How to Improve Small Business Customer Service by Analyzing Data

When you start a business, you don’t yet have much data to assess and analyze. Sure, you performed market research when writing your business plan and likely analyzed lots of market data. But in most cases, that data was aggregated and summarized for you.

This changes when you launch your business and start generating your own data. Now, you’re responsible for reviewing and analyzing that data to make decisions.

For example, periodically (every quarter), we review our customer service data to determine whether we are best using our resources.

This is an excellent practice for a small company, especially one that is ambitious and, by its very nature, capacity constrained.

At crowdspring, we strive to provide world-class quality support, but our team is small and their responsibilities complex. We do this through the use of great tools and by implementing technology that helps us handle a hefty load.

For instance, in the quarter that just ended, our team of 4.5 (including part-timers) responded to 8,158 email and webform requests, received 2,658 incoming phone calls, made 1,033 outbound calls to customers and chatted online with another 783 users.

This is not to mention the 469 intellectual property violations they investigated, the 601 prospects they followed upon, or the hundreds of instances of possible fraudulent activity they scrutinized.

This gets even more impressive when you consider that the average request to our support team was answered in under 24 minutes, and the average issue was resolved in less than a day!

To me, the truly amazing thing is that this team not only handled that kind of volume with that kind of efficiency but that they did it with smiles on their faces and a relative minimum of frustration.

How is it possible that such a small team can respond so quickly and effectively in a high-volume customer service context?

Well, it starts with hiring the right people (a discussion we will leave for a different blog post) and continues with strong training regimens (another future post, ok?), as well as having clear protocols and systems in place.

But one of the most important factors in building efficient customer support machinery is the art of scheduling.

And guess what? Great scheduling starts with great data!

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If you know when the work needs to be done and how much work you can expect, you are halfway to delivering exceptional service to your customers.

Right now, you are probably asking yourself, “What exactly are they doing over at crowdspring?”

Well, let me answer you. At the end of every quarter, we pull a huge amount of data from the SAAS tools that we use to deliver support: Zendesk is our help desk software and is the tool we use to manage all support tickets; DialogTech (formerly IfByPhone) is our voice phone system for incoming calls, call automation, and analytics; Olark is our provider of live chat software and allows us to interface in real-time as well as track metrics, and Skype is our phone system software that we also use for all outbound calls to customers.

These tools all interface with Zendesk, and an incoming voicemail will create a new ticket in ZD, and an Olark chat will also be transcribed and available through Zendesk.

Four times per year, our Customer Service Manager downloads reports from each of these services and aggregates them into one supersize Excel spreadsheet, allowing us to perform fairly sophisticated analyses of the results. Among the analyses, we look closely at:

  • weekday distribution data to determine which day has the heaviest volume (Monday – no surprise there)
  • hourly distribution data allows us to schedule extra help for the busiest times of each day.
  • support agent data to see whether any of the team are struggling to keep up with the load
  • topical “group” data to help us better understand what kind of questions users have (and to create content to help our users find the answers for themselves)
  • time data for phone calls and chats to best understand and determine staffing allocation for busy times of the day and week.
  • reply-time and solve-time data to help us track our own productivity and efficiency

Collecting, aggregating, and analyzing this data allows us to devise the most efficient schedules, keep person-hours under control, maintain high morale and low employee turnover rates, better understand our user’s pain points, and in general do more with less.

By sharing the data and analysis with our entire team, we inspire new ideas, generate new and improved workflows, and empower the team through ownership and a shared sense of responsibility and achievement.

That’s what I think of when I think about better living (and scheduling!) through data!

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