Why would you want to forecast tickets for the future when you’re already drowning in them? Because reasons—plenty of reasons! Here are a few for your consideration.
Hiring: By knowing your current capacity, backlog and having a solid ticket forecast, you can come up with a great rationale for hiring new teammates. For example, if you know you that your current SLA will drop dramatically if your company adds 1000 active users, it’s a strong indicator that you’ll need to add capacity to your team—and that hand pump expresso machine you’ve always wanted.
Budgeting: If your department/team has a set budget, make sure you allow for additional staff if you know you’ll need more help. When you’re fighting for resources, it can be especially helpful to have data to back your support spider senses about what you’ll need next month, next quarter or next year.
Shift planning: On a more tactical level, a forecast can help you plan your hourly and weekend coverage if you do extended hours or 24/7 support. Do all your tickets come in on during business hours, or do you get a steady trickle all the time? Have someone on your team who likes coming in late, super early, or prefers weekend shifts at home in their shag carpeted basement? This can be a great rationale for giving them that flexibility and ensuring you have solid coverage at the same time.
Holiday/Vacation scheduling: When’s the best time to schedule the goat-themed offsite you’ve always wanted to do or the 4 hour supermegabrainstorm for your distributed team? How many people can take vacation simultaneously to celebrate National S’mores Day in style? Forecasting can help!
VP/CEO bugging you: Sometimes, we’re so busy with tickets that it’s hard to plan for the future. Set the right expectation and pre-empt questions from higher-ups about dips in SLA or increases in ticket backlog with a rational explanation.
How to create a forecast
Below, I have a very basic ticket forecast using a sample data set. Here’s how I came up with it:
Use a report from your helpdesk software to get a 12 week rolling average of tickets per week. I have a report that does this for me automatically. Depending on your business needs, you might want to use a longer or shorter average (monthly, quarterly, every 6m, etc.)
- Divide that average by the # of users you are currently supporting (this could also be # of accounts, # of sites, # of active users, etc.) to get average tickets per user.
Include lowest and highest data points from your 12-week average for perspective (slow weeks vs. hectic ones)
Scale your data by additional users (make sure you’re applying the same formula)
Compare the # of tickets per week to the # your agents can handle per day/week and make sure your team is ready! In this example, a team of 5 could handle around 10000 users on an average week, but would likely fall behind during a busy week.
If you’d like a copy of the sample data, let me know!
- Use variables in the forecast so you can quickly change things like high/low weeks, agent output, and # of users.
- Change the volume per user avg, low and high based on your data and the timeframe you’re using.
- If you have different types of tickets that are easily bucketable (e.g. support, billing, Terms of Service), you can do separate forecasts for each type!
- Do forecasts based on time of day (afternoon opens, evening opens, etc.) to help with shift scheduling
And of course, some caveats:
- My example above uses a pretty basic and unsophisticated forecast. There’s plenty of room for improvement (some obvious ones would be scaling via a given growth rate or shrink rate).
- This forecast uses a pretty aggressive average ticket submission rate (15% of users). In previous roles I’ve had, the weekly ticket submission rate was more like 1-5% of users per week.
- This is a good tool for giving a general idea of what things might look like at a certain amount of users, but won’t cover cases like massive downtime, a failed product launch, a data breach or a controversial business decision. Be prepared!
- You may want to be more aggressive with the average and “high” volumes or use previous high/low weeks as custom baselines to help you plan accordingly.
Play around with your forecast and figure out what makes the most sense for your team. And please share your own customizations and improvements with the rest of us on Support Driven!
About the Author: Aaron Ilika (@aaroneovs) heads up Advocacy and Support at Accompany.