Customer experience is the new competitive battleground — and companies that can leverage their customers' preferences and data to create more timely, relevant experiences will win the war for their engagement.
However, there are a few major roadblocks that keep enterprises from harnessing machine learning to provide personalized experiences at scale: siloed systems and data, machine-learned behavioral models that are hard to operationalize across all those disconnected systems, and conflicting models that make customer communications repetitive or irrelevant.
Some vendors are presenting machine learning as a panacea to cure all of a company’s customer experience problems — but machine learning is just math. To use the insights gained from building models on customer behavior and data, they have to be actionable, across systems.
Last week at Forrester's Customer Experience Forum, our CEO Michel Feaster presented on how to deploy machine learning insights within the context of an individual customer's end-to-end experience with your company.
You can find the full presentation below, and here are the highlights of our approach:
1. Replace fragile point-to-point integrations with an integration hub.
Moving business logic out of siloed systems and into a layer that spans data sources, apps, and legacy systems is the first step to building a single customer view and orchestrating personalized experiences.
2. Connect data and map it into a universal ID in Usermind’s Customer Data Platform (CDP), which is connected to a machine learning environment.
With a unified 360° view of customers, partners, employees, and other important business entities, you can build better models based on historical and real-time data. Without this contextual history, it’s impossible to build effective models.
3. Baseline and collect data on key customer journeys across systems, channels, and teams.
You can build, train, and optimize models using the data from Usermind’s CDP — and use these models to run automation across systems, directly through the Usermind platform.
4. Operationalize machine learning models in journeys.
Use models to generate next best actions at individual touchpoints — personalization in near-real-time, at scale.
5. Continuously experiment and optimize models and journeys.
Customer experience is rarely linear, and almost never looks the same for two customers. With a Customer Engagement Hub like Usermind, you can continuously iterate on end-to-end customer experience, with insights into where a human touch, and automated email, or other relevant activity will help you achieve your business outcomes.
Here is the full presentation, with examples of how to operationalize a model in Usermind: