Have you ever been sent a promo code as a pre-emptive apology for an order snafu? Received a proactive message about a CRM glitch with a plan to make your life easier in the meantime? Gotten quick compensation for a flight delay? 

Happy customers tend to stick around, grow, and publicly advocate for your business. Unhappy customers often churn and can deter others from doing business with you. According to Temkin, after a poor experience, 22% of customers reduced spend and 19% terminated their relationship with the company entirely. According to Zendesk, 54% of customers share bad experiences with more than five people. It’s impossible to overstate the value of customer advocacy and the cost of vocal detractors.

CX teams measure team effort to understand and manage their overall cost of serving customers and to cultivate happy and engaged teams that play a starring role in great customer experiences. Team effort can also make a huge difference in popular metrics like employee promoter scores (“EPS”), quality assurance (“QA”), and operational metrics like case resolution times and escalation rates. When customer-facing employees’ jobs are harder than they need to be, CX teams often see high turnover rates and low EPS scores, lower QA scores, more escalations, and delays in case resolution times — the list goes on. And each of these unfortunate side effects increases your cost of service and creates stubborn obstacles that stand in the way of improving customer experience. 

Why Measure Team Effort?

Team effort is the collective amount of time that customer-facing teams allocate to supporting your customer experience.

Customer effort is a mix of customers’ subjective feelings and what they objectively experience during interactions with your business. Measuring and reducing customer effort is a top priority of many CX organizations and with good reason. 

Last week, we were excited to contribute to the conversation at CCW Digital’s online conference, Customer Experience Challenges, Trends, and Innovations. In case you missed it, we’ve recapped our session, Getting to Frictionless CX, below. Our session featured Dexter Horthy, VP Customer Success & Solution Engineering at Replicated, Frame AI CEO & Co-Founder George Davis, and Matt Wujciak, Market Research Analyst at Customer Management Practice.

We recently discussed why measuring customer effort is important and how it’s different from surveying customer effort. This post introduces a five-step framework to help you think through how to measure customer effort using organic customer feedback and AI. 

Themes = What Makes and Breaks CX

Understanding the themes that drive customer experience enables CX teams to explain changes in customer sentiment and effort, and ultimately, business outcomes. It helps them achieve goals like improving outcomes, finding early warning signals to get ahead of customer expectations, and testing hypotheses about how to improve. 

We recently discussed why high-impact CX needs high-impact measurement and, more specifically, measuring customer effort. This post focuses on measuring customer sentiment with organic customer feedback. 

We recently discussed why high-impact CX needs high-impact measurement that clearly supports CX goals. This post focuses on measuring customer effort. 

Customer feedback is the lifeblood of CX. Without it, CX teams can’t measure the quality of their experience and have no compass to help them improve it. Many teams start by listening to feedback in two ways:

History is full of infamously bad predictions. 

If you’re reading this, you probably know that a customer health score is a composite data point assigned to individual customers to summarize relationship health. It should enable you to understand how your customer relationships are likely to evolve in the future and know which accounts need attention at any given time. It needs to consider and weigh a variety of factors, and it’s only as good as the signals that feed it. 

Sentiment analysis has become quite the buzzword in the CX community, and with good reason. It’s never been more important to understand customer sentiment, and failing to do so brings unfortunate consequences.

Stop throwing away organic customer feedback.

Artificial Intelligence (AI) is transforming many industries, and many CX teams are navigating mandates to incorporate it into their strategies. But let’s face it — today, most of the ways we use AI in CX are pointed at our teams and customers, not with them. Consider the following scene:

The three-headed CX monster plagues CX leaders with three core problems — siloes, dark data, and the “too-hard” pile. According to Deloitte, customer feedback, both organic and unsolicited, is often spread across as many as 23 different channels, leading to a siloed view of your customer experience. Dark data is typically unstructured data that is impossible to understand without a significant data science effort or tons of manual analysis, which often happens late or never. Unfortunately, this is the common fate of 90% of your customer feedback.  Because of siloes and dark data, some of the most brilliant CX insights are cast aside onto the “too-hard” pile. If it’s too complicated for any stakeholder to understand the impact of customer feedback and to execute on it, it stays stranded on the “too-hard” pile. Most customer feedback has a short time window to make an impact on any given customer’s experience — allowing it to linger here translates to failure to act, and failing to act in a timely fashion opens the floodgates to unfortunate consequences, from churn to a tarnished reputation. 

