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As a customer, I’ve taken plenty of surveys for my favorite brands, and the best ones always have survey logic. Before I started working on this post, I always assumed this flow was coincidental. The survey seemed to magically change based on my responses. Spoiler alert: that’s exactly what was happening.

Thanks to survey logic, the best surveys feel connected, the question order makes sense, and the experience is smooth — almost like a real conversation. These types of surveys make it easier to give thoughtful answers because each question seems to build on the one before it.

Savvy teams use survey logic to adjust questions based on how respondents answer. In this article, let’s explore the basics of survey logic and the different types of logic that make surveys smarter and more engaging.

Table of Contents

• What is survey logic?
• Types of Survey Logic
• Survey Logic Rules
• Survey Logic Triggers
• Benefits of Survey Logic
• Survey Logic Best Practices

What is survey logic?

Survey logic is a feature where surveys adapt to a respondent’s behavior. The order of pages may change. The question order may shift, and notifications may appear. The goal is to create a survey with a logical order, improving the experience and the quality of responses.

“I see survey logic as a tool that transforms a list of generic questions into an engaging and relevant conversation,” says Eylül Nowakowska Beyazıt, director of customer success at surveying company Survicate. “It allows us to ask the right questions to the right people.”

Survey logic improves the quality of data that is collected by ensuring respondents are only asked relevant questions. This leads to surveys with improved response rates and data accuracy, making results more actionable for businesses and researchers.

Here’s an example of survey logic in action:

Think of your survey as an engaging chat versus an interrogation. “In our day-to-day interactions, we adapt our questions based on the behavior of who we’re talking to. Survey logic empowers us to do just that,” Beyazıt says.

Now that we know what exactly survey logic is, we can explore the different types of survey logic.

Types of Survey Logic

1. Skip Logic (Conditional Branching)

Think of your survey like a branching tree. Certain responses lead to more relevant questions. Less relevant queries are tossed out. Survey logic can help you create these branching paths in your surveys.

A respondent’s survey direction depends on their answers to specific questions. This ensures that respondents are only asked questions that are relevant to them.

Keep in mind that skip logic can only move respondents ahead in the survey. Only future questions are altered based on responses.

2. Branching Logic

Branching logic is a more generalized term that encompasses various ways to route respondents through a survey. Skip logic is one specific form of branching, but branching logic can also include directing respondents to different paths based on their demographics, preferences, or other criteria.

Here is a link to Anna Szabo’s tutorial on how she created a Salesforce survey with survey logic. She explains in depth how she used branching logic o her advantage.

3. Randomization Logic

I’ve definitely taken surveys where I could predict the next question before even reading it. That’s where randomization logic comes in. It mixes up the order of questions or answer choices to keep things fresh for each respondent.

This not only makes the survey feel less predictable but also helps remove biases that come from seeing options in a set order. In my opinion, it’s a great way to get more honest and thoughtful responses since people aren’t just picking the first option out of habit.

4. Display/Relevance Logic

Display logic helps you show additional questions or information based on a respondent’s answer. For instance, if a customer expresses interest in lip gloss, the survey might display more detailed questions about the kind of lip gloss they like.

Jeff Sirkin, founder of Sirkin Research, refers to the importance of screener questions.

“We always need to make sure that we’re using screener questions… So it’s not just a single question about your title or your role,” he says. “We want to make sure that it’s very specific to what it is that the survey is about.”

5. Quota Control Logic

As a survey creator, you can use quota control logic to set quotas or limits for specific groups of respondents. Once a quota is met, the survey is closed for that particular group.

For example, let’s say I have a product that appeals to both young parents and college students. I may have more than enough responses from parents. If another parent takes the survey, they’ll receive a notification that ends the process sooner. I can then focus on gathering data from college students.

6. Piping Logic (Data Insertion)

I love when a survey feels like it’s actually speaking to me rather than just running through a list of questions. This advanced survey feature personalizes the experience by reusing respondents’ answers in later questions.

For example, if someone enters their name as “Erica,” the survey can greet them with “Hello, Erica!” This makes the experience more engaging because it feels like a real conversation. In my opinion, small details like this can make a big difference in keeping people interested and willing to complete the survey.

