Any good and effective customer engagement strategy relies heavily on 3 important pillars:
- Understanding your customers
- Engaging them at the right time with right offers
- Measuring the impact of each customer interaction
Customer segmentation is the foundation over which customer understanding is built upon. Ineffective customer segmentation would lead to faulty or incomplete customer insights which in turn will lead 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 behavioural 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 behavioural segmentation strategies. If done right, you can significantly improve your retail KPIs like ATV, Repeat rate, Churn rate, CLTV, etc.
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The majority of the retailers are still using traditional customer segmentation techniques like demographics segmentation. Demographic and other customer attribute segmentations are important. However, to see substantial results, they must be used with other highly powerful data-driven segmentation schemes like behavioural segmentation.
Behavior segmentation is all about understanding the latent needs and wants of your customers. You do this by carefully analysing the behaviours they exhibit while interacting with your brand/retail store. It transcends channels and offers a holistic look. So, you could understand the different behaviours that your customers exhibit in their journey with your brand.
A deep understanding of your customers’ purchase can surely go a long way in devising a personalised engagement strategy. Behavioural segmentation goes far beyond just customer engagement and has uses across the entire retail value chain. Some common ways in which behavioural segmentation is used are discussed below.
Personalised campaigns based on some triggers/actions (Cart abandonment, time spent on a page, purchase, etc).
Bundling campaigns based on items frequently bought together. So x% discount on A if you already bought B.
Defining promotion strategies after carefully analysing the purchase behaviours of different segments.
4. Assortment Optimisation
Different customer segments would have a relative importance of item groups. Such items that influence key behavioural segments 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 behaviour segmentation examples you can use to deeply understand and engage your customers:
- Purchase behaviour
- Browsing behaviour
- Attitude towards shopping
- Brand love/loyalty
- Customer journey
- Attribute segmentation
- Price flexibility
This is the most common and most widely used behavioural segmentation scheme in retail. RFM or RFV narrates how you can segment your customers based on their purchase patterns/behaviors with you.
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 segments that emerge out of a typical RFM analysis include:
Knowing about the exact purchase behavior patterns of these different segments helps in targeting them with very personalised strategies/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.
Similarly, for splurges, we need 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 very common and important way in which retailers segment their customer base.
Our customers’ browsing behaviour is a very good indication of their actual purchase intent. This can be used wisely to make them do that eventual transaction with us. The entire browsing behavior of every customer is captured. Then they are rolled up to identify interesting and actionable patterns from the same.
case in point, all the customers spending more than 5 mins on a particular product page can be tracked. They can then be sent an immediate email. This could talk about some key features of the product, and you could send maybe a discount coupon if possible.
Similar 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 the browsing data with tags like customers who viewed this also saw this.
Attitude Towards Shopping
Another interesting way retailers can use behavioural segmentation is through attitudinal segmentation. Often, the transactional behaviour of our customers is not enough to understand their latent needs and to upgrade our solutions.
It’s imperative to understand these deep needs/preferences of our customers 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 has multiple questions about the needs/ wants/desires of customers. And this data can be easily analysed 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's comfortable and easy to wear. Let’s call them functional buyers. Now the marketing communication 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/wants although both of them would buy from us. To engage both these segments, the retailer must understand them well and segment them aptly.
Besides engaging them, this helps to nurture them for life as loyal customers!
Another means of analysing your customer base and goes far beyond just looking at their purchase behaviour.
Brand love can be defined as a combination of emotional and purchase behaviour. 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, etc.
Customers can be given scores on both the emotional aspect as well as the pure purchase behavior aspect. Brand love can then be a combination of both these aspects which can be used to segment customers. The scaling factor can vary from being very loyal and in love with the brand to not being loyal at all. Deeper purchase behaviour (at items/category) level can then be used to profile these brand segments. And the insights can be used to reach out to these segments respectively. For instance, highly brand loyal customers would have a higher affinity towards certain types of products.
This insight will also have a bearing on inventory and merchandising decisions. If these items are exhausted they would lead to a bad customer experience for your most loyal customers.
A typical customer goes through a lifecycle journey with any retail brand. And his/her needs/requirements continue to vary depending upon the exact 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 behaviours and patterns. A typical pre-purchase journey would follow the ACPP cycle where
Finding out the exact customer 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 our 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 favourably. Delving deeper, a typical post-purchase journey could start from newbies (first purchase) -> regular user -> decrease in engagement -> churn. Of course, customers can leak out of the process at any given stage but this is the most usual path followed.
As a retailer, your communication with a new customer would 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.
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 considered to be an engagement metric.
Usage patterns, purchase frequency, etc. are all measures of engagement depending upon the context of the business. For this article let’s consider the engagement in your Loyalty program as an engagement metric.
It could be defined as identifying customers who leveraged 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 high engagement 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.
An item is a combination of attributes. You need to carefully analyse the purchase behaviours of distinct 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 high actionable attribute segmentation schema for our 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, etc. Or as another example, consider a watch retailer. Segments could mostly be interested in round dial with size > 32 and leather strap mostly in dark colours.
Such detailed segment specifications can be very helpful in future campaign execution 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 our customers’ buying behaviour.
For example, one group of customers would only purchase whenever there is a heavy discount/offer going on. These are typically called cherry pickers.
On the other end, we might have 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.
Choosing the right customer engagement platform is key to be able to engage and retain your customers. There are multiple tools available that can help in analysing 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 segments based on multiple behavioural attributes, you need a comprehensive platform. Besides, it should also make room to use those segments in personalised and relevant campaigns to your customers.
Customer engagement in retail is also different from customer engagement in any other industry. And as such it's always advisable to go with context-driven platforms/products as opposed to horizontal generic platforms.
Behavioural segmentation is a key component in effective customer engagement and retention. If done well it can surely lead to an improvement in key CRM KPIs. These include repeat rate, ATV, Churn rate, etc. Leverage the different types of behavioural segmentation explained in this article above. Layout the foundation of a robust customer engagement strategy today!