in demand shift every quarter. Reading the postings shows how.
- Good for
- A skills research team · A training provider · A labour market analyst
- Each run gives
- An email
- Time saved
- About 4 hours a month (an estimate)
One brief a week from everything your AI employees found.
Every Monday: a Market analyst reads everything your other AI employees found and writes the brief: openings, closures and strong fits by city against the week before, rivals’ new branches near your stores, who is hiring. Charts and plain findings in a PDF, by email and in your Team inbox. Each employee sees its own corner. The brief puts them together, with the figures, so the team reads one page on Monday.
- Good for
- A head of strategy · A regional manager · An analyst
- Each run gives
- A PDF · An email · A Team inbox note
- Time saved
- About 8 hours a month (an estimate)
Activity at industrial sites from space, into your database.
Every month: the latest satellite picture of each factory, mine or port in your watch table, with an activity note by xMap AI, saved to your database for a research model. Stockpiles and trucks show output before reports do. The notes feed your model.
- Good for
- A hedge fund’s research team · A commodity analyst · A supply chain risk team
- Each run gives
- Rows in your database
- Time saved
- About 2 hours a month (an estimate)
Fast food branches by brand and city.
Every quarter: branches of the big fast food brands in your cities, counted by brand, with people per branch, in a note by xMap AI for a franchise investor. Franchise investors need to know who dominates where. The density note says.
- Good for
- A franchise investor · A QSR consultant · A mall leasing team
- Each run gives
- An email
- Time saved
- About 3 hours a month (an estimate)
Grocers in each city sorted into price tiers.
Every quarter: the supermarkets and grocery stores in the cities you compare, sorted by xMap AI into discount, mainstream and premium, with a note on how the mix differs between cities. The share of discount and premium grocers says a lot about a market. The same count in every city makes them comparable.
- Good for
- A retail analyst · A consumer goods strategy team · A property investor
- Each run gives
- An email
- Time saved
- About 4 hours a month (an estimate)
Weather and road closures at logistics hubs, every week.
Every Monday: the week’s weather and the road closures around the logistics hubs you follow, joined by xMap AI into a short risk note for supply chain clients. Freight slows when storms and closures hit a hub. A weekly note lets clients plan before it happens.
- Good for
- A supply chain risk firm · A freight analyst · A procurement team
- Each run gives
- An email
- Time saved
- About 6 hours a month (an estimate)
Bars and clubs counted and rated across cities.
Every quarter: bars, night clubs and late cafes in several cities, counted and rated, in an index by xMap AI for a hospitality investor. Nightlife shows where young spending goes. The index compares cities.
- Good for
- A hospitality investor · A drinks brand · A city tourism analyst
- Each run gives
- An email
- Time saved
- About 4 hours a month (an estimate)