Customer intelligence is the methodology behind collecting and analyzing customer data to uncover actionable insights. Customers interact with a business in all sorts of different ways: using the product, customer support channels, social channels and communities, sales channels, and orchestrated surveys. All of those interactions provide data points, and these data points can inform critical business decisions like product direction, packaging, and more. Customer intelligence is the foundation of any strong CX strategy. 

💡 Did you know?

  • According to research done by Bain & Company, 80% of companies believe they deliver “super experiences,” but only 8% of customers agree.

That’s quite a few companies failing to deliver which gives those who are delivering “super experiences” a strong competitive advantage. How much of an advantage? In 2017, Microsoft found 47% of consumers switched brands after poor customer experiences. This can happen after just one interaction!

A PwC report identified one in three consumers (32%) say they will walk away from a brand they love after just one bad experience. In fact, 65% of US consumers found a positive customer experience more influential than compelling advertising to encourage repeat business. Adobe’s research into 2020 digital trends surfaced an interesting finding. Companies who prioritized CX are three times more likely than competitors to exceed top business goals. Customer experience is the cornerstone of successful strategy and nothing drives customers away more than terrible customer experience. Delivering exceptional customer experience demands that businesses know what their customers actually want. Mastering customer intelligence is the most effective way to do this.


What is customer intelligence?

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Customer intelligence is the methodology behind collecting and analyzing customer data to uncover actionable insights. Customers interact with a business in all sorts of different ways: using the product, customer support channels, social channels and communities, sales channels, and orchestrated surveys. All of those interactions provide data points, and these data points can inform critical business decisions like product direction, packaging, and more. Customer intelligence is the foundation of any strong CX strategy. 

A customer intelligence strategy has four phases:

  1. Collecting information 
  2. Aggregating data into a single source of truth
  3. Deriving insights from data 
  4. Transforming insights into action

The maturity and sophistication of a customer intelligence strategy directly correlates with how many components a company has adopted, with many businesses struggling even at phase one. According to research done by Forbes, only 13% of companies surveyed were ahead of the pack on customer intelligence, despite findings that customer data and analytics were key to enabling disruption in their industry. 

Data collection

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You don’t need to conduct endless surveys to collect customer data. Surveys can be helpful, absolutely, but customer data lives everywhere! There’s a strong chance you’re collecting customer data right now without even knowing it. Meaningful insights can come from support and sales conversations, customer behavior and activity, and yes - sometimes even surveys. The key is collecting information at every touch point and knowing how to measure what matters.

Create listening paths across channels

Customers communicate with your business all the time! They communicate when things go wrong, sing praises when things go right, and sometimes communicate by not saying anything at all, dropping off silently never to be seen again. The key to successful customer relationships isn’t all that different from nurturing other important relationships in our lives: listening is key.

Customer support and sales teams receive a treasure trove of valuable information from all of the aggregated customer channels they typically service: email, chat, social channels, or even face-to-face interactions. Recording and aggregating feedback from those customer channels delivers the kind of business intel that’s usually only acquired by costly market research programs. 

Here are some of the listening channels where valuable customer insights live:

Help desk

Email, chat, phone, and social conversation data is often stored in a help desk like Zendesk or Help Scout for support conversations. Support conversation data is typically rich with sentiment, feature requests, common UI/UX confusions, or serious problems that require immediate attention. In other words, everything you need to improve customer experience!


Sales emails along with logged phone and meeting notes usually live inside your CRM. Sales conversations can inform what prospective customers expect from your product, what problems are important for them to solve, or what they find most valuable in the product. Sales conversations can also help to inform product direction in order to better align with customer expectations, thus increasing acquisition rates.  


Support teams, marketing teams, and product teams all conduct a variety of surveys to measure customer experience. Market research, NPS, CSAT, CES, and exit surveys all collect a variety of different insights to improve different aspects of the customer experience, and often live in different, disjointed tools across teams as a result. 

Business Intelligence tool

Conversion rate, churn rate, retention rate, and customer LTV all give quantifiable insights into your customer’s behavior and the impact of that behavior on your business. While quantifiable insights are important for assessing business health and areas for improvement, it does little to show what needs to happen in order to improve (or maintain) those metrics.  

Paint a holistic picture across data channels

Traditional business analytics might look at the quantifiable side of the business, like the numerical values of conversion rate, churn rate, retention rate, and customer LTV to detail the health of the business. But, while those metrics are important for understanding business health and areas for improvement, they don’t tell you anything about how to improve. In other words, they don’t result in actionable insights.

