There’s no shortage of data: according to Statista, the total volume of data created, captured, copied, and consumed is expected to reach 180 zettabytes by 2025.
This data can be incredibly impactful for all industries, from technology to non profits to consumer goods. With the right analysis and software, customer data can provide actionable insights into customer demand that organizations can use to improve their products, services, and policies.
But it’s a challenge to get to those insights. Businesses are inundated with vast amounts of data from multiple sources but lack access to tools that can automatically pull, analyze, and determine how to make the biggest impact on the customer experience.
Finding a solution to this data challenge is critical for customer success, loyalty–and ultimately revenue.
While customer demand is often understood as the volume of requests that customers make to businesses, it’s also a measure of the motivations and needs that drive customers to contact customer service at all.
Customer demand insights help contact center leaders identify actionable opportunities to improve customer experience and reduce customer service costs. Without customer demand insights, customer service teams are largely reliant on lagging indicators of performance and as a result must reactively address customer queries and complaints.
While they have endless amounts of data, they don’t have the tools they need to systematically drive changes at a product, service, and company level.
Also Read: Unpacking Customer Demand: The Next Frontier for Customer Experience Transformation
We’re generating, storing, and analyzing more data than ever before. Customer service organizations receive input from every customer interaction–from phone calls and emails to social media and customer surveys. However, this data is often siloed across multiple platforms, making it difficult to gain a comprehensive view of the customer journey.
While customer data has the potential to unlock improvements for the customer journey, there’s too much of it to analyze at scale without the right tools. It’s challenging to get answers to even the simplest questions without investing millions of dollars into analytics software and analyst teams.
The current processes involve substantial upfront costs in software, hardware, and personnel. They often require (an expensive) dedicated analysis team that must continually extract insights or the implementation and maintenance of analytics systems that are complex and resource-intensive for IT. You need to know exactly what to look for in your mountains of data–and even then there’s no guarantee that the results are reliable.
These data problems are universal: it’s no fault of the contact centers or any individual department within the organization. Before now, the customer service industry just hasn’t had the right technologies and scalable processes to collect, store, and analyze data to unlock the true needs of customers’ at scale,
Let’s unpack these data problems a bit further.
The sheer volume of data generated by customer interactions can make it difficult to extract meaningful insights and take action based on that information without extensive resources.
Another challenge associated with data volume is the need to just manage and store these large quantities of data. Yet, a study by Forrester shows that over 60% of companies do not prioritize investing in a customer data platform due to reasons ranging from the complexity of implementation and a lack of executive support.
The industry needs a solution that can be implemented quickly and efficiently and offers a demonstrable ROI for improving company revenue.
Data quality can be impacted in a few ways. One is through the methods companies use to measure the customer experience: we often look at lagging indicators of performance that focus on agent-customer interactions after an issue has already occurred. Retention rates, average handle time, customer effort scores–and the majority of other customer service metrics–are all measured post-interaction.
The methods of gathering data can also degrade data quality. For example, customer surveys are limited in providing accurate insights: they tend to have a low response rate of between 4-12% and provide a little context on why the customer answers the way they do. For example, the Net Promoter Score, once considered the gold standard for measuring customer loyalty, is now being abandoned by customer service leaders. Gartner has predicted that 75% of companies will stop measuring NPS because it doesn’t relay context and therefore fails to provide actionable information.
Another example of data measuring limitations is when contact centers only listen to a handful of calls or read a selection of online reviews to determine customer satisfaction. This limited information can make it challenging to obtain a complete and accurate understanding of the customer experience and any challenges they’re facing.
Poor data quality can result in inaccurate or incomplete customer profiles, which in turn can lead to a reduction in service quality. It makes it difficult to analyze customer feedback, which further hampers the ability to provide timely and personalized customer service.
