3 paths to deeper insight
Making the most of your customer data
So you’ve got plenty of customer data – what now? Analytics helps you realise the value of your data by developing real insight. Who are your customers? What defines them? How do they behave? It’s about going beyond the surface and finding out what your customer data really means.
There’s no single data analytics method that works for everyone – the technique you choose depends on your goals and priorities in the short and long term.
Here’s a look at three key analytics techniques, and how they can help build understanding in your business.
Customer knowledge discovery
Knowledge discovery, sometimes called data mining, is analytics at its most fundamental. The three-stage process goes from surface-level discovery and visualisation to profiling groups and analysing trends over time.
Knowledge discovery starts with goal-setting and information-gathering. What do you want to know about your customers? Where will the data come from? How will it be used? The clearer your goals, the easier it will be to refine your process and get the answers you need. You may need to access datasets from different parts of the business, including email lists, CRM data, sales information, social media analytics and anything else you have to hand.
After setting goals, there are generally three stages of discovery:
- Basic analysis: this involves trawling through your data and pulling out surface-level insights. However, while the information may be accessible, that doesn’t mean it’s not valuable – particularly if you haven’t done this type of analysis before. Number of unique customers, number of new customers during a set period, demographic information like age and income bracket, geographic location – all this information is valuable when it comes to your marketing.
- Profiling: the next stage involves pulling out specific groups – for example, new customers, high-value customers, active and inactive – and finding out what makes them different. What do your new customers have in common? Are active customers more likely to live in a certain area or fit into a specific demographic? This level of analysis takes you under the surface, helping you develop real insight into what’s driving customer behaviour.
- Trend-spotting: this looks at patterns over time, rather than within groups. This could include examining seasonal sales trends year-on-year, looking at time between sales, analysing the length of the customer journey from the first interaction to conversion and even studying the time leading up to a customer loss to find out more about churn.
Segmentation is a key part of your analytics process. Segmenting involves dividing your base group of customers into smaller groups for deeper insight.
Customers can be segmented in multiple ways based on age, gender, location, income bracket, spending habits and even interests and preferences if you have that data on hand. You can also choose to build two-factor segments: for example, you might combine age and income data. The key is grouping customers into a manageable number of groups – too many groups will be difficult to analyse and target effectively.
When your customers sit in defined groups, it’s easier to reach them with targeted marketing. For example, behaviour segmentation could be used to reach out to customers after a set inactive period – potentially preventing turnover.
Segmentation can also help you maximise your marketing budget. Rather than spending time and energy targeting low-value customers, you can focus your efforts on the bigger spenders.
Churn analysis and predictive modelling
Churn happens when a customer stops interacting with your business for some reason. Churn is multi-faceted, and can happen because of poor service or product quality, or outside factors such as changing circumstances in a customer’s life. While some level of churn is inevitable, some is preventable with the right analysis and quick action.
Churn analysis looks at your customer data with turnover in mind. It can be quite complex to examine customer behaviour over time and analyse that information against particular segments. With churn modelling, you can spot signs that a customer is likely to churn and target them before that happens. This type of predictive model isn’t just valuable during the analytics phase – it can be used as part of your ongoing marketing efforts, helping you reduce churn and boost retention.
From analytics to insight
Customer data analytics comes in many forms. In most cases, getting the best insight means using several analytics methods in tandem. Basic analysis gives you the scope of your audience along with basic information, while deeper insight can come from segmentation, churn analysis and profiling. It’s about using the information you already have to build real insight about your customers, which then flows through into super-targeted marketing and better business decisions.