No two people are alike. The same applies to your audience. No two audiences are alike.  Over the years, I understand that segmentation can be very different, and it strongly influences targeting. How do you segment? Based on data. 👉🏽 Location  👉🏽 Industry  👉🏽 Revenue  👉🏽Tech stack But besides that, some data is far more impactful than what you see on the surface. I call these nonstandard segmentation parameters. You won’t be able to deduct on Linkedin’s Sales Navigator because it needs a human touch, and you have to play a little bit of Sherlock Holmes to discover it 🔍. Nonstandard criteria for segmentation could use other types of data, such as: 👉🏽Company culture 👉🏽Goals and values  👉🏽Company openness 👉🏽Purchasing process: who makes the decisions?  👉🏽 Religion or culture of the decision makers? 👉🏽 Any personal data you can find in open access (please use this wisely as it’s not okay to congratulate someone’s daughter with a happy birthday without knowing them) 👉🏽Any point of data or pattern that you’ve had success with can be as little as going to the same university and being a member of any association you had in common.  👉🏽What is the origin of the decision-makers? One of the companies I consulted was really serious about research, not that others were not serious. Still, those guys were an exemplary company for getting data about their target accounts 🧲. They researched on their own. They called the company, went to the office, and bribed the secretary, the lobby boy, and the security to gather every single piece of information on the decision makers or whoever might influence that 😂. While I don’t advise doing the above, some companies are so goal-oriented that they will do anything to get it done 😉. Since they were offering software development services, which is pretty generic, let’s face it, it’s a hard job to show a unique value proposition 🤷‍♂️. But they were very successful in filtering the accounts that are a good or a no-good fit! How did they do? They had a target 100 list. 🎯 Once they had it, they filtered all the decision-makers or people close to the decision-making. And they did that in a particularly interesting way. Since they were targeting companies in the USA and Canada, they were super granular about one particular thing: none of the decision-makers should come from one of the countries where offshore software development is vast (Eastern Europe, India, Brazil) 🌍 Why is that? Very simply, they knew that once one of the decision-makers had some roots in one of those countries, they would undoubtedly be biased towards hiring offshore development from that region, and in particular, that person might already know a ton of competitors, which put them into an uncomfortable position 😬. Once that step was done, they had a much higher chance of success 🏆.  #DataDrivenMarketing #AudienceSegmentation #MarketingInsights