Executives are also understandably skeptical about results or business cases created from this data–even if the customer service leader is bringing up valid suggestions. It’s easier to ignore data that’s considered watered down or anecdotal than irrefutable results pulled and reviewed by artificial intelligence and machine learning algorithms.
Despite having access to large volumes of data, it’s challenging to determine the exact actions to take to resolve customer concerns. While a metric like customer effort score, for example, can tell customer service leaders how much effort customers need to resolve an issue, it doesn’t necessarily explain why it takes so much effort and how organizations can help reduce this effort.
To find better insights, businesses often rely on skilled analysts that can look for connections between various data points to create actionable business cases. However, manually stitching together different pieces of data is complex, expensive, and time-consuming. Organizations must also already have a clear understanding of the issue they're trying to solve and what specific data points to pull. This doesn’t leave room for different discoveries that could have a greater impact on the customer experience and bottom-line revenue.
Today’s organizations need better tools that quickly and accurately analyze their customer service data–and deliver meaningful insights into how they can improve their operations.
Customers express their needs in various ways that are challenging for humans to understand at scale. Finding insights from unstructured verbatim data requires the ability to identify phrases, measure occurrence frequency, conduct sentiment analysis, and much more. Without artificial intelligence and machine learning, an individual can’t manually analyze more than a few customer interactions.
You can use professional analysis teams, but thanks to the cost and time required to create the analyses, this method isn’t scalable–especially because the insights will always lag behind the current customer experience.
The customer service industry has historically been missing software that can automatically pull customer demand data from multiple sources and extract the insights they need to make data-driven decisions.
With the right technology, contact and call center teams can shift from reactive problem solving to proactive, revenue-generating activities. Using a customer demand intelligence platform, CS and CX leaders can:
Customer demand software provides businesses with an easier and more efficient way to analyze customer data. These tools automate the process of connecting different data points, identifying patterns and trends, and generating actionable insights from customer feedback across all inbound and outbound channels. Businesses can access insights quickly without needing a team of analysts, complex software, or knowing exactly what they’re looking for.
We built Operative Intelligence to analyze 100% of all inbound interactions and the language customers use to articulate their requests and needs. Pulling insights from customer interactions using customers’ own words–instead of keywords or phrases–call and contact centers can identify the true driver of customer contact.
Customer pain points often aren’t at the customer-service level: contact center agents spend the majority of their time bridging the gap between what a product or service promises to do and what it successfully delivers. Operative Intelligence uncovers the root causes behind customer contact at both micro and macro levels–from product bugs to confusing company policies–as well as how much they’re costing the business and the opportunities to resolve the issues.
By identifying the root causes of issues, contact center teams have time to focus on prevention–not management. Contact center leaders can take irrefutable data to the C-suite to show them exactly what customers’ pain points are and what improvements can be made to the product, service, or company to stop the problem at its core.
These changes can keep customer issues from occurring in the first place–giving time back to customer service agents to focus on value-generating tasks like upselling, better onboarding, and proactive retention.
We can tackle the industry-wide data challenges by working collaboratively to build better processes, tools, and systems. My co-founder and I designed Operative Intelligence to be a part of the solution. Having worked at call centers throughout college, we knew the industry needed a solution that could unlock the reality of customers’ needs at scale.
Operative Intelligence extracts, analyzes, and visualizes customer demand data to provide organizations with a better understanding of their customers’ needs. By automating the entire process, contact center leaders save time, money, and effort while gaining accurate results every time.
Businesses can then make smarter decisions based on real-time data rather than relying on outdated insights or costly research efforts. Rebecca Burns, Head of Support at MYOB, explains: “Understanding that it takes more than ‘numbers on a page’ to drive true transformation, OI enabled our leadership team to target high-impact initiatives and drive data-based continuous improvement in our contact centres.”
If you’re interested in learning more about customer demand and how we can solve these data challenges together, book a call with me to continue the conversation.
When considering the future of customer service, the industry is largely evaluating new call center technology and the growth of artificial intelligence.