A study by Forrester revealed that companies that prioritize customer experience outperform their peers by nearly 80%.
This statistic probably comes as no surprise: a great customer experience is critical for customer engagement, retention, loyalty, and spending.
As an industry, we've become highly proficient in managing and optimizing support interactions as they occur within the contact center. These strategies have been guided by traditional and important metrics like Average Speed of Answer, Average Handle Time and in the last decade, Customer Satisfaction and NPS.
The next frontier of transforming the customer experience goes beyond these metrics and looks to unpack what is driving customers to contact in the first place, also known as Customer Demand. Unearthing these insights is the key to improving not just customer service but the entire customer experience–and growing bottom line revenue as a result.
Customer demand is commonly understood as the volume of requests that customers make to a business, such as the number of customer service inquiries received in a given time period.
Customer demand, however, encompasses much more than just volume. It both quantitatively and qualitatively measures the core reasons customers are contacting an organization and its customer service operations. It uncovers the motivations and needs that drive customers to contact customer service in the first place.
Despite being the single biggest driver of cost for customer service businesses, unpacking what drives customer contact is often overlooked. Traditionally, the industry has focused on measuring their response to customer inquiries–not what is driving the inquiries themselves.
Servicing customer issues promptly and efficiently is of course important — improving customer experience helps improve brand loyalty and retention. But, the deeper value in unpacking customer demand lies in understanding the underlying motivations and root causes behind customer contact.
Introducing customer demand as a different way of thinking about the customer experience allows companies to focus not only on solving customer issues but also on proactively preventing them from occurring. This shift in thinking enables businesses to meet–and then exceed–their customers' expectations.
Contact and call centers are hard at work implementing innovative strategies to provide the best level of service that results in a satisfied customer.
However, despite advances in contact center technology and management practices, the American Customer Satisfaction Index is at the same level as it was in 2002, and the number of customers experiencing issues with products or services is actually increasing.
How could this be so? We have customer service leaders and frontline agents working harder than ever before alongside modern technology–but customer satisfaction and experience is stagnant or, in some cases, declining.
One perspective is that focusing on managing symptoms of customer issues–versus the root cause–has created these outcomes. We’ve been optimizing for great service only when our customers need it instead of addressing the issues that are driving the contact in the first place.
Let's dig into this further by looking at how customer service has historically been managed and addressed.
Agents play a crucial role in bridging the gap between what a product or service promises to do and what it can deliver successfully. They’re often the first point of contact for customers experiencing issues with a product or service and their role is to respond to and resolve these issues in a timely and efficient manner.
While these services are essential, the role is naturally reactive: they help at the time of the customer’s need. This sort of customer demand will always be present, but the volume and costliness of the issues can be reduced by using customer demand insights effectively. Without this actionable data, service reps will always have to spend time preventing customer churn instead of driving revenue.
The customer service metrics used today are typically focused on measuring past behaviors. They look at how many calls were answered in the last week, how long individual agents were on each call, how long it took to answer customer service requests and other lagging indicators of performance.
While it’s important to understand how to improve these metrics, they all focus on agent-customer interactions after an issue has already occurred. They don’t allow customer service teams to uncover why the issue occurred in the first place.
Common methods of determining customer needs can be subjective, biased, and even inaccurate. This understandably can limit contact and call center teams as well as organizational success.
Take this example: a traditional customer service practice consists of agents quickly answering post-contact why a customer contacted them. They may have about five seconds to choose from a predefined list the company created.
Not only does the list limit the agent to interpreting the interaction through the lens of the company (not the customer) but it also means the agent is answering an objective question subjectively and quickly.
Customer feedback, such as through post-interaction surveys, has also become a popular measurement in the last decade. The surveys are typically managed through a standalone platform and results are interpreted in isolation from the actual customer need that drove the contact.
There are a few issues with this method. At an industry level, response rates to surveys remain low, with the top end at a 15% survey response rate. This makes achieving a relevant, unbiased sample size difficult.
Plus, businesses often hear from customers who are the happiest or the angriest, which can also skew results. And again, the customers’ needs are being interpreted through the lens of an organization’s questions.
These lagging indicators immediately put contact center leaders and teams at a disadvantage. Instead, they need to be able to identify and measure the specific problems that lead customers to contact them via customer demand intelligence and insights.
Customer demand insights are the data points that identify opportunities to reduce customer queries, effort, and wait times–and much more. They allow businesses to understand what customers are looking for, what challenges they run into, and how they prefer to interact with services and products.
At both the macro and micro level, they tell you exactly what challenges are occurring throughout the customer journey from the moment a potential customer interacts with a brand to after they purchase and use the product or service.
Businesses with actionable and quantifiable business intelligence that can pull customer demand insights are in a better position to impact positive change in the customer experience. This is exactly why Operative Intelligence was created: so contact center leaders could change not only the outcome of customer interactions with their agents–but the product and service as a whole.
