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4 types of data to help you get the most out of advanced segmentation

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According to the most recent Email Industry Census, 25% of email marketers rate their return on investment in this channel as ‘excellent’. But what happens to the other 75%? One factor that could be affecting their results is that they’re not sending the right message to the right customer.

To do that, you need to up your segmentation game. Read on to find out what can help you to get there.

Why should you segment?

If you’re still sending batch and blast emails, with a single message for your entire list, I’m pretty sure you’re not in the top 25% of email marketers. The reason for that is simple. Your customers are at different stages of the buying cycle, so they will only respond to relevant information that aids them in their particular stage. You should apply this to all your segments, from those who have just signed up to a newsletter, right down to those who have items in their basket ready to buy.

Case study: The Gambia Experience used advanced segmentation, and saw an uplift in revenue of 120%.

Separating your top customers from the unengaged ones

Who are your most active, engaged and biggest spenders? The answer to this million-dollar question could come from an RFM model (Recency, Frequency, and Monetary Value). If you cross compare this section of your database with those who are less active and spend less, you can put the value of your subscribers into perspective.

That is not to say that you shouldn’t send emails to subscribers who don’t spend as much with you. It simply means you need to approach them differently.

Types of data to feed into advanced segmentation

To help you understand the different levels of segmentation you could achieve, we’ve summarised it below:

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Each type of data layer allows you to capture more information about your users to create the experience your customers are crying out for.

How should you use it?

Reported data from your website will help you piece together the journey a customer may take starting with the welcome email. Look at these aspects:

  • Do they click on the links in your email?
  • Do they browse their favourite departments?
  • Do they jump to the sale section?
  • Have you asked for more? Putting together a series of progressive profiling automated emails could help you fill in your data gaps.

All of this data can give you an insight into the steps leading to a purchase, or to basket abandonment.

Behavioural data captured from your ESP can show you historical engagement with your emails. Use this to target your most recently active customers with a “loyalty” offer, or to target your competition entrants with a further discount after they’ve converted again.

Behavioural modelled data allows you to make certain assumptions from what you already know. Do you know that people within a certain postcode area tend to go to one particular event? Do you know that certain lifestyles can be grouped into purchase behaviour?

This level of segmentation begins to really take notice of the makeup of your customers, and what makes them tick. It takes it a step further from observing what they do, or don’t do in relation to your emails and website.

Plugging into external sources, like social APIs, takes your segmentation to an even deeper level. This allows you to derive data about a person’s online behaviour based on their interactions. For example:

  • Do they prefer discounts?
  • Are they time constrained?
  • When are they most active?
  • How they like to be communicated with?

If you’re ready to make your email marketing work harder for you, and bring an uplift in ROI, advanced segmentation is the way forward.

One size doesn’t fit all: using smart data for personalization

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