Four-Wheel Data Drive

Big data is on the agenda of most corporations and represents an opportunity to learn more and do more—to get better, you might say.

Consider this scenario from the customer of a recently purchased four-wheel drive vehicle:

“As a family, we have been experiencing difficulties with an 18-month-old car we had from new—a dashboard notice telling us the car will stop working after so many more miles until we have the fault resolved.

“This has happened at least four times since new—twice in the last three months—and each time the dealer has resolved the issue. In between these irritations, we have been looking at one of their new models due for release early next year. However, these recent quality issues have given us pause for thought

While the sales team is exemplary, their performance is not matched by the quality of the product.”

This is a real-life example where a consolidated view of the customer journey would be of real advantage to one OEM. Unfortunately, many enterprises are still reliant on legacy systems for data visibility.

The technology marketplace is awash with systems that can collect, analyse and re-model data in real time, enabling business to move at real pace. This newfound agility through data analytics is enabling businesses to respond to customers and competitors so much faster than ever before.

There are four styles of data analytics:

Descriptive – What has happened?
Diagnostic – Why has it happened?
Predictive – What’s going to happen?
Prescriptive – What should I do?

If these four wheels were turning to the advantage of the OEM, it would be possible to gain a complete view of the customer experience and decide on the appropriate contact points to ensure the customer is satisfied and engaged.

And yet, it is uncommon for established businesses to have a single platform that handles all their data. It is more the case that multiple legacy systems hold data that, in many instances, are interconnected and which cannot easily pool data.

Automotive supply chains are facing a massive revolution—find out what this could mean for your enterprise.

The importance of data pooling

Pooling data or creating a data lake provides for real deep dives so that data can be turned into meaningful information upon which sound decisions can be made.

The experiences of the unfortunate four wheel drive owner prompts one to ask if the manufacturer has visibility of the entire relationship with the customer—particularly as the customer is experiencing numerous quality issues that will detract from propensity to buy again.

Can your organisation see across relationship systems or have you got some considerable blind spots?

Can you tie the data together to identify current or potential issues and start protecting a future relationship/revenue?

Avoiding the blind spot can be a worthwhile investment in protecting brand, reputation and a steady stream of repeat customers. To do that, all four wheels have to be synchronised and rolling.