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A 2011 U.S. government study determined 32% of homes no longer used landlines – instead preferring to use a mobile phone as their home contact number. With this trend gaining momentum, wireless service providers, in turn, are seeing an increase in customer support inquiries. Influenced deeply by technology, customers today expect customer service experiences that simplify their tasks and reduce effort.
An overwhelming majority of consumers (74%) use at least three channels when interacting with their wireless provider for customer-service-related issues. Every voice, chat or Web interaction still typically starts with a clueless agent or an opaque website that seems to ask the customer: “Who are you and why do you want to talk to us?” Getting quality customer service from wireless, telco and other enterprises continues to be an inherently painful exercise for most customers. Customers want intelligent interactions with companies, and they expect these experiences to be seamless and consistent across channels. The key to delivering these experiences lies in the data.
Big data and customer service
Big data is already a part of the consumer’s everyday life, even though they may not realize it. The vast majority of such data is unstructured: e-mails, Facebook posts, tweets, chat transcripts, call center interactions, website activity and support forum conversations. All of this data is gathered from multiple channels such as your website, mobile applications, contact centers, e-mails and social media interactions.
While many enterprises are becoming increasingly adept at using analytical tools and big data to increase transactional sales, customer service and support interactions have not received an equal share of attention. Moreover, customer data is only now beginning to evolve from a tool to enhance transactions to a true strategic asset.
Your customer data can tell you who your customer is, their history with you, their past interaction data, how their current journey on your website or at your toll-free number is going and if your customer is vocal about you on social media.
Integrated with the right tools and methods, this data across thousands and millions of customers reveals patterns, trends and insights. These new tools and technologies allow us to use big data to transform customer service, engineering a quantum leap from the old, reactive way of doing things, to new, differentiated models of customer experience.
Anticipate, simplify, learn: A framework for big data
Collecting and analyzing data is one thing. Applying it in real-time to drive business outcomes is another. Companies are learning they must apply a simple yet powerful framework to describe the philosophy behind applying data analytics to customer interactions:
Anticipate: Determine who you are talking to, what they are trying to get done and when they require help. Consumers reveal their intent through their behavior on multiple channels. This happens in real time during the regular course of the customer’s journey on a wireless provider’s webpage without requiring them to provide any explicit input.
Simplify: Once you have determined a customer’s identity and predicted their intent, the data can help you decide the best interaction type, to make the experience simple and fruitful for your customer. This includes decisions on the right channel (or channel combinations) through which to engage the customer.
Learn: The most critical step in this framework is applying the results from the previous steps to making subsequent interactions better. The critical fuel that makes subsequent interactions better is more of the data itself. Smart customer service applications can use the data that they generate to self-correct, automatically learning from each interaction to improve customer targeting, prediction accuracy and outcomes.
The A-S-L framework in motion
Anticipate-Simplify-Learn (A-S-L) is a powerful and effective framework to organize and orchestrate the range of tools and processes at our disposal and drive relevant outcomes.
A wireless provider can drive value from big data and the ASL framework to improve the customer experience. Specific customer data, (service plan type, billing cycle information, device, etc.), in conjunction with the Web journey (search term, URL referral, clickstream, etc.) can be used to anticipate customers needs and accurately predict intent.
Let’s say that a customer is on a family plan with an iPhone. She goes to the wireless provider’s website, logs in and does a search on bill charges and goes to the billing section of the site. The ASL framework then uses the customer’s details and Web journey information in conjunction with statistical models to predict the top three likely intents of the customer. For example, the most likely intents could be – bill due date extension, a bill inquiry or a payment inquiry. Based on the prediction of possible intents and the user’s history, the channel propensity models in the ASL framework determine the right form of intervention (self-service, chat, phone call) that would simplify the issue resolution process for the customer. If the right intervention is chat then the intent prediction information is passed on to the chat agent. When a chat agent intervenes, the agent already knows the likely intents and is provided all the information they need about the customer related to bill due date extension, bill inquiry or payment inquiry. The agent can quickly offer the right service. The customer’s issue is resolved. The interaction is then “mined” to learn and further improve future interactions. With the benefit of big data and predictive models, the customer and wireless provider has achieved successful outcomes.
In order for wireless providers to implement a successful customer care strategy, they must look beyond traditional measures. With no major technical limitations in the wireless industry, customers will continue to expect more in their customer service experiences despite the number of channels they may use to get there.
