We know that data is valuable; we know that people, companies and governments are creating and collecting more of it all the time. What we don’t understand very well is what the actual business model is as a result, and what the risks are of building a data business.
It’s clear when we look at somebody like Amazon how they make money from data: they hold it and process it on behalf of clients. But why do those clients pay? How does a non-data business start recognising that data is valuable enough to pay somebody like Amazon?
1. The value of your audience to yourself
The most obvious starting point is how the data you own about how people interact with your brand is valuable. I define insight as something new and useful that I’ve learned as a result of poking around my data. Insight might tell me about consumer trends I can use in my buying team. It might help me get a better understanding of my audience to improve my TV buying. It could even show me a major competitive advantage I hadn’t been exploiting.
How do you unlock the potential of your data? You can use segmentation. The better you understand the people who make up your audience, the better you can service them. A data management platform (DMP) is the easiest way to do this, but what’s more important than the technology is the set of rules about how you segment your audience based on their behaviour. For example, if a person reads five car reviews, you can assume they’re more likely than the general population to be in the market for a new car.
2. The value of your audience to your partners
You may be a data owner as much as you are a data consumer. If your website has a lot of content then a user’s behaviour can tell you a lot about their interests. If you host ads, then your inventory is worth money to advertisers. Your data is worth far more. You can sell space on your ‘automotive’ section to a car brand, or you could sell space anywhere on the site but served only to users who regularly read the automotive section. The latter offers advertisers a wider range of inventory and better targeting of relevant readers.
3. Maximising your data’s value to the market
To keep your data as valuable as possible, simply work on the things you would want to know before you use data to serve ads. For example, how recent is the data? If a person’s behaviour on your site indicates they’re in the market for a new smartphone, it’s safe to assume they won’t stay in market for long. Charge more while you can and let your price tail off as their ‘in market’ status signals age.
Also consider how valuable the user is. If a person reached your site by searching for ‘cheapest furniture’, that’s a very different signal than a user who searched for ‘painted wood bespoke kitchen’ for example. There are a lot of data points that tell you whether a user has a large budget or a small one.
These pricing differences should be applied whether you’re serving ads on your own site or selling the data segments to advertisers to be used elsewhere.
Your data is valuable, so collect it, analyse it, use it and where necessary, monetise it.
