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Practical ways to drive customer service, looking forward.
By Atanu Basu (left) and Tim Worth (right)
A quick google search of the phrase “predictive analytics” produces more than 375,000 results – the number of search results crosses 1 million when the phrase “predictive modeling” is used instead. Just five years ago things were quite different. Some of the early converts in the business world – primarily, experienced professionals who are “mathematical enthusiasts” – still remember our struggles to convince senior executives in large corporations why predictive analytics and related technologies will change the way they do business. The good news is those days are behind us, thanks primarily to IBM and Accenture who have been spending serious marketing dollars since mid-2009 to evangelize to executives and managers in almost all verticals – and horizontals – the virtues of business analytics, in general, and predictive analytics, in particular. As readers of this magazine, you and I are probably “believers” – and we owe a debt of gratitude to these two large consulting firms (especially IBM) and their primary competitors, who are following very similar footsteps, for making analytics an integral part of many important business discussions.
This article is Part 1 of a two-part series highlighting some of the major uses of predictive analytics in the after-sales service domain. Part 1 focuses on customer service; Part 2 will focus on field service.
Let’s define a few simple terms to keep us all on the same canvas.
Customer Service indicates the after- sales service provided through contact centers. This after-sales service can be for technical or non-technical issues that the customer is experiencing with the product or service she purchased from a company. Contact, usually inbound (i.e., a customer contacting a company), can be made by phone, e-mail, chat and Web.
Field Service indicates the after-sales service provided by sending an expert to the customer site to resolve an issue, technical or otherwise, that a customer is experiencing with the product or service she purchased from a company. This usually takes place after the customer has already communicated with the company’s customer service division, and Customer Service has decided to engage Field Service to address the issue.
Predictive Analytics answers the questions what will happen, and when. It is a domain – coined by practitioners and industry experts – that uses data, algorithms and business rules to provide forward-looking visibility for a process or an initiative. Predictive analytics is much more of a business discipline than a scientific discipline – however, it borrows heavily from the mathematical sciences.
Metrics That Matter in Customer Service
Today’s large contact center operations have three primary objectives:
- Improve customer satisfaction and loyalty
- Reduce cost per contact
- Transform from a cost center to a profit center
Customer service executives monitor and manage a variety of metrics – also known as key performance indicators (KPIs) – to track progress toward the above objectives. These KPIs can be broadly grouped into the following four categories:
Satisfaction and Loyalty. This category includes KPIs that are specifically designed to understand if a customer is satisfied with after-sales service provided by a company’s contact center(s) and whether this customer is likely to continue to do business with the company. Examples include customer satisfaction (also known as event satisfaction), net promoter score, brand satisfaction, satisfaction with agent, recommend likelihood, repurchase likelihood, etc.
Resolution. Usually a customer contacts a company’s after-sales service to seek resolution for an issue – technical or non-technical – that she may be experiencing with one of the company’s offerings that she has purchased. It is of paramount importance to the company to resolve the customer’s issue quickly to ensure her satisfaction, loyalty, etc. Examples of KPIs in this category include first contact resolution (also known as resolved in one), resolved within two, etc.
Operations & Productivity. This is the oldest and the most widely understood category in the contact center world. Examples of KPIs in this category include incoming contact volume, variance from forecast, handle time, queue time, utilization, occupancy, adherence to schedule, etc.
Cost and Revenue. This category is about saving and making money. The latter piece is achieved by selling through the service channel, a practice many large companies have embraced during the past few years (perhaps the recent recession accelerated the adoption). Examples of KPIs here include cost per contact, revenue per contact (also per shift, per day, per week, per agent, etc.), sales conversion rate, actual cost vs. budget, etc.
Customer Service Meets Predictive Analytics
Predictive analytics is starting to have a profound effect on customer service. The value proposition of knowing about an issue or an opportunity, before it becomes an issue or an opportunity, is compelling in the customer service environment. Proper identification and quantification of an upcoming issue can enable a corporation to take measures to preempt the same. Similarly, proper identification and quantification of an upcoming opportunity can enable a corporation to take advantage of it – otherwise, a missed opportunity can be looked upon as an issue by itself.
While contact center practitioners have been projecting forward the KPIs in the Operations & Productivity category for a while now, it hasn’t been the case for most of the KPIs in the other three categories until recently. So, let’s explore the applications of predictive analytics in these three categories in more detail.
Each of the KPIs in the Satisfaction and Loyalty category is a response variable (i.e., output) that depends on many moving parts (i.e., inputs or explanatory variables). Take for example customer satisfaction (CSAT). Say, we are talking about a customer service environment providing phone-based technical support for a technology product (such as hardware, software, electronics, etc.). In this scenario, the CSAT depends on the following factors:
- The customer. Most large companies collect and store lots of data on their customers. For example, CSAT may be different based on how tech-savvy the caller is.
- The product. CSAT may also vary depending on the product. Some products are more complex than others; some have more issues than others do; and so on. Large corporations usually have lots of data on their products.
- The agent. CSAT may depend on the characteristics of the agent taking the service call. Some agents have more extensive training than others; some are more experienced; some are easier to understand. Leading companies actively collect and manage data on their contact center agents.
