One-minute narrated stories of a flow at work. The scenarios and figures are illustrative, not customer results.
Phone repair
The challenge. Elsa runs a phone repair shop on Södermalm in Stockholm. Cracked screens and tired batteries pay her rent. Seven repair shops sit within a short walk. When one of them slips, its customers go looking, and she finds out months later. Last spring a rival closed, and its regulars walked straight past her door to a chain store.
The flow. With xMap Flows, Elsa built Sentinel. Every morning at eight, across Södermalm, Sentinel searches the map for phone repair shops, spots any whose rating fell or that closed, explains what it means for her shop, and sends it to her phone on WhatsApp.
The result. In March, Sentinel tells her a rival’s rating fell from four point six to three point eight in two weeks. Elsa puts a same-day promise in her window and online. Within a month, twenty-three new customers mention the other shop by name. When a rival stumbles, Elsa’s door is already open.
7rival shops watched daily
3.8a rival’s rating, from 4.6
23customers who switched
Blocks: Schedule → Area of interest → Map business scan → What changed → xMap LLM → WhatsApp Business
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Sneakers and streetwear
The challenge. Wei Ling leads retail marketing for a sneaker brand in Singapore. Every month she opens a pop-up store for a new drop. Choosing the spot is a gamble. Her team walks the malls, takes photos, and argues for a week. The last pop-up sat in a quiet corner of a brand new mall, and half the stock went back to the warehouse.
The flow. With xMap Flows, Wei Ling built Watchtower. On the first Monday of every month, from Orchard Road to Tampines, Watchtower finds the malls and food halls, keeps the ones with a high traffic score where people stay longest, picks the top five, explains why each one fits the drop, and turns it all into slides for the team.
The result. This month, Watchtower ranks five spots, with Bugis on top: people there stay well over an hour. The team agrees in one meeting instead of five. The pop-up sells out in nine days, a week faster than the last one. Wei Ling’s team stopped arguing about corners. Now they argue about restocking.
5spots ranked each month
1meeting, not five
9 daysto sell out the drop
Blocks: Schedule → Area of interest → Nearby places → Filter → Sort and limit → xMap LLM → Make a report
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Eyewear retail
The challenge. Priya plans new stores for an eyewear chain in Mumbai. She has the budget for six openings this year. Reading glasses sell to people over forty, but her shortlist comes from brokers and gut feeling. Two stores opened last year in suburbs full of students and young families. One has already closed, and the other barely pays its rent.
The flow. With xMap Flows, Priya built Keystone. Every quarter, across twelve Mumbai suburbs, Keystone counts the people over forty who live there, finds the opticians already open, works out the over-forties for each optician, keeps the six best, and makes the report for the board.
The result. This quarter, Keystone puts Thane and Chembur on top: about nine thousand people over forty for every optician, three times the city average. Priya walks into the board meeting with one report and leaves with six approvals. Priya stopped guessing where customers live. Now she opens where they squint.
12suburbs compared
3xover-forties per optician
6approvals in one meeting
Blocks: Schedule → Area of interest → Demographics → Nearby places → Calculated field → Sort and limit → Make a report
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Luxury watches
The challenge. Nina manages the dealer network for a Swiss watch brand in Zurich. Two hundred shops sell its watches. Her dealer list is a spreadsheet that ages every day. Shops move, rename or quietly close, and the store finder keeps sending customers to them. A visitor from Tokyo once walked to an address that had become a bakery.
The flow. With xMap Flows, Nina built Meridian. Every Sunday night, Meridian reads the two hundred dealers, looks each one up on the map, spots any that closed, moved or changed their name, updates the dealer sheet, and emails the area manager.
The result. This week, Meridian finds two dealers closed and one moved across the Limmat. By Monday morning the sheet is right, and the store finder reads from it. Nina’s team spends the week selling watches, not checking addresses. Nina’s dealer list no longer ages. It wakes up right every Monday.
200dealers checked weekly
3changes caught this week
0wrong addresses online
Blocks: Schedule → My data → Map business scan → What changed → Google Sheets → Email
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Organic cosmetics
The challenge. Jonas leads sales for an organic cosmetics brand in Hamburg. His team of five sells to concept stores and beauty shops. Their prospect list comes from three places, so the same shop shows up two or three times. Reps call stores that already stock them. One owner in Ottensen got four calls in a week, and asked them to stop.
The flow. With xMap Flows, Jonas built Prospector. Every Monday at eight, across greater Hamburg, Prospector finds the beauty and concept stores, searches the map for zero-waste and organic shops, keeps each shop once, leaves out the ones that already stock the brand, scores how well each one fits, and puts them in the team’s prospect sheet.
