A guide to data centricity: 4 steps to get started
According to the latest report of the CMO Council, a high number of marketers (43 percent) say that it’s not the lack of data, but rather the absence of the ability to transform this data into real-time action, that’s holding them back in implementing their company’s strategy. It’s a fact, data is all around: its volume is continuously doubling every three years, out of new sources every day: virtual-reality tools, digital media, sensors, and of course billions of mobile phones. Nevertheless starting to work with this data and transforming it into knowledge and strategic input is something else…
Yet the perks of using your data to gain understandings on your clients are clear: companies who use customer behavioural insights outperform their peers by 85 percent in sales growth and over 25 percent in gross margin. Becoming a data- and customer-driven company is key if you want to be able to compete in the age of the customer.
So how do you get started?
1) Get an overview of the data you have available
What is in there? What is not? Is it complete and correct? How is it defined? Is everything documented? What is the source? Can there be typing errors or are you sure it is 100% correct?
2) Investigate linking possibilities
Next, you want to make sure there is a possibility to link all your sources. Do they have a similar primary key? Do the variables have the same format if in different tables? What about missing values? These first investigations might seem obsolete to you, but are of great importance if your ambition is to start doing real analytics and building models on this data. As we all know: garbage in = garbage out, and this is something that is very true for data models. Once the base is ok, you can start concluding on your data:
3) Descriptive statistics
Once your data is cleaned, you can get an overview of who your customers really are: in which age range do most of your clients fall? Where do they live? What’s their household composition? Quite quickly you can already have some valuable insights.
4) Evaluate data maturity and determine the next step
In a next phase, it’s time to evaluate your data maturity and link it with your company’s strategy. If your strategy is to have a customer retention rate of 95%, then building a churn model (predicting which customers are most likely to leave) could help you a lot with reaching that goal. However, if you don’t capture certain basic needed input, such as which products a client has, what’s their start and end date, or if this data is not correct, the outcome of your churn model would not be valid, and incomplete as a base for your actions.
Seems a bit overwhelming? In the whole of this process, 4C can be of help. We dive into your data, summarize our findings and give you advice on what to capture (better) and how to resolve anomalies. Next we can offer you a first tailormade model. Interested in churn, rather a segmentation or an up-sell model? We discuss with you what would suit your needs best. Finally, we give you advice on what could be valuable next steps in reaching the state of a customer company. Got interested? Get in touch, and we can discuss how we can assist you!