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Traditionally, decision-makers and marketers have relied on surveys to measure customer success. These customer experience analytics tools are useful — you shouldn't throw them out the window just yet.
However, backward-looking metrics have their downsides. And with large companies deploying the latest technology at speed, those who don't maintain pace risk getting left behind.
CX analytics have evolved, and McKinsey's report, Prediction: The Future of CX, proposes a solution — predictive analytics.
In this article, you'll gain an in-depth insight into customer experience metrics, their benefits and how to analyze CX data and use it to improve customer experience.
What Are Customer Experience Analytics?
Customer experience analytics is the capture and analysis of data relating to customers' interactions with your brand.
With today's tech solutions, you can gather customer data from every touchpoint. Calls, websites, live chat, chatbots, social media, SMS, reviews, ecommerce metrics and CX surveys — these sources are varied and rich with insight.
However, having a bunch of data doesn't automatically mean you know how to derive actionable insights. In fact, many companies underutilize the swathes of data they have access to. The time it takes manual teams to quantify and qualify data can be restrictive — especially considering the new approaches forward-thinking operators now deploy, namely real-time and predictive CX analytics tools.
According to McKinsey, "These companies can better understand their interactions with customers and even preempt problems in customer journeys. Their customers are reaping benefits: think quick compensation for a flight delay, or outreach from an insurance company when a patient is having trouble resolving a problem."
As useful as surveys are for measuring success, they shouldn't be your only CX analytics strategy. With the status quo quickly moving toward people-centric practices, deeper, more comprehensive insights are required to understand and optimize customer journeys. Most importantly, a survey can't prompt a purchase decision in real-time — it only offers you insight into what might help next time.
Examples of CX Metrics and Tools
Examples of tools and metrics you might use to analyze digital behavior, purchase decision-making and customer attitudes include:
- Social media activity
- In-store behavior
- Location
- Internet of Things (IoT) data
- Social listening
- Voice of the customer
- Net promoter score
- Average handle time
- Customer lifetime value
- Customer satisfaction scores
- Churn rate
- Customer effort score
- Renewal rate
- Average spend
- Voice and chat transcripts, metadata and analysis
- Review monitoring
With the digital economy taking a stronghold, most of these tools can be automated. But savvy business leaders are going one step further and using predictive analytics wherever possible.
Ever been given an excellent recommendation by Netflix or Spotify? These companies use algorithms to predict which programs will have you sitting back to enjoy another few hours of quality viewing.
Related Article: Eliminating Vanity Metrics From the Analytics Portfolio
Why Are CX Analytics So Important?
Considering the concerning effects of global warming, the rise of data as the “new oil” can only be a good thing.
Data provides insights unlike anything the business world has seen before. In the not-so-distant past, forecasting and customer profiling relied on guesswork and limited interactions with focus groups or interviews. These days, companies can draw upon every interaction every customer makes to inform decisions and drive growth.
That said, it's not just businesses that benefit from customer experience analytics. Smoother, personalized customer experiences reduce complaints and increase satisfaction. People feel more seen, heard and understood by the organizations they invest their hard-earned cash in.
By making the conversation a two-way street, everyone benefits. And with predictive analytics, consumers get more of what they want without the rigmarole of any back and forth — just seamless interactions that deliver desired outcomes.
Benefits of Measuring Customer Experience
Improving customer experience comes with a plethora of business benefits. However, there are three core advantages that any company can derive from optimizing their current CX analytics strategy:
- Improved personalization: Millennials and Gen Zers take pride in being more than just a number. These savvy consumers want to be treated as individuals, and any sign that your company doesn't see them as such could be disastrous. Analytics gives you the power to drill down into your target audience to understand who they are and what they want. The better you can personalize output for each individual customer, the more likely they are to become lifelong loyal advocates.
- Churn reduction: In the past, the only signal a company had that a customer was unhappy was if they left. One of the major advantages of having a varied CX analytics strategy is being able to identify at-risk clients and mitigate their concerns. What's more, using a data-based approach lets you spot patterns and trends that signal dissatisfaction so that you can adapt as necessary.
- Increased customer retention: Too many businesses are so hyper-focused on acquiring customers that they neglect post-purchase efforts to retain customers. For most companies, customer retention is where the real money hides. And understanding what drives your loyal clients to stick around is imperative. Using surveys, predictive modeling and customer segmentation, you can take unilateral action to gain an army of loyal brand advocates.