7. Termination Logic

Termination logic helps you determine when a respondent wants to exit the survey. For example, if a respondent selects “I do not wish to continue” or provides certain disqualifying responses, the survey can be set to end.

These different types of survey logic can be combined and applied to create complex survey designs. This allows you to tailor the experience to your specific research objectives and the characteristics of your respondents.

Personally, I also love how this feature allows me to respect the respondent’s time and decision, preventing them from feeling stuck in a survey they don’t want to complete.

Properly implemented survey logic ensures that respondents have a more engaging and relevant survey experience while creators collect accurate and meaningful data.

So, survey logic is not just a “nice to have” if you want to create effective surveys.

Survey Logic Rules

Why do you need survey logic rules? First, they make surveys more efficient by guiding respondents to relevant questions based on their answers. Second, they create a more personalized experience, ensuring participants only see questions that apply to them.

Each rule consists of two key elements:

• Criteria — the condition that must be met, for example, a specific response to a question.
• Action — what happens when the criteria are met, like showing a follow-up question or skipping to a different section.

You can set up rules using:

• Single-question branching. A rule based on just one question. For example, if a respondent answers “Yes” to “Do you own a car?”, a follow-up question about car brands appears.
• Multi-question branching. A rule that considers multiple criteria. For example, if someone selects “Yes” for “Do you own a car?” AND “Electric” for “What type of car do you own?”, they get a question about EV charging habits.

You can mix and match multiple criteria using AND/OR logic:

• AND means all conditions must be met. For example, if a respondent is between 25-35 years old AND selects “Frequent traveler,” they see a question about travel preferences.
• OR means any one of the conditions can trigger the action. For example, if they select “Business traveler” OR “Frequent traveler,” they get the same follow-up question.

Once your criteria are met, you can apply different actions to guide respondents through the survey smoothly.

1. Display a question.

This survey logic rule shows a follow-up question only if it’s relevant to the respondent. Let’s say I’m sending a survey for a line of products, including options for pets. If a respondent selects “Yes” to “Do you have pets?”, they’ll see “What type of pet do you have?” instead of unrelated questions.

2. Skip to a new page.

This rule directs respondents to a specific section based on their answers. For example, if a user selects “Beginner” in a tech skills survey, they skip past advanced-level questions and go straight to beginner-friendly ones.

Pro tip: Avoid sending users back to a previous page, as it can create loops that prevent them from completing the survey.

3. End the survey and show a thank-you page.

Here, the survey ends early and shows a customized thank-you message, if relevant.

If a respondent doesn’t qualify for a study (e.g., selects “No” for “Do you work in the tech industry?”), they see a message like, “Thank you for your time! Unfortunately, this survey is for tech professionals.”

4. End the survey and redirect to a URL.

With this rule, the respondent is sent to a specific website when the survey ends. A survey panel company might redirect disqualified respondents to another available survey that better fits their profile.

Survey Logic Triggers

Survey logic triggers are the criteria that activate specific actions and trigger certain rules in a survey.

Let’s assume that I’m running an employee satisfaction survey. My goal is to follow up with respondents differently, depending on their feedback. I am particularly interested in learning about the reasons why some employees might rate their job satisfaction as “Low.” I’m using a scale of 1-5, where “1” and “2” answers are the lowest scores.

So, I create triggers for these “1” and “2” replies, and display a follow-up question, “Can you share what aspects of your job contribute to your dissatisfaction?” I also set up an automatic notification to HR any time a respondent replies with a low score, so that they can follow up proactively.

Survey logic triggers make surveys “smarter” and more insightful, by adjusting the experience based on how people respond. They focus on each individual case and make sure you don’t deter people’s attention.

For example, the survey might show or hide certain questions and close once enough responses are collected. The survey can even send notifications when a key response comes in. These triggers help keep surveys both relevant to you as someone who runs them and more engaging for respondents.

Benefits of Survey Logic

Survey logic plays a valuable role when designing surveys. Here are some key benefits of using survey logic.

