The world is awash in data. Even “Big Data” is in the news because of its promise. But only through the use of algorithms can we make sense of all this information.
In the contact center in particular, terabytes of operational data are usually available at any given time. When this information is analyzed effectively, it can help contact center management maintain consistent and appropriate service delivery across the seasonal peaks and valleys of contact volumes. Consider for instance that centers have used scheduling algorithms for years to manage the short term, “day of ” service delivery. Now, however, new algorithms enable the forecasting, evaluation, and optimization of contact center strategies across seasons and years.
For long-term planning, the use of algorithms and data presents opportunities to prepare more successfully for seasonal fluctuations, changes in operational performance, and changes in contact volume. Consider again that, typically, contact center operations are not stable over medium- and long-term time horizons. It therefore is not unusual for contact volume forecasts to have error rates of over 20 percent a few months out. More so, the resourcing decisions that contact center executives make are not short term decisions at all. Just look at hiring. Hiring staff is often a long-term decision, and yet many executives often make such judgments in the face of significant forecasting error.
The Importance of Strategic Planning Algorithms The contact center strategic plan or capacity plan focuses on resourcing the contact center network over the next week to 18 months. A capacity plan is the best big picture decision- making device for a contact center executive. In many ways, this plan and the resourcing decisions it expresses are the overarching statement of how management wants to treat its customers and agents. This is where customer service executives puts their “money where their mouth is,” in that a well-managed and funded strategic plan leads to a well- managed operation. Further, an effective strategic plan is a great aid to achieving wanted customer and agent satisfaction levels.
Which models are best?
Many service failures result from an unmanaged or inefficient plan and could be avoided with proper foresight, analyses, and algorithms. Advanced strategic planning systems have mathematical models that both simulate the operational performance under different planning scenarios and develop resourcing plans that are most efficient while still achieving service goals. When variance to the plan is noticed, these simulation and optimization algorithms are key to understanding the trade-offs between service, cost, customer experience, and revenues. These algorithms make plain the service, cost, and experience repercussions of alternative resource decisions, and lead to better informed resourcing decisions.
Simulations are descriptive models; they describe how the operation will perform under different agent resource levels of customer contact volumes. Simulation models can be proved accurate through a validation exercise where the model’s predictions are compared to historical contact center performance through good service levels and bad. Once validated, descriptive models can be used as predictive models of future contact center performance (hint: always insist on model validation for any predictive system). Proving model accuracy gives decision-makers confidence in the analyses that flow from these models. The best simulation models are multichannel (simulates email, back office, inbound, outbound, chat centers), multi-skill, and multisite models. These models are also used to determine staffing “requirements,” how many agents are needed week- over-week to ensure service delivery.
The best contact center resourcing algorithms are staffing optimization prescriptive models. These models prescribe the best hiring, overtime, undertime, and controllable shrinkage plans that meet servicing objectives at least cost. These models ensure consistent service delivery as they achieve just-in-time staffing plans (as real world constraints allow), never hiring too many or too few contact center agents.
The combination of predictive and prescriptive algorithms let analysts determine the optimal resource plan that will meet service goals at least cost under any expected scenario. This approach produces the “best” management decisions.
Used by a clever analyst, these algorithms will also accurately predict the repercussions and risks of making the wrong resourcing decisions. Given that the future is unknown and variable, a creative analyst can quantify the operational risk of making the wrong staffing decision. These scenarios can be evaluated beforehand. For instance, performing the what-if analysis of what would happen to service if we staffed optimally for today’s forecast, but it was wildly off! This analysis could be used to alter the staffing decision and protect from this real-world possibility.
The contact center industry has many cautionary tales of strategic service failures, where wrong resourcing decisions led to service catastrophes and customer experience nightmares that took many months to fix. Avoid these catastrophes with a better strategy for planning.
