The ScoreData Team
A Customer Engagement center is a central point from which all customer contacts, including voice calls, email, social media, faxes, letters, etc., of an enterprise are managed. It is part of a company’s customer relationship management. With the large amounts of data collected by modern engagement centers, the possibilities of applying predictive analytics to improve engagement center efficiency and customer satisfaction are very many. The applications can be broadly classified into the following categories:
- Enhancing Customer Engagement and improving customer experience. The possible applications include:
- Improved caller/agent matching
- Enhanced cross-selling and upselling
- Superior customer retention
- Interactive Voice Response (IVR) analytics
- Behavioral targeting to better serve customers
- Optimizing contact center management and control. Possible applications include:
- Agent ranking and performance measurement
- Improved call routing and distribution
- Centralized global queueing
- Staffing optimization
ScoreData has extensive experience in building predictive models using their ScoreFast™ engine, many of which can be integrated with caller-business engagement scenarios, e.g., improved customer retention by churn prediction and mitigation, enhanced cross-selling and upselling, risk analytics, etc. This paper is about a staffing optimization project undertaken in collaboration with our partner Avaya. Then we go on to examine how predictive analytics will help contact centers of the future.
Customer Engagement centers employ a large number of contract agents as the workloads tend to vary significantly over the course of a year. Being able to predict workload and staffing requirements to keep the customer wait times less than a specified threshold is critical for agent capacity planning.
Engagement Center Optimization Example
Business Objective: To build a workload prediction system that accurately forecasts call volumes and agent capacity requirements for the following week. While forecasting is a common use case in many verticals, as the data, the number of variables, and the requirements for finer levels of forecasting increase, the modeling problem becomes more challenging. Avaya provided the data for this project from a demonstration system using historical data sets.
Methodology: We used the following methodology for the forecasting project:
- Data preparation, audit and understanding- We engaged with Avaya teams to grasp the business context and understand the data (tables, fields and their meanings). We normalized and merged all datasets to form a single view of the data for modeling.
- Feature Engineering and Data Loading – ScoreData created information rich features from the single view dataset. The resultant dataset was loaded into the ScoreData analytics platform.
- Feature Extraction and Model Development – We conducted statistical analyses on the ScoreData platform to generate the best predictors of the target variables. Then we developed various models for forecasting workload and staffing.
- Insights and Report Preparation – Analyses outcomes and insights were collated in a report along with model characteristics.
Data Audit and Insights:
- Looking at per-day level call volume data, one can see two flat periods (with no activity) in the data (‘call volume vs. days’ graph below). As a consequence, ScoreData did not use this as a homogeneous time series data (from 6/11/13 to 7/22/13), rather we only used the data for the two time periods that have call volumes, ignoring the flat periods.
- Call Segment Data Funnel – Step by step data stats on applying filters to remove Interactive Voice Response (IVR) calls and data stats:
- Total number of segments (or calls): 9,242,604
- After removing IVR Calls which we can not use: 2,107,867
- Removed several more rows that did not meet Avaya’s criteria for including in the analysis
- Average Number of agents handling calls for each hour
- Average Queue Group Time (Ring + Talk + After call)
- Average Queue Group Time (without the Talk time)
- Time Distributions
- Disposition Time of Agents
Models: Since the call volumes varied by day, prediction models were built for each day of the week. We used the Generalized Linear Model (GLM) algorithm with Poisson distribution to predict the number of agents required for a given call volume, wait time, day of week and queue group. The following figure shows the features with the highest relative importance. The most important feature turns out to be the Number of Calls, with wait time coming in next; there is a negative dependence on wait time (highlighted in orange). The greater the wait time allowed, the fewer the agents required.
The project was useful to the ScoreData team to understand the nature of call center data and to create a unified view of the data. Forecast of call volumes was done on a daily basis. The next step in the project, given enough data, is to forecast call volumes on an hourly basis and then predict staffing requirements also on an hourly basis.
Analytics for the Engagement Centers of the Future:
In this section, we will examine a few emerging trends in the contact centers of the future and discuss how analytics will play a crucial role in the transformation of such centers. The engagement centers of the future must be agile enough to adapt quickly as customers’ expectations shift with advances in and varieties of interaction options open to them. Analytics will play a most important role as contact centers adapt to the changing demands of the future. According to Laurent Philonenko, SVP of Corporate Strategy & Development, CTO of Avaya, most businesses that are making analytics an urgent investment are doing so to be better positioned to (1) compete more successfully, and (2) grow their business to increase revenue potential.