1. Improved Relevance

Survey logic helps you give your responders relevant questions to make the experience, well, logical.

Imagine filling out a feedback form for a retail store. You are asked, “Have you tried our new range of denim shorts?” You reply, “No.” How would you feel if the next question was, “How would you rate the quality of our new range of denim shorts?”

That would be annoying, right?

Survey logic removes this pain point for your participants. The fewer pain points there are, the more accurate data you’ll receive.

2. Reduced Survey Fatigue

Long, complicated surveys bore respondents, and when that happens, they leave. In fact, here’s a scary stat: One in five customers will stop doing business with a company if its surveys are too long.

That’s where survey logic comes in. Showing or skipping questions based on previous answers helps create shorter, more focused surveys. The result? A better experience for your customers and higher completion rates for you.

3. Higher Response Rates

Online surveys often have a wide range of response rates, from as low as 2% to as high as 30%. It largely depends on how you draw the sample and how you recruit participants.

Response rates tend to be higher when participants have a vested interest in the topic. Survey logic can help with this by personalizing the experience, tailoring questions based on previous answers, skipping irrelevant sections, or directing respondents to the most relevant content.

This makes participants more likely to complete the survey, boosting response rates.

4. Data Accuracy

By asking only relevant questions to each respondent, survey logic helps ensure that data is accurate and meaningful. When you use survey logic effectively, you collect cleaner and more structured data. This makes it easier to analyze, draw insights, and make informed decisions.

5. Customization

Survey logic enables you to customize the survey experience. You can create personalized paths for different segments of your audience, ensuring each group is asked questions that address their unique perspectives.

For example, let’s say you want to carry out an employee engagement survey. However, not all employees are the same. You may want to customize your survey for different segments of employees based on their roles, locations, departments, or other criteria.

And guess what? Personalizing web surveys can boost response rates by 8.6% — that’s pretty impressive.

How do you do that effectively and efficiently? Yes, you got it right, through survey logic!

6. Personalization

​​Survey logic helps personalize surveys by using respondents’ names or tailoring follow-up questions based on their answers. This kind of customization makes a big impact — 80% of consumers are more likely to make a purchase when brands offer personalized experiences.

A more personal approach doesn’t just boost engagement; it also leads to higher-quality responses and more valuable feedback for your brand.

With HubSpot’s customer feedback software, you can easily create custom surveys that capture meaningful insights. Choose from various question types, use flexible templates, and send surveys via web link or email. Then, seamlessly share findings with your team to strengthen customer relationships and improve retention.

Now, let’s explore best practices for using survey logic.

Survey Logic Best Practices

Here’s what Beyazıt of Survicate had to say about using survey logic:

“If used correctly, survey logic can help you transition from gathering feedback to taking action by seizing the power of the moment. It helps you craft surveys that … create dynamic conversations tailored to each respondent’s unique perspective.”

Looking to unlock the power of survey logic? Here are the best practices and expert tips to help you get it right.

1. Plan your survey carefully.

Before implementing survey logic, have a clear understanding of your research objectives and the specific data that you need to collect. Plan your survey questions, logic, and branching paths accordingly.

2. Keep it simple.

While survey logic can be powerful, avoid overcomplicating your survey. Excessive branching or complex skip patterns can confuse respondents. Try to make your surveys simple and clear.

“I once worked with this online shop, and they got so wrapped up in creating the ‘perfect’ survey that they ended up leaving out a chunk of their customers,” says Sudhir Khatwani, founder at The Money Mongers. “It just goes to show, sometimes you can overthink things and end up missing the forest for the trees.”

Pro tip: Don’t add hide-and-skip logic functions together. Skip logic is used to skip questions based on respondent choices. Hide logic removes questions that may or may not be relevant based on the specific responses. Using both of them in one survey could result in a disorderly flow of questions.

3. Test your survey.

Always thoroughly test your survey with a small group of participants before launching it to a larger audience. This helps you identify any issues with the logic, wording, or flow of questions.

Be sure that your survey settings work the way you want and that your questions are not biased, leading, or confusing.

