Updated on August 17, 2021
Any good and effective customer engagement strategy should rely heavily on three important pillars:
- Understanding your audience.
- Engaging your potential customers at the right time with the right offers.
- Measuring the impact of each audience and customer interaction.
Customer behavior segmentation is the foundation upon which customer understanding is built upon. Ineffective customer segmentation would lead to faulty or incomplete customer insights, leading to poor campaign performance. Customer segmentation thus is a very important part of the overall customer engagement ecosystem.
Now there are many types of segmentation in the retail context. But behavioral marketing segmentation is used widely since it is the most effective. It assists in engaging and retaining your valuable customers.
In this blog post, we will share with you practical and data-driven behavioral segmentation examples and strategies. If done right, you can significantly improve your retail KPIs like average transaction value (ATV), customer retention, churn rate, customer lifetime value (CLTV), and others.
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The majority of the retailers are still using traditional customer segmentation techniques like demographic segmentation. Demographic makeup, psychographic segmentation, geographic segmentation, and other customer attribute segmentations are important. However, they must be used with other highly powerful data-driven segmentation schemes like behavioral marketing segmentation to see substantial results.
Behavior segmentation is all about understanding the latent needs and wants of your users. You do this by carefully analyzing the behaviors they exhibit while interacting with your businesses. It transcends channels and offers a holistic look. So, you could understand the different behaviors of your customers by journey stage.
A deep understanding of your customers’ purchases can surely go a long way in devising a better personalized experience. Behavioral segmentation strategy goes far beyond just customer engagement and has uses across the entire purchasing process. Some common examples in which behavioral segmentation is used are discussed below.
1. Promotion and Behavioral Marketing Strategies
Besides solid buyer personas, you need behavioral segmentation to engage and personalize conversations with your target audience.
This will even help you launch marketing messages based on some triggers and actions such as cart abandonment, time spent on a page, purchase, etc. Moreover, based on a visitor's browsing pattern, your recommendation engines would be able to give relevant, useful, and personalized product recommendations, and in turn, optimize conversion rates.
Remember, for your marketing efforts to succeed, you need to carve out marketing strategies in line with your potential customer behavior.
The buying process and pattern vary for individual customers. So, capturing the buyer's behavior will give you valuable insights and help you with smarter decisions for merchandising.
This can assist you in bundling campaigns based on items frequently bought together. Another example is to give X% discount on product A if you already bought product B.
3. Assortment Optimisation
Different segments of customers would have relative importance on certain products or brands.
Such items that influence purchasing decisions need to always be maintained in stock. If they are exhausted, it could lead to dismay which in turn might lead to churn.
Let’s look at some behavior segmentation examples you can use to deeply understand and engage your target market and current customers:
- Purchasing behavior
- Browsing behavior
- Attitude towards shopping
- Customer loyalty
- Buyer journey
- Customer engagement
- Attribute segmentation
- Price flexibility
This is the most common and most widely used behavioral segmentation example in retail. You can analyze your customer's purchase decisions and segment customers based on their purchase behavioral patterns with you using RFM.
RFM stands for recency, frequency, and monetary. Recency refers to the last purchase date for a customer. Frequency is the number of purchases in a given time. And monetary refers to the cumulative value of purchases within a given period.
Some common customer segments that emerge out of a typical RFM analysis include:
- Frequentists: Customers with high frequency but low ATV.
- Splurgers: Customer with high ATV but a limited frequency.
- Lapsing: Customers who haven't purchased in the recent past.
- Newbies: First-time buyers who've made their initial purchase.
- Moderates: Average customers who do not fall into the above segments.
Knowing about the exact purchase behavior patterns of these different segments helps in targeting them with very personalized offers.
For instance, to increase the ATV of the frequentists, we could give them an ATV stretch coupon. This way, they would be encouraged to spend a bit more with every visit while the customer experience could also be enriched with that pint of personalization.
Alternatively, for splurges, we would have to increase their frequency. For that, chain reaction coupons can be given. This can trigger them to come again and again to the store or the website.
This is another behavioral segmentation type in which retailers segment and engage their target market.
Customers’ browsing behavior is a very good indication of their actual purchase decisions and intents. This can be used wisely to make them do that eventual transaction with us. The entire digital behavior patterns of every customer should be captured for this using a customer data platform or a customer relationship management software. Then you can analyze the data to identify interesting and actionable patterns from the same.
Let's show you an example to understand the segmentation of regular customers based on their online behavior.
Let's consider all the customers spending more than 5 mins on a particular product page and track their activity. They can then be sent an immediate email. This marketing message can talk about some key features of the product, and you could also send a discount coupon if possible for the engaged customers to make a purchase.
Similarly, other key events like cart abandonment, checkout abandonment can be used to trigger real-time campaigns. This will prevent customers from logging off without making a purchase. Product recommendation campaigns are also run using browsing data with tags like customers who viewed this also saw this.
Attitude Towards Shopping
Another interesting behavioral segmentation example is clustering ideal customers based on their attitude towards shopping.
Often, the transactional behavior of your customers might not be enough to understand their latent needs to upgrade your solutions.
It’s imperative to understand these deep needs or preferences of your target customer, prospective customer, and to leverage this intelligence while interacting with them. One way of doing this is through running a primary research survey to a handful of our customer base.
The attitudinal survey should have multiple questions about the needs, wants, and desires of customers. And this data can be easily analyzed to arrive at very distinct segments based on those needs.
