‘AI-driven’ and ‘Hyper-personalisation’ are key terms that are surrounding the retail industry now. Businesses are using data to provide customers with their individual, tailored experiences.
In 2023, it has become a top priority for retailers to improve and enhance their customer engagement and experience.
Let’s delve into the five types of data that retailers need for customer segmentation, making it one of the key factors for personalisation. We will also look at the common mistakes that retailers make when using data for customer segmentation, such as making “Incorrect Assumptions”.
What kind of data do retailers need for customer segmentation?
Retail businesses generate lots of data through their operations and marketing campaigns, and that can be used for segmentation. Data that any retailer would need can be classified into five themes. I call them DIGITS.
- Demographics & Behavioural Data– This provides a basic understanding of customers’ characteristics, such as age, gender, income, education, marital status, and occupation. It captures information such as buying history, frequency of visits, average order value, and category preferences.
- Interaction & Click-Stream Data– Generated online, this captures the event clicks, behaviour on the website, cart additions, wishlisting, social media interactions, engagement levels, and email opening rates.
- Geolocation Data– Location-based information such as city, state, or ZIP code etc.
- Transactional Data– Data that relates to specific transactions, including product SKU, date, time, location, and amount spent.
- Service and Customer Support Data– Information related to a customer’s complaints, NPS scores, and reviews. This provides direct insight into a customer’s post-purchase satisfaction levels.
What must retailers do with the segmented data?
The most important use case of segmented data isPERSONALISATION. Whether it is a campaign, an offer, or a landing page, all of them can be personalised for each customer by combining and analysing these various data types.
Retailers can also identify distinct customer segments based on shared characteristics, needs, and behaviours that allow for targeted marketing strategies, and improved customer experiences.
How can one use behavioural data and segments to identify newer trends?
Behavioural data sets capture customers’ actual behaviour such as visit frequency, purchase patterns, time spent, categories evaluated, preferences, and website analytics. When this data is combined with other segments, it unveils newer trends and patterns. One easy way of spotting trends is to first have extensive behavioural data segmented by demographics, geolocation, purchase patterns, or any other relevant criteria. Next, look for changes such as changes in purchase behaviour, shifts in engagement patterns, emerging preferences, or any other significant changes.
This analysis will help in spotting potential trends that may indicate a larger shift in consumer behaviour in the near future. Customer behaviour is constantly evolving, and trends are very dynamic by nature. They can surprise us if we are not continuously analysing the behaviour data, refining the segments, and monitoring emerging patterns and trends over time.
What are some common mistakes to avoid when implementing customer segmentation?
While customer segmentation can help businesses significantly in personalisation, trend spotting, and rethinking the product offerings among other benefits, it may also be a spoilsport if not implemented properly. There are five common mistakes (Insufficient Data, Poor Quality of Data, Generalisation, Incorrect Assumptions and Lack of KPIs and Objectives) that one should avoid to ensure that the segmentation is effective.
The first two mistakes are around the data itself, most businesses either start with “Insufficient Data” or “Poor Quality of Data”. Data-based segmentation follows the GIGO concept, “Garbage In, Garbage Out”. Hence it is important to be cognisant of the quality as well as the quantity of the data. By avoiding these two mistakes, we can enhance the accuracy of the segments.
The next two mistakes rise out of our inability to think and process the data for segmentation correctly. We tend to either “Generalise” the customers based on a small set of data or single characteristics or go with “Incorrect Assumptions” while neglecting qualitative insights or customer dynamics. By avoiding the above two mistakes, we can enhance the relevance and value of our customer segments.
And finally, a very important point that gets overlooked while implementing segmentation is to clearly define the outcome one is expecting through segmentation and what KPIs will define the success of the implementation. The “Lack of KPIs and Objectives” can lead to haphazard or irrelevant segmentation efforts.
What are some of the key topics which you are keen to listen to during eTail Asia?
I would be keen to listen and learn from other brands on what are they doing for hyper-personalisation and how are they measuring the impact of their activities.
I am also looking forward to joining the sessions on Omnichannel to understand how retailers are blending in-store and digital experiences to transform customer journeys.
And why must people not miss eTail Asia this year?
eTail Asia is a great platform to connect with industry peers, gain valuable insights, and stay ahead in the rapidly evolving e-commerce space. Since it showcases the latest trends, innovations, and technologies that are shaping the future of retail, it is a great opportunity to become better at digital retail.
People who wish to listen to and learn from top-notch speakers, gain valuable knowledge about emerging trends, and gain some valuable inspiration, practical tips and lessons that they can apply in their businesses must not miss this event.
What are your thoughts on this article? Share in the comments below.
If you liked this article, then please consider sharing it with your friends. Also, don’t forget to follow me onTwitterandLinkedIn.
तमसो मा ज्योतिर्गमय:
