How to get the most out of your investment in analytics technology.
There is no question that the increased availability of customer data has positive benefits for global organizations. Companies that know how to effectively use customer data to guide their strategic direction are the most successful in improving the customer experience. The challenge for many, however, is how to achieve this level of “data nirvana.”
For many years, contact centers have served as the power user of analytics software, and continues to function as an integral component of the customer care infrastructure. Equally interesting is the fact that analytics solutions are now gaining traction across the enterprise in a variety of departments and functions due primarily to its increasingly strategic role and influence in shaping business decisions and outcomes.
The most important aspect of developing effective customer engagement programs is having analysts and data scientists who understand the business problem that the data is addressing. While these skilled people have historically devoted their talents to the contact center, it is rapidly becoming apparent that the benefits of analytics extend beyond the customer service function to reach the entire organization. As companies work toward removing the silos of data and functional skills between departments, the impact of analytics is being felt throughout the enterprise.
In this interview, Saddletree Research Chief Analyst Paul Stockford asks Verint’s Daniel Ziv—vice president, customer analytics—several burning questions about the ways global organizations can maximize their investment in analytics technology.
PAUL STOCKFORD: You know that I’m an analytics devotee. Analytics of all varieties are becoming a topic of organizational interest in enterprises around the globe. Any thoughts on how an organization should approach an analytics implementation?
DANIEL ZIV: Analytics is definitely a hot topic these days. Most organizations use some form of analytics, but that doesn’t mean they all reap the same benefits or see high levels of success tied to their deployments. I have found that the following tips help create a more successful analytics implementation:
- Implement analytics across the organization, versus in a silo department, to allow multiple departments and users to benefit.
- Empower internal teams to take advantage of the strong correlation between the use of data analytics and higher customer experience performance.
- Secure executive buy-in to your analytics deployment and outline its contributions toward the prioritization of customer experience.
- Take advantage of both structured and unstructured analytics.
STOCKFORD: Analytics applies to both structured and unstructured data. Is one form of data more important or useful than the other?
ZIV: While structured analytics is very important, it’s not as effective in determining customer preferences when used as a standalone offering. Unstructured analytics, on the other hand, provides deeper insights that help organizations better understand their customers beyond just what they purchased, their age and home address. When a customer spends five to 10 minutes speaking to a contact center agent, think about what transpires during that interaction, and how it can allow an opportunity to mine for a deeper understanding of the customer’s profile.
There is a misconception involving comfort with unstructured data. Aggregates can be derived from numbers, run against an algorithm, and summed up to get an exact answer. The misconception is that unstructured data is less effective because it’s less accurate. It involves analysis of emotion, attitudes, perceptions and other things that are deemed fuzzy when compared with hard numbers. This is why the use of both structured and unstructured data together is paramount. You can know exactly how much a customer spent with you, but that doesn’t reveal details that will identify what’s going to increase purchases, improve loyalty or influence their decision to share positive feedback with other customers. Those outcomes are usually derived from the unstructured data. It may not be as precise as structured data, but these insights and perceptions can be trended and aggregated to create a rich picture of customer influencers and behavior.
STOCKFORD: OK, we agree that there is a great deal of value in both structured and unstructured data in terms of mining for business intelligence. So the next question has to cover best practices. Can you give us some examples of the different approaches an organization can take to get the most out of their analytics solutions?
ZIV: Absolutely. I would recommend looking at several different approaches, including a digital channel strategy, upsell and cross-sell opportunities, as well as retention.
Digital Channel Strategy: In order to really build effective self-service applications and to increase usage of digital channels, organizations have to understand the potential points of failure of those channels. When considering or launching a new application, organizations need to ask themselves how it will be built, what questions to ask, and how to improve and refine it. It’s never right the first time, so constant refinement should be expected as customer needs and questions change.
Almost every inbound voice call is a potential opportunity for improving non-voice, self-service channels. Often a customer has tried to self-serve—but when that fails, they pick up the phone. In these cases, speech analytics can be leveraged to mine everything that should have been achieved through the other channels. In fact, one of our customers shared that “analytics transforms the contact center into a business intelligence center,” and estimates that 50% of voice channel calls are actually triggered from a prior digital self-service failed attempt. Using speech analytics, the organization is constantly providing input to the owners of those digital channels to refine and improve them.
Upsell and Cross-Sell: This is a tricky area for many agents. However, when done right, upselling and cross selling can actually increase customer satisfaction and help transform the contact center into a revenue center. The role of contact center agents has evolved to identify and act on new sales opportunities in real time—aiding a standard salesforce in their efforts to drum up business.
With this new role, the use of analytics becomes imperative in helping to determine the right message, the right offer and the right timing. Speech analytics can assess what’s effective, comparing exact words and phrases and their timing, linking them to the customer’s profile to help an agent determine when and if a cross sell should be attempted.
Retention: Research shows that it’s cheaper to retain a current customer than it is to acquire a new one. But, without a deep understanding of where the organization creates value for customers, making amends to those who are dissatisfied, customer retention initiatives can be spotty.
Today, organizations are using a deeper customer analytics approach to understand several things: (1) why consumers find certain products or services valuable; (2) how to devise the right solutions for customers who are thinking of leaving; (3) how to efficiently craft the right retention offer (without overshooting with expensive freebies); and (4) how to identify which customers should just be let go.
By leveraging analytics, organizations now have the ability to identify clear drivers of satisfaction and dissatisfaction. Structured and unstructured data obtained from contact center engagements play a big role in proactively addressing issues and devising the right solutions to put customer experiences back on track.
STOCKFORD: Give us a brief case study, maybe an example of how these best practices are applied in a specific instance.
ZIV: There are so many, but one that comes to mind is a highly regarded financial services organizations in the United States that has effectively applied analytics to secure its gold-standard reputation in customer experience metrics. The company’s program takes its best and brightest from across the enterprise to join the analytics team for 18 months—equipping these employees with a deeper knowledge of the analytics tools available. Coming from different departments including the contact center, marketing, IT and back office, this team is knowledgeable about the business issues and pain points from across its departments. The trained group works in collaboration to leverage knowledge gleaned from the contact center, break silos and apply analytics to solve problems across the business.
This approach provides the employees with skills, insights and connections to help their individual sectors of the organization, acting as analytics champions, even after they finish their 18-month term. The best practice at play here is that analytics is not a single department residing in the contact center or marketing—it crosses boundaries, and is ongoing. The rewards can be significant too, because this team identified $14 million worth of savings and additional revenue and retention opportunities last year alone.
STOCKFORD: Launching a successful analytics initiative is more than just downloading software and bringing in a couple of skilled analysts, and now we’re talking about extending the process to the entire enterprise. What else should enterprise analytics users be thinking about?
ZIV: Securing executive buy-in is critical. Several years ago, customer service departments used analytics mostly internally and within their department to reduce cost, handle time and repeat calls, solving their own pain points, so executive buy-in was less critical.
Customer engagement has since evolved into a more strategic corporate priority, especially after research has shown the strong correlation between the use of analytics and customer experience performance, and the many examples of how customer experience directly impacts revenue and company valuation. The contact center and analytics teams have earned a seat at the table with the CEO, presenting customer feedback and getting buy-in for taking actions to improve.
Analytics in the contact center is still important for improving operations, but it becomes even more important in collaboration with other functions. With customer service teams becoming more open and connected, more departments and employees across the enterprise can become more customer focused, changing the organizational culture, and leading to greater success and impact.