Virtualized Engagement Centers: The engagement center of the future is not likely to be a centralized facility, but will be distributed geographically with agents working out of their homes. High turnover in the engagement center staff is causing business leaders to look for effective ways to attract and retain the best talent. Flexibility in working hours and workplace is an important factor in this. Virtualization technology will play a key role in making this practicable. Key issues here are security and privacy of customer data. Machine Learning and Analytics are increasingly playing a critical role in detecting security breaches and avoiding future attacks. Analytics techniques are now being applied across the network, application and data layers to provide increased security and privacy. One of the techniques used to provide privacy is data encryption. Ability to process encrypted data and draw insights will become increasingly important.
Future Agents: It turns out that today contact center staff accounts for almost 75% of the cost of running a center. Therefore, it is important to optimize agent performance with the right tools and information. Agents will increasingly have to multitask among voice calls, social, chat and email interactions with the customers. Today, most agents have to toggle between a set of standalone software applications to access the information they need to service customers. Agents will need a seamless view of customer information to fluidly meet customer needs. With the digital transformation of an enterprise, the data silos that are entrenched in today’s enterprises can be overcome. Again according to Philonenko, in a digital enterprise, with a single analytics application it becomes much easier to have a single view into the entire journey of customer data, partner data, employee data, process data, etc.
Instead of specific agent skill groups that today’s contact centers employ, future centers will have a fluid workforce in terms of skills. For example, a particular agent may have foreign language skills as well as the ability to cross-sell or up-sell effectively. Agents will not belong to specific skill groups, but will be called upon to service customers based on the needs and demand. Analytics will play an important role in forecasting the workload, optimizing the staff, and routing customer calls to the right agents to minimize the customer waiting times. Optimal workload forecasting is an extremely challenging problem under those constraints. ScoreData has worked out an approach to solving this challenging problem with predictive analytics. We hope that will be the subject of another report in the future.
The way agent performance is measured and ranked will also need to change to keep up with customer demands. Contact centers will need to transition from a reliance on efficiency-based metrics, such as Average Handling Time (AHT) and calls handled per hour, to customer and business focused measures like First Call Resolution (FCR), customer satisfaction, and ROI. Analytics can be leveraged in that transition as well. For example, customer feedback surveys – both ratings and comments – can be analyzed to assess customer satisfaction.
Future Customers: According to the American Express 2011 Global Service Barometer, U.S. consumers prefer to resolve their service issues using a variety of touch-points, including the telephone (90%), face to face (75%), company website or email (67%), online chat (47%), text message (22%), social networking site (22%), and using an automated response system (20%). And according to the 2014 Global Service Barometer, for simple issues consumers prefer going online (36% versus 14% by phone) and for difficult enquiries talking to an agent by phone (48% versus 10% by email). Consumer preferences will keep changing. When they escalate a service issue from chat to voice, they expect the new agent to know the interaction that has already happened. Cloud-based contact center and analytics can help agents to follow a customer’s journey seamlessly.
How ScoreFast™ Makes a difference
The entire project with the run-time engine was delivered in six weeks. Our models were built using the ScoreFast™ engine, the web based model development and management. It is built as an enterprise grade modeling system that can be used to develop a broad range of models for use cases inside and outside the Engagement Center.
Its data and model management modules are easy to use, dashboard driven and intuitive. It chooses models for specific use cases after trying out many algorithms internally and selecting the one with best performance metrics. ScoreFast has collaboration features encouraging sharing and collaboration within large cross functional teams, and access control features that are designed keeping in mind specific needs of ScoreData’s big enterprise clients. The collaboration features encourage cross-functional knowledge sharing and innovation within the companies.
The platform has built in hooks to link raw data feeds into the system and its one push provisioning features mean models once developed and tested can be deployed onto downstream systems with a single push of a button. These features make ScoreFast easy to integrate into existing business processes without any disruption or cost overheads.
The canonical engagement center use cases deal with Agent Ranking, Caller-Agent Mapping, Dashboards with cross-sell or upsell with detailed presentation of customer profiles, and Engagement Center workload optimization. All these yield substantial improvements in customer satisfaction and improved top-line and bottom-line benefits. The ScoreFast™ engine delivers unique value throughout the predictive analytics insights-to-decision process, with a dramatically lower total cost of ownership.
Conclusion: Consumer demands will continue to change and technology will continue to evolve. Predictive Analytics will play a crucial role in helping businesses to adapt to the changing world. Engagement centers that adapt to changes to empower agents while keeping customer satisfaction in mind will become “relationship centers.” As Philonenko says, analytics allow human beings to be smarter, act faster, evolve and grow, all of which are essential for an agile relationship center.