How to Boost Customer Analytics with Behavioral Segmentation

Despite all grumble about the restrictions Driven by privacy initiatives, today’s marketers have to deal with a growing stream 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 gain insight into huge logs of customer touch points.

The decline of traditional audience analysis is one of the side effects of increased data collection. While marketers once emphasized data points that catered to audience interests, demographics, and fundamental psychographics like beliefs and lifestyle choices, today it’s all about understanding behavior.

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

More insight into customer journeys

Behavioral segmentation uses audience data to understand how audience members interact with marketing assets, rather than focusing on who the prospect is.

Sarah Mehlman from Similarweb explains behavioral segmentation by citing some vertical-specific examples. †E-commerce companies analyze the buyer’s browsing behavior and journey to group their audiences by funnel stage and willingness to buy,” she notes. “If you’re using a SaaS platform, you may be more interested in segmentation based on user status, including measuring customer loyalty or visit frequency and the average time you spend on your site or app.”

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

The result is a very personalized customer journey that delivers offers at the right time. McKinsey marks a preview from an Eastern European telecom operator that used behavioral analysis to identify the most appropriate channel and timing for customer reach. The operator targeted people moving with broadband installation offers in one campaign.

Another campaign targeted people who had just received high phone bills with offers to reduce negative brand perception. The result was a huge increase in sales and a better understanding of the public.

Correlating preferences with demographics

Behavioral segmentation doesn’t ignore every element of traditional audience analysis. For example, demographics can play a role in audience segmentation. Behavioral data alone provides marketers with more context and insights when analyzing demographics.

A Meta study found that smaller audience campaigns focus on demographics rather than just interests saw 99% higher range and were 1.6 times more likely to convert. In that sense, going too detailed for your own good is a real danger.

There is also the fact that analyzing interests is a difficult task because everyone indulges in interests in a different way. For example, a novice surfer could consume much more surfing content than a professional surfer, despite being less committed to surfing as a lifestyle. Marketers looking to target beginners can narrow their target audience to the point that they no longer notice statistical patterns.

Demographics expand the audience and data analysis can better point to common behaviors. Simply put, by empowering yourself to notice patterns, you can optimize campaigns.

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

Interestingly, the company also marketed party dresses for women who frequented nightlife and bars more often compared to other cohorts. Using advanced A/B testing of advertising materials, the retailer presented promotions to members of the public who had previously expressed an interest in dresses. In this way, web traffic data, layered with conventional demographics, gave the retailer a more complete picture of their audience, leading to more effective behavioral segmentation models.

Getting to the bottom of value perception

Nonprofit organizations often find raising awareness a challenge. Most organizations cannot afford large marketing campaigns. Targeting the right people and using data to help nonprofits optimize their campaigns is essential.

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

The nonprofit took advantage of the custom audience list to create a comparable audience list to maximize reach. The result was a 32% increase in engagement rates. Using alternative data, such as activity on social media channels, technology preferences, and use of rideshare services, provided more context to traditional interest-based data, allowing the organization to correlate spending behavior with value perception.

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

Multi-layer segmentation for messages that resonate

Behavioral segmentation through deeper data analysis helps marketers differentiate their products and better understand prospects. As a result, it becomes easier to create custom customer journeys, segment existing demographics, and find new value drivers.

Ralph Tkatchuk is the owner of TK DataSec Consultancy.