In my blog series I highlight new functions in modern marketing. This time: The Data Engineer. A technical person with a love for numbers, but more importantly, an analyst who knows the ropes in the relevant branches. A person that knows when to press which button. It’s not a trick. It requires in-depth insight into the market and the decision-making processes. Because each case is different, the Data Engineer has to be involved in the development of a marketing campaign at an early stage and will continue to be involved throughout the process.
Initially, the focus is on the analysis of the target group. A wish list is drawn up and desired variables must be found. For example, you might want to have a list of prospects – who are doing renovations soon – but there is usually no database with this type of specific information. You will need to be creative in analysing and combining existing information. Because no matter what, without a well-stocked database, success is not easy. It’s all about the numbers. The target group, besides relevant, must be large enough. An expensive campaign for about 78 prospects will, as a rule, not lead to a positive business case.
Instead of defining and determining the target group, the Data Engineer ensures that measurable goals are set. Measurability is crucial in determining what is and what is not successful. At each stage of a campaign, results and behaviour are measured and – if necessary – the campaign is adjusted. This is not a one-off action. It’s a continuous process. (see infographic)
In the construction and execution of a campaign every marketer is an analyst. An A/B test whether an orange button converts better than a green one is performed. Or a small adjustment to a subjectline is made to improve the open ratio of an email. These are small pragmatic adjustments that make or break a campaign. One thing is for sure, if you don’t analyse anything at this stage, your chance of success is significantly smaller.
Once the campaign is running, the Data Engineer combines the data streams. Various sources such as organic, email, social, ad serving, etc. are taken into account. These data streams need to be converted. So they can all be interpreted the same way. Converting and loading data in reports and dashboards can be very useful. The sooner the analyst is involved in the marketing campaign, the easier he can automate the data streams. The insights than can be used to advise on improvements. That’s the added value!
Beautiful tools to process data automated are available today. They help you visualising data, providing deeper insights and discovering things that were invisible to the naked eye at first. Finding special segments or particular behaviours in the target audiences, as well as the analysation of sentiments, are among the possibilities. It helps us getting to know the target audience better with a bigger chance of success.
If you want to do some data engineering yourself, start with Google Data Studio. A free tool that uses data streams from Google Analytics, Youtube and Adwords. You easily create graphs and bring data from different sources into one dashboard. Information gathered offline or seperate Excel sheets are no problem. They can be added manually. Even SQL databases and Google Sheets are easily integrated.
Once you run into Google Data Studio’s limitations, have a look at Microsoft Power BI; a good alternative for a reasonable price. Or you can choose from the more traditional BI tools like Tableau, Qlik or Microstrategy.
I would like to stress that these tools don’t guarantee immediate success. They provide insights only. How to interprete these insights and turn them into a clear advice is the work of an experienced Data Engineer. It’s an art in itself.