For resource planning teams, relying on blunt, static shrinkage averages is outdated and ineffective, often triggering unwanted schedule chaos, broken SLAs, and endless firefighting.
That is why we launched QStory Shrinkage Forecasting because the planners we work with were wasting countless hours manually tweaking offline spreadsheets, ignoring the wealth of rich operational data they already had access to. Our tool replaces guesswork with data-driven accuracy, eliminating the madness and making a better everyday for planners, agents, and customers alike.
What is Shrinkage Forecasting?
Contact centre shrinkage forecasting is the process of predicting the exact percentage of paid working time lost to planned activities (like coaching, meetings, and annual leave) and unplanned activities (like sickness, lateness, and sudden absence), and dynamically factoring those losses into your forward schedules to ensure net headcount precisely meets customer demand.
Why “Average” is the Enemy of Resource Planning
In the contact centre world, we constantly strive to deliver customer experiences that are better than average. Yet, when it comes to forecasting shrinkage, most Resource Planning (RP) teams are still forced by legacy systems to rely on blunt, average percentages.
According to research highlighted by Call Centre Helper, typical contact centre shrinkage accounts for 30% to 40% of total workforce hours in the UK. Despite this massive operational weight, standard workforce management (WFM) tools push planners to apply a flat, static percentage (e.g., 32%) across every single operational day and hour.
Applying a static average to a dynamic environment guarantees failure. Monday mornings are not Wednesday afternoons. Applying a flat 32% across the board introduces two critical vulnerabilities into your scheduling:
- Understaffing During Spikes: Mondays and Fridays routinely experience higher unplanned absences, and many sources of external shrinkage are highly seasonal. A flat, static estimate undercalculates your required gross headcount, driving up queue times, breaking SLAs, and burning out your frontline teams.
- Overstaffing During Lulls: Midweek shifts see lower absence rates, and often training and 1-1s can be moved or postponed. Overestimating shrinkage here means excess rostered staff and wasted operational budget.
| Forecasting Method | Application | Operational Reality |
| Legacy Flat Estimate | 32% Static Every Day | Monday is severely understaffed; Wednesday is overstaffed. |
| QStory Dynamic Model | Dynamic Profile (e.g. Mon: 38% | Tue: 31% | Wed: 28%) | Real headcount is accurately matched to forecasted customer demand. |
Mapping the Realities of Shrinkage
To battle static averages, scheduling teams must stop treating shrinkage as a single block of lost time and start forecasting its specific components:
| Category | Typical Components | Impact on Scheduling |
| Planned Internal | 1-on-1s, coaching, team huddles, compliance training | Controllable. Can be scheduled dynamically around forecasted demand lulls. |
| Planned External | Annual leave, bank holidays, floating days | Predictable via seasonal booking patterns and historical allowances. |
| Unplanned | Short-term sickness, family emergencies, lateness | Highly volatile. Requires accurate pattern-based forecasting to prevent staffing deficits. |
Introducing QStory Shrinkage Forecasting
Built for the complex contact centre of the future, QStory is the trusted specialist that refuses to settle for “average.” Our Shrinkage Forecasting module treats shrinkage with the exact same predictive respect as volume and AHT. We use your existing operational data to accurately predict future lost time, replacing static offline spreadsheets with intelligent automation.
- Dynamic Pattern Recognition: Automatically identifies micro-trends across days of the week, weeks of the month, and seasonal cycles.
- Holistic Coverage: Seamlessly models both internal controllable events (coaching, huddles) and external events (holidays, sickness).
- Direct Schedule Injection: Feeds calculated shrinkage profiles straight into forward schedules. No more clunky manual CSV exports or static spreadsheet work.
- Data-Driven Precision: Replaces guesswork by analyzing historical variance and actual operational patterns to refine staffing requirement calculations.
“When was the last time your contact centre actually had an ‘average’ day?
Averages kill SLAs because they completely gloss over reality and ignore sickness spikes, shifted holiday patterns, and all the realities of large teams. To be better than average, you need to staff for what’s actually happening on the floor. We’re rolling out QStory Shrinkage Forecasting to help you plan for real conditions using the data you already own.“Barry Jones, Product Director at QStory
Frequently Asked Questions
What is the average shrinkage rate in UK contact centres?
Average contact centre shrinkage in the UK typically ranges between 30% and 40%. This varies by sector, the public sector and financial services often record higher planned shrinkage due to stringent regulatory training requirements.
How does QStory improve scheduling accuracy compared to standard WFM?
Standard WFM platforms require RP analysts to manually adjust static percentage overrides. QStory automatically calculates dynamic historical trends and incorporates them directly into future staffing requirements. With easy manipulation and a simple user interface.
Why does standard WFM software struggle with shrinkage forecasting?
Most legacy WFM tools treat shrinkage as a static input parameter rather than a living, dynamic variable. They rely on blunt averages, forcing WFM teams to perform manual offline calculations in spreadsheets to adjust for daily variance.
How does Shrinkage Forecasting reduce overtime expense?
By accurately predicting higher-shrinkage days (such as Mondays or post-holiday periods) using historical data, resource managers can align standard shift schedules with actual net demand. This eliminates staffing deficits and drastically reduces reliance on high-cost, last-minute emergency overtime.