The touchpoints revolution has created a big bang of rapidly emerging new data sources – but between these points of light, it’s also created black holes in our understanding that only a re-engineered approach to research can fill.
A new wave of connected touchpoints is taking over from survey-based research as the dominant source of data for brands. The streams of real-time information emanating from these touchpoints tell us exactly who does what and when, not just for a small sample of people but for everybody interacting with them. However, the touchpoints revolution that is bringing Big Data into being is also revealing Big Data black holes – significant gaps in our understanding that result from the things touchpoints can’t tell us. Filling these black holes is the new mission of research, and it’s a mission that is crucial to brands’ ability to market effectively. Unless we fully understand consumer behaviour in this new, emerging universe, we cannot hope to influence it.
The black holes start to emerge when we look for the 'why' to go with the 'who', 'what' and 'when' of how people behave. Touchpoints data alone can’t tell you what a person is thinking or feeling when they carry out a particular action. It can’t tell you what their mindset is, what their relationship to your brand is, and whether it’s at all worthwhile spending money on advertising to them. And without this contextual information, all of the other data loses much of its value.
How Big Data black holes form
This is a direct result of the nature of the touchpoint revolution itself. A particular action no longer has a pre-determined meaning as part of a linear path to purchase. One behaviour, like visiting a Nike website, does not automatically lead to another behaviour, such as buying a Nike product. Training algorithms to assume that it does simply blunts your targeting approach, confuses your strategy and wastes your budget.
To understand the real significance of someone’s interaction with a touchpoint, we need to understand that person’s preferences, the triggers for them to make a purchase, and the contexts that play a role in their decision-making. Our Nike website visitor, for example, may be there specifically to buy a pair of Nike running shoes, but they could equally be researching technologies for reducing the risk of shin splints, with no particular interest in the Nike brand or products at all. They could be an existing Nike customer seeking troubleshooting information for the wearable they bought a year ago; a journalist researching the company – or an activist checking on the origin of its products. Taken in isolation, the data generated from this one touchpoint has no way of differentiating between these people with very different relationships to the Nike brand. All too often, it can’t even distinguish whether the visitor is really a human being, as opposed to a bot.
The problems these black holes cause
This lack of contextual understanding creates two types of problems for marketers:
- It undermines the actionability of touchpoints data. Targeting based on behaviour in isolation tends to result in a lot of irrelevant advertising being delivered to the wrong people, with wasted budgets, frustrated consumers and plummeting ROI.
- It makes it difficult to optimise on the basis of touchpoints data.
If marketers are to make strategic decisions about where to spend their budgets, they need a way of assessing what the relative impact of all of their audience’s different touchpoints are. To which channels should they be directing more of their marketing budgets?
Re-establishing a single view of consumers
We gain much from the new streams of data that the touchpoints revolution has provided, but we lose something in exchange: a single-source view of the consumer that allows us to relate the actions they take to the feelings and motivations they say that they have. For all their weaknesses, traditional surveys provided the opportunity to explore the context for actions at the same time as establishing what actions had taken place. This enabled us to segment audiences based not just on what they did, but on the basis of their mindsets and receptivity to brands. We need to re-engineer and scale this form of understanding to capitalise on the flood of data being released by touchpoints. Only by doing so can we fill the Big Data black holes that are emerging.
Getting more value from behavioural targeting
Far from losing relevance in the age of Big Data, the art of segmenting an audience is becoming more