Customer 360 provides a single consistent view of customer data, bringing together insight from across your business. Finance might assume one of your business customers is a late payer, but then logistics uncovers blunders in your last three deliveries. Surprisingly, it turns out that that same troublesome customer is also a vendor that provides important marketing services to your business. Customer 360 reveals the full story.
Artificial Intelligence (AI) is having a profound effect on Customer 360. It’s now not just about understanding what customers do, it’s about understanding why they do it. Imagine the generation of hyper-personalised services, intelligent sentiment driven customer service and enhanced predictive fraud detection. All of this is possible with AI driven Customer 360.
At first glance, delivering a Customer 360 initiative seems straightforward enough – gather up customer data across the business, add some exciting external data sources, fire up your data integration, data virtualisation and MDM technology stack and consolidate everything. What could go wrong?
The answer is rather a lot.
A Gartner report in 2022 reported that only 14% of organisations have achieved Customer 360 success. And Standish Group’s last CHAOS report reminds us that 66% of IT projects end in partial or total failure. AI driven Customer 360 initiatives are as likely to run into problems as any other project. Not quite the experience we were hoping for…
So what typically goes wrong and how can you avoid making the same mistakes? Here are some key data management steps to consider:
- Right to left. Not left to right: there is a temptation to start with the question ‘what data do we have?’ rather than ‘what data do we need?’ Customer 360 needs to be begin with the end in mind. This means its goals must be aligned with your organisational goals. By first determining the outcomes you want, it is much easier to figure out the data sources you need. You might not need many data sources at the beginning and some may not be needed at all.
- Deliver incrementally: whilst Customer 360 can enhance decision making, customer engagement, fraud detection and others, it does not mean you should deliver all of these use cases at once. Instead, develop your vision, plan your journey and deliver a use case at a time. Each iteration should deliver concrete value, building on previous iterations and providing agility to change course as necessary.
- Develop a solid foundation: implementing a Customer 360 typically uncovers many wider data management questions which cannot easily be answered. For example, do we have the data we need? Are we allowed to use the data for new purposes? How can we identify or match the same customer? Do we define customer consistently? What do we do with missing, duplicate, inaccurate or otherwise poor quality data?
The role of solid data management and data governance can’t be understated. Customer 360 programmes require changes to policies, roles, responsibilities, standards, processes and rules for how data is used and managed. This is a big undertaking for the vast majority of organisations and the journey for each is unique.
That’s not to say you have to have finished this journey to embark on a Customer 360 initiative. In fact, the momentum around a Customer 360 initiative is the perfect opportunity to introduce the necessary data governance elements at the same time.
The business benefits AI driven Customer 360 can bring are undisputed – but the right data management and governance approach has to be in place to make it fly. And when it is, the sky’s the limit.
If you would like to discuss how to take your Customer 360 and data governance to the next level, please contact [email protected]