The business world has been dealing with big data — and trying to capitalize on it — for decades. As channels and omnichannel experience grows, so does the data.
While the topic of data may be overwhelming due to its volume and complexity, it also presents valuable opportunities with the right guidance and strategy. When gathered and properly organized, data can deliver essential, actionable insights. These insights can be used on both macro and micro levels within an organization. From the top, data insights support high-level decision making. Down in the trenches, data can empower contact center agents to best serve individual customers. This improves the customer experience (CX) while supporting a fulfilling employee experience (EX). How? Data insights help agents better support customers by providing a fuller picture of who they are, including:
- How they want to engage with a company
- What they’re shopping for
- How they research
- What they end up buying
- What additional products or support they’re likely to need
- Their real and potential customer lifetime value
As organizations look to gather, understand and use their contact center data, including data on agent performance, they’re presented with several hurdles. The first step to overcome them is proactively planning a data strategy.
Start with a high-level strategy for contact center data
Without a strategy, an organization risks getting lost in the data. Having too much data without a plan for how to house, access and use it can lead to:
- Uncertainty around which data to collect vs. what is already captured
- Having the wrong data sets in the wrong places
- Siloed data that cannot be utilized across channels
- Lack of the right technology to automate collection accuracy and consolidated reporting
- Frustrating self-service experiences
- Failure to effectively serve customers and anticipate their needs
In short, organizations drowning in data will struggle to keep up. They may even start to lose customers to competitors. To avoid this quagmire, it’s crucial to create a strategy with plans for appropriately gathering, organizing and utilizing data across all channels. Consider, too, how your organization can use data to create a single, 360-degree view-of-the-customer which passes freely between channels. Otherwise, you’ll miss the mark on delivering the hyper-personalized, omnichannel experience today’s customers expect and want.
3 categories of data contact centers should track
Contact centers should track data that correlates to agent performance, customer profiles and business insights. Below are examples of data points organizations can track for each.
Data on customer preferences and behavior
- Customer interaction history – What comprised their prior calls and what was their sentiment associated with those experiences?
- Customer purchase history - What did they buy and what can that tell you about potential solutions they may need in the future?
- Contact method - How have they interacted with your organization in the past (chat, call, text) in relation to their service history and sentiment?
Agent performance data
- Average handle time (AHT) – Track how long agents are staying on calls with customers. Consider how this relates to successful resolution and customer satisfaction. If paired with auto-resolve options, for instance, agents’ call times may go up, indicating that they’re spending time on more complex issues.
- Call sentiment - Sentiment analysis can offer both valuable customer insights and agent training opportunities. AI-powered tools with natural language processing can even measure empathy levels.
- Customer feedback/surveys – Collection can be automated for this data which offers both qualitative and quantitative feedback on agent performance.
- Listening data – Recording calls offers the opportunity to capture and review customer interactions. Some call recording features even allow you to automatically rate call quality with a pre-designed scoring system.
Business insights data
- Customer satisfaction by channel – Capturing customer satisfaction and comparing it among channels informs CXM leaders on which channels work well, which are experiencing issues, and where to invest future dollars.
- Volume per channel by hour – Having this data on hand can inform staffing decisions and determine whether other channels can assist agents during high-volume call hours.
- Customer surveys – Ask customers how they prefer to interact with your contact center depending on various situations. This allows organizations to proactively implement various technology solutions and staff appropriately.
4 ways data can enhance your CX
As companies harness their contact center data, they discover many opportunities to better serve customers and improve outcomes. So, once you have an intelligent data strategy in place, make sure you leverage it to enhance the overall CX. Use data intentionally to inform your organization’s personalization efforts and provide a full view of your customers. Empower agents to deliver the best possible service and support. Look for analytics platforms that include built-in AI and machine learning to help your organization harvest valuable insights that improve contact center outcomes and help you make better overall business decisions. Below are four specific ways to leverage data.
- Start with a defined goal prior to looking at data. Too often, we are presented with data and attempt to infer what it’s trying to tell us. But data only has clear answers when we know what we’re asking of it. We must start with a specific question to answer or a hypothesis to test. That guidance directs how we then interpret data, find places to drill down, and use data to enable educated decisions.
- Find opportunities for elevating agents to best-use scenarios. Use analytics to categorize critical situations. As your data sets grow, you’re better able to anticipate issues and their resolutions. Armed with this information, your teams can proactively plan automated workflows that can either auto-resolve simple requests or route customers with more complex needs to the right person or even a specialized team. This saves time and money.
- Drive real-time business decisions. Leverage data to help solve business problems, enhance organizational performance, and produce useful business insights for more informed decision-making.
Six out of 10 of business leaders say that it is extremely important to use real-time customer analytics to improve customer experience across touch points and devices. AI-based call analytics paired with natural language processing, for instance, can uncover insights on agent- and call-level metrics. Predictive analytics and sentiment analysis can help you measure agent empathy and better understand a customer’s emotional state at the start and end of their call.
- Support increased personalization to drive loyalty and growth. Well over half of business leaders say that their organizations improved customer retention and loyalty after gathering and acting on insights from customer data. Yet, just 15% have both a single 360-degree view of customer data as well as the organizational structure to make use of those insights. Work toward this goal to house data and present it to your agents in a real-time single view-of-the-customer. When they have the right information at the right time, your agents can quickly understand the situation and provide the best service and outcomes for your customers.
Ninety percent of global executives who use data analytics report that they’ve improved their ability to deliver a great customer experience. So, don’t let insights from your frontline go to waste. Gather the right people to craft your data strategy. Plan how you want to use data and which data points you need to capture. With that information in hand, you’ll get a better idea of next steps, including any additional support you may need in the form of expert partners and/or new technology. To further improve your contact center operations and outcomes with guidance beyond data insights, download Conduent’s eBook, The Evolving Customer Contact Center: 5 Operational Imperatives.
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