Now is the time to understand your customers

As brands, this means that understanding our digital customers has a greater level of importance and so it’s vital our databases are capable of housing this information and our processes are set up to analyse it effectively.

The here and now

Now is the time to prioritise areas of investment that we tend to put off – specifically database strategy and customer analytics.

Let’s take a moment to think about our databases. We all have one. As marketers, they are our most valuable resource. They tell us everything we know about our customers, who they are, how they behave and what their transaction history looks like.

Kept well, this information is gold dust and allows us to segment and target our customers with information that is most relevant to their behaviours and preferences. Kept badly and our marketing activity can be a bit of a lottery. This is why it’s important to have a strategy to answer vital questions such as:

● What data do we need to collect from our customers?

● How and when should we go about collecting it?

● How should we store this information?

● How do we link this to other data sources, e.g. web analytics

Part of an advanced data strategy is the consideration of how you use and analyse your customer data. We should all have basic customer insight at the tip of our tongues, for example:

● Who is our customer?

● How do they behave online?

● What constitutes high value?

● What is their average lifetime value

If you’re struggling to answer any of these, using this time wisely and getting under the skin of your customer data will empower your campaigns for when things do start getting stronger.

The future

There’s no doubt about it, customer behaviour is going to change over the next few months and even the best of predictive models may struggle to pick up what that change might look like.

What about when things pick up again and some of our old habits kick back in? This is where predictive modelling will have a role to play.

Let’s think about the post-lockdown world and consider your first marketing campaign. Which customers are most likely to make a purchase? Who will pick up from where they left off as if nothing happened? Who might need a bit more encouragement? These are all questions that can be answered using a predictive model, built around your data.

Pattern recognition techniques can analyse purchase histories (and sets of customers that haven’t purchased for some time) to make projections on future purchase behaviour at a customer level. These models can be complex in their mathematical computation and therefore often take several weeks to build and validate – so starting now will ensure you are ready for the post-lockdown world.

Of course, these models are only as good as the information you feed them – good models are underpinned by strong datasets, so tying this into your data strategy is equally as vital.

The current situation means that consumer behaviour is moving faster than ever and this will have significant implications on the data in your CRMs. Of course, every bit of this data represents a person - and it’s the collective behaviour of these people that will ultimately determine your success.

Today may be an opportune time to focus on your data strategy in order to deliver business performance tomorrow.

Jeremy Horne, head of data science, RocketMill