4. Don’t overwhelm respondents.

A long list of questions can leave your respondents overwhelmed. This is especially true if your survey involves several branching logical conditions. Use more pages and spread out information, instead of cramming everything on one page.

“One question per page keeps the flow going; stacked questions on the same page can feel overwhelming,” says Maya Kislykh, head of content at Bluestone PIM.

Page number aside, however, I’m sure you’re wondering — what’s the ideal number of questions? According to 42% of respondents in a HubSpot survey, there should be anywhere between 7 and 10 questions.

5. Consider mobile responsiveness.

More and more people access the internet on their smartphones. This means your survey must be responsive so it looks great (and includes your intended survey logic) on all devices. Test the survey on various devices and browsers to ensure compatibility.

I recommend choosing a survey creation software that has mobile design basked in. Looking for a place to start? Hubspot’s survey tool can help.

6. Monitor survey progress.

While you are collecting data, I recommend monitoring your survey progress and completion rates. If you notice a high dropout rate at a particular question or section, investigate the reason why. It’s possible that the survey logic is causing confusion or frustration.

Regularly check the survey data to verify that the logic is functioning as intended and that the collected data is accurate and complete.

7. End your survey right.

How you conclude your survey is just as important as the questions you ask throughout. If your respondent makes it all the way to the end, you can gather more information to follow up later.

“[You can] add an email gate on the final question page, as participants are typically more engaged at that stage and more likely to provide their contact details,” says Kislykh.

For those who don’t want to complete the survey, you can let them go. Always include an option for respondents to exit the survey at any point if they choose to do so. I find this respects their autonomy and prevents complaints.

The Future of Survey Logic

Today, we have surveys that adapt to respondent behaviors. These conditional elements already surface better insights and lead to higher completion rates. So what’s next?

“If you ask me where surveys are headed… Well, I reckon we’re on the brink of some cool stuff,” Khatwani says. “Imagine a survey that adjusts in real time, tapping into past data and predicting the best next question. That’s the future, and I’m here for it.”

Editor’s note: This post was originally published in November 2023 and has been updated for comprehensiveness.

Talking about statistics and representative samples might not sound like the most exciting topic. I can get it. But stick with me, because getting this right is hands down the most critical part of making smart, customer-focused decisions.

Through my years of working with customer data – sometimes learning the hard way – I’ve landed on a practical way to build survey samples that genuinely mirror the customers I need to hear from. It’s shifted how I operate, moving away from guesses based on potentially skewed data toward strategies built on a more solid foundation.

So, let’s ditch the textbook feel. I want to walk you through how I approach this, the methods I use, and why it matters so much – just like I’d explain it if we were hashing out how to really listen to our customers.

In this article:

• What is a representative sample?
• The Importance of Representative Samples in Customer Surveys
• Representative Sample Methods
• How to Get a Representative Sample
• Use Cases for AI Agents in Surveying
• My Final Thoughts: Listening to the Right Voices

What is a representative sample?

Let’s demystify this term “representative sample.” When I use it, I’m talking about a smaller group of people carefully picked from a larger group (your population) in a way that accurately mirrors the key characteristics of that entire larger group.

If your population is your whole customer base, your representative sample is a slice chosen so it looks, feels, and behaves like the whole pie, just scaled down.

Source

Think of it like a blood test. Your doctor doesn’t need to drain all your blood to understand your health – they take a small, representative sample because they know that sample accurately reflects the composition of the whole.

Similarly, if your customer base is 40% enterprise clients and 60% small business, a representative sample should maintain that same 40/60 split.

What makes a good representative sample?

A good sample aims to mirror the population across multiple relevant dimensions. This could be things like demographics (age, location), firmographics (company size), behavioral data (purchase habits, product usage), or even attitudes (past satisfaction levels).

The goal is to create this miniature reflection so that the feedback you get from the sample is highly likely to be the same feedback you’d get if you could ask everyone.

It’s about achieving generalizability – the ability to confidently apply insights from your sample to the broader population you care about. Without representativeness, you’re just getting some opinions, not necessarily a reliable pulse of the whole group.

This matters because customers notice when brands don’t seem to understand them.

