Common Forecasting Mistakes and How To Avoid Them

Dover Forecasting
May 20, 2018 · 3 min read

Forecasting in WFM (Workforce Management) can be fraught with errors and pitfalls if you don’t know what to avoid. Often, bad information gets inherited from tenured staff to new, analyst to analyst, or employees build forecast and capacity models that they love, but aren’t either accurate or flexible to accommodate growth and change. Effective planning strategies can really change based on the environment. Here are some common myths and errors and how to correct them.

One: Mis-Calculated Shrinkage Factors

This is a common mistake. Applying a Shrinkage factor (a percentage of required time lost to non-working events), many analysts simply add 30 points to their base requirement, so for example, 30% shrinkage added to 150 is 150 * 1.3 = 195. Or, they multiply 30% by 150, and add the total to the base, so 45 + 150 = 195. Either way, they end up at 195.

But, that’s wrong. 30% shrinkage applied to 195 is not 45 but 58.5, meaning you’ve just left yourself 136.5 agents to handle 150 agents’ worth of work. So, you’ve just under-forecasted and missed your service level. Not good.

The correct formula to add shrinkage to your base requirement is to divide your total by the inverse of your shrinkage, so 150 / (1–0.30). This will result in 214.2 required agents, with 64.2 allocated towards your 30% shrinkage.

WFM forecasting applications will usually handle this for you so you won’t have to worry about this.

Two: Use Your Historical Data

Historical data is vital input data for your forecast. But, it’s not all of it! Many factors can change your forecast from what it has been historically — marketing campaigns, product changes, promotions, new hires and training, attrition, etc. You have to know the answers to these questions:
1. Will operations manage performance tighter/better or looser/worse than previously?
2. Are there any changes that may cause higher volume and/or attrition?

Three: Forecasts are Entirely a WFM (Workforce Management) Function

This is absolutely not true, though it’s often thought it is. The simple fact is your business units and teams will often drive much of your service volume. Product changes, platform changes, feature changes or improvements, changes to cost structures or models, hiring plans, attrition cycles, training and coaching cycles, script changes, marketing campaigns, sales blitzes, blasts, mail outs, etc. They can all heavily influence forecasts.

However, critical information sits outside the WFM team that helps to build an accurate forecast. You should have regular meetings with other departments that drive volume — your sales team will have information on expected revenues, your marketing team will have plans for campaigns, etc. As you forecast, trial-and-error is critical — experiment with different methods and see what gets the best result. Most importantly, measure the actual against the forecast. Build a continuous improvement loop to improve your forecast on an ongoing basis.

Four: Average-Based Forecasts

Averages based on historical data are extremely important. They help set a baseline of understanding and a place to apply assumptions on. If you need a high-level forecast for how many agents or FTEs you need, you can take simple averages of contact volume and AHT (average handle time) to get this. But, you really need to understand what goes into these averages. For example, if a large cohort of your agent workforce was working on a campaign that caused increases in handle time that are no longer present, your AHT average will be higher than it should be. Or, if a large cohort of your workforce is relatively new, your AHT may be lower in future. Understanding this will help you generate an FTE requirement forecast that’s accurate, as opposed to overstated, and save significant labour costs.

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