The big 5 for structural value creation with data & analytics

Read below about our big 5 for structural value creation with data and analytics.

1. Form multidisciplinary teams

Multidisciplinary teams consist of several specialists with their own expertise working toward one common goal.

If you want to be successful with data and analytics, you need to set up teams that include the various areas of expertise across the data and value chain. We call this the 4Ds: data, decisions, design & delivery. Roles to think of then are data engineers, marketers and data scientists, designers and developers, respectively. Give them a mandate and make them jointly responsible for the realization of the improvement initiatives and their results.

2. Think big, start small, scale up

The world around us is changing rapidly. So do our customers. Therefore, we can no longer afford to spend months working on an improvement initiative that we launch all at once. We have to move toward partial deliverables that we continuously develop.

This means we need to design and deliver many more Minimum Viable Products (MVPs). Propositions that we offer our customers that provide them with a better experience than the current situation. The starting point is no longer a 100% perfect solution, but an improvement for today. Quite a change in the mindset of specialists who are used to going for the best possible solution.

3. Make proper use of customer input

If you truly work "customer centric," then customer feedback is the only truth for your team.

Therefore, test your MVPs with the customer as soon as possible and gather his input. Preferably based on his behavior, but research can also be an excellent first step. Make sure that you can test different versions and develop them further. Again, what is the customer's current situation? What does he see, hear or read now in our interactions and how do we measure improvement?

4. Go truly data driven

Working data driven means using facts as the starting point of all issues and as an (intermediate) measurement point of all outputs to make improvements.

This involves numerical substantiation so that there can be no discussion about utility and need, reason, adjustment and success or go-/no-go decisions. Make sure you can substantiate your efforts, build in measurement points and moments and define when you are successful and when you are not. And don't forget to define the conditions to scale up success further.

5. Provide structural and scalable solutions

Take your data-driven improvement initiatives out of the experimental realm and push the professionals working on them to deliver scalable solutions.

This means that successful solutions must be able to be rolled out broadly across the organization, delivering sustainable value for customer and business. So tie each initiative to an objective: Why are we doing this? For whom are we doing this? What do we want to achieve? What are the risks? And, when are we successful and what do we need if we want to deliver this structurally?

Turning CX insights into improvements with the Business Accelerator

As a data-driven marketer, your ambition should be to structurally turn CX insights into improvements and thereby create value for your customers. Do you want to be helped with this? We have developed the Business Accelerator. It allows you to create immediate value for your customer within 3 months and at the same time provides you with a foundation to move forward:

Improved customer journey, which you can continuously improve based on a dashboard.
Data driven multidisciplinary team, able to convert data into value for customers faster and structurally.
Scorecard, showing the most important development points for the organization based on customer and employee experiences.