Customer Analytics 101

Each interaction a customer has with your brand, from initial awareness to repeat purchases offers valuable insights into their needs, behaviours, and potential pain points. But understanding this granular data at scale is a major challenge for most businesses. In this article we take a look through key data initiatives that can help.

Mapping Your Customer Journey

With every client, we start with the customer journey. Why? Because this helps us to understand the different departments, touchpoints and tools which are used within that journey. Mapping out every interaction and eventuality can be something of a minefield, and so it is best to keep things simple. We typically divide the journey into three sections. Acquisition, everything that happens for that customer to discover your brand, Conversion everything that happens in them becoming your customer and finally Lifetime Value is everything that goes into retaining, upselling and growing their loyalty.

Start by defining key milestones that reflect significant actions or decisions made by the customer, such as signing up, making a first purchase, or returning for repeat business. It is essential to view this journey from the customer’s perspective, focusing on their motivations, pain points, and expectations at each stage. This structured journey map serves as a foundation to first understand which data sources need to be integrated and centralised.

Centralising Data Sources

To maximise the effectiveness of your customer journey mapping, it is essential to extract and centralise data from all customer touchpoints. Bringing together insights from marketing, sales, customer support, and external feedback channels into a unified system provides a comprehensive view of each customer’s interactions and needs. With all data sources in one place, you can ensure consistency in how you track and manage each stage of the journey, minimising silos and reducing the risk of misaligned communications.

At 173tech we always recommend however working iteratively. So start with the data source that covers the largest area of your journey (typically a CRM or marketing tool) and then build out. No doubt you may already have some form of reporting or dashboard within your CRM, and it might seem counterintuitive to extract this, but the reason you do this is to create that one source of the truth later by overlapping data from different sources.

Customer Life Stages

When plotting out your customer journey, it is important that each life stage 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 causes confusion more than anything else. It is better to have five stages vs fifteen, 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 eliminate 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 and you should not design your journey to accommodate the few.

Once you start using data to automatically apply life stages, 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 simple yet powerful way to organise life stages within your database or CRM. By tagging customers according to their current stage, you make it easier to track progression through the customer journey. This tagging is especially valuable for segmentation, which involves grouping customers based on shared characteristics to better understand and serve their needs on a large scale.

Effective segmentation should prioritise meaningful differences that guide tailored messaging strategies. For example, rather than segmenting by basic demographics like gender, a restaurant might focus on grouping customers as family diners versus solo diners— two segments that require distinct messaging and offers.

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

Once segments are established based on your brand messages, you can further cohort your customers to create dynamic groupings based on factors like value, activity level, or likelihood to churn.

Using our restaurant example again. You have different messages for solo diners vs family diners. Perhaps solo diners are more likely to want a quick, cheap meal at a higher frequency. Within the solo-diner segment, you would have a cohort of these customers at risk of churning. Maybe they haven't visited your restaurant in one month. As such you understand what to offer these customers (a discount will likely appeal as they are more cost-conscious) and who to offer it to.

Cohort analysis allows for a deeper understanding of the diverse needs and behaviours within each segment, enabling more personalised marketing and retention strategies. By leveraging data modelling, these cohorts become fluid and responsive. Instead of relying on static weekly or monthly reports, you gain near real-time insights into customer actions and trends. Customers are automatically reclassified from one activity cohort to another as their engagement with your brand fluctuates, giving you an up-to-date, actionable view of customer behavior.

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 insights into each customer’s lifetime value, you can easily identify where to focus your time and resources for the most impactful interventions.

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. Quite often there will be a fuzzy match between customers who leave a negative review for instance, and customers who recently contacted support with a problem.

Conclusion

To effectively acquire, retain, and expand your customer base, it’s essential to recognise and adapt to the evolving nature of your relationships with customers. Achieving this requires a comprehensive approach that integrates data from multiple touchpoints, leverages ongoing segmentation and cohort analysis, and incorporates predictive analytics.

By continually deepening your understanding of customer behaviour and preferences, you can fine-tune your strategies to align with their needs, ultimately building stronger connections and driving long-term growth.

For more insights into optimising customer analytics, feel free to connect with a knowledgeable member of the 173tech team.