Consumers often go showrooming before buying electronics. A well known price comparison site used this to their advantage. They tracked visits to specialized Hi-Fi stores and immediately targeted these prospects with special offers of their merchants. This way they took control of their online journey before people started checking Google, Amazon and eBay and thus increased conversion significantly.
Competition is hard. How do you get those lunch groups or regular diners away from the competition? A local Berlin business wanted to increase lunchtime velocity for their three Italian Food places. All visitors with work address near one of their venues where filtered by regular visits to other Italians. Special lunchtime offers then did the trick to lure lunch turnover over to them. Pizza and Pixels working together like a charm…
How do you know a driver is considering to switch brands? That is exactly what a leading premium brand asked themselves. The solution was found in location data, of course! Visits to multiple competitive dealers and showrooms indicated the driver’s churn tendency. That was when the brand started moving hell and earth to interact with the driver. Personalized branding ads got placed for these churn candidates. Some prospects could even be phoned personally through customer service. They had proper opt-in and translated the device IDs into phone numbers from the CRM. Sometimes it helps to just listen to why someone is leaving to make them stay!
A new Chinese apparel brand was entering the European market. They knew that students and people with sporty habits are their target group. The created audience included everybody who goes to university every day and to a gym at least once a week. Further filtering by home location and everybody who passes the stores on their commute led to higher ROAS by cutting less targeted media.
The transport authority of a European capital was testing a new mobility as a service offering. Now, how do you know which commuters could be interested in their flat fee model? They needed to find commuters with long commutes and multiple stopovers within the city. For those their flat fee offering made particular sense as it included multiple transport modes from train to bike and car. Targeted commuters reacted very well. And the test was a huge success and may be rolled out into full production.
Yep, you read that right. A strong local Accounting Firm needed to outsmart the big four. So they went to target the clients of their competition. Taking the work address of all of their competitive subsidiaries told them who works there. Now analyzing which clients they visited allowed them to target everybody who works in the building of the client. And there you have it. Genius!
