A Contact Center Crystal Ball:

A Contact Center Crystal Ball: Marrying the Analyses of Service, Cost, Revenue, and Now, Customer Experience Ric Kosiba, Ph.D. Vice President Interactive Intelligence, Inc.

Table of Contents Introduction... 3 Managing by Gut... 3 Performance Metrics... 6 Decisions When There are Competing Objectives... 8 Enter Customer Experience Metrics... 10 Technologies Enable Great Decision-Making... 10 Copyright 2012-2014 Interactive Intelligence, Inc. All rights reserved. Brand, product, and service names referred to in this document are the trademarks or registered trademarks of their respective companies. Interactive Intelligence, Inc. 7601 Interactive Way Indianapolis, Indiana 46278 Telephone 800.267.1364 www.inin.com Update 2/14, version 2 2012-2014 Interactive Intelligence, Inc. 2 A Contact Center Crystal Ball

Introduction If a contact center executive could have anything, it would probably be certainty. It would include certainty of contact demand, resource availability, operational efficiency, and maybe most importantly, the certainty of agent performance and customer experience delivery. However, certainty is the one thing that the manager of such a complex operation will never have. In the absence of a crystal ball, the best equipped contact center executives tame the uncertainty of their environment with good management and with the help of mathematical models. These models provide the next best thing to an operational crystal ball they alert, evaluate operational risk, and propose business and resourcing solutions. And now, the best of these models determine expected experience delivery. Managing by Gut The goal of every contact center executive is to plan and run the operation so that it provides an appropriate and consistent service each day at least cost. This is not an easy task as demand variability and seasonality are the norm in contact center operations. In 2003, the Society for Workforce Planning Professionals (SWPP) produced a survey of workforce managers that asked a simple question: How accurate are your 30-day contact volume and handle time forecasts? The results were telling. Of those surveyed, 51% reported that their contact volume forecast was at variance to their actual volumes by 16% to 20%. Seventy four percent responded that their average handle time forecast varied against actual performance by 5% to 10%, one month out. Forecast variability of business drivers is a great measure of real operational variability. A forecast of, say, contact volume represents an organization s best effort to know the future demand; variability to plan represents not just forecast error, but the unknowable. The top companies view their forecasts as their best understanding of their operation at the time their forecasts are created. Any variability to their forecasts necessarily represents business change or business unknowns. Each forecast is viewed as their operational baseline. So what does this imply for service variability? Because operations make staffing decisions in accordance with the original forecast long before "day of" production, any forecast variability will lead inevitably to service delivery variability. Figure 1 on the next page represents the sensitivity of service level to a change in contact volume for a midsized customer service contact center. Each data point was developed using a validated discrete-event simulation model by holding all variables constant (i.e. staff levels, handle times), varying only contact volume. By changing volume and determining the resulting service, the sensitivity of the contact center can be determined and, more importantly, the natural variability of service delivery can also be determined. 2012-2014 Interactive Intelligence, Inc. 3 A Contact Center Crystal Ball

In this model, a volume variance of 10% (the x-axis) yields a service level variance of plus 8% and minus 11% (the y-axis), meaning that less-than-normal variability, as defined by the SWPP survey, still implies service level delivery between 69% and 89%. Figure 1: Sensitivity Analysis of Volume versus Service Level for a Midsized Contact Center This range is the daily as produced service delivery variance that these operations can expect due to their better-than-normal forecast variance. Similarly, varying handle times in a medium sized contact center by an industry normal 5% and developing sensitivity graphs (Figure 2, see next page) yields a variability in service level of about plus or minus 5%. Since it is likely that both contact volumes and handle times will be at variance to forecast, it is easy to see that achieving service consistency may be a challenge. 2012-2014 Interactive Intelligence, Inc. 4 A Contact Center Crystal Ball

