In this era of disconnected systems, siloed data, and fragmented customer experience, most companies believe that a single view of the customer is the holy grail for providing a better experience. While building this connected 360° view of the customer is absolutely necessary, your quest shouldn’t stop there.
Unifying customer data in a Customer Data Platform (CDP) is an excellent start. But most CDPs don’t deliver on the full potential of unified customer data.
It’s impossible to build a world-class enterprise customer experience if your business teams can’t easily glean insights from that data, and operationalize those insights.
When evaluating a CDP, don’t just ask if it creates a golden record. If you’re trying to scale personalized, data-driven customer engagement, here are the questions you should be asking:
Every CDP on the market collects and maps customer data — and a good CDP will have native analytics to mine all that historical customer data for actionable insights. Once you’ve gone through all the effort to unify your customer data into a single view, the most important thing you can do is to make those insights actionable to everyone in your business who needs them.
To maximize the reach of customer insights in your organization, it’s vital to connect to both third-party analytics systems like Tableau or Looker and third-party predictive modeling platforms like JupyterHub, so that you can build PMML data models on all that valuable historical customer data.
That’s why Usermind’s CDP is open and vendor-agnostic. We store the data in Redshift so you can connect it to the tools your various business teams are already using, in their format of choice.
Data-driven customer insights are great — data-driven customer engagement is better. How do you use customer insights to power your company differently? If you’ve leveraged your CDP to discover a valuable segment of customers, how do you then engage with them in the best possible way, across teams and systems?
When you think about implementing a CDP, it’s not just about getting the data in one place — it’s about how fast you can take action on it. If your CDP isn’t tightly coupled with a rules engine, you’re probably going to have to kick off an IT project to move that data into all the appropriate systems. It can take weeks or months to implement new processes across all of the systems that impact customer engagement (CRM, marketing automation, transactional email, help desk, etc.). Without journey orchestration, you end up needing to build point integrations, ETL pipelines, task-based rules, and complex workflows to actually get value out of the insight.
The longer it takes you to take action on any insight or high-value customer segment, the more opportunities you’re going to lose. If your CDP only delivers insights, and not action, then it’s providing less value, at a much slower pace. And if you’ve built these processes around the wrong hypothesis, it’s difficult to change. What if those emails don’t work? What if you were wrong? If you’re not improving conversion, you’ll have to kick off another IT project, and start the whole process over.
That’s why Usermind’s CDP is tightly coupled to our natural language rules engine. The connected customer records in Usermind’s CDP make it possible to operationalize customer insights, within the context of every customer’s journeys.
With access to data about all your customers’ interactions over time, within the context of their individual customer journeys, you can build better data science models around customer behavior. You can identify low-hanging fruit and guide customers to the ideal next best action for their segment.
But without governance, if you put all those into production, the models are going to collide. If the same user fits the criteria for seven different models at the same time, they’re going to receive seven next best actions.
Ungoverned, data models can actually cause terrible customer experiences. Mature organizations look beyond segmentation, and apply models within the context of customer journeys. For example, a customer might still fit the criteria for those seven models, but they’re only going to get the two campaigns that apply to the journey they’re currently in.
Systems that only provide rules or next best actions, without the context of the journey, don’t provide the level of orchestration and governance needed to ensure good CX.
Maturing into orchestration is key to data-driven engagement. Getting all your data into a CDP is a great first step; but the real holy grail is: unified data that is open and accessible to all of your business teams, rapid action through all systems of engagement through orchestration, and a governed CDP where models are executed within the context of a journey, so machine learning doesn’t cause more customer experience problems than no action at all.