The Importance of Omnichannel CX Data
The proliferation of social media has done an amazing thing for the world: given everyone a voice and a platform. As such, modern consumers love telling you what you're doing right — but especially what they think you're doing wrong.
As a business leader, you should celebrate this enormous social shift. One person shouting and letting off steam because they're annoyed after a bad day won't give you much insight. But, as soon as patterns emerge, it's time to pay attention. To gain the maximum amount of customer data, it's vital that you meet customers where they're at.
As you'll know from your customer profiles and journey maps, different demographics have preferences over which channels they use. Furthermore, consumers might use different channels at different times of the day. All this information is critical for you to get a complete, end-to-end picture of how your customer experience is received across the platforms you exist on.
How To Make Use of CX Data
Gathering data is one side of the coin, but you could have an ocean-sized pool of data and still not use it. In fact, this is the very situation most companies find themselves in today.
If you find yourself overwhelmed by the amount of data you have and don't know where to begin making sense of it, keep reading.
Prioritize Low-Hanging Fruit
Gather insights from your most-visited channels and use technology to aggregate them en masse. Software dashboards can provide a quick overview of customer interactions, quickly identifying pain points from metrics such as bounce and click-through rates. Look for trends by channel, frequency of interaction, demographics, reviews and survey scores.
When working with CX analytics data, always start with the areas that cause the most problems but are easiest to fix. If there are multiple areas for improvement, begin with solutions that have the fastest impact on revenue.
For example, if people frequently complain about your customer care team not being available around the clock, consider implementing live chat or chatbots.
Align Output With Customer Value Expectations
Whether they interact directly or indirectly with your brand, your customers are constantly feeding you actionable information.
Is there a specific button design that prompts conversions? Use that design across the board. Are there specific posts that have higher click-through rates and conversions? Promote that content across social media and use it as a benchmark for future pieces.
Tracking customer data in this manner provides all the information you need to continually refine output.
Be Proactive
It's impossible to emphasize the need for proactive, predictive solutions in addition to reactive solutions. Social listening, IoT and other predictive CX analytics tools help you identify and act on trends in real-time. You don't need a CX or marketing department to quantify and qualify any data because software automates the entire process.
As McKinsey's report said, "Today, companies can regularly, lawfully and seamlessly collect smartphone and interaction data from across their customer, financial and operations systems. This yields deep insights about their customers."
Differentiate From Competitors
If you're looking to differentiate by keeping up-to-date with upcoming trends, you're already one step ahead of the crowd. Many C-suite professionals find a tactic that works and tend to stick with it. In today's agile, fast-paced world, deploying the latest CX tools is one of the best ways to stand out ahead of your competitors.
Business leaders who take an approach of relying on historically effective CX strategies are at the greatest risk of getting left behind. If you can detach from your ego and adapt based on informed instincts, you'll keep up with the increasingly fast-paced economy.
Get the Timing Right
There are few situations in which the maxim "timing is everything" doesn't apply. With technology driving modern businesses, little room remains for the human touch. But that personal touch is more important than ever.
People don't want to be inundated with information or requests from your company. As such, sending out follow-up correspondence, surveys and personal requests for testimonials must be elegantly timed.
Related Article: How to Prepare Data for Ingestion and Integration
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Why Customer Experience Surveys Are No Longer Enough
McKinsey's report highlighted CX surveys' weaknesses and backs up its claims with some insightful statistics.
It said, "Ninety-three percent of respondents reported using a survey-based metric (such as Customer Satisfaction Score or Customer Effort Score) as their primary means of measuring CX performance.
“Only 15% of leaders said they were fully satisfied with how their company was measuring CX. And, only 6% expressed confidence that their measurement system enables both strategic and tactical decision-making. Leaders pointed to low response rates, data lags, ambiguity about performance drivers and the lack of a clear link to financial value as critical shortcomings."
4 Problems With CX Surveys
Delving further into the reasons why survey-based CX analytics are limited, let's look at those four standout points:
- Quantitatively limited: Most people aren't inclined to answer customer surveys. In fact, McKinsey's research suggested that only 7% of customers actually respond. While it might be better than nothing, only 13% of business leaders felt this would reflect their entire customer base.
- Slow: Surveys are backward looking — with many being sent out at least a day after the experience itself. As such, customer experience surveys are often analyzing memories instead of real-time information. What's more, while sending out a survey demonstrates some level of care, it doesn't provide customers with any proactive solutions to issues.
