Leverage Analytics to Cash in on Customer Insights
The customer care industry has a huge opportunity to capitalize on Big Data by applying analytics to customer interaction data in order to gather customer insights. These insights will allow service professionals to deliver a more personalized experience at the time of engagement and ultimately build loyalty and grow brand evangelists.
Decoding the Voice of the Customer
Data is a company’s competitive advantage if leveraged correctly. New ideas, product enhancements, quality or warranty issues, operational efficiencies, sales and marketing insights and legal risks are all outcomes that can be uncovered and used to develop strategic business plans. The reason most companies don’t tap into their goldmine is that the massive amounts of data tends to be: overwhelming to filter; usual stored in multiple places; rarely integrated; hard to get at for security purposes; and requires a lot of IT time and resources to access it which tends to be very costly.
Despite all of these challenges, it remains critical that customer service professionals do their best to leverage this data. The only way to get an accurate view of the voice of the customer is to leverage analytical tools that can help decode each different channel and form of data.
The three most beneficial analytic tools service professionals can use are speech, text and predictive analytics. Let’s take a look at examples of how service professionals can apply and use these tools for each channel they service.
Phone Analytics
When it comes to the phone communication channel there are three different opportunities to leverage analytics: inbound, outbound and QA.
· Inbound Customer Service
Micah Solomon, the author of High-tech, High touch Customer Service describes the concept of anticipatory customer service where companies predict customer needs and proactively address them. Anticipating a customer’s needs gives customer service an opportunity to provide a WOW experience by fixing the problem before it amplifies. This is where predictive analytics technology plays a big role. By analyzing past order history, predictive models may uncover a specific loyal customer who calls in every Tuesday at 4:30 p.m. to place his same order of five widgets. Wouldn’t it be a WOW moment for that customer if instead customer service called him on Tuesday at 4:20 p.m. and ask if he wanted to place his usual order? This type of proactive service would deepen relationships because customers would feel like brands really know and care about them. No longer does customer service need to be reactionary. Reactionary service is not going to hold today’s less loyal and ready to jump customer.
· Outbound Telemarketing
When it comes to outbound telemarketing programs, success is usually based on a number of factors including: lead sources, agent skills, time of day, phone number dialed, script and offer. The challenge with most outbound programs is the success of campaign is based on controlling lead cost. Another challenge is every phone agent excels at converting different types of leads. For example, Agent Suzy may be good at converting leads that are generated online, dealing with product X, and geographically reside in Texas. Whereas, Agent Bobby may be good at converting leads that are generated from List A, dealing with product B, and geographically live in Michigan. In order to measure success, managers need to analyze conversion rates by source, day, time, rep, and product in real-time. Unfortunately, running data through a dialer makes it difficult to get timely reports with actionable intelligence. At the end of the day, many contact centers find it hard to prioritized leads in order of highest likelihood to convert, assigned them to the right sales agent, and ensures they get followed up on.
To conquer this problem, leading companies are building predictive models to analyze their historical conversions to identify trends. Then, with the help of predictive analytics contacts centers are able to implement conversion based routing techniques to ensure leads are being prioritized and routed to the best agent, at the right time of day, using the best channel, and finally presented with the right offer. The end result is a decrease in lead acquisition cost, increase in contacts made, decrease in dial attempts, and most important an increase in conversions.
· Quality Assurance
When it comes to monitoring the quality of phone calls, service professionals can leverage speech analytics technology to detect silence, emotion and even fraud. When there is a long period of silence on a phone call a lot of times that is a red flag for a system or process failure. For example, maybe the representative’s computer is taking a long time to load or maybe the representative wasn’t trained well enough on the correct process. Either way, by leveraging speech analytics technology you can detect these periods of silence and discover whether they are happening on every call or just a few. Next, you can leverage speech analytics to detect high emotion in a customer’s voice. Most of the time, a customer will start off a conversation with customer care in a low, monotone voice. If bad service or policies are being communicated, then the level of voice will become amplified and the speech analytics technology will detect this and alert a supervisor.
Social Analytics
When customer care starts providing support via social media channels, one of the biggest challenges they will experience will be to learn how to efficiently swift through the social conversions to determine which posts are actionable. With over 250 million blogs in the social media landscape, this is no easy task. However, it’s absolutely critical brands prioritize and assign the relevant, actionable posts at the top due to the limited resources available to monitor and engage. The solution? Once your social media customer service program has been running for a period of at least 3 months, take all of your tagged and categorized posts and build a predictive model. The model will then help sort through all the new incoming posts; scoring and prioritizing them in order of relevancy. The great part of predictive analytics is the model continues to learn from itself; getting smarter each month as more data continues to get analyzed.
Web Analytics
Contact centers that service online ecommerce businesses can use predictive analytic technology to personalize the online shopping experience. The technology will use clickstream data and/or historical transaction data from websites to predict the best content to display to that visitor. If a customer already has an account on an e-commerce website, then the chances are the company knows what that customer has purchased in the past. Using predictive analytic technology the company can display images, offers, and product recommendations that cater to that customer. What this customer sees on your website will be different from what another customer will see. If a first time consumer stumbles upon your website, there will not be any historical data to predict. Instead, the technology will leverage clickstream data to predict what this customer may be interested in purchasing.
Consumers are starting to expect a level of personalization at every step of their customer experience journey. The thought process of customers is “if I am giving up the right to my privacy by supplying you with my personal data, then the least you could do is use it to personalize my customer experience.” This type of WOW customer experience will prove to consumers that brands values their loyalty and business.
Written by Lauren Ziskie, Customer Engagement Officer at Dialogue Marketing Inc., a nationwide provider of outsourced customer engagement services to help its clients acquire, support, and retain customers. The company is also a leader in leveraging speech, text, and predictive analytics in the contact center space. Lauren also is a social media strategy professor at Walsh College. To learn more or ask a question, feel free to follow her on Twitter (@LaurenZiskie) or call 248.836.2642.