• Customer survey data at a broad perception, or overall relationship level
• Customer survey data at a specific event or transactional level
• Customer survey data at the completion of specific journeys (e.g. mortgage application)
• Social media feed for overall perception and ‘shock’ tracking (e.g. product failure)
• Mobile phone tracking technology to measure retail store flow (e.g. RetailNext)
• Customer ad display tracking to measure awareness and action (e.g. Nomi)
• Wearable tech to collect data during key activities (e.g. location-orientated tracking)

Relevant reporting then ensures that the data collected is delivered to an organisation in a way that is specific to the various audiences and stakeholders using the information. Insight programmes are in danger of being side-lined into marketing and communications tracking, or being used as a high level metric tracker with little diagnosis of the symptoms being reported. An integrated measurement framework should be the driving force of how organisations truly deliver customer centricity, and the key to insightful engagement within a business. Each layer of an organisation demands a different view of the customer. Understanding this helps to build relevancy into the reporting and outputs produced, allowing for engagement with senior decision-makers and operational teams.

Depth of data

As we consider the relevancy, we must also understand the variable depths of data needed at different levels within the organisation. Again, if we consider the need for ‘symptom’ level data as KPI measurement, and ‘root cause’ data as the underlying reasons, the variable depths of data have differing uses. These include:

• Tracking and benchmarking of KPI
• Experience diagnosis through attributes
• Verbatim analysis for sentiment and emotion
• Unstructured data-gathering for sentiment tracking
• Campaign reaction and evaluation on social media
• Connecting operational data
• Connecting employee engagement surveys

Depending on the stakeholder and sector needs, the depth can then be controlled to ensure the relevancy is maintained. This is the most effective way of creating engagement and mobilising an organisation to use insight to drive growth.

Simply put, too much depth can be overwhelming and unclear at certain levels. Equally, too little data will not provide enough information for action and experience recovery.

Speed of data

As depth is aligned to relevancy, speed is aligned to depth. Gathering rapid, ‘in-the-moment’ data allows for a fast feed, but it’s specific. Conversely, gathering considered and broad data allows for depth, but a potentially slower feed.

As with relevancy, there are two considerations to speed – namely, the speed of data capture and the need for variable speeds of information to flow into the organisation. High level metrics and C-suite key performance measures will be delivered on a timely basis for strategic decision-making, but this will likely occur at a thematic level, and the timing will probably need to be more measured. Moreover, the flow of granular tactical measures into operational teams may need to occur rapidly in order to allow for effective service recovery.

Within the future of customer experience measurement is the ever-growing need for closed loop processes to be built on the back of measurement. This pushes the development of integration into customer relationship management (CRM) systems, (including digital ‘in-the-moment’ capturing systems such as mobile technology), but again, this positions CXM as a true business driver. A closed loop can only be brought about by a changed culture within an organisation, particularly one with the adequate systems and people in place to close the loop where technology alone will not suffice.

Speed of data, therefore, creates the need for evolution in data gathering, but the need for relevancy and depth creates the parameters around the quality of data being gathered. The interplay of the three pillars should result in a quality process, aligned to specific business needs to inform growth. Businesses still need to have a strong principle of quality, and the ability to question whether it is gathering the right data in the right way from the right people. Any company failing to do so risks falling foul of the old maxim: “rubbish in, rubbish out.”