The importance of data freshness in predicting customer lifestyle changes

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Cultural shifts in today’s consumer behaviors have coincided with an overall decline of traditional shopping expectations. Typical audience demographics like age and gender no longer hold the same inferential potential for marketers; for example, a target audience between the ages of 25-35 is not necessarily married, buying a house or having a child any time soon. Or a 55+ audience may be just as tech-savvy as a millennial consumer and may prefer the convenience of shopping in-app vs. in-store. As linear lifestyle milestones become increasingly fluid and the path to purchase becomes more instantaneous, marketers can no longer rely on one-size-fits-all behavioral assumptions to understand and reach their target audiences.

As a result, marketers are investing more heavily in gathering and analyzing the massive amounts of data at their fingertips to better understand the ever-evolving consumer. So, how can brands maintain the ability to pivot to meet consumers’ needs and predict changes in their lifestyle with the immediacy needed to stand out today? The answer lies with data freshness – incorporating “live” real-time data that details consumer behavior in recent hours, days and weeks and is vastly more powerful than the static behavioral data that many CMOs have traditionally relied on for insight.

Hitting the refresh button

We know we’re living in an era of unprecedented access to data. But lots of data in the wild is stale – you can buy categories of purchase data from credit card companies, for example, but that only tells you (broadly) what people bought months ago, not what they’re doing now. Or, in the case of location data, not all insights are of equal weight; the value of a signal decays exponentially with time, and it becomes inconsequential to know if consumers visited a phone store a month ago when buying cycles for new phones are measured in days.

This is not to negate long-term behavioral data completely; it gives valuable background into broad classifications and preferences, but alone it cannot accurately capture customers’ fast-changing shopping needs. Relying on lifestyle segmentations based on static behaviors – e.g. classifying an audience of golfers because of a longstanding affinity – will index well for fixed habits and hobbies, but it will fail to capture the new golfer or the existing golfer’s new hobby.

Conversely, if an active-wear brand layered in real-time location data, they might catch new gym-goers who were historically classified as junk food aficionados. Or, a furniture store could identify an uptick in foot traffic to a paint shop or hardware store, for example, and reach audiences who are planning to move houses early on in their shopping cycle — a change that might be overlooked by less agile brands. With these “fresh” live insights, marketers are less likely to be caught off guard if a major lifestyle shift occurs; instead, they can better capture current customer purchasing patterns, improve personalization and predict emerging shopping behavior, which will ultimately help to boost sales conversions.

The (consumer) cycle of life  

It’s abundantly clear that today, more than ever, marketers need to properly leverage the right data — not just the data that’s been historically available to them — to understand a shopper whose habits lack the sequential progression of other generations. While this complexity gives way to new challenges, it’s also an unprecedented opportunity for brands to connect with a new age of consumers in a timely way with experiences that are more tailored, and relevant, to them than ever before.

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