- The issue. CSAT can vary based on the issue about which the customer is calling. Some issues are more difficult to solve over the phone – and some of these complex issues may even need follow-up calls. Leading companies track and store lots of data on the issues related to their major product offerings.
- The call. CSAT may also be different based on the quality of the call itself. Maybe the customer waited in queue for a long time before speaking with a live agent. Maybe the customer found the IVR (interactive voice response) directions misleading or confusing. Today’s sophisticated phone switches, also known as automatic call distributors, collect a wealth of information on each call.
In addition, customer satisfaction – which is usually measured by surveying a representative sample of the customers via e-mail or automated post-call IVR – may also be affected depending on whether the customer bought the product through a channel (e.g., a notebook computer manufactured by HP bought from a retail store such as Best Buy) or directly from the manufacturer. Then there are environmental factors (for example, economic factors such as recession) that may play a role. CSAT is the response variable that can be modeled – and its value predicted for future time horizons of interest – by talking into account the data from all the moving parts listed above. This is the case for net promoter score (“NPS”) and the other KPIs in this category as well. For NPS, it may be more insightful to model the promoters and the detractors separately and then combine them to get the resultant NPS for the future time horizons that may be of interest to a company.
It is important to understand that the relationship between a response variable (such as CSAT, NPS, etc.) and its explanatory variables (such as data on customer/agent/product/issue/call/etc.) is dynamic. So, the models need to be refitted, and the predictions need to be updated, continually (the periodicity of recalibration can be decided by the domain experts at the company). Another important matter is to focus on the explanatory variables under management control so they can be properly manipulated, where possible, to preempt an upcoming issue or to take advantage of an upcoming opportunity. By the way, we are not suggesting you exclude factors not under management control from your models. We are suggesting you put an additional emphasis on the factors that you can control so you can get as much insight as possible on how to best control them for your specific objectives.
The KPIs in the Resolution category, for this phone-based technical support environment, can be modeled and predicted similarly to those in the Satisfaction and Loyalty. The Resolution KPIs are also response variables that are affected by the evolving dynamics among the customer, the agent, the product, the issue and the call (and the channel, if there is one). Also, as is well known among the experienced practitioners, first contact resolution (“FCR”) significantly influences customer satisfaction – when FCR goes up, so does CSAT.
The KPIs in the Cost and Revenue category, for this phone tech-support example, may behave somewhat differently. Predictive analytics can predict cost per contact, for the future time horizons of interest, since cost per contact is also dependent on customer/agent/product/issue/call/(channel). A very useful application of predictive analytics in the revenue generation category – i.e., selling through the service channel – is to automatically generate an appropriate offer during the call itself. The predictive model is producing an offer with the highest likelihood of customer acceptance; in short – the right offer to the right customer by the right agent at the right time. The right time is immediately upon resolution of the customer issue. It is imperative to remember that the customer called the company to resolve an issue she is having with the company’s product. The first responsibility of customer service is to address the primary reason for the call, and then leverage the good will generated (through effective problem resolution) by making an offer that the customer has a high likelihood to accept. Figure 1 explains how this predictive up-/cross-selling may work in the customer service environment.
Another interesting place where predictive analytics is having a significant impact is in customer service for new product introductions. The IBM CEO Study of 2010 shows that most CEOs, across industries, are expecting an increasing portion of their future revenue to come from new offerings. As product lifecycles continue to shrink and innovations continue to proliferate, predicting what the contact centers can expect upon a new product launch is getting more and more difficult. Leading companies are using predictive analytics today to predict incoming contact volumes, for the future time horizons of interest, so they can appropriately staff their contact centers in anticipation of the new product launches. This is not only in terms of the number of agents per shift/day/week per region, but also in terms of the qualifications of these agents.
The Future is Convergence and Social
Customer service is evolving. The introduction of social media is already impacting how companies are approaching this key function. Social media have made possible what most of us already knew intuitively – good news travels slowly, but bad news travels instantly. Even though the false positives (i.e., flagging something that shouldn’t be flagged) and the false negatives (i.e., not flagging something that should be flagged) have slowed the adoption of some new technologies (such as speech analytics, sentiment analysis, etc.), these technologies are getting better.
Advancements in predictive analytics technologies – and the promise to synergistically include different data types (numbers, text, audio, video, etc.) with huge data volumes to make useful predictions – should change customer service as we know it. In fact, the change has already begun.
Atanu Basu is the CEO and president of DataInfoCom, an analytics software company headquartered in Texas. Basu has more than 16 years of experience in the semiconductor and software industry. Dell and Microsoft are DataInfoCom’s reference customers. DataInfoCom has recently won an “emerging technology” investment award from the Texas Governor’s office. Basu can be reached at firstname.lastname@example.org.
Tim Worth is the senior manager of Delivery Operations at DataInfoCom. Worth has 20 years of frontline service industry experience with Dell (16 years in contact center analytics), Sallie Mae and Aditya Birla Minacs. He can be reached at email@example.com and 512-635-9203.