Research frequently shows a gap between how well companies think they know their customers and how understood customers actually feel. A Koros study found that businesses typically miscalculate the number of times customers have poor experience by around 38%.

The Importance of Representative Samples in Customer Surveys

So why the fuss? Why the extra effort to get a sample that truly represents? Because I’ve seen the alternative: resources wasted on initiatives based on feedback from the wrong slice of customers.

Basing important decisions on feedback from a skewed sample is like asking only your friends if your new business idea is good. You’ll likely get overly positive feedback that doesn’t reflect the broader market reality.

Getting the sample right delivers these crucial benefits.

Guarantees Accuracy and Reliable Insights

This is the big one for me. When your sample accurately reflects your customer base, the data – the scores, the trends, the comments – are far more likely to be true.

A representative sample forces you to confront the whole picture, good and bad. This is critical because inaccurate data doesn’t just mislead, it can actively harm.

Estimates suggest poor data quality costs the U.S. economy trillions annually, impacting everything from marketing spend effectiveness to strategic planning. Organizations can lose between 15% to 25% of their annual revenue due to data errors, including missed sales opportunities and compliance fines.

Practically, this means your metrics like NPS and CSAT become trustworthy indicators.

I’ve found that when I trust the data’s accuracy because the sample was solid, I can diagnose issues much more effectively. If a representative sample shows a satisfaction dip among a specific user segment after an update, that’s a clear signal.

Skewed data might completely hide that.

This accuracy isn’t just a nice-to-have — it drives real value. Industry analysts like Forrester have quantified this, suggesting even single-point improvements in CX scores (which rely on accurate measurement) can equate to millions in revenue for large enterprises.

You need accurate data, rooted in good sampling, to even measure that progress reliably.

Saves Significant Time and Resources (Cost Efficiency)

Let’s be practical. Surveying every customer is usually out of the question – too expensive, too slow. Representative sampling is the efficient alternative.

By studying a smaller, carefully chosen group, you get statistically valid insights without the massive overhead. My experience consistently shows that the time spent planning the sample saves far more time and money than dealing with the consequences of bad data later.

Consider that analysts estimate data scientists spend a huge portion of their time, sometimes up to 80%, just cleaning and preparing data. Starting with a well-defined, representative sample approach can streamline this entire process.

Think about the costs: platform fees, team hours, analysis time. A representative sample, often needing just a few hundred well-chosen responses, drastically cuts these compared to a consensus attempt.

This efficiency translates to speed.

Being able to gather trustworthy insights quickly allows businesses to adapt faster. As Aptitude Research found, companies using quality data sources make decisions nearly 3x faster than those using poor data.

A representative sample helps ensure you have quality data, enabling agility.

Enables Confident, Data-Driven Decision-Making

Ultimately, we gather feedback to make better choices about products, marketing, support, and strategy. When those choices are informed by data from a representative sample, you can act with greater confidence.

Research backs this up. A study by McKinsey indicated that companies extensively using data-driven decision-making are 5% more productive and 6% more profitable than their competitors.

Presenting findings backed by a solid sampling plan carries more weight. It shifts the conversation from “Here’s what a few people said” to “Here’s what our customers likely think, based on a reliable sample.”

This confidence is key for getting buy-in.

If representative data clearly shows a pain point that affects a significant, valuable segment, the case for investing in a solution becomes much stronger. It reduces risk. Launching something based on feedback from only enthusiasts is a gamble. Testing with a representative group gives a more realistic forecast.

Given that making a wrong strategic bet can be incredibly costly, grounding decisions in representative data isn’t just good practice – it’s smart risk management.

Companies that consistently make customer-centric decisions based on solid data tend to see higher customer lifetime value and reduced churn rates. For instance, predictive analytics have been shown to reduce churn by 10% to 30% and increase CLV by up to 50%, as businesses leverage data-driven insights to address customer needs and enhance satisfaction proactively.

Representative Sample Methods

So, how do we actually build a representative sample? It’s not random guesswork. It involves specific techniques designed to give everyone (or key groups) a fair chance of being included, minimizing bias. These are generally called “probability sampling methods.”