To understand the why behind the business metrics, listen to channels where customers already ask questions and share feedback openly, like support and sales conversations. Combined with surveys and standard business health metrics, a comprehensive picture emerges of both the customer wants and needs, and how those wants and needs correlate to business health. This will help you prioritize potential improvements and determine where to act first for the best result.

Measure what matters

Customer experience teams have several key performance indicators that measure customer happiness, loyalty, and most importantly customer-driven growth. 

CSAT, NPS, CES, alongside critical business health metrics are some of the most popular ways CX teams measure the success of their customer experience initiatives. Each measurement comes with its own unique set of customer insights and business value.

CSAT: How happy are your customers with the service they’ve received?

Customer Satisfaction (CSAT) is a popular performance metric for customer support teams, used to measure how happy customers are with the service they received. It can either measure a customer’s happiness with the overall product or service experience, or it can measure the quality of a 1:1 interaction with a team member that provided them help with a particular problem or issue. 

A CSAT survey is a single question asking the customer how happy they are with the product or service. If it’s being used to measure the quality of customer support, it arrives after a support interaction or tucked in a support email signature with a question like “How happy are you with the service you received today?

If it’s used to measure the quality of the product or service as a whole, the question might arrive directly in-app after a recent purchase or an account milestone, and instead be phrased as “How happy are you with..?”

It’s a bit like a star rating for a restaurant on Yelp.

CSAT can be the canary in the coal mine for larger business problems. It’s a good litmus test for general trends around customer sentiment about your product or service. That said, it comes with a fair amount of criticism as it only paints a very incomplete picture.

Oftentimes only a very small percentage of customers participate in CSAT surveys, and ratings typically only happen when customers are effusively positive or destructively upset. Excellent CX teams know they can’t necessarily hang their hat on a 5-star CSAT rating, but instead use low CSAT ratings as an opportunity to take action, reach out and engage with customers that are clearly unhappy.

NPS: How likely are your customers to recommend your business?

NPS stands for Net Promoter Score and measures customer loyalty, and the potential for organic, customer-driven growth. Year after year, Neilson research on trust in advertising consistently finds that 90% of consumers consider recommendations from peers as the most trusted channel when trying a new product or service. NPS gives you a window into how likely your business will grow by word of mouth marketing. 

Calculate Net Promoter Score by asking a simple question: How likely are you to recommend X product or service to a friend (or colleague, in the B2B ecosystem) and a scale from 1-10.

Then followed by the single, open-ended question: Why?

Responders are classified in the following ways:

  • Promoters (score 9-10) Your loyal fan club. Promoters not only stay customers but they also refer your product to others, generating word-of-mouth growth.
  • Passives (score 7-8) Satisfied customers, but at risk to jump ship if a better competitive offer comes along.
  • Detractors (score 0-6) Unhappy customers who may even do brand damage with negative word-of-mouth.

To calculate your Net Promoter Score, subtract the percentage of Detractors from the percentage of Promoters. NPS Benchmarks vary between industries, but in general, a higher score ranges in the 50s and 60s, with a lower score being anything lower than 20s or 30s. 

Critics of NPS call it a lagging indicator, only shedding insights into past performance vs. future growth. When used correctly, learning from the past is necessary to drive future growth. The key to successfully implementing NPS is not to focus solely on the number alone, but to use the customer feedback delivered in the open-ended “Why” question to inform business direction.

CES: How easy was it for your customers to use your product (or receive the help needed)?

CES stands for Customer Effort Score, and measures how much effort a customer exerts to resolve an issue, fulfill a request, or more generally use your product.

Research from CEB discovered customers respond much more favorably to effortless service than delightful service. Reducing friction and making help fast, easy, and accessible pays off in dividends compared to delivering above and beyond service. As a result, the Customer Effort Score was born. 

Similar to CSAT, CES asks one single question: 

To what extent do you agree or disagree with the following statement:

[Your Business] made it easy for me to handle my issue.

And then a scale of 1-7 from Strongly Disagree to Strongly Agree.

The CEB study found moving a customer from 1 to 5 increases their loyalty every step of the way, with “loyalty” defined as repeat business and stronger retention rates. A customer moving from 1 to 5 on the scale will increase retention rates by 22%. Increasing from 5 to 7 offers a less dramatic return of 2% increase in loyalty, making 5 and above the score to aim for. 

The challenge of Customer Effort Score is one of survey fatigue. The best place to stick CES is often in tandem with CSAT, but the more questions you ask of your customers, the lower your response rates turn out. Not to mention that the CES measurement is all about reducing friction, and survey questions easily add friction to an experience. 

Companies that leverage both CSAT and CES either ask two questions on the same survey, sending it out after the close of a support interaction, or ask CSAT on every interaction from the email signature, with CES coming in an emailed survey after closing the conversation.

