Consider the following contact center scenario.
Janet, a mobile consumer, calls her telecom provider and lands at the Contact Center.
Janet: Your network connectivity is poor in the Dukes and 53rd area. I keep dropping business calls when I commute through it every day.
Advisor: Thank you for the location details. Is there any specific time of the day that you notice this more often? …
The advisor goes through a script to elicit information about the problem. She then asks Janet if she could help her with anything else.
Janet: Oh yes! I need to know more about your A-Plus business package.
Advisor: (Explains the features and plans of this value-added service.) Can I sign you up? It will take only a minute.
Janet confirms her order.)
Advisor: Is there anything else I can help you with today?
Janet: Yes, there is one more thing. I am approaching the end of my present subscription and my phone is almost two years old. I’m wondering what my options are. Maybe I’ll upgrade to the <model>. But I hear that you need a micro-SIM card, while my present phone uses the larger SIM card.
The advisor helps Janet with details of the new phone, SIM card and plan options. She also gives Janet the link to the online channel where she can upgrade and informs her that she will email the names of the distribution partners available in the local area where she can evaluate phones and sign up.
Using the previous example, this article will examine the role of data in customer experience management in contact centers through three different prisms.
1. Harnessing Data for Long-term Process Excellence
A process excellence approach to business seeks to answer how the building blocks of your value chain are working. In the above example with Janet, after her interaction with the advisor, the Contact Center needed to initiate some important service delivery or follow-up actions.
The information regarding her problems with signal quality has to be passed on to field operations. Then, the sales department needs to get an email off to Janet listing local partner options in her neighborhood. There can easily be slips through the cracks due to poor process handovers.
Traditionally, data has been used in contact centers to investigate how existing processes are working. Are they operating to design? This is a change-oriented approach, where a Black Belt collects data and studies call metrics, using DMAIC (Define, Measure, Analzye, Improve, Control) methodology and tools, like business assessment and value chain mapping to understand where tweaking a process is necessary for different/desired results. The value additions come from process engineering after detailed data analysis.
Contact center technologies, like switch and interactive voice response (IVR), have long provided the detailed voice of the process (VOP) data needed to undertake such (relatively large-scale and, therefore, not frequent) Six Sigma interventions. Such projects aim to make long-term changes, such as to rework processes to deliver better products or redesign workflows for more efficient service delivery.
Also, tools like speech analytics provide information on actual customer needs and sentiment from their calls, forming the voice of the customer (VOC). Together, they form rich sources of customer information for process excellence initiatives for sales, marketing, product development, operations/manufacturing, procurement and finance departments.
2. Using Data to Inject Real-time Process Efficiencies
The second role of these data sources is in actual operations. Contact centers routinely mine data for (relatively) immediate needs, like quality measurement for advisor training, and call forecasting for staff and schedule optimization.
By looking at the constant flow of process data, operations leaders are able to respond to call trends changing throughout the day/week in near real-time. What is important to note, however, is that now it is also possible to react to micro-trends that are easily missed on the operations floor due to everyone’s preoccupation with actual service delivery. With advanced dashboards and analytics, monitoring data in real-time allows you to focus on the data itself.
For instance, even a remote command center can see that a contact center in Manila, Phillipines, is deploying excess staff three weeks after a new product launch. While some staffing was increased to manage an expected rise in call volumes with the launch, more advisors are now being deployed owing to an increased average handle time (AHT) situation as well. Meantime, the center in Windsor, Ontario, Canada, needs to bring in more staff given the projections on call volumes due to the snow expected from a severe cold front approaching Southern Ontario and Michigan. With the command-center paradigm, analysis, decisions and actions can happen in real-time or near real-time to keep output efficiencies up. Somebody’s looking at the data!
Some command centers, for example, provide real-time analysis and recommendations to contact centers around the globe, to help customer service operations make informed decisions quickly. While process excellence teams may take a more holistic approach to driving process results, the command center fills in with actionable insights to create better transaction-level outcomes.
As workplaces becoming more responsive, real-time and event-based, there is a perpetually greater need for “clean” data for accurate analysis and decision making. This is not easy, given the multiple sources of data and their variety, volume and velocity. To ensure accuracy, companies must build in best practices for data warehousing and data management, which at the very least require specialized expertise, skill sets and the right tools.
3. Leveraging Data to Proactively Deliver Personalized Customer Experiences
Many businesses deal with millions of calls such as Janet’s every year. Would Janet rate the service and the advisor as “good” if she took a customer satisfaction survey? It appears the advisor did well; contact centers train their advisors to ask the right questions, solve problems and upsell from the portfolio.
