Get Closer To Your Customers
Introduction
In the first of our blog series, we discussed how mapping out the customer journey was a great exercise in which to better understand which data sources are the most valuable, key performance indicators across the journey as well as business decisions that data can inform. We also discussed how optimising your advertising was a great first project for data teams after they have set up their data stack and how Lifetime Value is a great metrics to optimise acquisition. With that win under your belt, the next place in which to create value from your data is by building out your customer analytics.
Customer Journey Mapping
When plotting out your customer journey, it is important that each lifestage is distinct from each other and that you have removed any overlaps or subjectivity. Too often, companies are guilty of trying to accommodate every possible permutation of where a customer could be in their journey, and this actually causes more confusion than anything else. It is better to have 5 stages vs 15 and for each of these stages there needs to be a hard rule as to when a customer moves from one stage to another. For example, perhaps you would only classify them as a customer when they have purchased from you and not when they have signed up or created an account.
It is also important to get rid of areas where customers are ‘hot’ or ‘cold’ as these are often open to different interpretations depending on the person. By having a clear set of rules, it becomes much easier to then classify, and tag customers based on these rules. Whilst there will be outliers, customers who may not neatly follow the journey you’ve laid out, they should be the exception to the rule and you should not design your journey to accommodate the few.
Once you start using data to automatically apply lifestages, it becomes much easier to understand your pipeline at scale and see which areas might need to be optimised, ensuring every step is fine-tuned for maximum efficiency and customer satisfaction.
Customer Tagging & Segmentation
Customer tagging is a straightforward method to add your life stages to your database or CRM, and is also useful when considering segmentation. Segmentation is all about categorising customers based on shared characteristics, enabling a deeper understanding of their interests on a large scale.
Effective segmentation should focus on distinct differences from a messaging perspective, so how will you communicate differently to different groups throughout your relationship, and so it’s important to focus on the aspects which will have the biggest impact. For example, a restaurant would not segment its customer by gender, but would be family vs solo eaters, as the messaging to these groups is quite different. Once you have established your segment, you can then track what proportion of your customer base they represent, their average lifetime value and their journey as a whole across your pipeline.
Cohorts Within Segments
With our segments drawn in messaging lines, we can then cohort our users. This might be to bucket our customers in terms of value, activity or likelihood to churn. Cohort analysis helps us to understand the varying needs and behaviours within broader segments, facilitating more personalised marketing and retention strategies. The great thing about doing this through data modelling is that it will be dynamic. Instead of relying on weekly or monthly reporting, you can understand the actions that your customers are taking with minimal latency. Customers will automatically be moved from one activity bucket to another as they interact more/less with your brand.
Predictive Analytics
Once you start to understand your customer behaviours, you can start to predict them. What is predictive analytics? It is a way of looking at the past behaviour of your customers and then giving a percentage score as to how closely they match those people. You may find 4 or 5 different actions taken by customers which signify they will end their subscription with you. As a customer takes these actions, flags can automatically be appended to their profile, giving your customer success or sales teams an early indication that this customer might leave. By combining this with an understanding of their lifetime value, you can easily understand which customers you should spend the time and effort intervening.
Do Not Forget About External Touchpoints
While most of your interaction with customers may be through marketing channels or be captured in your CRM, it is important not to overlook areas such as customer support, which may sit in its own software or silo. If this is not integrated back into your CRM you run the risk of your marketing team trying to send an offer to a customer who recently had a bad experience. By consistently monitoring and responding to customer feedback on review sites, companies can improve their products and services, thereby enhancing customer loyalty and driving growth, but these learnings need to be captured and analysed to find trends.
Conclusion
To successfully acquire, retain, and grow your customer base, it is imperative to understand the evolving nature of your relationship with your customers. This requires a holistic approach that combines data from various touchpoints, ongoing segmentation and cohort analysis, and the integration of predictive analytics. By continuously refining your understanding of customer behaviours and preferences, you can tailor your strategies to meet their needs more effectively, fostering stronger relationships and ensuring sustained business growth. If you want to understand more about customer analytics, be sure to get in touch with a friendly member of the 173tech team.
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