Consumers are more likely to choose brands that engage them through their passions and interests, rather than those that urge them to buy products through advertising.
So, for the marketer, it’s all about helping them “do what they already want to do”.
It sounds like a simple enough task, but in reality, it is something that marketers will find tricky to achieve. The concept assumes that we already “know” what that person wants to do, and for the most part, we don’t. For all our effort, marketing and advertising tends to operate in a sub-optimal world of simple demographics, ratings, static CRM systems and short lived cookies.
What we do know (or can sense) is that the data holds the key, but prizing value out of data in a useable, simple and scalable enough form is a challenge. When it comes to a digital audience, any attempt to get closer falls short because:
- Most of what people say (particularly on social networks and call centres) is captured as text, which means that computers need to interpret the meaning of the text correctly. For example, if the word ‘house’ is mentioned, does it mean ‘house’ as in ‘home’, the TV show ‘House’ or the genre of music? Given the ambiguity of language this is an immensely difficult problem.
- Even if a computer understands the meaning correctly, the next challenge is in knowing how each individual “as a person” values it. Simplistically, long haired Dachshunds might mean everything to you but little to me.
So how can these major barriers to understanding be overcome?
Increasingly digital pioneers are turning to a new genre of big data and predictive analytics companies offering machine learning and semantic analysis to help find the natural patterns or clusters that turn this data into gold.
These new methods combine interests extracted from what you read and do on the websites you browse with social graph data to unlock a more intimate and relevant understanding of consumers, including the contextual relevance of each topic a consumer pays attention to.
Solving the second question about how each individual values these topics, requires the ability to reference two maps (technically graphs) – first a map of how everything in the world relates to everything else (an ontology) and second, a map of how each person values everything in that map (an individual interest fingerprint).
Marketers can use this data to personalise an individual consumer’s messages, content or experiences, or they can use it in aggregate, finding and understanding the natural (and often hidden) clusters in the data; the places where similar behaviours and tastes co-exist. These clusters can then be used in multiple ways, reducing churn or finding the right prospecting parameters.
Understanding how each person values the things in their lives means marketers can increase performance by hundreds of percent and spend far less. For the first time, it’s possible to understand a person’s core interests and affinities in detail as context changes (perfect for brand marketers) as well as quantifying what they want to do “right now” as they land on your sites (perfect for direct response).
In essence it’s a new way of media planning, targeting and personalisation – providing the foresight on what consumers want to do next. Google, Facebook and other tech giants are already well on the way to reaching this understanding.
Now the technology is available for a new wave of digital pioneers to get to this point – and make the vital leap from customer management to customer intelligence. Helping others “do what they already want to do” is, for the first time, a real possibility.