For a better understanding, let's take the example of a fashion retailer. One segment could be high on fashion and could try every new design that comes out. They can be called fashionistas.
While on the other hand, there can be other segments of customers who view clothing more from a functional perspective. They are fine with just anything as long as it is comfortable and easy to wear. We'll call them functional buyers.
Now the marketing messages to fashionistas will be very different as compared to the messaging for functional buyers. This is because they are two distinct segments having very diverse needs and wants although both of them would buy from you. To engage both these segments, the retailer must understand them well and segment them aptly.
Such tailored messaging that resonates with the customer lifecycle stage can go a long way in boosting customer satisfaction, and eventually, keep your customers happy.
Another type of segmentation example is customer loyalty behavioral segmentation.
Brand love or customer loyalty can be defined as a combination of emotional and purchasing behavior. An emotional connection for the brand can again be ascertained through simple surveys. It could include asking for things like how likely they are to recommend the brand to family and friends. Or, how likely are they to switch the brand given the price difference?
Consumers can be given scores on both the emotional aspect as well as the pure purchase behavior aspect. Loyal customers would then be a combination of both these aspects which can be used to segment them.
The scaling factor can vary from being very loyal and in love with the brand to not being loyal at all. Deeper purchase behavior (at items/category) level can then be used to profile these brand segments to derive actionable insights. And accordingly, your communication to the broad audience would narrow down to these segments.
For instance, a highly brand loyal buyer might have a higher affinity towards a certain type of product. Such customer loyalty segmentation will give you insights to properly manage inventory and merchandising.
If these items are exhausted they would lead to a bad customer experience for your most loyal customers and can even tarnish the customer relationship if unaddressed.
A typical customer goes through a lifecycle journey with any retail brand. And his/her needs/requirements continue to vary depending upon the exact buying stage of the journey they are in.
Right from the time when they recognize their need, to making that eventual purchase, customers do exhibit distinct behaviors and patterns. A typical pre-purchase buyer journey would follow the ACPP cycle where:
Finding out the exact buyer journey is always a challenging task. But there are ways available to ascertain the same to a reasonable amount of accuracy.
For example, a search query to solve their pain point with your company name would indicate brand awareness. However, a cart abandonment event on the website would surely indicate a preference.
As a retailer, it’s important to identify the customer journey stage to engage them favorably. Deep dive into this, a typical post-purchase journey could start from newbies (first purchase) to a regular user to a decrease in engagement and eventually churn.
Of course, consumers can leak out of the process at any given stage but this is what we see on universal occasions.
As a retailer, your communication with a new customer should be different as compared to a customer who has churned. As customers move from one stage to the other, your course of action must also be data-driven if you want to make a positive potential business impact.
Customer engagement is a tricky metric, in the sense that it can encompass multiple other metrics within itself.
As an illustration, for an eCommerce retailer A, a visit to a site can be considered to be engaged. While for some other retailer B, a cart abandonment event can be an engagement metric.
Usage patterns, purchase frequency, and others are all measures of behavioral engagement depending upon the context of the company. For this article let’s consider the engagement in your customer loyalty program as an engagement metric.
It could be defined as identifying customers who redeemed the points given to them in a given timeframe. We can further break this down into High, Medium, and Low engagement depending upon the actual score.
Again, our messaging and communication to highly engaged customers will significantly be different from our messaging to low-engagement customers. And this would only be possible if we have a way to first define and then measure these engagement buckets.
Here's a customer engagement calculator to measure your engagement score and tips to improve the same.
An item is a combination of attributes. You need to carefully analyze the purchase behaviors of basic customer segments. Then, tie it back to the attributes that make up the items bought. This way, you can arrive at a very insightful and effective segmentation strategy for your customers.
Let’s again consider the case of a fashion retailer. One segment could be interested in tight-fit clothing with cotton as material and printed patterns.
Or as another example, consider a watch retailer. Segments could mostly be interested in round dial with size > 32 and dark-colored leather straps.
Such detailed segment specifications can be very helpful in executing future campaigns as well as new product introductions. With these granular attribute segments available, our communication and messaging to these customers would be very personalized and useful. This will lead to the betterment of the campaign results.
Focussing on the price of the items bought is yet another way to distinguish customer purchases. Simple analysis can reveal some very interesting facts about your customers’ buying behavior patterns.
For example, one group of customers would only purchase whenever there is a heavy discount or offer going on. These are typically called cherry pickers.
On the other end, we might have high-value customers who would only buy high-value items. They would usually be undeterred by a drop in prices. So, they would continue to buy their preferred brand even if the competitor brand has significantly reduced its price.
Focus on the price of the merchandise brought by customers to unearth insights and segment your customers accordingly.
Behavioral segments play a key component in effective customer satisfaction, engagement and retention. If done well it can surely lead to an improvement in key customer relationship management KPIs. These include repeat rate, ATV, customer churn rate, and others.
However, choosing the right customer data platform is key to be able to accomplish the desired benefits. There are multiple tools available that can help in analyzing and segmenting customers across different channels and attributes. However, working with many different tools is a nightmare that any retail marketer would want to avoid.
To create these dynamic behavioral marketing campaigns based on multiple behavioral attributes, you need a comprehensive customer data platform. Besides, it should also make room to use those customer segments in personalized and relevant campaigns to your customers.
So, besides leveraging the behavioral segments discussed in this blog, make use of a retail customer data platform such as Ingage. It's time to lay out the foundation of a robust customer engagement strategy today!