Consumers today are largely in control of the marketing messages they receive, able to tune out of anything that doesn’t interest them, whenever they like. Their inboxes are overloaded with unwanted emails – so they unsubscribe. Their phones buzz with irrelevant text messages – so they opt out.
To convince customers to tune in, marketers must engage them with personalised, relevant messages. In fact, most consumers now expect tailored comms – research by IBM found that 75% of customers “expect organisations to understand their individual needs”.
To meet these expectations, you need customer data. ‘Big data’ in particular is a favourite buzzword at the moment, referring to the analysis of extremely large (and often unstructured) data sets.
The problem with ‘big data’ is that it’s dangerously easy to get lost in it if you don’t know what you’re looking for. As marketers gain access to increasing amounts (and types) of data, there can be a temptation to gather every piece of customer data possible without really knowing why you need it.
It’s not really the size of the data that matters, but rather the insight you can draw from it. Modern marketers need to go beyond ‘big data’ and look for actionable insight.
So what kind of insight do you need? And how do you find it?
Data or insight… or both?
It might help to start by defining what we mean by insight – or rather how ‘insight-driven marketing’ differs from ‘data-driven marketing’. It’s not a case of one or the other, because they’re not actually opposing ideas.
Data-driven marketing is the next step from a simple batch-and-blast approach to emails (or SMS messages, or app notifications). Data-driven marketers go beyond email addresses and demographic information, pulling together things like customers’ location, purchase history and interests into a single system and using it to start targeting and segmenting marketing messages.
Data tells you the basics of who your customers are and what they’re doing – purchases, visits, clicks – the facts. When you pull this data together and start looking at what it really means for your marketing strategy – that’s insight.
The value of customer insight is that it helps you answer the most important questions, such as:
- Who are my best customers? Which ones are the most…
- Where are my customers at in their lifecycle? Which ones am I at risk of losing?
- What kinds of campaigns and offers will appeal to the types of customers I want to target?
- Who can I cross-sell or up-sell to?
Insight-driven marketing means pulling information like this from your customer data, and using it to make more informed marketing decisions.
For example, let’s say you’ve identified a group of customers who are at high risk of churn, and you want to develop a retention strategy. Knowing which of these customers are the most valuable (the ones you really need to win back) can help you decide where to focus your efforts. It’s also helpful to know which customers are leaving with bad feelings – and a big social media following to share them with.
Customer insight can also help you identify areas that need attention. For example, by comparing your most loyal customers to those who are most valuable, you might find that your loyal fans aren’t actually spending that much money. Based on this information, you might adjust your marketing strategy to focus on cross-selling or upselling to these lower-value customers.
By focusing your collection and analysis efforts on the right types of data, you can draw out actionable insights that will help guide your marketing strategy – allowing you to adapt your approach to suit the needs of your customers.
Identifying the questions you’re trying to answer first will help you determine what data you need to collect, even if you’re not quite ready to start implementing an insight-driven approach.
It’s important to recognise that a data-driven marketing strategy isn’t the end goal, it’s merely the first step. Aiming for insight rather than data will help you focus your efforts and start collecting the data you need to develop an insight-driven approach.