How to supercharge customer analysis with behavioral segmentation

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Despite all the grumbling around the limitations prompted by privacy initiatives, today’s marketers are dealing with a growing onslaught of data. As companies increasingly rely on every customer action as a data point, marketing teams are turning to data scientists and engineers to help them make sense of vast logs of customer touchpoint engagements.

The demise of traditional audience analysis has been one of the side-effects of increased data collection. Whereas marketers once emphasized data points that speak to audience interests, demographics and basic psychographic factors such as beliefs and lifestyle choices, today it’s all about making sense of behavior.

Data proliferation has eliminated any advantage that marketers once enjoyed thanks to traditional audience analysis. In the never-ending quest to find a competitive edge, marketers have had to dig deeper into their data and leverage insights creatively. In this sense, behavioral segmentation has provided the key.

Deeper understanding of buyer journeys

Behavioral segmentation leverages audience data to understand how audience members engage with marketing assets, instead of focusing on who the prospect is. 

Similarweb’s Sarah Mehlman explains behavioral segmentation by citing some vertical-specific examples. “Ecommerce companies analyze browsing behavior and buyer journey to group their audience by funnel stage and readiness to buy,” she notes. “If you run a SaaS platform, you may be more interested in segmenting according to user status, including gauging customer loyalty or frequency of visits, and average time spent on your site or app.”

Behavioral segmentation likewise helps companies understand buyer needs better. Some metrics used in the process – attention minutes, scroll depth and micro-conversions – play a role in traditional audience analysis as well. However, behavioral segmentation offers greater audience context by connecting these signals to others such as spending habits, the impact of special occasions on conversions and the payment methods used.

The result is a highly personalized customer journey that delivers offers at the right time. McKinsey highlights an example of an Eastern European telecom operator that leveraged behavioral analysis to identify the most appropriate customer outreach channel and timing. The operator targeted people moving homes with broadband installation offers in one campaign.

Another campaign targeted people who had just received large phone bills with offers to mitigate negative brand perception. The result was a massive revenue boost and a better understanding of its audience.

Correlating preferences with demographics

Behavioral segmentation doesn’t discard every element of traditional audience analysis. For instance, demographics can play a role in audience segmentation – it’s just that behavioral data offers greater context and insights to marketers when analyzing demographics.

A study conducted by Meta highlighted that campaigns with smaller audiences targeting demographic factors instead of just interests saw 99% higher reach and were 1.6 times more likely to result in conversions. In this sense, going too granular for your own good is a real danger.

There’s also the fact that analyzing interests is a tough task since everyone indulges in interests differently. For instance, a beginner surfer might consume much more surfing content than a professional surfer, despite having a lower level of commitment to surfing as a lifestyle. Marketers seeking to target beginners might reduce their audience sizes to such an extent that they won’t discern any statistical patterns.

Demographics widen audience sizes, and data analysis can point to common behavioral points better. Simply put, it’s by putting yourself in a position to notice patterns that you’re able to optimize campaigns. 

For instance, a leading casual fashion retailer boosted conversions by 75% using conventional demographic data merged with geolocation data. The results suggest that even if store proximity isn’t always a buyer-intent signal, it can still be used effectively to tip the scales and prompt conversions. 

Interestingly, the company also marketed party dresses to women who frequented nightlife spots and bars more frequently compared to other cohorts. With sophisticated A/B testing of message creative, the retailer served up promotions to audience members who had previously expressed interest in dresses. In this manner, web traffic data layered with conventional demographics gave the retailer a more complete picture of its audience, leading to more effective behavioral segmentation models.

Getting to the bottom of value perception

Nonprofits often find raising awareness challenging. Most organizations cannot afford huge marketing campaigns. Targeting the right people is of the essence, and data is helping nonprofits optimize their campaigns.

A national nonprofit used alternative data to raise awareness amongst donors and achieved remarkable results in their paid ad campaigns. The nonprofit wanted to create a custom audience list of potential donors based on their interests, demographics and value perception. The organization achieved this task by leveraging alternative data, such as credit card spending details, affinity toward cryptocurrency, the motivation behind previous charitable donations, and donor channel preference. These datasets, mixed with traditional interest-based data, helped the nonprofit determine their audience’s value perception and target them better.

The nonprofit leveraged the custom audience list to create a lookalike audience list to maximize reach. The result was a 32% increase in engagement rates. The use of alternative data such as social media channel activity, technological preferences and rideshare service usage offered greater context to traditional interest-based data, helping the organization correlate spending behavior to value perception.

While pricing wasn’t an issue in the nonprofit’s case, it’s easy to see how companies could leverage behavioral data such as these to segment customers and offer ideal prices.

Multilayered segmentation for messaging that resonates

Behavioral segmentation through deeper data analysis is helping marketers differentiate their products and understand prospects better. As a result, creating custom buyer journeys, segmenting existing demographics and finding new value drivers is becoming simpler. 

Ralph Tkatchuk is the owner of TK DataSec Consultancy.

Originally appeared on: TheSpuzz