‘I wish this customer just thinks straight.’ And that never happens. Thinking patterns conflict, coincide and converge each other. As a marketer, prevalence of multiple thinking patterns emerging out of your customer journeys bothers you.
Just like there is more than one way to reach your office from point A, there are many ways in which a customer ends up discovering your product and decides to purchase it. What you fumble as a marketer is to predict paths that customers choose. It gets even more complex when customers start exploring more than one channel to evaluate and engage with your brand.
It is beyond human thinking to imagine all possibilities that can segment existing and emerging customers.
SQL tables and IT tickets
SQL tables brought some order to the madness. They have been able to organize raw data and nothing more. IT ticketing systems were the next step to the evolution of customer segmentation. But, neither of them gave any real-time insight, weighing the larger picture.
They threatened businesses with the lack of data privacy. Tools like SQL tables and tickets could not store data in an authentic way. Customers shied away from brands that misused their private data. Poor management of private data and lack of an intent to establish transparency translated into leaving the platform or abandoning the product.
For marketers, it was not being able to efficiently filter data. The mediocre data could not be used to identify target audience and target real campaigns.
While marketers are grappling to find a single solution, we have it sorted for you. Automating the process of segmentation customers into comprehensible and manageable segments is the problem that we are addressing in this article, below.
One of the most sought after features in a CDP for audience segmentation is the auto-segmentation that is available in FirstHive.
“How can Marketers master automation of customer segmentation?” Customer segmentation is a process that is now collectively done by marketers (humans) and technology tools (machines). This brings us to define auto-segmentation.
Auto-segmentation is the process of segmenting customers into cohorts that fulfill a certain criteria by leveraging machine learning that can provide recommendations over the system, beyond demographic attributes and transaction history.
We deploy powerful algorithms that involve k-means for clustering cohorts. To automate the cohort segmentation process we rely on unsupervised machine learning. While this sounds simple, the complexity increases when you are mapping your cohorts using a criteria that has more than two or three dimensions. FirstHive applies the auto-segmentation to realize real cohorts that can be used to construct fruitful campaigns.
In the campaign activation module, these customer segments could be selected for implementation. During live campaigns, customer segment cohorts are automatically updated. This adds more efficiency to the campaign optimization process too. It also enables automation of altering triggers that control campaigns.
Apart from automated updates, the algorithm also recommends. There are two layers to the recommendations made. The customer segment recommendations are made to marketers. And, the segments are also enabled to make product and content recommendations directly to the customers within different cohorts. Thus increasing relevance.
Visual representations validate and augment the recommendations determined by the algorithm. This also means that you not only promote and focus on the high performing customer segments, but find out those negative audiences that are not contributing or hurting your business.
It is assumed that customer segmentation is to segregate audiences by geography, demography and transaction value plus behavior. But, it is more. A marketer needs to analyze deeper into customer behavior and personality using many dimensions. Engagement channel preference, first touch point, product preference, content subscription are few other parameters that could be applied to build cohorts.
Auto-segmentation combined with recommendation systems brings forth many advantages.
- Finds hidden clusters within the dataset that represent important customer segments.
- Remains devoid of errors that are caused by human assumptions.
- Automation ensures scalability and productivity
- Individualized personalization can be achieved
- Database and customer information is kept up-to-data, round-the-clock.
To enjoy any of the benefits, email us at email@example.com and discuss how you can automate your customer segmentation process.