Can Big Data analytics solve the cross-channel service conundrum?by
If you could take everything you know about your customers and prospects, and combine that with new data sources from social media to location and even the weather, then you could truly offer the very best customer service. Big data analytics enables you to do just that.
Customer service used to be all about measuring individual data points such as number of calls handled and time per interaction, and these were used as vital indicators of both how well a company's new product or service has fared as well as the success of the customer service itself. The explosion of multichannel, mobile and social customer relationships demands a new Big Data approach to analysing customer service interactions.
Big data analytics enables you to collect vast amounts of data on individual consumers – their consumption habits, their preferences, their interactions with the company – and then analyse those data sets for predictive behaviour and pro-actively apply those insights both to your existing customers and to customers coming into your contact centre, and build a truly personal customer experience.
Throw out the Excels
Traditionally, customer service measurements have slotted neatly into organised columns in an Excel spreadsheet. But, with the digital boom and proliferation of devices, the customer journey has become fragmented, making it increasingly complex to measure interactions between individuals and the company at hand.
A service call might seem successfully resolved in record time, but that might be because the customer is multi-tasking and logged on to the website. And what if they’ve been tweeting and getting suggestions from the social sphere that either help resolve or exacerbate their reason for calling? Where is the customer when making the call and what impact does that have on the interaction and the outcome?
How cross-channel customer service can benefit from big-data initiatives
Cross-channel customer service naturally produces a mass and variety of structured and unstructured data coming from individual web-logs, email and telephone transcripts, customer interaction records and statistics, Tweets, and Facebook posts to name a few.
Only by breaking down silos to analyse the trends and patterns found within the interconnected troughs of structured and unstructured data can a company truly optimise its customer service efforts. Three key benefits which can be realised are as follows:
1. Predict the future, increase loyalty
Companies are adept at chasing new customers while watching the churn. Why else would they spend around $500B on advertising and acquiring new customers, $50B on CRM spend, and just $9B on the call centre? They offer new customers price incentives, free service, unlimited this and that, all the while not listening to the sentiment of the current customer base.
Social channels have thrown up the opportunity for customers to vent frustrations and opinions that wouldn't be worth the effort of writing a more formal e-mail or picking up the phone. Big Data analytics has the potential to unlock invaluable customer insights that can be channelled into a company's development strategy, thereby reducing the customer churn – and increasing top-line revenue. Organisations can start not just to understand customers, but to provide a more personal customer experience, to let customers know that they have been listened to – all the things that helps customers stay loyal to the brand and spend more going forward.
A new generation of cloud-based data analytics solutions will be key to understanding and visualising customer service interactions in the fullest context. Sales and marketing departments will be able to gain actionable business intelligence, retain customers, increase employee effectiveness and improve the bottom-line.
2. Actionable business intelligence
By mining and consolidating the treasure trove of structured and unstructured customer data presented by cross-channel customer service, companies can yield rich and actionable business intelligence. For example, telcos and mobile carriers often receive the brunt of complaints within the contact centre and social sphere. Based upon all customer service interactions and social media postings, these organisations can identify and pinpoint unknown network outages, service interruptions or latency issues. Similarly, social media sentiment analysis can be used to respond to unplanned calls into the contact centre.
3. Increase employee effectiveness
With cloud-based data analytics, companies can assess a multichannel customer's lifetime value based upon in-store and online sales, customer service history across all channels, and social media amplification. With this information, and with the right solution, the most valuable customers can then be brought to the top of the workload pile – and dealt with by the most appropriate employee.
For years, contact centres have rated employee performance by average handle time, average hold time and so on. Intelligent solutions can now store more detailed information about how employees work, what they know in terms of skills and knowledge, and how they score in quality management solutions.
Analyse the right data in the right way
The right solution can combine all of this information – from the customer and employee spheres – so that contact centre managers can understand important correlations, such as common attributes between top performing employees, whether there is a relationship between years of service and average handle time or whether for example more agent soft-skill training does actually increase customer satisfaction.
The right data analysed in the right way can produce answers to important questions that will help managers ensure their workforce has the right skills to provide exceptional customer service and build a more engaged, efficient, and effective organisation. Employees will be happier, customers more satisfied and corporate balance sheets will be healthier.
Brendan Dykes is senior solution marketing manager of Genesys.