Consumer product companies live by their customers, but their data is often anything but customer-centric. In many cases, there are multiple entities that are the same person, or data is incomplete.
It might be perfectly understandable that someone who bought a car in Spain, but had it serviced while visiting France, and again after a move to Portugal is listed in three different systems with slight variations to the person’s name, but that is still the car brand’s problem to solve if it wants the best possible relationship with the car owner. That simple example doesn’t even introduce data from social media monitoring, web analytics and other ways modern companies learn about their customers.
This data diffusion is a serious stumbling block to creating a customer-centric organization. As much as the culture of the company puts customer service first, only so much of that will be felt by a customer that receives three identical offers, or fails to note a customer has already taken advantage of the offer. But with data locked in multiple systems, it can seem like the best a company could do is try to overcome these missteps with kindness and deference.
Most large, mature companies, despite diligent attempts to consolidate their data for the best possible treatment of their customers, have far-flung data resources that resist valiant efforts to provide a single view of all interactions with a customer. There are regional boundaries, layers of legacy systems, data that came with acquired companies, and more. The work and time required to bring data from multiple sources often takes longer than is practical. By the time the data is consolidated, it could be out of date, or the costs incurred might outpace the value such a consolidation would provide.
This is precisely where agile data mastering excels. It’s human-guided machine learning finds the connections between data in any number of sources to quickly provide a single view of data related to each customer. The data across sources is presented as one entity when it clearly matches, and when matches seem likely, but aren’t certain, the system asks experts where matches should be made. This information is then used to make further matches until there is a clean, reliable record for each customer with all relevant data, regardless of where it comes from.
Further, those connection points between systems, once established, continue to keep those data sources unified as the data changes. Using the example of the car owner, if that customer buys a new car, there is no need to track down all iterations of that customer in the various systems again.
This unified view of all interactions with each customer puts the company in a truly one to one relationship with customers, and reflects the customer-centric mindset. From this solid data foundation, companies can stop wasting time and money on efforts that frustrate customers and show the seams in the company’s organization, and build stronger connections that reflect the full extent of their customer relationships.