This is the final post in a three-part series about assembling your customer feedback stack so that you can take action faster and more effectively than ever before. In case you missed it, catch up on part I here and part II here. 

This post is the second in a three-part series about assembling your customer feedback stack so that you can take action faster and more effectively than ever before. In case you missed it, catch up on the first post here.

This post is the first in a three-part series about assembling your customer feedback stack so that you can take action faster and more effectively than ever before. 

81% of companies are competing on customer experience. The bad news is that it's getting harder to differentiate your business, but the good news is that CX technology is better than ever and makes it easier to activate the customer feedback that moves the needle for your customers and your business. To understand what’s changing and why we combined findings across the year of customer feedback data and a survey of CX professionals. Check out our infographic that highlights the top five things to know about activating customer feedback in 2021. 

Too often, when organizations think about getting started with a traditional CX program, things generally go one of two ways: 

You’d be hard pressed to find an executive at any company billing itself as customer-centric who will say that listening to customer feedback isn’t important. So it’s quite possible that a customer experience program has been on your mind for some time, but it’s also quite possible that your version still exists as a quarterly survey program, or maybe as an aspirational bullet point in an internal strategy deck. Unfortunately, CX initiatives have long been easy to cast aside in favor of priorities perceived as lower effort and higher impact. But CX programs have come a long way — it’s never been more important to have a sound strategy in place, nor has it ever been more achievable to drive and measure positive outcomes for your customers and your organization. Best-in-class CX organizations are trading old-school CX programs for CX Ops, a new-action-oriented approach that unifies customer feedback and measures every interaction from every channel to help CX teams turn insight into action. 

Traditional CX programs fall short of measurable impact because they often rely on siloed, one-dimensional, and stale data. Poor CX data management results in an inaccurate and illegible portrayal of your CX and uncertainty about how to improve. According to Forrester, 77% of companies say that they don’t model the drivers of CX quality regularly, leaving them in the dark about what matters most to their customers. Furthermore, 65% of companies say improving their data analysis is critical to improving customer experience. 

Used correctly, Voice of Customer is one of the most impactful ways you can improve your company's customer experience. According to a recent HBR Salesforce report, companies agree that customer-sourced "data has a clear role to play in developing a superior customer experience." Yet only 23% actually act on the customer feedback they receive. 

Data is growing by leaps and bounds, the convergence of extremely large data sets both structured and unstructured define Big Data. The increasing awareness of the Internet of Things (IoT) devices among organizations and volume, variety, velocity and veracity at which data is generated have caught the attention of the enterprise in a bid to enhance digital technologies and guide digital transformation.

The emergence of customer experience (CX) as a primary differentiator has turned up the volume on Voice of Customer (VoC) programs. When 84% of customers say that experience is just as important as products and services, the pressure is on for organizations (especially those dependent on recurring revenue) to listen to their customer voice. Without incorporating VoC insights into the product, organizations lose touch of what their customers need.

New York startup Frame AI raised $6.3 million in Series A funding to help companies engage more efficiently with their customers using artificial intelligence and machine learning.

73% of all people point to customer experience (CX) as a determining factor in their purchasing decision. Frame AI challenges the status quo through its Voice-of-Customer data platform that helps companies identify, prioritize, and take proactive action to address drivers of customer experience, in real-time and across all channels.

Frame AI, a New York City startup that uses artificial intelligence and machine learning to help companies understand their customers better across multiple channels, announced a $6.3 million Series A investment today.

With invaluable feedback from customers (hat tip to Bret and Ashley at Articulate and Jackie and Beck at Practice Ignition!), we devised some new ways for teams to coordinate their reviews in Frame and to permanently hide irrelevant conversations.

A checklist to understand what customers experience and how much effort it takes to support them.

A growing collection of templates, tips, and guides for working with customers.

Using Google Apps Script to automatically find users and apply the desired tag — for free.

We’re excited to have hosted a productive event with the Customer Success community at Work-Bench last week, where we discussed the role of data in customer success with a line-up of all-star panelists.

How we retrofit a framework into a scalable, self-managed SaaS tool.

Open the floodgates!

Customer Success has become one of the first functions at successful SaaS businesses because trial and churn dynamics impact the bottom line so dramatically. Recurring revenue from monthly subscriptions provides more predictable growth — but only if it recurs and expands.