How can you segment customers based on purchase history for better CX personalization?

Make note of expectations voiced by customers. What caused a change in pace or tone of voice? What are they combining your solution with, and what is their overall purpose with your solution? Group customers by expectation patterns primarily, and traditional criteria secondarily. Highest success in repurchase, expansion, referrals, and margin rely on meeting or exceeding expectations. Next, meet with Service to understand costs to serve. Prioritize expectation segments by profitability. Educate all work groups about expectations they affect. This is the path to being the most loved, agile, and financially successful firm. It will improve employee experience, reputation, budgets, and growth. Use secondary segments for touchpoint management.

Purchase history also allows you to set SMART goals for sales and tweak products and services. It's also more cost-effective and efficient.

The purpose of capturing customer purchase history is to improve organisation’s Top Line and the Bottom Line. While purchase history of consumer is important to understand trends , buyer’s patterns, buyer behaviour, choices, needs etc. These are a traditional ways of knowing a customer. There are undiscovered and untapped markets/ products which customers want but they are unaware, what they want. To discover it, we need to follow ‘Consumer Journey Map’. There are many sucess stories i.e Netflix, Southwest Airlines, Uber etc. 10-15 years back most of us have not heard name of Netflix. What Netflix did, simply eliminated DVD by creating OTT platform. Southwest airlines team . Contnued……

Each customer is different, although one thing they all have common is that they are on a journey with you and your company, and just like every journey, every one of them would at different milestones in their adoption, understanding of the product, the company, their asks and needs of the company and product. Segmenting the customers by purchase history and analyzing their trends and data, will provide an apples to apples comparison of similar customers, which would help the leaders understand customers similar asks and needs better, which in turn will help to understand the customer better.

Purchase history is a key input in segmentation, providing data on purchase frequency, time, type of purchase (repeat, net new, cross-sell/up-sell), and method (store, online, with/without sales support, etc). These data help inform seasonal, cross-sell, product, and loyalty campaigns, inform willingness to buy and pricing strategies, and inform demand planning to drive efficiency throughout the supply chain. However segmenting customers on purchase history alone leaves key elements of the story out of the picture. Leaders combine purchase data, engagement data (online, digital campaigns), demo data, etc to inform propensity modeling and a/b marketing and product testing strategies. Power comes from the synthesis of multiple data points.

There are some traditional methods to segment customers such as RFM, which categorizes customers based on Frequency, Recency, and Monetary value. But in more practical and modern segmentations, nowadays, businesses segment their customers according to their predicted purchase due date, purchase preferences, etc. using ML and big data. They try to predict their customers' behavior and customize the product and relationship based on their requirements and predicted retention cycle.

Adding a product/ SKU/ category preference centre for your customer to explicitly add preferences for products along with the preferred purchase frequency, mode of communication and subscriptions can add depth to the purchase data. It allows for more personalisation and creates opportunities for recommendations and cross-selling.

Segmenting customers by purchase history should not be done in a vacuum, there is a need to use a limited number of other factors to consider. Recency, frequency, monetary value, product adoption, customer loyalty scores and value realization %. In addition to all of these, it is important to understand customers vision and end objective, so that the future offers are targeted towards the value and as step closer towards realizing their end goal.

Trend analysis of purchase history and value is best. How many times in a month does the customer patronize, and how much cumulatively was spent in a month? Segmentation can be based on this criteria to set priorities.

Segmenting customers by purchase history is a strategic process that involves dividing your customer base into distinct groups based on their past buying behaviors. To do this, begin by collecting and organizing comprehensive transactional data, including details on what, when, and how much customers have purchased. Define your segmentation criteria, such as recency, frequency, and monetary value, and then use analytical techniques like clustering or RFM analysis to group customers into segments with similar purchase patterns. Once segments are defined, assign labels and create customer profiles for each group.

Segmenting customers by purchase history, gives us a clear indication of there purchase preferences, and interests. Which helps in precision targeting, You can target right products at the right time to right audience for better conversion and ROI.

It gives you a clear image to ROI, KPI, Customer behaviour and also improves your strategy either marketing or customer base.

Firstly, it enables highly personalized marketing strategies, allowing companies to tailor their messages and product recommendations to individual customer preferences based on their past buying behaviors. This personalization leads to increased customer engagement, higher conversion rates, and ultimately, greater sales revenue. Secondly, segmentation aids in customer retention and loyalty efforts by recognizing and rewarding loyal customers, thereby reducing churn. It also uncovers cross-selling and upselling opportunities, optimizing revenue generation. Additionally, by understanding purchase history, businesses can efficiently manage inventory and allocate resources more effectively, resulting in cost savings.

Not all customers should be treated the same. Purchase behaviour goes a long way in understanding what motivates a customer to buy. If you understand their motivations on a more personal level, you can tailor your offers to them to improve conversion rates.

In my opinion, the biggest challenge in segmenting by purchase history, is that past behavior by itself isn’t necessarily a reflection of future desire. It’s more important and useful to also capture the “why” of customers’ past purchases to understand their motivations, ask them about their satisfaction with it, and also their desires for future purchases. These added together help provide insight into customers’ relationship with us.

It’s quite challenging, considering the fact that few individuals might want varieties of products at the same time. But when the CRM system is on track with vital details of each customer is helps in solving the problem

Purchase history is not something constant. It changes over time and by changes in the customer's life. It is affected by a lot of events and guessing a precise behavior and communicating EXACTLY what the customers want is almost impossible. The segmentation's confidence level is not highly reliable

A critical challenge is to navigate the privacy concerns for the customers buying from you. It is critical have your customer understand the scope of data collection usage and obtain explicit consent for the same. This helps with your customer trusting you and being more receptive to the subsequent communications. Another challenge is to have the real-time purchase and customer service data to stay updated with what is the current relationship status with a given customer. This is extremely important for certain industries like banking and healthcare to develop the customer relationship, their faith and drive adoption.

There is a balancing act between clever personalisation and making the customer uncomfortable with just how much you know about them. Bare this in mind when choosing how explicit your personalisation is.

One thing to implement in segmenting your customers, should be their needs. Categorize them according to their needs. If you can specify each customer by their needs, it helps in keeping track of what’s to offer.

Good article emphasizing the importance of segmentation. This not only applies to purchase history, but overall customer profile. Today is a world of personalization, and segmentation is the key to personalize effectively.

One vital aspect to highlight when we delve into customer segmentation based on purchase history is the potential for inadvertent bias. Bias can sneak in during data collection, interpretation, or even during model training. For instance, if a segment is predominantly based on high spenders from a particular demographic, it may inadvertently exclude others and lead to missed opportunities or unintentionally skewed personalization. What's helped me prevent bias is: -Drawing from diverse data sources -Regularly re-evaluating segmentation criteria -Using clear, transparent models -Valuing customer feedback immensely Bias-free segmentation isn’t just ethical, it’s good for business. Let’s ensure personalization benefits all customers.

Segmenting customers based on purchase history improves CX personalization by identifying high-value customers, offering tailored recommendations, running targeted promotions, and providing customized communication, leading to stronger customer relationships and business growth.