Effective marketing requires distilling consumers into attribute-based groups to better cater to their needs. This is called marketing segmentation and it’s the process by which ecommerce is able to identify what to promote to a site visitor. Since its inception in the early 1950s, marketing segmentation evolved into three key areas: demographic, geographic, and psychographic.
Demographic segmentation: this is focused entirely on who the customer is be that their age, education, gender, occupation, income level, etc.
Geographic segmentation: this markets to those based on their location, including region, continent, country, city, and district.
Psychographic segmentation: this separates consumers based on personality traits such as lifestyle, interests, and values.
While the three segmentation methods above proved beneficial to ecommerce marketing, their efficacy is on the decline due to new privacy laws restricting the data required for them to reliably perform. By implementing machine learning capabilities to analyze consumer behavior in-session, marketers can accurately identify important patterns and target their efforts while remaining compliant. This is called behavioral segmentation.
What is behavioral segmentation?
Behavioral segmentation categorizes customers according to behavior patterns as they interact with a company or brand. As the name suggests, behavioral segmentation studies the behavioral traits of consumers — looking at a consumer’s knowledge of, attitude towards, use of, likes/dislikes of, and response to a product, service, promotion, or brand. Some of the primary traits within this segmentation include product knowledge, purchase patterns, digital behavior, previous purchases, awareness of your business, and product rating.
Modern advances in AI from Session AI include the ability to segment based on predictions of who will make a purchase, who will not, and who is influenceable. New behavioral models allow ecommerce brands to segment based on predictions of who will return within 7 days, who is likely to make a future purchase, which visitors only check out using coupons, and more.
What are the benefits of behavioral segmentation?
Behavioral targeting comes with four key advantages for marketers: personalization, prediction, prioritization, and performance. Personalization synthesizes a visitor’s attributes and places them into a sub-group for one-to-one messaging. Predictions are determined by patterns readily identified by AI and ML models and help inform whether a consumer is likely to purchase or not and deliver the correct actions to optimize conversions. Prioritization uncovers high-value prospects with the greatest returns on investment — empowering data-driven decisions around the allocation of time, budget, and resources for that visitor. Lastly, performance monitors growth patterns and changes to segments over time to clarify results and track success against preconfigured indicators.
Why is behavioral segmentation important for marketers?
Dividing consumers into smaller segments, each with a common variable, allows ecommerce brands to better optimize the use of time and resources. When all consumers experience the same marketing message, it only works for a small percentage. As brands better understand a particular market and divide it appropriately, they’re able to implement personalization and convert at a significantly higher rate.
Types of behavioral segmentation
Initially, behavioral segmentation was limited to four types before quickly expanding as the model grew in sophistication. Below we’ll focus on the top eight types: purchase behavior, benefits sought, customer journey, usage behavior, occasion, customer satisfaction, customer loyalty, and interest.
Note: each mode of behavioral segmentation can be used in unison, so no need for a Sophie’s choice.
Behavior segmentation: Purchasing
Segmenting based on purchase behavior involves identifying trends of how different customers behave while making a decision on whether or not to buy a product.
Purchasing behavior helps marketers understand:
- How different consumers approach a purchase decision
- The complexity of the purchasing process
- The role the consumer plays in the purchasing process
- Barriers along the path to purchase
- Behaviors that are most and least predictive of a customer making a purchase
Predictive behavioral segments
By implementing machine learning capabilities to analyze consumer behavior throughout the customer journey, marketers can more accurately identify important patterns over time. Ecommerce brands can now build predictive segments based on purchase propensity (likely to buy, unlikely to buy, or on-the-fence).
Digital behavior patterns
Using patterns in digital behavior can help brands understand the variety of ways different customers approach the buying process in order to remove any friction between the journey’s start and checkout. There are a variety of ways to approach this, depending on the type of buyer.
Some examples of digital behavior patterns could be:
- The “price-conscious” buyer: someone who is looking for the lowest price possible.
- The “smart” buyer: a meticulous researcher who wants to understand every variable and feature before committing.
