Is “Dark Data” the Key to Closing Your Customer Experience Gap?

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We start 2018 facing an experience gap – that’s to say a gap between what customers need from their experiences with brands and organisations and the quality of the experience that they actually receive. Improvement of customer experiences stalled from 2016 to 2017 in many markets. Despite talk, action and investment in VOC & CEM programmes, few companies are making any headway in responding to customers’ journey needs and experiences for customer have become ‘average’ at best.

There is much fixing and removal of functional pain points without any tangible enhancement to the overall quality of experiences, let alone creative innovation and delight. The experience gap between what customers expect and what they receive, is getting bigger. The only way in 2018 is up!

What a Wealth of Data!

More data was created in 2017 than the previous 5,000 years of humanity. Every day, 2.5 quintillion bytes of new data is generated, enough to fill 10 million Blu-ray discs, the height of which stacked would measure the height of 4 Eiffel Towers on top of each other! What’s more, by the year 2020 it is estimated that about 1.7 megabytes of new information will be created every second for every human being on the planet; these are astonishing facts.

This data challenge (its volume, speed, accuracy and sheer variety) could ironically make the current customer experience worse, not better. It potentially brings greater complexity when, as a CX industry, we already have the tendency to over-complicate. Our ability to use data to identify and find tangible customer led solutions hinges on our ability to understand and interpret it effectively.

Organisations need data exploration and decision tools to overcome data complexity and derive meaning and real insight. They need to be able to reliably and confidently extract value from these abundant and divergent data sources and identify the cause and effect that impact the relationship between customer behaviour and customer experience.

Given we cannot rely on the future being the same as the past, we also cannot depend on historical models. We need to get better at predicting customer behaviour. AI (artificial intelligence) in the form of machine learning and data enrichment will help organisations get a better handle on data in 2018.

Data Rich, Insight Poor?

Voice of Customer Programmes (VoC) and Customer Experience Management & Measurement (CEM) platforms have helped bring together data from all sorts of sources from social media reviews, to surveys to call centre interactions, and from many disconnected engagement channels. These platforms have connected together the differing ‘voices’ of customers into one common and shared place where an organisation can allow leadership and employees of all levels and in geographically disparate markets can have access to customer metrics and to a “360 degree” view of the customer.

Platforms have relied heavily on unwieldy surveys and there is a growing understanding that customers are becoming increasingly frustrating with the length and volume of these surveys. There is a feeling that these surveys alone represent organisations focusing on themselves rather than the customer. It’s usually about “how did we do” rather than “how did it feel for you”. Therefore, as some customers skip through these surveys quickly or others put off responding, the quality of the output and the representation of the customer base (and surveys as a feedback mechanic) is coming into question.

According to the Temkin Group, only 15% of voice of customer (VoC) programmes are considered “very successful”- that’s startling given the size of the market and the investment made by organisations over recent years. A major downfall of these platforms is that they have only reliably been able to collect and analyse structured data – structured data according to Oracle only accounts for 12% of enterprise data. So up to 88% of customer feedback is not being reliably and confidently regarded (or if it is, it is being put through basic text characterization techniques that yield no meaningful output).

Using Dark Data

So, the secrets to Customer Experience success potentially lie hidden in this “dark” or unstructured data. Many companies are only scratching the surface of its potential — a more qualitative approach is required.

Take the example of an international hotel chain where an intelligence engine leveraged its proprietary machine and deep learning and AI to interpret unstructured customer feedback from multiple sources to establish what matters most to customers. It was found for example that whilst a greater number of customers might feedback about the quality of their breakfast, ensuring parking facilities are up to scratch would have a greater impact on the overall customer experience.

Its powerful ‘AI Impact Analysis’ algorithm distinguishes between what people talk about most frequently (the easy bit), and the things that people mention that have the greatest impact on their behaviour. It can accurately detect how customers think, feel and act about an experience and reveal how those thoughts and feelings translate into behaviour, to understand the relative importance of each interaction in their journey in real-time. Having seen this in action, it is an exciting development.

AI-Powered Listening

We are finding companies increasingly frustrated with the lack of real insights, beyond CX metrics or the identification of where things are going wrong. There appears to be a concern that they are not learning anything new and that the more data they get, the “dumber” they become. This means that they are not able to draw real meaning or make sense of the data that they are collecting and have at their disposal.

The customer has a wider voice than how well they would rate an organization or how likely they are to recommend a company to a friend or relative. Data analysis done well based on the true, full, unfiltered voice of the customer (i.e. all customer feedback data), served by AI, should help companies understand what customers really value, how it feels to be a customer along their multi touchpoint journey and most importantly the ‘why’ behind customer’s behaviour, choice, beliefs and expectations. Organisations, aided by newly developed AI engines, can have a thorough understanding of what’s moving the needle in their customer experience and those of their competitors, and why.

Data should lead to insights that then lead to the correct strategic, prioritised responses, that lead to the design and delivery of excellent customer experiences – not mediocre ones. 

AI software will help organisations get their hands around their data so that they can use it either strategically to make important long-term customer focused decisions, or they can leverage it in real-time to make operational decisions in the moment. What’s clear is that there is no value to the data that is being created and collected, if there are no insights that can be used.

Driving Change

If organisations can unlock value out of their data, this alone will not lead to Customer Experience success. The customer insight story needs to be shared clearly, consistently and continuously so that everyone in the organisation ‘leans in’ on the insight and ‘leans in’ on the customer. Going one step further, the organisation needs to be able to take these insights and operationalise them.

The number one thing that we find makes the difference between Customer Experience leaders and laggards is not how good their VoC programme is. It is how well connected or aligned the organisation is around the customer to deliver on the insights. If they are, those clever people tasked with customer experience excellence can effectively drive the customer agenda and affect change.

Here are ten features of an aligned organisation:

  1. Leadership: The leadership team is openly empathetic and committed to the customer
  2. Line of sight: A clearly communicated Customer Experience vision is intrinsically linked to the overall business strategy and imperatives
  3. Linked metrics: CX and journey metrics are linked to overall business metrics; there is company-wide adoption of customer measurement and performance
  4. Employee engagement: All staff are engaged around the customer which culminates in a pride and a passion to improve things for customers. Frontline staff are also empowered and enabled so they can proactively interact with customers, without question
  5. Re-designed processes: Supporting processes are connected to the customer journey – old processes have been overhauled, streamlined and internal barriers in delivery, have been removed
  6. Customer centric behaviours: Behaviours that support the customer and enhance the Customer Experience are clear, understood, encouraged, endorsed and widely observed
  7. Rigorous execution: There is a robust and prioritised roadmap of activities which balances alleviation of friction or pain points and which also innovates to exceed customer expectations
  8. Technology integration: Technology is established as a company-wide enabler and it functions hand in with customer teams, not in isolation
  9. Operationalisation of insights: Ownership and accountability for both data and customer feedback and action is clear, connected and monitored
  10. Company-wide collaboration: There is active and seamless collaboration as one holistic customer team

CX success will come from a commitment from companies to truly put the customer first, throughout. This means harnessing this valuable customer data, taking intelligent action and aligning the organisation based on these customer truths. Hopefully then, we will begin to close the growing experience gap.

Image Source: Pixabay

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