Apart from Janet’s service problem, however, which the telecom company can analyze to improve its product, it appears that there were some missed opportunities.
The third role that data can play is in enabling intelligent business operations. Real-time command centers can help operations routinely be more proactive in managing overall business by deploying algorithmic systems that are automatically initiated by rules-based triggers. Automating workflows across the enterprise can leverage prescriptive analytics to minimize human dependencies and yet deliver highly targeted actions. Let’s see how:
- Customer call patterns: How do customers’ calls correlate to product or service performance if calls peak at specific times of the day? Can your advisor knowledge bases be truly “intelligent” and provide troubleshooting recommendations? Can they decide that a survey needs to be rolled out to quickly gather more information about the product problem?
- Drivers of customer satisfaction scores: Contact centers have transaction and VOC data that contribute significantly to a company’s customer satisfaction ratings and point to the underlying drivers of your customers’ perceptions. Are smart engines feeding recommendations and predictive analytics to operations and sales leadership by analyzing transaction data and customer feedback? For example, modeling can help identify customers that are a “flight risk” by mapping dissatisfaction from various sources of contact center data.
- Personalized customer engagement: Do you map customer experiences along their multi-touch journey to develop product usage or customer history profiles? These could again be algorithmically built into prompting your advisors with relevant, customized “packages” that they can walk customers through while on the phone.
Ostensibly, Janet offered the telecom company two opportunities: 1) to sell more to her (the A-Plus package) and 2) to retain her business when her present contract expires. Her call to the contact center, however, seems to indicate that it has information systems operating in isolation from each other. While the advisor signed her up for the value-added package, she missed an opportunity to renew Janet’s contract (the larger opportunity given the telecom marketplace). Also, she gave Janet the online channel’s URL over the phone (did Janet write it down?) without closing the deal, which means that Janet’s business is still open to poaching by the competition. But that’s not all!
The accuracy of any automated algorithm will depend on the quality of data curation and mining. The missed opportunities with Janet could arise from the fact that the enterprise today deals with structured and unstructured data types that need to be combined, contrasted and analyzed to arrive at the “big picture.” This needs to cover all process building blocks and handoffs, all customer touch points, and all sources of the diverse data flowing through the enterprise. Predictive analytics then help a business identify customer patterns and pain areas, which they may not articulate on their calls (unsaid needs), but fully expect to be educated about or understood as part of your service.
Blend Process Excellence, Real-time Analytics and Proactive Algorithmic Workflows
More importantly, the information that Janet’s two-year contract is coming up for renewal should have popped up on the advisor’s screen when she was assigned Janet’s incoming call. Believe it or not, many contact centers do not have the ability to feed such crucial information, along with the options, to make a personalized offer that is more likely seal the deal on the spot.
Given their history, data-driven actions in contact centers tend to focus on process efficiencies or product problems, both reactive in their basic nature of application. But today’s customers have many options and they don’t need to wait given their access to various social, mobile and cloud technologies. In a customer’s omni-channel journey with a company, a contact center could fail to deliver to its potential as one of the most important customer touch points that has been invested in.
Assuming Janet’s conversation is typical, then all three uses of contact center data must be brought together in a harmonious blend to achieve business results:
- Use the VOC and VOP to evaluate continued process efficiencies in the long term. Process excellence teams can use Six Sigma methodologies, analyze what is controllable and non-controllable, and reengineer processes to ensure improvements of operational efficiencies for clients.
- Deploy real-time analytics to garner operational insights that help a company take quick action. Contact centers can leverage dashboards, recommendations and real-time insights from transactions to help client programs manage their objectives and metrics on a day-to-day basis.
- Enable a level of data-driven insights that not only allows an organization to see a line of business intimately, but also to understand the business’ drivers. Integrate back-end and front-end systems to enable the design of seamless and automated workflows. This could produce more opportunities to engage with customers proactively, even before they call the contact center. For example, how about connecting with Janet with a personalized offer to renew her subscription with options of the latest devices, payment terms and a direct sign-up on the phone – even before she calls in her service problem?
The most critical challenge is the access to “all” of the data. This is a distant dream in many corporations, where vast quantities of data remain locked in silos/departments/partners, generated by different platforms and from various switches/servers/databases. With poorly managed or nonexistent enterprise-wide codes for variables being measured, they are unable to integrate the data infrastructure and common storage into a robust, speedy data warehouse. Contact centers and their back offices/management must be able to access structured and unstructured data with equal ease and speed – to generate the algorithmic triggers or alerts from prescriptive or predictive analytics.
Combining the use of data across long-term, real-time and proactive needs provides a win-win for all contact center stakeholders. Use this data to garner actionable insights that help a company realize competitive advantage through enhanced customer experience.