Figure 2: Sensitivity Analysis of Handle Time versus Service Level for a Midsized Contact Center For those contact center executives without tools like contact center simulation models and sensitivity analyses, natural variability can reveal itself as unexpected service disruptions. Without these models, managers simply cannot see negative variability coming, and as such management experience is much more important, as a manager s gut instinct is the only safeguard from unknown resource shortages (or overages). For executives that incorporate mathematical models into their planning process, variability can be managed. When simulation models are part and parcel of a resource planning process, natural variability can be minimized not by knowing the unknown, but by resourcing in such a way that the magnitude of the unknown can be understood and managed through the judicious use of resource flexibility (e.g. use of agent overtime or flexible shrinkage). Figure 3 on the next page demonstrates the agent resources (and hence, the costs) required to improve service levels. This sensitivity graph, along with Figures 1 and 2 can be used to ensure that natural variability in volumes and handle times can be covered by allocating the right amount of flexibility to the operation s budget. Let s do a quick exercise. Assume that for our network, we typically see contact variance of plus or minus 5%. Using the sensitivity curves in Figures 1 and 3, variability in contact volume of 5% translates to a miss in service level of around 4% (Figure 1). This service variability of 4% can be managed by having 13 agents available to flex onto the floor (Figure 3) when volumes begin trending higher. Note that these sensitivity graphs are unique to each contact center, contact type, and economy of scale. No gut instinct is required. 2012-2014 Interactive Intelligence, Inc. 5 A Contact Center Crystal Ball

Figure 3: Sensitivity Analysis of Staffed Agents versus Resulting Service Level Performance Metrics Service level is far from the only important contact center metric. We need to predict the performance and tame the variability of several key metrics. Let's say we had a crystal ball. What metrics would be important to predict? What sorts of analyses would be useful to enable more certainty in performance? First, the metrics that are important to the contact center execs are clear; they are focused around areas appealing to the various stakeholders of the operation customer satisfaction, center efficiency, service attainment and consistency, agent satisfaction, variable labor costs, and for sales or collections centers, revenues. All of which are, in some ways, competing objectives. Let s discuss these areas in turn. Customer Experience: Inarguably, the metrics associated with customer satisfaction are among the most important to a contact center organization; delivering customer satisfaction is for most companies the whole purpose of their contact centers. The metrics each company uses are most often custom to each operation (or even each contact type within each operation), but include variants of agent quality score (again, usually custom to the contact type), net promoter score, customer satisfaction score, and others. Their goal is to reflect the voice of the customer and the customer's opinion of their interaction with the contact center agent. Many operations measure first call resolution as a proxy for customer satisfaction. Customer satisfaction metrics exhibit interesting patterns. They are often seasonal, as the purposes for customers interacting with contact centers can vary seasonally (think holiday season versus right after). Because of this, they are also predictable customer satisfaction can be measured, like other time series metrics (e.g. contact 2012-2014 Interactive Intelligence, Inc. 6 A Contact Center Crystal Ball

volume), and can often be forecasted. Also, through the use of good management, customer satisfaction scores can be managed, using agent training or incentives. We will return to this. Contact Center Efficiency: Contact center managers track the efficiency of their operation mostly in the form of simple, but effective, ratios. The ratio between agent paid time and on phone time delivers one of the best overall measures of a center s efficiency, but includes efficiencies that management cannot affect (e.g. the efficiencies of economies of scale). Time that an agent is either in an idle state (waiting for an interaction) or assisting a customer divided by paid time is also a very effective efficiency metric (and removes economies of scale). While many managers track agent occupancy, it is not a great measure of efficiency and it can be easily manipulated. Service Attainment: Operational performance metrics are probably the most widely measured in contact centers because they are good measures of performance and easy to capture. Most contact center managers focus on service level (percent of calls answered within some threshold) or average speed of answer (ASA), while keeping an eye on customer abandon rates (number of customers that remove themselves from queue, usually due to long wait times). It is interesting that, while abandon rate is a great measure of customer patience and obvious dissatisfaction, it is rarely used for staffing purposes, simply because workforce management systems do not support this metric, as it is difficult to model. Also, more and more multi-skill or multi-channel contact centers are beginning to utilize capture rate as a metric (the percentage of contacts that are successfully routed to the preferred agent group). Consistency of service attainment is a primary goal of many contact center operations. Given the variable nature of contact center demand, consistency is, as we've shown through sensitivity analysis, difficult for many to achieve. However, other contact center operations focus on staffing for controlled inconsistency, changing service attainment goals as the marginal customer value (usually revenue) changes per season. These companies use abandon rates, lost revenue, and variable labor costs as the drivers for calculating the service goal that delivers the highest marginal profit. For example, a reservations center may have different service goals by season as the marginal value of their product changes when their customer calls shift between a business and leisure focus, business travelers being less cost elastic than leisure travelers. This is the equivalent of using marginal profit as their main service goal treating their profit-making contact center as though it was a business in its own right. These shifting goals are evaluated regularly and can lead to substantially better profit performance than those using static service objectives. Agent Satisfaction: Like customer satisfaction, those companies that actively measure and monitor their employee satisfaction do so with custom metrics and periodic employee surveys. However, most operations closely monitor agent occupancy which measures how busy agents are. It is well known that high occupancies usually result in longer handle times and possibly higher agent attrition. 2012-2014 Interactive Intelligence, Inc. 7 A Contact Center Crystal Ball