What started as an unexpected career path for me and my co-founder–working in contact centers through college–pushed us to create a solution that would help contact centers better meet the needs of their customers at scale.
We realized the customer service industry didn’t have access to reliable ways to understand why customers contacted support and how the contact center performed against those requests. Without reliable, irrefutable, and actionable data, it’s a huge challenge to measure and articulate exactly why customers are contacting businesses and what to do about it.
But with this data, customer service organizations can reduce the overall number of customer contacts. Their teams would finally have the opportunity to go from solving problems to proactively improving the customer journey.
Customer demand insights can be used to identify more than agent handle time and customer satisfaction. They can also help determine actual errors within a product (and how the engineering and product teams could address them), or a mismatch in messaging (and how the marketing or design teams could improve communication).
By analyzing customer interactions using customers’ own words–instead of keywords or phrases–call and contact centers can identify the true driver of customer contact. Operative Intelligence does this by analyzing 100% of all inbound interactions, or more specifically the language that customers use to articulate their requests and needs.
The results provide clear insights into the root cause of customer inquiries. It not only identifies what the issues are but also how much they’re costing the business and the opportunities to resolve the issues from happening at all.
Let’s say an organization uses a speech analytics solution and discovers that 34% of customer support interactions are related to billing. However, the speech analytics tool doesn’t tell the organization the specific reasons behind these billing inquiries. Is it a question about the bill amount? Is the online bill payment not working? Do they need to change their billing address?
Knowing the context of what drives the contact for the customer is crucial to be able to address current customer concerns and prevent future customer issues. So that’s why we built Operative Intelligence: to automatically identify the root cause of each customer contact while also providing context for each customer's issue.
There are no longer just issues with “billing.” Instead, the platform breaks down the percentage and volume of billing inquiries related to any type of customer inquiry as told by the customer. Instead of guessing what the billing issue is–or spending extensive time and resources to figure it out–Operative Intelligence can automatically pull these insights from existing contact center data.
If businesses know that 80% of the billing inquiries are due to an inability to change a billing address online, then they can add a feature or system that allows users to do that. With that change, there is reduced customer contact for that issue and customers need to put in less effort to interact with your product.
Operative Intelligence customer demand insights also identify the top cost drivers for the company as it relates to why customers contact support. Once you’re able to identify the root causes of issues and top cost drivers for the company, contact center teams can drive the changes necessary to prevent the problem from happening at all.
The time spent managing support drops significantly, allowing businesses to reduce customer service spending and retain customers–and reinvest these savings back into activities that drive revenue, lifetime customer value, and retention.
Armed with irrefutable data on customer contacts, customer service leaders can identify areas for improvement throughout the entire customer journey – and then take this information to the C-suite to drive company-wide improvements.
Michael Callahan, Vice President E-Commerce and Customer Experience at Orbit, found that Operative Intelligence’s artificial intelligence and machine learning allowed them to “automatically pull key drivers of customer complaints and challenges, sorted by topic and product. This enables us to go back and close the loop with our product development teams, helping us to be an active partner in improving our products.”
Operative Intelligence automatically transforms the mountains of data produced by customer service activities into actionable, direct steps on what customer service, product, or company changes will have the greatest impact and highest ROI. Teams could drive actions faster and more reliably than they’ve ever imagined by providing that “ultimate unlock” into exactly how to improve the customer journey.
In an ideal (but attainable) world, customer service agents could spend 80% of their time on tasks that generate value for customers – and only 20% of their time on addressing product or service issues.
However, in many organizations today, the situation is reversed. Customer service organizations are often viewed as cost centers that work independently from the profit generators of the business.
With the help of customer demand insights, this balance can be shifted. Customer service reps can have more time for upselling, proper onboarding, proactive retention, and other revenue-generating activities.
If you can understand what’s failing your customers to the point they need to contact support, then you’ve found the key to truly transforming the customer experience. You can help your organization address the issues before they start–giving back time to both your customers and customer support teams.
The ability to execute such impactful changes across an entire company makes prioritizing customer demand insights a win for everyone: customers have better experiences, the company sees reduced costs and improved customer satisfaction, and customer service reps can focus on value-generating activities.
We built Operative Intelligence to make this possible. We wanted contact center leaders to be able to empower their teams by easily identifying opportunities to improve both the customer experience and operational performance.
We couldn’t have created Operative Intelligence without our direct experience in the industry and the visionary goals of our clients. We’re continuously working with them so they can drive meaningful changes in the organization that are tied directly to revenue. If you’d like to connect to speak more about scaling your customer demand data, feel free to set up a time to speak with me now. Or, share with your insights so we can continue the conversation on the importance of driving the right types of decisions for our customers and business.
When considering the future of customer service, the industry is largely evaluating new call center technology and the growth of artificial intelligence.