Smart Data, Not Big Data, For Email

One of the great things about email marketing is that there can be a large amount of data available to be used to improve email marketing performance.

However lots of something (data) doesn’t necessarily make it easier; in fact it can make it tricky to know where to start and what data to use.

The term ‘big data’ is out there, but not that useful for email marketers. At Kickdynamic we like to think about smart data, not big data.

With smart data you can find and utilise the data that is going to give you the biggest impact for the entirety of your email marketing list with the least effort – therefore positively impacting your ROI.

We believe the use of smart data is a way to consistently send email messages that are relevant to each recipient – every time they open your email – in every context.

I like to break down data into three categories:

1. Context – behaviour at time of open
2. Previous behaviour
3. User generated preferences

These categories should be used together in different areas of your email strategy. The goal is to make your email timely relevant and contextual for every recipient every time they open your email.

Having divided the data into three categories, it is useful to prioritise them in order of their corresponding impact. Although contextual data is new, the impact it is having on click through rates and revenue per email (RPE) is significant.

1. Context – behavior at time of open

Context is something we are big believers in at Kickdynamic. It is targeting content based on your customer’s environment at the time of – not just at the time of send. This involves utlilising data that has not been used before to make email relevant and timely to every recipient.

All email content can now change – at the time of open – for every recipient based on the following factors, which can also be combined together:

Location:

Update content based on your customer’s location at the time they open email. Your customers are always on the move and your email should update with them. Therefore your content is now based on location giving your customers a rich experience.

Opening environment:

Tailor your content based on the opening environment. You can now update content and change the landing page depending if your customers open on iPhone, laptop or desktop.

Deep linking now allows you to have a rich user experience so that the following doesn’t happen:

There is nothing worse than reading an email on your mobile device, clicking the link and being taken to the mobile or normal site, as opposed to the brand’s app, which you always use.

Timing:

Email is read at different times of the day and week, sales begins and sales end, products are revealed and products run out of stock: so your email content should reflect this.

Combining location, timing, and opening environment gives your email context. It can now be mobile aware giving your customers an amazing experience improving performance and driving revenue.

2. Previous behaviour

Previous behavior involves using a previous action your customer has taken to determine what content you will target them with and when with future email messages. This can include the following:

  • Website browsing behaviour
  • Previous purchase and value to form a RFM segment
  • Email open and click action
  • Email open and click non action

This type of data has been successful in producing uplift in email conversion and engagement; however it can be difficult to implement and you can normally only utilise 30-50% of your database.

3. User generated preferences

These are preferences that a customer selects in either a preference center, survey or via another means that can be used for targeting via segmentation a subset of your subscribers.

Very useful for ‘quick’ segmentation based campaigns that have obvious preference uses. For example, if a subscriber has selected that they are interested in mountain bikes when signing up for an email, then you are able to send them mountain bike specific emails when there is a sale.

The downsides to user generated preferences are:

  • You are only likely to get 10-15% of your database because not all signups will give their preference data
  • It becomes stale; preferences are rarely updated and the impact falls off over time

Using data in a smart way, and not getting overwhelmed by ‘big’ data, can dramatically improve your email content, context drive performance and revenue from email.

What have you seen success with?