Dear Marketers and Data Scientists,
I get it. Our lives are changing rapidly and it’s making understanding individuals more complicated than ever.
Change has never happened at a faster pace.
- We’re consuming media differently. (Hello, TikTok and CTV streaming services.)
- We’re living and working in new places. (Nearly 7 in 10 workers are doing jobs from home.)
- We’re spending and investing our dollars creatively. (Ever heard of Bitcoin?)
- We’re managing our health in new ways. (Our attitudes & actions don’t always align.)
So as our culture and everyday lifestyles, preferences and spending styles evolve, how can marketers keep up? Data. Yes. It sounds like the easy answer. But not just any type of data can help marketers keep their finger on the pulse of their audience; it must be predictive.
What is predictive analytics? The industry definition is “predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical and present-day data. The goal is to go beyond knowing what has happened to provide a best assessment of what will happen in the future.”
By tapping into the power of predictive data, marketers and data scientists have the ability to not only keep pace withbut also stay in front of their audience’s wants and needs (not to mention the competition). But predictive analytics is a broad term on its own. Let’s double-click specifically into what marketers and data scientists like you should be looking for when on the hunt for fresh data that reflects the modern consumer.
To guide you through the journey of selecting the best (and most predictive data) to leverage as the anchor for your omnichannel marketing strategies, I’m sharing a checklist of the attributes that you should consider table stakes.
- Relevant – First of all, is the data you’re evaluating reflective of today’s modern consumers? Personally, I have seen other data providers still offering audience attributes to denote if someone prefers to use the Yellow Pages. Talk about a throwback. At AnalyticsIQ, we conduct our own proprietary research led by a team of cognitive psychologists. Our research reflects today’s consumers, from streaming habits to using buy-now-pay-later services to whether or not they prefer Uber-ing around town. It’s the type of data that really helps marketers and data scientists understand the actions, attitudes, and motivations behind an individual’s way of life – and how they may interact with your brand.
- Detailed – Once you dig into the data, you may find that some companies provide binary data points – basically a simple yes or no flag. For example, does an individual prefer to use coupons? Yes or no. The better, more flexible approach is to leverage data that has been modeled and package as to highlight the predictiveness of a data point. For example, instead of saying yes or no as to whether a consumer utilizes coupons, a better approach would be to have a scale of 1 to 7 so you can identify those coupon-fanatics (a 7) vs someone who may be open to using one (a 3) or someone who has zero interest (a 1). With this type of data, now you’ve given yourself the gift of turning the dial up and down on your segmentation and analytics strategies.
- Scalable – There are a number of data sources that you may find incredibly interesting on your data journey. But are those insights only available on a portion of the population, like your social media audience for example? By leveraging a national data source, you can not only link the predictive data to your first party data, but you can see and tap into a broader view of prospects who fall outside of your CRM who you hope to capture as customers, as well.
- Predictive – Predictive data must be backed up by just that – more data. To prove that data is actually predictive, ensure that your data partner conducts multiple validation tests. By back testing data, you can apply a predictive model to historical data to determine its accuracy.
- Privacy compliant & future proof – Over the past years, the world has seen a number of privacy regulations fall into place from GDPR in the EU to CCPA in California. There are a number of additional proposed regulations and industry changes (like Google’s third-party cookie deprecation) that are pushing marketers to evolve their data-driven strategies. It is key to choose a partner that not only meets today’s guidelines but is planning for potential future privacy shifts, too.
If you’re looking to calm the storm of consumer changes and root your marketing, advertising, and data strategies in data you can trust, then a predictive data partner may be just what you, your team, and your organization need.