Customer Health Score: How deep are customers adopting your product or service?

The Customer Health Score takes several different aspects of customer data and assigns them a classification of green (healthy), yellow (meh), or red (flight risk). 

Customer health takes several data points into account, including:

  • Product usage depth: how many features/products is the customer using?
  • Usage frequency: how frequently is the customer using the product or service
  • Support received: Are they happy with the support they receive, have we always met SLAs?
  • Customer feedback: Do they engage with providing feedback? Is it positive?
  • Business outcomes: Are they accomplishing larger business goals with the product or service?

What makes for a good customer health metric is bespoke for each business. Determining a customer health score relies on understanding what behaviors and trends a customer exhibits that lead to a healthy customer long term. It can be a bit tricky to pin down.

Customer health scores matter because they are a predictive churn risk indicator. By understanding how many customers exhibit trends reflective of healthy customers, you have a clearer picture of how many customers you’ll keep around for the long haul. You’ll also know who to focus your energies on (less healthy customers) to get them up to speed and mitigate churn down the road. 

Critics of customer health scores say they are too subjective and too prone to false positives (or negatives) as the criteria are often set by speculation. It’s also tricky to implement. In addition to defining a customer health score, monitoring and tracking will need to be established for each component that makes up the total score.

Conversion Rate: How many people try, then buy?

Conversion rate is the number of customers that check out or try your product or service and end up buying. It can be measured by any point of entry, from a sales touch point, marketing touch point, or trial period. It’s relevant to customer intelligence if the customer has interacted with a customer-facing team member like sales or support while in the presale evaluation stage.

Conversion rate is considered one of those critical business health metrics. It can indicate product-market fit and the success of your marketing messaging. Improving your conversion rate directly correlates to business revenue.

Measuring conversion rates as part of your customer intelligence strategy is important to customer acquisition. Clues to poor conversion rates can come from presale conversations and questions asked during the sales process.

Churn Rate: How many customers are you losing?

Churn rate is a popular metric for recurring revenue products or services and measures the percentage of your customers that leave your service over a given time period.

You find your churn rate by subtracting Users at the end of a given period, usually monthly, quarterly or annually, from Users at the beginning of that same period of time, then dividing it by Users at the beginning of a given period. If you have 80 customers at the end of a month, but started with 100 customers, your churn rate is 20%. 

Churn can also be calculated by a revenue amount, instead of by the number of lost customers. That looks at revenue lost vs. revenue gained. Revenue churn becomes more important for companies that have multiple payment tiers for their business. 20% churn on your free plan isn’t the same degree of disaster as 20% of your premium plan customers. 

Churn is considered one of the single most important metrics to recurring revenue services when it comes to calculating the health of the business. The cost of customer acquisition is typically higher than the cost of customer retention, making customer acquisition meaningless if those customers don’t stick around. 

Incorporating churn into customer intelligence is vital to assess how customer trends directly correlate to business health. If your customers are asking for X feature and leaving when you don’t have it, that’s much more important to address than if customers ask for X feature but stick around long term.

CLV: How much value do your customers bring the business long term?

CLV stands for customer lifetime value, and measures how much revenue a customer brings to your business across the lifetime of their account. If one customer spends a lot on a single purchase but never returns, but another customer makes lots of small return purchases over a period of time, those customers have the same CLV. 

The basic formula for customer lifetime value is the average single purchase amount multiplied by the average number of purchases per year multiplied by average years retained. Or, if you’re a recurring subscription business, MRR x the number of months retained.

CLV is one of the most important metrics for any business to measure, as it calculates how long it takes to recover sales and marketing costs required to acquire a new customer, and how much each customer generates for your business once that cost is recovered. 

CLV is especially valuable when tied to other customer intelligence metrics, like NPS, customer health, or CSAT as it draws a line from customer experience efforts directly to revenue.

Aggregate data into a single source of truth

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You have mountains and mountains of customer data from all stages of the customer journey living in your help desk, CRM, survey results, and other KPIs, but each data source likely has a different owner and location. Sales data lives in CRMs, support data lives in help desks, survey data usually lives in an entirely different home entirely, and revenue metrics like conversion rate, retention, churn, and customer LTV frequently lives inside a BI tool. 

How can you make sense of all this data to make customer intelligence your advantage? 

The danger of data silos

You may be collecting the right customer data, but if it’s fragmented across teams and stored in siloed departments and tools you won’t get very far with it. Fragmented data results in fragmented reporting that makes drawing meaningful insights challenging. Organizations that outperform competitors in customer experience are 9 times more likely to integrate data across multiple sources, analyzing customer data and feedback across channels. 

