In marketing, the more facts we know about our target consumers, the more relevant our communications are likely to be. Despite this, we are far more likely to reach out to individuals based on assumptions rather than facts – especially in digital advertising. The majority of campaigns are based on inferred (probabilistic) data which tends to come from observed behaviour, such as online searches. It is used to target people using a best guess about what this behaviour is likely to indicate regarding products they will want or need.
However, research shows that 32% of marketing and advertising executives find targeting using factual (deterministic) data to be more effective in driving revenue compared to 22% who believed inferred data was better. Deterministic data comes from someone self-declaring information about themselves (e.g. providing birthday or gender data to register on a website) or can be based on a known action someone has carried out (e.g. having a baby or selling a house) which leads to behaviour change.
In spite of its greater value, though, deterministic data tends not to form part of media plans as planners don’t believe it exists at the scale they need for big brand campaigns. Probabilistic data can certainly be created in large volumes as it uses much broader definitions – but size isn’t everything! Factual data sets may not be as big, but their scale is growing, providing planners with what they need to make it worthwhile including them in the mix across all channels, from digital and mobile advertising to direct mail and email – especially if they want to improve the effectiveness of client campaigns.
A good example of deterministic data at scale is information captured on life events, such as home mover data. With 5-6 million people in the UK moving home every year it is a sizeable audience. For every home mover, it is possible to collect a rich amount of data which informs where they are in the home moving process – from putting their house on the market, to energy audits, surveys, searches and Land Registry data on completion.
Home movers have a purchasing power of £12 billion annually in the UK, with each spending an average of £10,000 on a variety of goods and services across an extended period of many months - typically across at least 12 months before, during and after a move. So for many brands, from mortgage providers, home insurance companies, retailers, broadband providers and utility firms, this is a rich source of consumers who will be actively looking for their products and services. Even fast food delivery firms can tap into this opportunity as people typically are time poor and have more need for takeaways when they first move in.
Similarly, ‘baby’ data is an extremely valuable factual data set. Over 600,000 babies are born every year and, according to ONS, in 2016 there were 18.9 million families with children in the UK. Brands that identify customers and prospects from pregnancy to birth and then track them as the children get older so they can adapt their offering to maintain relevance, can create years of opportunity that will also create powerful customer engagement.
This isn’t just true for products relating to babies, children or family offerings – but also for more lateral markets, such as automotive companies. Our data shows that having a second baby is a major trigger for upgrading to a bigger car. Interestingly, research carried out by Nielsen in Australia a few years ago revealed that people looking to move house in the next three months are also 251% more likely than average to purchase a car in the same time frame.
There are other deterministic life event datasets too, like people coming of age, going into higher education and retiring. The great thing about all types of factual life event data is that they build a long-term, in-depth picture of an individual rather than the snapshot view you get from inferred data.
Each of these events can be months or even years in the planning and so provide a ‘big picture’ view of an individual who will have a variety of qualified needs as they go through the process – which can be fulfilled by savvy marketers using deterministic data. For instance, mortgage providers should contact home movers as soon as they have put their house on the market and furniture companies should target them two months before the move as this is when they start to actively purchase new furniture.
Deterministic data should not replace inferred data though. The ideal scenario is to use both. Probabilistic data is useful for driving retargeting campaigns, but when you add in deterministic data at scale you can create a 360 degree view of the consumer and a factual basis that adds context to an individual’s probabilistic profile. In our experience, this addition of context when targeting campaigns can have a massive impact on ROI, making up to £28 for every £1 spent. that’s another factual piece of information worth noting!
By Paul Hickey, director of digital solutions at TwentyCi
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