- The “risk-averse” buyer: a cautious, economically-careful shopper, who struggles to make a decision on a purchase without insurance and a solid return policy.
- The “needs-proof” buyer: a shopper who needs social proof from peers that the product is popular and performs well.
- The “I’ll get it later” buyer: the customer that procrastinates on purchases and lacks urgency.
- The “persuadable” buyer: an impulse shopper who is highly susceptible to real-time offers.
Benefits sought segmentation
As a prospect researches a product or service, their behavior can reveal valuable insights into which benefits are the motivating factors that influence their purchase decision. A simple example would be a customer who continues to shop at a brand or store because of a rewards program. When brands know that a consumer is looking to be rewarded, they can group them with others looking for the same benefit and personalize their marketing message accordingly.
The customer journey
Building behavioral segments throughout the customer journey stage allow companies to align communications and personalize experiences for increased conversion at every stage. Additionally, businesses can discover stages where customers stop progressing and identify opportunities for improvement. However, attempting to identify which journey stage a prospect is in based on one or two behaviors can result in the wrong assumption. The most effective way to accurately determine a customer’s current journey stage is by leveraging their behavioral data across channels and touchpoints and building weighted algorithms.
Knowing how, how often, and how much a customer is using your product or service is extremely valuable. From social media to online games and SaaS brands, businesses need insights into the use of their product in order to optimize the experience — whether that’s maintaining current traffic or building a user base. Understanding usage behavior provides the data needed to predict customer loyalty and lifetime value.
Segments based on frequency of usage
Heavy Users – customers that spend the most time using your solution or product and make the most frequent purchases. They are the company’s most avid and engaged customers.
Average Users – customers that semi-regularly use or purchase, but not frequently. Use is often time or event-based.
Light Users – customers that use or purchase much less compared to other customers. Depending on your business, they could even be one-time users.
Usage-based behavioral segments are valuable for understanding why certain types of customers become heavy or light users. When companies segment this way, they can test different actions and approaches to increase usage from existing customers and attract new customers that are likely to match the same usage behavior patterns as heavy users.
Occasion or timing-based segments refer to both universal and personal occasions. Universal occasions include holiday and seasonal events where consumers are more likely to make certain purchases based on the time of year.
Personal occasions can either be one-offs (like a wedding) or recurring (such as an anniversary). Marketing for universal occasions is easy, but personal occasions require more data. Taking data across touchpoints can connect one search query to another and allow brands to understand the occasion and direct a converting customer journey.
Understanding customer behavior can provide an accurate measurement of customer satisfaction as it delivers real-time understanding of each interaction and every click. When ecommcerce brands segment customers by satisfaction, they can decide on the appropriate set of actions for each group and quantify and prioritize them by their potential business impact.
When you know who your most loyal customers are, you can maximize their value and find more customers like them. Loyal customers are extremely valuable as they cost less to retain and can become a brand’s biggest advocate.
Using customer loyalty, behavioral segmentation can yield answers to valuable questions like:
- What factors and behaviors along the customer journey lead to loyalty?
- Which customers are the best candidates for loyalty programs?
- How can companies keep the most loyal customers happy and maximize their lifetime value?
Understanding consumer interests is key to personalization, customer engagement, and delivering value. Brands like Netflix, Amazon, and Spotify use recommendation engines to suggest content and products based on that user’s interests. A big advantage of segmenting by interest is the ability to connect specific preferences with other potentially related items. Machine learning can help scale this process as more customers engage and interact with your product(s).
We’ve come a long way since the early days of marketing segmentation. Today, grouping users by their behaviors is the clearest way to understand how you can meet their needs and desires consistently. Using the power of AI and machine learning to predict what consumers are looking for, and where they’re at in the customer journey is the only way to stay competitive in today’s in-session marketing paradigm. The most competitive ecommerce brands are the ones capable of delivering a personalized experience for each customer (even anonymous) at every step.
See how you can use real-time behavioral segmentation to tailor the customer experience for anonymous and known consumers with Session AI’s in-session marketing platform.