Back to the future: Predictive analytics
While predictive analytics is not a new concept – marketers have often tried to use past performance to predict future behaviour – the dawn of the information age has amplified its effectiveness and usability. Predictive analytics allows marketers to focus efforts and maximise their budgets by identifying targets who are ready to buy and by eliminating those who aren’t.
To accurately predict buyer behaviour, you need more than focus groups and surveys. The era of Big Data has armed marketers with a wealth of information on consumers – including engagement with marketing automation activity and ‘intent’ data from across the web. The technology to crunch this data and make sense of it is rapidly evolving, providing marketers with a roadmap to reach the right audience at the right time.
Data in Action
The Big Data era has produced an incredible amount of information about habits, desires and tendencies of consumers. Marketers who follow these digital footprints can optimise their marketing efforts to target individual audience segments and personalise messages to speak directly to potential customers. Predictive analytics can help create highly-specified buyer personas – marketers no longer need to rely on broad demographic data and guestimates of what a particular buyer prefers. Enhanced buyer personas lay the groundwork for highly personalised messaging for nurture campaigns, which multiple studies show leads to significant increases in conversion and revenue. Predictive analytics also provides the benefit of targeted spending. Knowing what audiences to target and which platforms to target them through significantly increases the impact of marketing budgets.
B2B marketers are a little behind their B2C counterparts in the adoption of marketing technology ¬¬– predictive analytics included. And while it’s true that personalised data from individual consumers offers a clearer view into purchasing habits and tendencies, plenty of data exists for B2B customers that can be utilised to implement more intelligent marketing tactics. Purchase history, for instance, is a great predictor of current and future behaviour. If a customer has recently purchased a software system that won’t need an upgrade for three years, targeting that customer with marketing messages is not only inefficient, but could negatively affect that customers’ perception of your brand. Existing software licenses, log-in frequency, help desk calls and firmographics can also help B2B companies predict the need and desire for their products. Normally this kind of data will predict the type of customers that buy your products. Add in social data sources to the mix and you can predict customers that are ready to buy
Depending on the level of sophistication and budget resources, B2B marketers can deploy analyst-led solutions or automated ‘black box’ solutions to perform predictive analytics. For larger, more comprehensive data operations, an analyst-led approach is preferred. Computers are wonderful, but a human touch – specifically when there are oddities in the data – can more accurately utilise the information output to design programs and messaging that take into account both the customer and the nuances of the company. However, there are various automated solutions that are more than sufficient for less sophisticated marketing automation programs. Both approaches have their own merits, but one thing is clear: predictive analytics allows businesses to focus on what’s important and discard what’s not, leading to amplified revenue growth – and happy customers.