Using data to transform the customer experience #TheNewNow

01 April 2016 / Jaywing

There are myriad reasons why companies treat customers poorly then fail to realise future opportunities for further revenue. One of the most common, costly and yet easily resolvable issues, is a lack of understanding of customers themselves.


In his talk, ‘Big Data for Big Change: using data to understand and transform the customer experience’, Chris Bryson, Data and Analytics Director, from customer experience outsourcer Webhelp, explained how customer experience transformation starts with getting to know and understand each customer better through the use of Big Data and analytics.


The first step is to understand the current experience

When seeking to understand customers better, the first thing Webhelp always does is analyse the current experience. Chris told us “You want to understand how each touch point can be as good as it can. In order to build the best relationship with the customer, you need to understand what experience each customer is getting from your organisation before you start cross-selling and retention strategies.”


Investigate your data to shed light on customer experience

If companies are unaware of how customers perceive them, it’s not for a lack of data. Today’s businesses collect more data than ever in the customer-facing parts of their organisation, such as the contact centre, branch offices and even web chat engagements. By organising and analysing this data you can shed light on your customers’ current experience.


The challenge often lies in translating this data into actionable information and making it available company-wide. To address this, Webhelp looked for insight from web chat engagements about their clients and clients’ competitors to work out how to respond better.


Using data to increase sales conversion by 15% and chat efficiency by 30%

Chris told us about one example, where Webhelp analysed chat duration and overlaid Net Promoter Score (NPS) data to understand the different dynamics and areas for improvement in different customer and interaction groupings. Their aim was to reduce the chat length where NPS was high and find ways of closing the sale quicker.


Analysis showed that chats were going on for a long time, around 50-60 minutes. So they needed to understand where both the customer experience and sales conversions were already good and reduce that time, freeing resource to focus more on customers with a lower NPS score and who needed more time spending on them.


They also looked at chat themes by analysing keywords. This enabled them to map out different topics and profile this information against sales conversions, revealing that they needed to help advisors to deal with conversations about competitors in a better way.  To address this, they provided advisors with a tool to play with different facets of the offer, meaning they could provide the most favourable costs to customers a lot quicker and, therefore, get to check out much quicker. The overall outcome of this project increased sales conversions by 15% and chat efficiency by 30%.


How Big Data analytics improved customer experience scores by 20%

In another example, Chris talked about how Webhelp joined unstructured data with traditional data (from customer surveys) to improve the customer experience online. Jaywing was instrumental in this project and you can read more about it here


Using deep data analysis and advanced modelling techniques, Jaywing tested the relationship between the circumstances of a customer interaction and the resulting NPS score. Factors for analysis included the specific agent and their level of experience, time of day, call duration and hold times, the customer’s tenure with the brand and the type of interaction.


We also looked at the effect of each of these variables individually to identify which characteristics had the strongest relationship to NPS. Finally, we examined the interactions between variables to detect more complex patterns. This provided Webhelp with a very clear focus on what factors have the greatest impact on improving NPS scores, both by reducing ‘Detractors’ and increasing ‘Promotors’, resulting in a 20% improvement in NPS scores overall.


It starts but it doesn’t end with Big Data

While big data can help companies of all industries can understand their customers better and provide better service, there’s more to be done beyond simply collecting customer data.


Companies that use big data well excel in sorting through the white noise, filtering out the relevant information and drawing insight from its analysis. Only then can companies begin to put big data to work to target and retarget the right customers, personalise their experience, solve their problems or build products suited to their needs. Big data can certainly be valuable — but only with actionable insight.