Data-driven marketing: do it!

Is it complicated?

When you talk to companies about customer data, there are usually two reactions: Representatives of large companies roll their eyes because different departments sit on different, poorly networked data silos and their hands are tied in using the data. Smaller and medium-sized companies, on the other hand, often have reservations. Yet SMEs in particular are all about efficiency.

While marketing continues to rely on expensive incentives and reach, it fails to recognize the growing importance of an alternative: relevance. This means nothing more than sending the right message to the right person at the right time, in the right way. However, with hundreds and thousands of customers, this only works in a data-driven and automated way. 

This is precisely where SMEs can demonstrate their agility. Where the big players struggle with their data silos and dependencies, SMEs often have optimization options open to them with their existing infrastructure. Therefore: Get rid of your fear of contact - and in five steps to your first attempt!

1. know what you can

Companies often don't know what they can already do. For example, many send out e-newsletters or have a web store. This means that they have data and can measure. These are good prerequisites for initial measures. These can initially also include an optimization of the existing and do not have to reinvent the world (yet).

Tip: Even if the infrastructure is not what you want it to be: If possible, start with it anyway. This is the best way to find out what you actually need and how you want to develop. 

2. know what they want

The beauty of data-driven marketing and marketing automation is: You have to think carefully about what you want - and what you don't want. Ask yourself which customer groups you want to reach what with: Those customers who mostly buy online should download the new app? The new customers should make a follow-up purchase as soon as possible? The existing customers should renew their subscription? Assess possible combinations and also define what they do NOT want to do. This also creates clarity. 

Tip: It is best to analyze existing behavioral data. This prevents speculation and puts you on the right path: When is the probability of a follow-up purchase highest? What interests do certain customer segments have? But don't analyze too much, or it will get too confusing.

3. where the magic happens

You now know what you can do and what you want. Then do it. But: Take it one step at a time and learn as you go. Start with simple, clear segments with high potential. For example, distinguish new customers from existing customers. Or active from inactive customers. Don't stop at a single contact, but for each customer response to your message, think about what could happen next. Design purposeful customer journeys. But don't overdo it: observe, learn, and then refine.

Tip: Customer behavior in a situation or reaction to a message is usually more meaningful than demographic data such as age and gender. Build on that - let your customers guide you.

4. reality check

Now it turns out whether you did everything right before or still need to correct a bit: Is everything implementable like this? If not: What is needed - or are there alternatives? Pay attention to the methodology and set everything up so that it is as measurable as possible. Because that's the only way you can improve (and communicate the good numbers). So when designing the customer journeys, think about reporting as well. What should be tested and how? When will you next revise or refine your measure? Watch out for overlaps with external (e.g. seasonal) events or other planned activities.

Tip: Take this step very consciously. Because once the customer journey is up and running, it's up and running.

5. go!

You know who you want to play what, when, why, and how. Now tailor the content to the exact purpose. What information does the recipient need? Which formats are suitable for the selected channels? And then let the machine do its job for the time being. Until you have enough data records together so that you can make initial statements about results. This varies greatly depending on the number of customers and the complexity of the processing route. Be aware: You will only learn with time. Use this time to optimize your processing routes.

Tip: Content can often be reused. It is therefore worth combing through what already exists and deliberately preparing content so that it can be reused.