Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

The Important Difference Between Cohorts And Segments

It’s official, cookies and other identifiers are slowly becoming a part of the history books. That means marketers are inevitably trying to wrap their heads around the latest buzzword… This time, it’s “cohorts”.

So, what is a cohort? Something elite marketers have been looking at for ages, whether they know it or not.

Top AI ML insights: Blockchain and AI, a Perfect Match

For the past decade or so, the most coveted piece of digital marketing has been deterministic data. This often starts with some sort of personally identifiable information (PII), like an email, device address or IP address. This PII helps marketers create segments like “discount shopper”, identifying groups of consumers that are likely to purchase and then target them with ads.

This, however, is only the first step for marketers. Next, is when we layer in extra elements of specificity and eventuality like time period or weather. This is where the term “cohort” is born. This is also where simple “discount shopper” segments become more actionable “discount shoppers for back to school season” cohorts.

And now, with global privacy regulations like GDPR and CCPA, marketers can no longer just rely on deterministic data, which means cohorts are going to play an increasingly important part of the digital marketing ecosystem.

What’s next in the age of cohorts?

The next phase of digital marketing will undoubtedly place a renewed importance on probabilistic data, where marketers are taking a one-to-many approach and building large groups of people with similar characteristics. The first evolution of machine learning in advertising was to build people with similar interests, shopping and consumption patterns. Now, the next big challenge for machine learning is to leverage privacy compliant data and figure out even narrower consumption pattern groups.

Related Posts
1 of 1,749

This approach will not only help create a digital ecosystem with privacy-by-design, but it will help brands find new customers. This is, afterall, how some of the most successful brands have been expanding their markets for years: Building cohorts of likely customers, based on their similarities.

Read Also: How AI-based Video Solutions are Creating Smarter Supply Chains

It will also drive a renewed importance on creating the best possible customer journey, instead of the most personalized one. Currently, many brands are constantly worried about collecting another data point to create this one-of-a-kind marketing plan. Instead, brands must optimize the path to purchase for consumers based on the groups of cohorts they are trying to speak to.

This approach includes all platforms – linear, CTV, mobile, OOH – for different cohorts at different stages of the customer journey. Then analyzing data to follow footprints across platforms. Then brands will be able to develop a truly impactful approach to advertising. One that respects privacy. And one that is not limited to any narrow segment of consumer.

This change is inevitable and necessary.

And change can be scary.

Especially when you are doing away with the “gold standard” that has been a top priority for the last decade. But the days of relying solely on deterministic data are done, and brands need to start identifying how they can expand their potential consumer base. The best way is by building cohorts through probabilistic data.

[To share your insights with us, please write to]

Comments are closed.