- Ambiguous: Written survey responses are highly subjective, while numerical data can be vague. Plus, it's difficult to gauge how honest people are in their responses. For instance, if you've offered a discount in return for the survey getting filled out, might the respondent be inclined to be extra positive simply because they're happy to get the gift?
- Theoretical: Taking all of the above into account, the information derived from CX analytics surveys is vague. Tying scores back to key performance indicators (KPIs) is open to bias, and any links back to business outcomes are largely theoretical. McKinsey said, "Remarkably, of the CX leaders we surveyed, only 4% said that their system lets them calculate the ROI of CX decisions."
Transforming Data Into Actionable Insights
McKinsey said, "The CX programs of the future will be holistic, predictive, precise and clearly tied to business outcomes."
If you're starting from scratch with CX analytics, you might be shocked by the level of change required to implement them effectively. Your company must take a top-down approach and shift to a people-centric but data-led culture. If you don't, implementing a CX strategy is just a shallow tick-box exercise.
When done correctly, making people and data your priorities can have a real impact on your bottom line. When customers feel like you understand their needs and are invested in making their life better, loyalty is intuitive.
Start With Mindset and Company Culture
The first challenge for many companies is understanding that data analytics aren't just for the IT department. Every CX team member should have a firm grasp of how to read data and take action based on what they learn.
A chief experience officer told McKinsey, "People associate CX with marketing, not technology. That is changing as more and more companies take up predictive analytics, and it’s up to CX leaders to help encourage the change in perception."
Define Organizational Goals
Once the shift in culture is underway, work collaboratively with all departments to set goals. Do you want to increase conversions, get better at forecasting or fix pain points along the customer journey?
Bounce ideas on a cross-departmental scale to find out which goals resonate with everyone. To tie your customer service analytics to business outcomes, those KPIs must be clearly defined, with the full workforce on board.
Look at Preferred Channels
With culture and goals defined and aligned, it's time to take action. Look at the direct and indirect ways consumers interact with your brand, and aggregate data wherever possible.
Look into implementing software that delivers predictive analytics so that you can reach your customers in real-time. Each channel they use presents a different opportunity and a varied picture of your customer journey. While it's important to start with the most popular, you shouldn't leave any stone unturned.
5 Ways Predictive Analytics Can Improve Customer Experience
Looking ahead to the future, it looks like predictive analytics will become the norm. Getting ahead of the crowd puts your company in an excellent position to solidify strong bonds with your current customers and reach new ones.
Below are more ways this type of tech can boost your customer experience efforts.
1. Predict Customer Needs
Predictive analytics can provide a wide range of insights. For example, beauty brands can predict when their buyers are likely to run out of a certain product. They'll hold off from marketing to them until the perfect moment, making shopping with that company feel ultra-convenient.
2. Inform Recommendations
When you make accurate recommendations to your customers, they feel validated. Have you ever had YouTube or Spotify lineup a favorite track you haven't heard for years? It makes you feel seen and understood, which has a ton of value in CX terms.
3. Identify At-Risk Customers
McKinsey outlined how predictive customer experience analytics can help you hold on to at-risk customers.
"One company applied its predictive system to its issue-resolution journey after realizing that its contingency funds could be applied more strategically,” the report stated. “The company developed an algorithm that could identify high-priority customers as measured by lifetime value and recent experiences. It used the algorithm to allocate contingency funds toward dissatisfied, high-value customers. This first use case proved successful, saving the organization more than 25% of its planned budget and paving the way for future applications."
4. Accurately Set Labor Budgets
Knowing when call volumes or footfall are likely to be high or low can revolutionize your labor strategy. In the past, everyone relied on seasonal information and guesswork. And with labor coming at such a high premium, data-based labor spending can be a game-changer.
5. Predict Customer Lifecycles
Customer experience analytics can help you predict notable events in the customer lifecycle. Insurance companies are an example of an industry that uses this type of modeling. For instance, predicting when kids are about to start driving or when a family decides to move home.
The scope of predictive data analytics is impressive, and now is the time to start implementing them where possible. Although surveys have their downfalls, they can still be useful when combined with a data-led CX approach.
Related Article: How Predictive and Prescriptive Analytics Improve the Call Center Experience
Don’t Ignore Predictive Analytics in CX Measurement
Surveys shouldn’t be the only go-to in your CX tool belt. Instead, it’s time to embrace the future of predictive analytics. Not only can this tech free up burdens on your in-house teams (and save money), but it can deliver actionable insights that will lead to better customer experiences and outcomes.
