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How to Use Data to Meet Incoming Customer Demand

In today’s fast-paced and technology-driven world, when customers need assistance or an answer to their question, they expect nearly immediate customer support. Providing this level of service may require additional effort on your company’s part (whether you offer support in-house or through an external vendor), but it pays off.

These expectations are rewarded by customer patronage, loyalty, and referrals.

  • 90% of U.S. consumers consider customer support when determining whether or not to purchase from a company.
  • 93% of customers will likely purchase again from a business offering excellent customer service.
  • Approximately 97% of customers will tell others about very good or excellent customer service experiences.

According to Help Scout, more than 31% of customers expect a response to their email in one hour or less. And Hubspot research shows that 60% of customers define “immediate” as ten minutes or less.

The challenge is to offer consistently great support without keeping customers waiting. This is no mean feat — after all, some days are busier than others, and seasonal events may cause spikes in customer requests.

That’s why the best customer service leaders focus on qualitative and quantitative data to ensure customer demand is met. Every situation is unique, so it’s important to find the sweet spot within the various data to ensure customer satisfaction at the speed of response. Here’s how to apply data to your customer service strategy.

Quantitative Data

Quantitative data (the value of data in the form of counts or numbers) analysis is the bread and butter of meeting incoming customer demand. Here is a simple and common method for determining overall volume and hours to handle the volume.

  • Historical data norms plus any growth factors = expected volume.
  • Expected volume divided by handle-time norm = hours needed.

Qualitative Data

Many support organizations use qualitative data (data that describes qualities or characteristics) to analyze customer demand and intervals. This type of information provides a context in decision-making. For example, in a low/medium/high forecast, “high” may be selected if it is known that a marketing push is going to happen soon.

  • Strive to capture this type of data in frequent written, verbal, and video communication with clients.
  • Seek input from the client’s marketing, product, and growth teams to ensure you have a solid understanding of relevant qualitative information.

Interval Data

Proper staffing involves understanding when customers are reaching out. Schedule support based on the historic number of incoming tickets. As an example, let’s assume the client receives an average of 259 tickets per week and desires a 10-minute handle time (including auxiliary work, like ticket notes). This data indicates that 43 hours per week are needed to meet incoming demand. However, this doesn’t cover the real-world need; the actual number of hours required is higher, to meet the customer’s expectation of short wait times.

  • Sketch out the number of incoming tickets by using interval data (in this case, number of tickets by hour of the day and day of the week). This will show you how many incoming tickets the company typically receives in any given operating hour (say, 12 tickets from 6:00 to 7:00 on Mondays).
  • Combine the hours needed to handle the volume and the incoming ticket interval data to determine where to place staff. This means calculating the percentage of incoming tickets for any given operating hour based on total tickets. (In our theoretical example above, the Monday 6:00 hour might represent 4.6% of all incoming tickets in a week.)
  • From here, take each percentage and multiply by the number of hours needed for the week. Kudos! Now we know where to place our hours to ensure customer demand is met. (Continuing with our example, we’d want to schedule two coverage hours, or two Mods, to that 6:00 Monday hour to ensure sufficient coverage.)
  • Once more, now that you’ve determined the bare-bones schedule, add additional time to set up your CS team for success and short queue times. This will vary from project to project, so conduct weekly reviews to ensure that both qualitative and quantitative goals are being met.

Next Steps

Of course, this is not a one-and-done situation. Proper management of the engagement requires ongoing monitoring of scheduling and client communication and adjusting planning as the project demands.

  • Utilize workforce management techniques and tools to ensure resources are meeting customer demand.
  • Utilize burst coverage to meet demand.
  • Check each week of the month to see if forecasting demand is meeting actuals. Adjust with new information, ramping up and down as needed.
  • Analyze staffing levels going into the next time period.
  • Repeat hours needed and interval distribution of those hours, with data forecasting from the next time period.

By using data for predictive scheduling, you can help ensure each individual client engagement is properly staffed according to the exact needs of the client’s customer base. Here at ModSquad, we are experts at providing the exact amount of Mod hours required — no more, no less. Analyzing data in the manner described above helps us provide the perfect level of support to control costs while satisfying customer demand.

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