Improving the Mobile Customer Experience by Combining User Behaviour Analysis with Customer...
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Transcript of Improving the Mobile Customer Experience by Combining User Behaviour Analysis with Customer...
October 8 – 11 2013, London
WWW.MRMW.NET
The original, premier event for the Mobile Marketing Research Industry
WWW.MRMW.NET
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Improving the Mobile Customer Experience by Combining User Behaviour Analysis with Customer Perceptions
INSIGHT
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Content
Our service ambition
How do overall perceptions link to customer behaviour?
Understanding the impact of touchpoint experiences on perceptions
THE #1 FOR SERVICE
WE’VE SET OURSELVES THE CHALLENGE OF BEING
11/10/2013 EE TEMPLATE FOOTER AUTHOR V.1 3
ON THE ON THE AND
HIGH STREET PHONE ONLINE
INSIGHT
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But our category is complex with a multitude of customer journeys – how do we understand what drives value in the customer experience?
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Ultimately we want to understand all these relationships
Propositions
Network
Customer Service
Retail
Digital
Billing
Relationship perceptions (CSAT, NPS)
Behaviour £
INSIGHT
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We have no shortage of data…..
Operational data
Dropped call rate
Data session failure rate
Average waiting time
First call resolution
Sales conversion
Transactional surveys
Network NPS
Call centre NPS
Retail NPS
Digital NPS
Relationship Survey
Customer Focus
Outcomes
ARPU
Churn rate
INSIGHT
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Customer Focus is our strategic research programme to track the overall relationship our customers have with us
• Covers all touchpoints
• Monitors performance in a competitive context
• Used to prioritise improvement areas and drive action
• Likelihood to recommend is our KPI
INSIGHT
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The first step is to understand the relationship between customer perceptions and behaviour
Propositions
Network
Customer Service
Retail
Digital
Devices
Relationship perceptions (CSAT, NPS)
Behaviour £
INSIGHT
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We can achieve this by linking our Customer Focus results to actual customer behaviour at an individual level
We examine churn behaviour for customers that we survey in a +/-3 month window around their contract end date. For data linkage we ask customers to opt in and provide their mobile number
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The clear relationship between likelihood to recommend and churn allows us to calculate the revenue impact of improving our recommendation scores
£
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Survey and Database Variables Joint Modelling Framework
For example: • # of dropped calls • First call resolution
Operational Measures
For example: • Network coverage • Satisfaction with call
centre • Brand • Billing • Tariff plans
Customer Experience
For example: • Churn/Upgrade • Spend
Behavioural and Financial Outcomes
For example: • Likelihood to
Recommend • NPS
Headline KPIs
From Customer Experience Survey
Other Databases Arrows indicate the causal relationships modeled.
INSIGHT
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Examples of relationships modelled
Survey – to – Survey Survey – to – Other Databases
Value
Likelihood to Recommend
Network
Call Centre
Brand
Communication
Recognition
Website
Tariffs
Drop call rate Likelihood to Recommend
Customer Service Metrics - First call resolution - Net promoter score - Customer
satisfaction - Problem resolution
Likelihood to Recommend
Likelihood to Recommend
Churn/Upgrade Financial metrics
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Examples of relationships modelled (cont.)
Churn estimates from the model were translated into revenue implications based on a financial model
Churn
FCR
Dropped Calls
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Modelling benefits
• Putting a commercial value on Customer Experience improvements, by linking perceptions with churn behaviour, raises it up the corporate agenda
• Use of our models drives Customer Experience prioritisation by quantifying the revenue impact of different potential actions, and therefore informs resource allocation decisions
• Bringing in the operational metrics allows us to pinpoint the specific activity required to drive improvements – eg focusing on reducing the number of customers experiencing >x% dropped calls
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TITLE SPONSOR DIAMOND SPONSOR PLATINUM SPONSOR GOLD SPONSORS
WORKSHOP SPONSORS SILVER SPONSORS
PREMIERE SPONSOR
APP SPONSOR NETWORKING SPONSOR PREMIERE SPONSOR