Making Your Data a Competitive Differentiator

Daniel Stimpson focuses on how the data produced by text or speech analytics can be used to improve your contact centre.

A recent Harvard Business Review article on data strategy suggested that, on average, less than half an organisation’s structured data is actively used in making decisions.

Going further, it appears that less than 1% of organisations’ unstructured data – particularly in contact centres – is actually analysed or used at all.

Can this really be true? Particularly as we’re consistently all being told about the need for data-first strategies, and how data has a critical role in driving our digital transformation.

A recent Avaya blog highlighted the challenge of building clear data strategies in a world where ‘over 40% of customers now use up to seven different channels to interact with brands’. It’s perhaps hardly surprising that organisations find it hard to make sense of all this data, especially as 43% of firms suggest that they currently obtain little tangible benefit from their data.

The Harvard Business Review perhaps explains why this is the case, reporting that 80% of analysts’ time is actually spent just identifying and preparing data – leaving very little resource for considered analysis.

However, simply blaming the sheer volume of data for your inability to make sense of it is unlikely to be a winning strategy. Clearly – as an industry – we need to be doing more to provide organisations with the robust tools and processes that can help customer engagement teams make more sense of their unstructured data.

To achieve this, we need to encourage data strategies to move beyond more defensive goals – such as fraud prevention and compliance – to embrace more active goals such as simplifying interactions and reducing customer effort.

Without speech or text analytics, for example, it’s very hard for organisations to monitor much more than 1% of their contact centre interactions.

Analytics, which for some organisations is already easily accessible through their existing workforce optimisation tool set, can play a key role in helping organisations to identify broken or failing processes by automatically surfacing context and themes within interactions. It can also help particularly by highlighting emerging issues being raised by customers and flagging up potential problems ahead of time.

And without the latest machine-learning-enabled predictive intelligence technology, it’s hard to know just how well your digital front door investments are actually performing. If you really want to know what’s driving your web visitors to your contact centres, then there’s really no substitute for analysing multiple data points and surfacing themes in a timely manner.

Today’s best-practice organisations are now actively embracing multiple data sources – particularly the thousands of conversations that people are having with their business – to help make smarter decisions and achieve better customer outcomes.

However, organisations will need to go much further, particularly if they want to extend their data analytics capabilities across the broader customer journey. Regardless of how customers instigate contact with your business, you’ll increasingly need to have the technology in place to capture, interpret and understand behaviours and anticipate your customers’ needs.

Get it right and the benefits – in terms of competitive differentiation and business results – can be significant. However, there will be considerable risks for those firms that don’t choose to optimise their data.

The Harvard Business Review article concludes: “companies that have not yet built a data strategy and a strong data management function need to catch up very fast – or start planning for their exit.” Their message is clear – disruption can work both ways.

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