Fragmented data silos hurt overall business operations, especially in regards to strategy.

Companies with fragmented data experience:

  • Low visibility into customer trends: With disjointed data, it’s impossible to follow the customer journey and see what’s working - and what’s not. Having a single view of the customer is critical to understand what your customers want. 
  • Failure to execute on the customer experience - Customer-facing teams like sales and support need access to customer data to provide a great experience. Without cohesive data, discrepancies about what’s needed at each touchpoint of the customer journey can arise. 
  • Poor strategic decisions: Executives base 48% of decisions on intuition or personal experience. While “go with your gut” makes for a snappy catchphrase, it’s hardly an effective business strategy. 

Yet, despite these major pitfalls companies still operate within data and departmental silos, with only 34% of executives agreeing they have a united view of customer data.

Create a one-company customer experience

You’ve likely experienced a company with a fragmented customer experience: the marketing message says one thing, but when you try the product you find it doesn’t quite do what you expected. You write support, who is oblivious to the problem advertised by marketing you’re trying to solve. You end up with time and money wasted, and a bad enough taste in your mouth to never want to buy anything more from that company. They’ve broken your trust. 

The reason it feels like they’ve broken your trust is because the whole company was not aligned on the promise to begin with! Without a singular view of customer data, support stays in the dark about the specific reasons customers buy the product, sales and marketing remain unaware of serious problems that come with a particular use case and continue to sell on that value proposition, product plays catch-up with competitors or goes on gut feelings based on a few customer interviews with power users that may be outside of the mass audience, and engineers have no idea they’re losing massive accounts from that “edge case” bug that happens to be edge case because it only appears with customers already worried they are scaling out of your product’s capabilities. 

Customer Experience expert Jeanne Bliss lists creating a one-company customer experience as one of the 5 core competencies required to earn customer-driven growth. Creating a one-company customer experience requires one-company customer data to keep leadership across departments aware and informed of the customer’s needs at each stage of the customer journey. By bringing all of your customer data sources into a single source of truth, departments and teams come to the same conclusions on what’s best for the customer.

Derive insights from data

You now have all the data you’ve collected in one singular place, but what does it all mean? Collecting and aggregating data is meaningless if you don’t know what you’re looking at. When it comes to deriving meaningful insights from customer data, two key tips are to consider your data in terms of the customer journey, and to combine multiple data sources and types together to answer important business questions.

Consider the customer journey

It’s not just about what you collect in terms of data, but where the customer is when that data is collected. Segmenting customer data by each stage of the customer journey will provide insights and answers to unique questions and solve different business problems.

Presale and evaluation stage

Evaluating prospects will always ask for exactly what they’re looking for in your product, and what problems they are trying to solve. 

Analyzing conversations from the presale process answers questions like:

  • Who is buying your product or service?
  • How do prospects perceive what your product or service does?
  • What do they find most valuable about your product or service?
  • What apprehensions do evaluators have before signing on the dotted line?

Customer data from the presale and evaluation stage of the customer journey can be used to inform marketing messaging and product roadmap decisions.

Onboarding stage

Onboarding is one of the most critical parts of the customer journey to earning a successful, long term customer. It’s also the time when customers have the most questions, as they’re just starting to learn everything your product and service can do. 

Listening to customers during onboarding will help you answer:

  • How easy or confusing is your product to implement?
  • How intuitive is your product?
  • How easy is it for your customer to get help if they need it?
  • What are customers consistently most confused about?
  • What are they most interested in?
  • Does your product deliver on promised results?

Analyzing onboarding customer data can help to streamline UI/UX confusion and inform customer enablement. 

Existing Customers

Listening to existing customers gives you everything you need to keep solving customer problems long term. 

Questions that data from existing customer conversations can answer include:

  • What frustrates my customers about our product or service?
  • What challenges do my customers face as they scale?
  • What other problems are my customers interested in solving?
  • What routinely gets missed by our customers?

Existing customer data can help inform expansion opportunities and initiatives to keep growing from within your existing customer base. 


End of life customers are an often overlooked part of the customer journey, but learning why customers decide to leave is critical to business growth. Deploying a survey in-app at the point of cancellation is an effective way to collect these sentiments, or analyzing conversation data specific to churned customers. 

Collecting offboarding feedback can uncover:

  • Why do customers leave?
  • Are there any competitors we should watch more closely?
  • Is the product missing key features?
  • Does our pricing make sense for our offering?

Additionally, problems with onboarding can masquerade as churn problems, so knowing how much customer loss is from a failure to truly learn the value of the product is an additional business opportunity!