The result. This Monday, the sheet holds thirty-one fresh shops, each listed once, best fit first. No rep calls a current stockist. In a month the team opens nine new accounts, and the shop in Ottensen orders its first box. Jonas’s team stopped calling the same shop four times. One call is enough now.
31fresh shops a week
0calls to stockists
9new accounts in a month
Blocks: Schedule → Area of interest → Nearby places → Map business scan → Remove duplicates → Match with my table → xMap LLM → Google Sheets
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Retail banking
The challenge. Mei runs the cash machine network for one of Shanghai’s largest banks. Her team looks after four hundred machines across the city. Every year she decides which machines to move, using last year’s withdrawals. But streets change fast: a mall opens, a market closes, and a busy machine goes quiet. Moving one costs a small fortune, so a wrong guess hurts twice.
The flow. With xMap Flows, Mei built Atlas. On the first day of each quarter, Atlas takes the bank’s four hundred machines, counts the shops and restaurants within three hundred metres of each, gives each machine a busy score, picks the quietest twenty, and adds them to the team’s network plan.
The result. This quarter, Atlas flagged twenty quiet machines. Six stood on streets where most of the shops had closed. Mei’s team moved those six first, and checked the other fourteen on the ground. Mei stopped moving machines on last year’s numbers. She moves them to where people are.
400machines checked each quarter
20quiet machines flagged
6moved first, no guessing
Blocks: Schedule → My data → Nearby places → Calculated field → Sort and limit → Microsoft Excel 365
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Mobile retail
The challenge. Andres plans where a big mobile phone company opens its stores in Lima. Lima has grown fast, out into districts like Comas and Ate. His store map is ten years old. People there ride an hour into the centre to buy a phone, or give up and buy from a street stall. He cannot see which districts want a store.
The flow. With xMap Flows, Andres built Radar. Every quarter, across all of Lima’s districts, Radar finds every phone shop, repair stall and accessory store, sorts them into clean categories, measures how far each one is from the company’s nearest store, keeps the ones more than three kilometres away, and maps where they cluster.
The result. Radar finds twelve hundred phone shops in clusters more than three kilometres from any store. The biggest are in Comas, Ate and Villa El Salvador. Andres opens two stores there in spring, and both reach their target in the first month. Andres found his next stores by following the phone shops.
1,200phone shops far from a store
3districts with no store
2new stores, on target
Blocks: Schedule → Area of interest → Map business scan → Place classifier → Find nearest → Filter → Show on a map
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Retail HR
The challenge. Oliver is HR director at a large department-store chain in London. In six weeks its Croydon store closes, and forty people need new jobs. He wants to place every one of them, not just hand out a leaflet. His team searches job sites at night, and the jobs they find are an hour away, or already gone. Most staff will only start looking after the doors close.
The flow. With xMap Flows, Oliver built Bridge. Every Monday until the store closes, around Croydon, Bridge finds the shop and warehouse jobs within ten kilometres, finds the hiring manager at each, matches each job to the colleagues who fit it, and emails the whole store the week’s list.
The result. Every Monday, all forty get a list of jobs near home, each with a named hiring manager. Oliver’s team makes the introductions. When the doors close, thirty-one have new jobs, and nine more have interviews booked. The store still closed. Thirty-one of its people walked straight into new jobs.
31placed before closing
9more with interviews booked
10 kmor closer to the store
Blocks: Schedule → Area of interest → Job postings → Apollo → xMap LLM → Gmail
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Supermarkets
The challenge. Khalid runs supply for a supermarket chain with forty stores across Dubai. Summer is his hardest season. When a heatwave hits, water, ice and cold drinks fly off the shelves in hours. Store managers phone in their orders too late, and the trucks are already out. Last August, nine stores ran out of water on the hottest weekend of the year.
The flow. With xMap Flows, Khalid built Shelf. Every day at five in the morning, Shelf reads the forty stores, checks the forecast at each one five days ahead, keeps the stores facing forty-five degrees or more, works out the extra water, ice and cold drinks each one needs, adds them to the restock sheet, and tells every store manager in Teams.
The result. On Sunday, Shelf sees forty-seven degrees coming on Thursday for the stores inland. Twenty-two stores get extra stock on Tuesday’s trucks, two days early. That Thursday, not one water shelf goes empty. The heat still comes to Dubai. Khalid’s shelves are just ready first.
5 daysof warning
22stores restocked early
0empty water shelves
Blocks: Schedule → My data → Weather forecast → Filter → Calculated field → Google Sheets → Microsoft Teams
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