Customer segmentation and being able to target profitable segments are key to an effective marketing strategy for companies using digital marketing. This is where the cost per acquisition is low and revenue derived from acquisition and lifetime value of a customer is high.
But, where digital marketing creates tremendous value as a business model is in analysing customers’ online behaviours to target them for advertising during specific online interactions. The classic example is, when you buy an air ticket, how likely are you to also book a hotel or rent a car? The answer is… highly likely. So, the value of being able to advertise these complementary goods on airline ticketing sites is much higher compared to any other sites.
Where marketing analytics platforms (and customer data platforms) truly shine is in providing scale to map these customer behaviours together to build long-term patterns for specific customer journeys and develop personas or customer archetypes based on what was purchased. For e.g. someone buying a business class ticket vs. someone buying an economy ticket. Once these patterns are identified, someone else’s new behaviour matching this pattern could be used as a target for advertising-related content (i.e. luxury hotels vs. economy hotels or premium cars vs. compact cars).
Today, unless we use Ad-Blockers (which only prevents ads), or a browser with “Do-Not-Track” enabled (which is not 100% effective), or a browser like Brave, we don’t have a choice to prevent our online behaviours from being captured and used for advertising purposes. Our online behaviours are catalogued (anonymously) by companies harvesting this data and sold to marketing agencies and/or exchanges. This allows them to generate revenue by letting ad space providers (i.e. website owners) and advertising content providers (i.e. agencies) trade on a two-sided platform for content and space “optimised using our online behaviours” for “displaying advertising on our browsers”. They haven’t breached any privacy regulations in doing so, as personas/journeys are anonymised (they don’t know who you are, but they do know you are someone that just bought a business class ticket, would likely stay in a luxury hotel and is likely to rent a premium car). The quality of these patterns improves with the increased number of data points, making them more valuable to ad exchanges and marketing agencies.
So, the question is… “if you were offered discounts on hotel booking and car rental, would you be more or less likely to let someone track your action of buying an air ticket and display hotel and car rental ads on your browser?” But, if we had a choice to say, I will only let this data be collected (and for tracking air travel and marketing-related products only) if I was paid for using this data, it creates a way for users who are the owners of this data, to be in control and be rewarded. It would also give consumers a choice to opt in (rather than opt-in by default) and have to resort to tactics such as “Do-Not-Track” and Brave browser to opt out. It will also allow those that are opting in to be rewarded for doing so.
I think this is the two sides of the argument that is currently being debated around “opt-in by default” vs. “opt-out by default”. Blockchain-based solutions such as Brave browser will help to solve this by giving control back to users and enabling a way for users to be rewarded. Similarly, by embedding these blockchain solutions in web-enabled televisions or IoT-enabled connected devices in your home, they could also turn into mini browsers for advertising content and for you to be rewarded for watching ads.
Imagine you are browsing a recipe on the connected screen in your fridge when the nearby local grocery store pushes an advertisement for a promotion they are running for some of the ingredients. Not only would you be able to purchase the item at a lower price, but you would also get rewarded for watching the advertisement.
PS: I have used a browser-based scenario for simplicity. But, it is possible to expand this to include multi-channel scenarios, where the same user is online via multiple channels such as social media, browser, web-enabled television sets, connected devices etc., as well as offline scenarios, where the same user is visiting a store or buying something over the phone.