Here are the main ones I’ve worked with in a business context.

Simple Random Sampling (SRS)

This is the classic setup where every single person in your target group has an equal chance of being selected. Think drawing names from a hat.

• How I approach it: Requires a complete list of the target population (the sampling frame). Then, use a random method (like a software random number generator) to pick individuals until the target sample size is reached.
• Example: If I need to survey 300 users for a specific HubSpot tool from a clean list of 5,000 eligible users, I’d use a tool to randomly select 300 unique IDs from that list.
• Practical note: SRS is unbiased in theory, but its big dependency is that perfect sampling frame. Getting to a truly complete and accurate list of all target customers can be very challenging in dynamic business environments. Flaws in the list mean the sample won’t be perfectly random.

Systematic Sampling

This one is a bit more structured. In this case, you’d select individuals from an ordered list at regular intervals, after a random start.

• How I approach it: Get the ordered list. Calculate the “sample interval” (k) by dividing population size (N) by desired sample size (n). Randomly choose a starting number between 1 and k. Select that person, then select every kth person after that.
• Example: From a list of 8,000 support interactions sorted by date, needing a sample of 400. Interval (k) = 8000 / 400 = 20. Randomly choose a starting number, say 12. Select interaction #12, then #32, then #52, etc.
• Practical note: Often easier than SRS, especially with digital lists. Works well unless there’s a hidden cycle in the list that matches the interval (e.g., every 20th customer signed up during a specific problematic promo). Always worth a quick check for such patterns.

Stratified Sampling

This method is often my go-to for customer surveys because it handles diversity so well. It involves dividing your population into distinct subgroups (“strata”) based on important characteristics, and then drawing a random sample (SRS or systematic) from within each subgroup.

• How I approach it: Identify key segments relevant to the survey (e.g., based on subscription plan, CLV, usage level, and industry). Determine the proportion of the total population each segment represents. Then, sample randomly from within each segment, usually ensuring the sample size for each segment matches its proportion in the population.
• Example: For a SaaS product with 60% “Standard” users, 30% “Premium,” 10% “Enterprise.” For a sample of 500, I’d ensure I randomly select 300 Standard (60% of 500), 150 Premium (30% of 500), and 50 Enterprise (10% of 500).
• Practical note: This guarantees representation from all key groups, even small ones, and often yields more precise overall results. It’s excellent for understanding segment-specific needs, crucial for personalization efforts which customers increasingly demand.

McKinsey research shows that personalization can reduce customer acquisition costs by as much as 50%, increase revenue by 5% to 15%, and improve marketing ROI by 10% to 30%. The main prerequisite is having the data to accurately define and size these segments.

Cluster Sampling

This is useful when the population is naturally grouped or geographically dispersed. You divide the population into groups (clusters), randomly select some clusters, and then survey all individuals (one-stage) or a random sample of individuals (two-stage) within the selected clusters.

• How I approach it: Identify natural clusters (e.g., sales territories, store locations, website visitor cohorts by day). Randomly choose a sample of these clusters. Then collect data from people within those chosen clusters.
• Example: A company wants feedback from attendees of its 50 nationwide workshops. Instead of sampling all attendees, they could treat each workshop as a cluster, randomly select 10 workshops, and survey all attendees from just those 10.
• Practical note: Can be much more cost-effective for large, spread-out populations. However, it might be less statistically precise than other methods if people within clusters are very similar to each other. Often requires a larger total sample size to achieve the same confidence level.

Choosing the right method involves balancing your goals, population, list quality, and practical constraints. There isn’t always one perfect answer, but understanding the trade-offs is key.

How to Get a Representative Sample

Knowing the methods is step one. Executing well is step two. Here’s the practical process I follow.

Step 1: Define your target population with laser focus.

I can’t stress this one enough – be absolutely clear about who this survey is for. Vague targets lead to vague results. You can consider asking:

• Who specifically are we trying to understand? (e.g., active paying customers? Trial users? Churned customers?)
• What defines them? (e.g., plan type? Usage threshold? Time as a customer? Location?)
• Who should be excluded? (e.g., employees? Competitors? Very new users?)