Costs and Revenues: While costs, and for sales centers, revenue, certainly receive management attention in the contact center operation, it is somewhat surprising how often center planning is mostly blind to costs and revenues. Often resource planning spreadsheets include capacity plans, but not variable labor costs. Clearly this is not optimal. Decisions When There are Competing Objectives The purpose of monitoring and reporting the various important contact center performance metrics is to provide managers information for making better resource decisions. There is obviously an art to this; contact center performance metrics are inherently in conflict (think cost versus service). In management science, the goal is to manage the performance in such a way that competing performance measures reach a form of Pareto optimality, where the center is managed such that it is impossible to improve one performance metric without degrading another. Figure 4 represents exactly this. It is a drawing of the trade-off between two competing metrics, service and cost per call, for an inbound contact center. In this sensitivity graph, all performance drivers are held constant, varying only the agent resources available and drawing the resulting service level on the x-axis and the resulting cost per call on the y-axis. This represents true Pareto optimality (so long as the center is otherwise operating efficiently) as each point on the curve represents the best cost and the best service level achievable. Figure 4: Sensitivity Analysis of Service Level versus Cost per Call 2012-2014 Interactive Intelligence, Inc. 8 A Contact Center Crystal Ball

For the contact center executive, making a decision becomes easy: if they would like to hit an 80% service level, the cost will be $5.90 per call. If they would like to hit a 70% service level, the cost is $5.60 per call. The beauty of these sorts of analyses is that they make the repercussions of every resource decision crystal clear. While sensitivity analyses are very powerful analytic tools, they are only truly useful if they are part of the overall planning and decision-making process. Best practices dictate that sensitivity analyses and its enabling technology, discrete-event simulation modeling, be embedded in the planning process and planning cycle. The best operations have developed business processes that include the following steps: 1) Monitor variance to plan and alert: As real world performance becomes catalogued, all variance to plan should be noted and explained. Weekly contact center performance data (ACD or similar, agent shrinkage and workforce management reports, payroll reports) are gathered and become comparison points between planned and actual performance. Items like contact volumes, handle times, agents available, agent shrinkage, agent attrition, cost per call, etc should be compared to plan to determine if there is any variance. If there is variance, then see step 2. 2) Use mathematical models to evaluate risk: If a metric is at variance to plan, there are two activities that should be performed right away. First, the variance to plan should be evaluated. Is the origin of the variance known? Can it be controlled? Is it expected to continue? If it is either unknown or expected to continue, the variance needs to be baked into the assumptions of a new plan. This entails building new time-series forecasts of that item. Because there is a new forecast and new forecast variability, the expected future operational performance should be determined along with the resulting service variability using simulation models. What happens to service levels? Abandons? Cost? Revenues? If the change to performance (the business risk) requires a resourcing decision, then see step 3. 3) Propose resource changes: At this step, because there is a significant change to one or more business drivers, it is necessary to determine the week-over-week staff required, and the new hiring, overtime, undertime, and controllable shrinkage plan that will deliver the desired service. While many companies develop these plans by hand, using a spreadsheet as a guide, the best companies utilize mathematical optimizers, like Integer Programming to perform this task. These models have a few advantages. They are fast, consistent, and provide the mathematically optimal resource plan, one that hits goal, week in and week out, at least cost. 4) Make decisions: Variance represents a decision point. When the operational environment changes, management must decide to a) staff to meet the change or b) live with the current resource plan and accept the change in service. Again, the best companies use their mathematical models to quickly evaluate all permutations of possible business scenarios against possible resource decisions to determine those most acceptable to the business. 2012-2014 Interactive Intelligence, Inc. 9 A Contact Center Crystal Ball

