Magazine article CRM Magazine

Managing the Contact Center Strategy More Intelligently

Magazine article CRM Magazine

Managing the Contact Center Strategy More Intelligently

Article excerpt

A modern contact center routinely has terabytes of operational data available at any given time. "Big data." When effectively analyzed, this information can help contact center management maintain consistent and appropriate service delivery across the seasonal peaks and valleys of contact volumes. Until recently, however, the methods used for such analysis and forecasting were inaccurate and often considerably off-base. Algorithms are now available that enable more accurate forecasting, evaluation, and optimization of operational strategies across seasons and years--introducing an intelligent new era of how contact centers are managed.


The contact center strategic plan or capacity plan focuses on resourcing the contact center network over the next week to 18 months. For a contact center executive, a capacity plan is the best big picture decision-making device available. This plan and the resourcing decisions it expresses is the overarching statement of how management wants to treat its customers and agents. Because a well-managed and funded strategic plan leads to a well-managed operation, it is a great aid to achieving wanted customer and agent satisfaction levels. With proper foresight, analyses, and algorithms, service failures that normally result from an unmanaged or inefficient plan can be avoided.

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 or 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. Proving model accuracy gives decisionmakers confidence in the analyses that flow from these models. The best simulation models are multichannel (that is, simulates email, back office, inbound, outbound, chat centers), multi-skill, and multisite models. These models are also used to determine how many agents are needed week-over-week to ensure service delivery.

The best contact center resourcing algorithms are staffing optimization prescriptive models, which 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. …

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