Contact Centers Benefit From Collection and Analysis of Real-Time Customer Data
1 Mar, 2007
By: Pat WhelanArguably, contact centers are measured in more ways than virtually any other group. Every day, contact centers measure themselves against a broad range of quantifiable objectives. Measurements are typically indicative of one or more of several key areas, including service levels, quality of service and operational efficiencies. Today, evolving automated communications solutions are changing how organizations interact with customers. And they are also generating new classes of data – real-time customer response data – that can be collected, analyzed and used as a basis for ongoing improvement.
The phrase customer communication solutions describes fully automated, interactive solutions that can maintain a “conversation” between an organization and customer, performing such tasks as right-party identification and validation, payment processing and flexible messaging based on organizational business rules. Some of these solutions can capture significant amounts of data about customer preferences and behaviors that simply were not previously available – data that can significantly impact the ability to profitably manage customer behaviors.
To manage toward continual improvement, it’s essential to find metrics that are relevant to the areas managed. Finding and using these metrics consistently is the key. For example, a call center’s internal operations might frequently include such metrics as service level and abandonment rates. More people-centric metrics include first call resolution rate (“one and done”). These broader metrics are representative of the full range of contact center operations; they are descriptive of both operational efficiencies, and quality of service.
Today, aided by these customer communication solutions, contact centers are adding customer response and preference data to their analytics mix. This data has been found to improve the ability to manage customer communication results, while enabling significant impact on customer behaviors and responses.
Descriptive metrics in this area include data about what communication channels customers prefer – to data measuring customer response to different voices (e.g., male v. female), different scripting styles, responses to contacts at different times of day, to retry strategies and more.
In addition, measurements can be derived by placing traps or “checkpoints” at specified points within the communication itself. Analysis of these checkpoints can offer a granular basis for tuning and revision that improves quality of service and operational efficiency.
Given the increasing tempo of competition, the ability to manage and analyze these new sources of customer information can provide a competitive advantage. Collaborating with automated communication solutions suppliers to aggregate and analyze this new class of data – real-time customer response data – offers opportunities to improve both operational efficiency and customer interaction quality.
Customer Response Analyses – A Valuable New Tool Based on Data Collection
Whether you’re attempting to collect a debt, creating a model for a marketing campaign or performing a customer survey, having this new data enables you to segment and tailor your outreach efforts more effectively, producing better returns. The next step is analysis. The results of analysis can guide conduct of efficient champion challenger tests to continuously evolve more effective programs.
Getting the best results involves a balancing act between list penetration, retry strategies, best times to call, number of attempts and many other factors. It’s not a trivial problem.
Many organizations have built a storehouse of empirical knowledge (sometimes called “tribal wisdom”) in an attempt to guide them in effective strategy design and to develop useful champion challenger tests. However, organizations that tackle these issues using an analytics-based approach are typically able to reach more people and enjoy better results.
An analytics-based process typically produces more consistent, predictable results – results based on hard data. Analytics-based testing can provide substantial improvements to contact center operations through a process called “tuning.”
Organizations typically know basic historical facts about customers. Today, working with data provided by automated communication solutions, organizations have the further ability to know how customers responded to being contacted using different voices or at different times of day or even through different channels of communication.
Thus, in addition to providing additional capabilities, these solutions can also provide real-time data that can be aggregated and analyzed to improve operations – data that both complements and augments existing storehouses of historical data.
Organizations can use this new class of data to understand and adapt to customers. While automated communications solutions offer significant potential for operational improvement, it’s unlikely that any solution chosen will deliver peak performance immediately on initial deployment. Optimization is necessary to deliver improvements in revenues, cost reductions, service improvements and further operational efficiencies.
Response-Data-Driven Optimization Offers Consistent Improvement
The question then becomes: “Now that I’ve deployed an automated communication solution, how can I collect and analyze its response data to improve results?” One reason contact centers are so intensely data driven is that performance optimization is essential. Today’s automated communication solutions offer a rich set of new data on which to perform optimization analyses. To take best advantage of these analyses, they must be supported with:
• Significant benchmark data. Having a few weeks’ or months’ worth of data is useful, but it’s better to have more data – not only from your own individual organization, but from organizations in similar market spaces.
• Industry-specific reports (sometimes called “peer reports”) that enable meaningful, realistic comparisons across your market.
• Process and practice expertise (“best processes”) based on experience across both your industry sector and other sectors.
Imagine that you have the opportunity to collect and analyze data from of one of these solutions. The following approach is reproducible, has demonstrated high value and follows a consistent data-driven methodology, namely:
Analyze: First, analyze the improvements gained after initial deployment. Create these analyses based on management reporting. Set goals, working with your solutions partner as appropriate.
Benchmark / Compare: Second, assess performance by making use of peer reports and industry benchmarks based on historical data. Your solutions partner may be able to provide this information.
Apply Data-Driven Best Processes: Analysis and benchmarking are critical steps. It helps to describe the gathered data in meaningful, applicable ways. The next step is inferential -- reaching conclusions that extend beyond the data. To reach those conclusions, you must apply empirical methods – based on industry and practice experience – a kind of “best-process” analysis.
As an example, data analysis for a debt collections operation might demonstrate that an organization is underperforming in, for example, average payment per account. That information is descriptive. It’s a critical piece of the puzzle, but it doesn’t explain what to do – whether it would be more effective to make a scripting or tone of voice change or to change retry or answering machine strategies.
To get the best results, organizations combine their own domain expertise with practices and processes derived from past analyses run within their industry sector and even other sectors. The key to success is that organizations must be able to use actual data generated by their contact program(s) – real-time information about current customer behaviors – as a starting point.
Test: Evolve your operations by running those champion/challenger tests suggested by the data. Ongoing testing is a must, and delivers excellent results when combined with data-driven analyses.
Repeat Regularly: Optimization is a process, not an event.
Describing new classes of data and methods of applying analyses to that data that yield useful customer insights is one thing. The example below illustrate both operational results and more customer-centric service quality results derived by organizations that have begun tapping the potential value in collecting and analyzing this new data.
Collections
A top five credit card issuer upgraded its collections operation using an automated customer communication solution with full data tracking and analytics capabilities. Out of the box, this fully managed solution enabled a range of improvements to the operational results of their contact center, including:
• Collecting more dollars than with its previous dialer/agent strategy
• Managing agent resources more effectively
• Improving list penetration
• Improving RPC
After approximately three months of collecting and analyzing real-time customer response data from the automated communications solution, three alternate strategies were proposed.
Results of applying these strategies across randomized sets of customers were instructive. Each of the three proposed strategies delivered greater RPC, more live answers and collected more money than the initial, “out of the box” solution.
After running these strategies side-by-side for several weeks, the clear winner was the third strategy.
If you manage in a contact center environment, or have contact centers in your business, you are vitally concerned with a wide range of measurements and analysis. This concern extends to both descriptive analysis:
”We’re not getting through to an unacceptably large percentage of our customers,” to perhaps the more important inferential analysis component, “Most of the customers we’re losing drop offline at a specific place in the message. Let’s adjust and tune that place.”
Having new sources of real-time customer response and preference data generated by automated customer communications solutions enables contact centers to generate new, valuable analyses that offer the potential to improve both operational and customer-focused results.
