While today, only 5-10% of advanced companies actually use data ahead of sending their email marketing campaigns, email and marketing data is very much the future. Marketing teams are relying more and more on data for the purposes of sending out email marketing campaigns. It is important that marketing teams are aware of exactly how data can be used for their benefit.
Email delivery platforms work by tracking and analysing delivery, open and click rates through data. They track all the meta-data in every email you send, including bounce back rates and unsubscribes. Open and click through data is the most important data for email marketing campaigns, and it is gathered in both of the following ways:
- When images are opened the pixel URL is tracked. Each URL can be made unique and so can be tracked when it is opened.
- Once you click on a link through your email you are automatically redirected to the website. As you are being redirected the information is sent to your email delivery platform before you reach the retailer’s website. Customers don’t normally see this because it takes fractions of a second for that information to be sent.
Although email delivery platforms analyse data, it is important that marketers are analysing this data as well; as it is, we still have a long way to go before a ‘data-driven’ culture is baked into the marketing organisation. Marketers need to start weaving data insights into their marketing strategies. For example, if you’re a daily deal sender and a specific customer doesn’t engage with your email within 60 days, it is best to remove that customer from your email lists to prevent being marked as spam. To do this automatically, you need to build a model to determine engagement frequency, which is one way to bake a data-driven culture into a marketing organisation.
The most enlightening result of data-driven marketing is being able to see the difference between the highest and lowest levels of engagement. It is often wrongly assumed that the most engaged recipients are just 2-3 times more engaged than less engaged recipients; an assumption which often leads to bad sending strategies. In fact, the top 10% of engaged recipients are 100 times more likely to engage with your email marketing campaign than the bottom 10%. By recognising this, marketers will see that sending strategies can’t be ‘one size fits all’, but rather, outliers and extremes are the norm, not the exception. In understanding this, data can have a huge impact.
One challenge being faced is how to determine the difference between a good and bad email marketing sender. Current email providers like Yahoo and Google use algorithms to track email engagement and determine if they are going to deliver the email to the inbox or the spam folder. Instead of fixing the problem by using data to enhance and add value to their email marketing campaigns, many marketers are trying to find loop holes in the algorithms which allow them to fake a good email; meaning, at the moment, it’s really an arms race.
There is still room for intuition and ‘gut-level’ decision making in marketing, but this should remain within a data-driven strategy. For instance, when you send an email to recipients of different levels of engagement, different levels of frequency are required, and this is information which should be driven by data. Email aesthetics such as colours and images should be left to the creative teams, since data won’t solve problems such as these. Therefore, whatever decision marketing teams make based on data, it’s important to remember to hold it up against the light of human reason.
So what practical steps can businesses take to up their marketing game to make their work more efficient?
First, businesses can use semantic analysis to characterise and predict recipient interest in new content: for example based on their engagement, one recipient might show a particular interest in cars.
Secondly they can cluster recipients by their engagement and interest in order to understand the distribution of a company’s customer base, which might involve looking at how many customers are interested in food as opposed to sports.
Lastly they can use trends in their current customer base to target advertising to new customers. For example a company might conclude: “Young males interested in cars make up our fastest growing segment. Let's target our advertising at them.”
For businesses thinking of going it alone, there are three things are important to consider:
- Access to useful data.
- The ability to process data at the necessary volume and speed.
- The ability to understand and drive decisions by using this data. If a business is incapable of working with data in these ways it might need to work with a specialist to fill in the gaps.
For companies that don’t have the in-house capacity, businesses can either hire their own data scientists and big data engineers to work with their marketing department or they can leverage companies that build tools on top of their own data. However, whether choosing to do it in-house or outsourced, the time to get working on a new data-driven marketing strategy is now!