It's no surprise that if you can convince a customer to visit one more time and make one more purchase, then you can theoretically, and across a large customer base, drive revenue by millions of pounds or dollars. For supermarkets, the visit frequency and essential nature of eating food to survive means that the dynamics are more predictable. However, achieving this at a macro level for non-food businesses is easier said than done. Emotional loyalty can only be nurtured if you understand – and speak to – customers' genuine passions and motivations, but how can brands remain relevant to a vast, ever-changing group of individuals, each with their own specific tastes and values?

The answer lies in the innovative use of data to get under the skin of real people, to help drive customer understanding at scale.

Traditionally, brands have segmented their audience through demographics such as age, gender, location and transaction history. While this data is incredibly valuable for retailers who see their customers very frequently, those who see their customers less frequently such as fashion brands (three or four times a year) or automotive manufacturers (every three to five years), require additional insight if they're to truly get to grips with and start to predict consumer behaviour.