Combine multiple data sources

Deriving insights from all of this aggregated data at points along the customer journey is a bit like stringing together ingredients for a recipe. A pinch of qualitative insight, stir in context from a CRM, add a dash of business metrics and you’ll be left with actionable insights that help you move your business forward. Here are some common data analysis recipes that can help solve larger business problems.

Improve conversion rate = CRM conversations + conversions

If you notice your conversion rate starts to slump, turn to CRM data in presale conversations. You’ll likely find requests for certain features hiding in there. While the customer might not explicitly say that’s their “closed lost” reason, the ask shows an interest, and the fact that they didn’t buy shows the offering was not otherwise compelling enough.

Looking at customers that failed to convert will show you what you’re missing, but looking at customers that did convert will show you trends in what customers find valuable (and who those customers are). Those trends can inform your value proposition and ICP to maximize the efforts of your sales and marketing team.

Increase CLV = Customer Health + NPS

Learn from your successes by examining the customer health of customers with high NPS. You might find out you only need to hit a couple of key product touch points to prove value to customers to keep them happy long term. Use those learnings to proactively monitor when customers don’t hit those touch points, and use it as an opportunity to engage to deepen customer product adoption where it might not have happened organically.

Reduce churn rate = NPS + Exit survey analysis + help desk conversations

Uptick in churn? Analyzing exit survey feedback in tandem with detractor NPS results will tell you why. Or, cross-reference help desk conversations with churned customers to view trends in conversations. Address those trends to keep future customers in tow.

Combining business health metrics like churn, CLV, and conversion rate, with quantitative data like NPS survey scores and CSAT, and adding qualitative customer data like CRM and help desk conversations, the solutions to all your business’ larger strategic problems begin to emerge. 

Transform insights into action

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Collecting, aggregating, and deriving insights from customer data does nothing for your business if it’s not used to drive business decisions. The bottom line is companies that identify as customer-data-driven experience more year-after-year growth than those who don’t. Driving the business forward is a whole company initiative, and it’s the reason why each department needs to buy-in to and learn from customer intelligence. 

Every team benefits from customer intelligence

Customer intelligence isn’t just for customer facing teams, the whole company can use the voice of the customer to inform business strategy. Here are just some ways departments across the business can benefit and take action from insights gleaned from customer intelligence. 


Customer intelligence helps product teams inform and prioritize the product roadmap by understanding what customer requests are dealbreaker features vs. nice to haves. By cross referencing trending feature requests with business health metrics like churned customers or customers that fail to convert, product teams get insights into what really matters to customers when it comes to feature development. 


Listening to the voice of the customer can unearth new value propositions, use cases, and real life problems your product or service solves. It can even expose entirely new audiences you’d never expect! You may think you’re selling yoga pants to athletes, but your customers indicate they love wearing them to Netflix and chill. Customer intelligence helps marketing teams craft more honest and organic messaging based on real customer experiences. Positive feedback from NPS surveys can fuel case studies or prompt customer reviews or referrals.

Customer Facing Teams

Customer facing teams like sales, support, and customer success rely on customer intelligence to better service the customers they speak to every day. Trends in common confusions or questions can help inform customer enablement projects like knowledge base articles or video guides. 

It can also inform internal enablement needs - like competitor intelligence for new competitors if a new name starts to emerge in CRM conversations paired with a lagging conversion rate. 


You might know a lot of customers report a particular bug, but do you know how much that bug is costing your business? Measuring customer complaints about bugs or site reliability issues against business health metrics like customer churn can help inform the importance of fixing certain issues. Engineers use customer intelligence to understand what issues are edge cases vs. annoying enough to merit a quick fix.

Revenue and finance

Measuring churn in tandem with customer intelligence stemming from help desk conversation trends, exit survey analysis, or negative NPS feedback puts a dollar amount to how much specific problems cost the business. Customer intelligence can help savvy finance and revenue teams optimize burn rate by prioritizing which larger business problems need fixing first. 


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Customer intelligence helps you edge out the competition by elevating the voice of the customer to inform larger strategic business decisions like product roadmap, marketing messaging, or new business initiatives. 

Collecting customer data across channels, aggregating into a single source of truth to create a “one company” customer experience, deriving insights by considering the customer journey and combining customer data with business health, and using those insights to take action across departments all make for a successful customer intelligence strategy sure to give you a leg up on the competition.

Everyone wants to do customer insights right but they're setting themselves up to fail if they rely on tools that automate what humans already do manually. That's just automation and isn't true innovation. Customer insights teams today are allowing technologies to reveal the areas that need the most improvement. This video shows you how it's done. 🚀

Customer Insights