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During early 2019, The Institute of Customer Service’s UK Customer Satisfaction Index recorded the fourth consecutive drop in satisfaction.

In an environment with access to higher quality and quantities of customer data, we should be able to anticipate customer needs and deliver higher satisfaction levels. But a recent study of C-suite leaders in established businesses reported that 69% had yet to create a data-driven culture, and 53% are not viewing data as a strategic business asset.

Why are businesses still unable to harness customer data to anticipate and deliver greater, more cost-effective, customer service?

Is your customer data broad enough?

A recent report from SAS found that 93% of businesses lacked the ability to accurately predict future customer needs, which contrasts with the 54% of businesses who claim they are ‘best-in-class’ or ‘transformational’ when using customer intelligence to drive activity.

Many customer behaviour models utilise a combination of Recency, Frequency and Monetary Value (RFM). It’s a popular suite of metrics because it’s easy to understand, doesn’t require large investment in new technology, and can be easily applied in a wide variety of sectors. However, it does have drawbacks too. RFM only looks at prior behaviour, is unable to accurately predict future behaviour, and ultimately fails to utilise broader sources of data to understand customer context and intent.

Siloed customer data is another significant factor limiting the use of broader behavioural data. Whether stored through marketing, sales or service channels, data can be spread across disparate platforms, locations and in a wide variety of formats. It results in difficult or unclear customer analysis and can also manifest in frustrating experiences for customers, as they are asked to re-provide data already given in another channel.

Breaking down these silos is high on the agenda for many businesses, who have identified that a broader view is necessary to drive value. Work has begun on utilising and integrating offline data to enhance digital experiences, with most businesses surveyed planning to implement this in the next twelve months.

When do businesses plan to make offline customer data available to personalise the online digital experiencewhen do you plan to make offline customer data avilable to personalise the online digital experience-1Source: Source: SAS Darkness of Digital Shadows Report

Are we asking the right questions?

As customer data moves towards all-encompassing views across past, present and future behaviour, we frequently find people not asking the right questions of the data.

Almost any data point can be pulled into live dashboard reports, but that doesn’t mean you’re looking at the right measures. It’s easier than ever to analyse data that is ‘interesting’ yet doesn’t drive improvement, or is only a manifestation of symptoms but doesn’t point to the root causes of poor service.

Often, improvement initiatives ask questions such as “How can we reduce the volume and cost of inbound calls?”. This intrinsic view tends to create efficiency improvements that benefit colleagues and internal processes, yet run the risk of frustrating customers and not resolving the root cause.

Instead, asking ”What frustrates customers when they contact us by phone” takes a more extrinsic viewpoint, and points towards why customers have to make contact in the first place, where interactions add value, and how queries and problems could be resolved faster, and on the first time of contact.

This approach leads to meaningful data being surfaced, with insight into the real issues customers face, from their viewpoint.

Are the right people exposed to the data?

From a recent study with senior leaders across 400 larger businesses, it was found that customer data isn’t available, or utilised, at all levels of the organisation. While 81% of leadership reported using customer data to guide strategic decisions, at more operational levels use of such data dropped to 26%.

When the same group were asked what becoming a data-driven organisation meant in practice, the top three scenarios were:

  • Employees being rewarded for identifying and acting on opportunities identified through analytics

  • All employees having the opportunity to become data analysts to some level

  • There are fewer lines of authority when making decisions backed by data

While this may involve significant change to provide open access to trusted data, it’s critical to empower teams to make customer-focused, commercially sensible decisions without getting caught up in bureaucracy.

Making it work in practice

A major airline had embarked on an ambitious programme to improve customer experience, reduce cost associated with poor performance, and improve the upsell ratio.

Where complex, customer scenarios and internal procedures intertwined, static behaviour models were struggling to provide enough insight to drive meaningful improvement.

Following in-depth mapping of customer journeys and identification of key data points, cross-channel correlations and trends could be drawn, uncovering high-impact targets for improvement. By implementing Predictive and Next Best Action Models, wide-reaching data across multiple channel journeys could be analysed, including customer queries, average holding time, average handling time, customer satisfaction score, etc.

Aside from deep analytics and customer modelling, performance data and insight can also be surfaced and analysed by senior leadership, ensuring future improvement programmes have the support and visibility needed to drive further outcomes.

This provided the team with the capability and support to rapidly identify and implement customer-focused improvements that had the largest impact on customer service, improving efficiency and reducing cost. This work is currently providing annual benefits of up to £700k and rising.

In summary

When the right people ask the right questions and have access to comprehensive data, you can drive improvements that make a positive impact on both your customers and your profitability.


nathalie choi

Nathalie Choi, Principal Consultant


Ben Matthewman

Ben Matthewman, Senior Analytics Consultant

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Dave Heywood, Digital Marketing Manager

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