Experience DNA is powered by cloud-based data lakes, predictive machine learning algorithms, and an API insight and action engine. Click on each of the elements below to learn how they increase performance, improve decision-making, and fortify real-time engagement.
How it works
1. Data lake
Customer, financial and operational data at individual and aggregate level are processed and stored in a cloud-based platform.
2. Predictive scores
Machine Learning algorithm scores every customer by studying relationship between journey features, sentiment, and loyalty.
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Dive deeper
API layer provides a single source of truth to fuel recommendation engines based on the data lake and customer scores. Insights and recommended actions deliver three types of value.
3. Action and insight engines
Performance measurement
Strategic decision-making
Real-time engagement
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Data lake
Many organizations are trying to create a “Customer 360” view by combining surveys, operational KPIs and financial metrics into aggregated dashboards. Experience DNA turns these traditional data points into real insight, using machine learning algorithms to serve up the most critical drivers of customer behavior with an added 4th dimension of predictive insight.
Creating “4D” customer insight
Customer data
Loyalty status
Latest feedback score
Transaction count
Operational data
Average handle time
Day/time of transaction
Wait time
Financial data
Price of last transaction
Household income
Redeemable points balance
Predictive insight
Data lake provides integrated, aggregated, predictive insights that go far beyond the standard “360” customer view.
Predictive scores
The machine learning algorithms predict customer satisfaction at an aggregate and individual level with 80% accuracy.
The machine learning algorithm predicts that 75% of customers have a positive experience. It also predicts the likelihood of each customer providing a positive score on a feedback survey based on their actual experiences.
Airline example
Eva has probability to be satisfied largely driven by her recent on-time flight
55%
Eva is likely to be satisfied
75%
Positive experience score
80%
Mike is likely to be satisfied
Mike has probability to be satisfied largely driven by his recent on-time flight and complimentary upgrade
12%
Joe is likely to be unsatisfied
Joe only has probability to be satisfied largely driven by lost luggage in his past two flights
Action and insights engines: Performance measurement
An API layer provides access to customized insights and action recommendations.
Functional leaders can track Experience DNA scores for their function, region and specific journeys over time and drill down to understand the specific journey features that are driving performance changes.
Action and insights engines: Strategic decision-making
Organizational leaders can plan changes to operations and investment in journey re-designs based on improving overall performance with both an ROI and customer experience lens to guide decision making.
Action and insights engines: Real-time engagement
Front line leaders can identify customers “in experience” and offer them personal treatment that fits their specific variables for uplifting satisfaction and revenue.
E-commerce real-time engagement example use case
Merchant.com is a large online retailer using a large e-commerce platform
Merchant.com has been flagged as at-risk because its customer experience score declined significantly over the past few weeks
Performance dashboard reveals large number of returns, resulting in additional costs for the merchant
The analytics engine alerts the CSM about the losses from returns; CSM contacts merchant to guide them through using the platform to provide better descriptions and reduce returns
Merchant.com is able to address the underlying issues and their customer experience score rebounds