Enter Customer Experience Metrics Until very recently, there were not methods available to model the hodgepodge of customer experience metrics that each company employs. With custom metrics for each company and contact type, one could not expect generic predictive models to exist. The nature of these metrics implies that they can be captured in a contact center plan as a time-series metric. They exhibit seasonality. This also implies that they can be forecasted using the same sorts of methodologies that the best planners use to forecast call volumes and sick time. For instance, analysts forecasting metrics that exhibit a time series trend might utilize exponential smoothing or regression models. If the metric displays seasonality, it might make sense for the analyst to use methods like Holt- Winters. But the good news is that these custom metrics can be modeled, predicted, and used in the normal course of strategic planning to determine, for each resource plan, the expected week over week service level, abandon rate, occupancy, cost, revenue, average speed of answer, and now customer service metrics like net promoter score or agent quality score. There is no reason that expected customer experience is not part of the regular capacity plan, forecasted along with all of the other service metrics. Technologies Enable Great Decision-Making Simulation and mathematical modeling have been commercially available over the last decade for contact center operations. These systems, commonly called strategic planning systems, automatically develop forecasts and resource plans for multichannel and multi-skill operations. Strategic planning models, like those used in Interaction Decisions, have a few terrific advantages over homegrown spreadsheets. First, they work in two directions. They evaluate for any week over week scenario the service, revenues, costs, and customer experience scores expected under any planning scenario. In the other direction, they determine the least cost staff plan required to hit the service goals associated with any scenario. Second, they are proven accurate. The best planning systems include a validation step to prove that, for each of the contact types and contact centers in a network, the model is accurate when compared to real contact center data. This is not easy, because every contact center and contact type is truly different. The models must be smart enough to consider these differences and be recalibrated as the operation changes. Third, they have to be fast. It helps no one if the models are too slow for the decision makers. The best systems can be run interactively, with only minutes required to evaluate any scenario. 2012-2014 Interactive Intelligence, Inc. 10 A Contact Center Crystal Ball

Speed, accuracy, and breadth of analytics enable a different sort of decision making process. With a static, spreadsheet-based planning process, decision-making and analytics were only passing acquaintances, where analysts had little time available to answer executive what-ifs. With speed and accuracy, using advanced modeling, an analyst can interactively answer the executive s query real-time. This enables a different relationship with decision-making, where all major decisions are vetted and all repercussions of resource decisions are known, including the expected customer experience. While complete certainty is still not attainable, using strategic planning systems, executives are a lot closer to their crystal ball. 2012-2014 Interactive Intelligence, Inc. 11 A Contact Center Crystal Ball

The Author Ric Kosiba is an expert in the field of call center management and modeling, call center strategy development, and the optimization of large-scale operational processes. He received a Ph. D in Operations Research and Engineering from Purdue University and an M.S.C.E. and B.S.C.E. from Purdue s School of Civil Engineering. He also has obtained a patent on the application of optimal collection strategies to delinquent portfolios in addition to a patent on the application of simulation and analytics to contact center planning. At the start of his career, Ric served as Manager of Customer Service Analytics for USAir s Operations Research Division, and as Operations Management Senior Analyst with Northwest Airlines. Later, he moved into Customer Support at First USA, where he served as Vice President of Operations Research and guided all facets of the company s call center process improvement, including collections strategy modeling and detailed staff plan development and call center budgeting. Ric then held a position as the Director of Management Science at Partners First, where his responsibilities included the detailed modeling of portfolio risks, in addition to predictive and prescriptive marketing and operations engineering. Ric ultimately founded Bay Bridge Decisions, which later joined Interactive Intelligence, and now serves as Interactive s Vice President in the strategic planning market. He continues to write for numerous contact center publications and speaks at highly acclaimed technical and contact center forums on a frequent basis. Contact him at: ric.kosiba@inin.com or 410.224.9883. 2012-2014 Interactive Intelligence, Inc. 12 A Contact Center Crystal Ball