Predictive Customer Engagement
How can we provide information that customers would value, but don’t know to ask for?
Support organizations sit on a gold mine of customer information. The Predictive Customer Engagement initiative aims to improve proactive value delivery to customers based on what we already know.
The Predictive Customer Engagement model begins with the assumption that you have begun moving up the the Value Stack model and have some experience with the Know-Me factor, business acumen, co-creating value, and maintaining trust with customers.
The Predictive Customer Engagement model is a double loop model consisting of the Event Loop and the Improve Loop.
The Event Loop includes:
- Accessing data sources
- Listening at scale
- Mining for actionable information
- Creating that action
- Communicating that action
- Accessing data sources to assess the impact
The Improve Loop includes assessing the effectiveness and quality of each part of the Event Loop.
The Consortium’s work on Predictive Customer Engagement is continuing to explore the following topics:
- Our ability to leverage big data analytics, emerging digital automation
- Our relationship with the customer – do they trust us?
- How do we get customers to take action on recommendations? How do we to provide compelling evidence of relevance, consequences of not taking action and information about effort involved?
- We have to know a lot about our products/services and how they are used
- We have to know a lot about customers as companies and as individuals
Machine Learning: A Path to Contextual Knowledge
Customer Service Landscape
- 70%-80% Known: Most inquiries handled by people have already been solved
- Self-Service: Designed using inside-out thinking
- People are solvers: People are creative thinkers and should work on new
- Knowledge-Centered Service: Dynamic content created & updated in the workflow
Artificial Intelligence is essential for digital transformations, freeing up employees from mundane and repetitive tasks so they can pursue creative ones.
The Challenge for Predictive Customer Engagement
- Provide context
- Evidence this issue is relevant to me
- Consequences that I will be impacted if I do not act
- To the appropriate individual
- Someone who is accountable/responsible
- Has authority / privileges
- To take action
- Ability, skill, confidence to act
Three Elements Drive Behavior
- I want to do this, I understand the value
- I am responsible or will receive positive feedback
- I have the skills, capability, privileges
- I have access to the right resources at the time of the trigger
- Event based call to action, told to, reminder, notified, alert
- When users have motivation and ability
Machine Learning Techniques: Solving Problems
- Visualizations: Consumable Outputs (reports, dashboards, augmented reality, chat, machine to machine)
- Capabilities: Analysis abilities (patterns, predictions, recommendations, optimization)
- Machine Learning Technology: Classification, Regression, Clustering, Anomaly detection, etc., and the associated algorithms resulting in a trained model
- Data Repository: Storage entity (Data Lake, Data Warehouse, or other model for storing data to be mined)
At the Consortium’s 2022 Member Summit, we celebrated 30 years of innovating together. Greg Oxton shared a history of the Consortium, reflecting on how the bodies of work developed, matured,…
We have seen many instances of KCS working very well outside Customer Support. KCS captures knowledge as a by-product of the interaction, and there are interactions everywhere. We have seen countless…
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