For customer service, “small data” can be more powerful than big data
In our research conversations with customer service teams across the globe, we’ve heard again and again about the challenge of finding worthwhile insights from all the voice-of-the-customer data (for CEB Customer Contact members) that the teams now collect. This is despite the time and resources spent on speech/text analytics.
That’s why a Harvard Business Review blog post on the challenges and shortfalls of “big data” resonated with the research team.
In one of the examples, the authors compare how two companies use customer information:
The first company aggregates big data and provides high level trend information to managers at the corporate headquarters. As the authors point out, the shortfall of this strategy is that while corporate managers might know that the store is out of yellow sweaters, say, they have no understanding of why that might be the case or how many orange sweaters they could have sold if the store had carried them.
Contrast this to a different store that arms each sales clerk with pieces of “little data”. Each employee is empowered to use these small pieces of data to supplement their intimate knowledge of customers and customer preferences. Because these sales clerks work closely with customers on a daily basis, they are in the best position to use small pieces of data to make quick decisions on a micro level that can culminate in big changes for the company.
Why the Post is In Line with Our Research
These examples highlight the opportunities that service organizations have to use the customer service “frontline” to find and use information that can provide low effort experiences for customers. Since the frontline engage most closely with customers, they are really in the best position to get the right customer information that can dramatically improve the level of customer service.
That’s why our research for 2014, will focus on the rep skills required to 1) use relevant small pieces of data that the service organization already collects and 2) find additional relevant small pieces of data from customers.