Write down a precise definition. For example, “Paying customers in the U.K. on the ‘Professional’ plan who have used Feature Z in the last 90 days.” This clarity guides everything else.

Step 2: Calculate your ideal sample size.

How many responses do you need for reliable results? Don’t guess, consider:

• Population size (N). How many people fit your Step 1 definition?
• Margin of error (e). How much uncertainty is acceptable (e.g., +/- 5%)?
• Confidence level. How sure do you need to be (usually 95%)?
• Expected variability (p). How diverse do you expect answers to be (use 0.5 if unsure)?

Use an online calculator. Plug in these numbers. It will estimate the number of completed responses needed.

Remember, this is completed responses. You MUST factor in your likely response rate. If you expect only 10% to respond, you need to invite 10 times the number of people you need responses from. Plan your outreach numbers based on this reality.

HubSpot’s blog offers good resources on thinking through survey sample sizes.

Step 3: Choose the right sampling method.

Based on Steps 1 and 2, choose the method (SRS, Systematic, Stratified, Cluster) that best fits. You can consider:

• Goals. Overall picture versus segment deep-dive?
• Population. Diverse or spread out?
• List quality. Is your frame complete and accurate?
• Resources. What’s your budget and time constraint?

Again, for understanding different customer experiences, I often find that stratified sampling delivers the most actionable insights if the data allows for it.

Step 4: Build your sampling frame.

This is your actual invite list, pulled from your database or CRM based on your Step 1 definition. Its quality is very important.

You’ll want to ensure it is:

• Comprehensive. Includes everyone who should be included. Missing groups equals coverage error.
• Accurate. Correct contact information and characteristics. Studies have shown that email marketing databases, for example, generally degrade by about 22.5% every year, highlighting the need for regular cleaning.
• Up-to-date. Filters out irrelevant contacts.
• No duplicates.

Spending time cleaning and validating this list before sampling is crucial. Use your CRM tools (like list segmentation in HubSpot) carefully.

Step 5: Execute the sampling plan and collect data.

Now, it’s time to implement your chosen method precisely. Use randomizers correctly and deploy your survey thoughtfully. Consider timing – HubSpot has explored the best times to send surveys. Make sure to use clear communication.

Monitor the responses. If you’re using stratification, watch if segments are responding proportionally. If a key group lags significantly, consider a polite, targeted reminder to that group to help balance the sample and reduce non-response bias (where non-responders differ systematically from responders).

For example, one study found that only 20% of participants donated data compared to 63% who intended to, indicating a substantial non-response gap that targeted reminders could help address.

Pro tip: Tools like HubSpot’s Customer Feedback Software, potentially using survey templates for consistency, can help manage this process.

Step 6: Evaluate representativeness and adjust if necessary.

Once you’re finished collecting, before analyzing, check your achieved sample against your target population’s known characteristics (from Step 1).

• Does the distribution by plan type, region, tenure, etc., in your responses match the overall population?

If it’s reasonably close, great. If it’s significantly off (e.g., way too many responses from one country), your raw results could be misleading.

In these cases, a very technical person might use statistical weighting, which involves mathematically adjusting the influence of responses to better reflect the true population size.

This is a more advanced step, and while some tools offer features for it, it still requires careful application. It can help correct moderate imbalances but can’t fix a fundamentally flawed sampling process. If you’re going to be using weighting, it should always be reported transparently.

Use Cases for AI Agents in Surveying

AI is definitely making waves in many fields, and survey sampling is no exception. While I don’t see AI replacing the need for a smart sampling strategy, it is growing as a powerful assistant in nearly every aspect of business.

Tools that can help streamline tricky parts of the process, potentially boosting accuracy, and maybe even surface insights we miss, are a huge benefit.

Source

Sometimes, I like to think of it as less automation and more as augmentation. Based on what I’m seeing and industry discussions, here are three clear ways AI can practically lend a hand.

Use Case 1: Automating sampling frame cleanup and maintenance.

• The challenge: As we discussed, building and maintaining a clean, accurate sampling frame (that master list in Step 4) is critical but incredibly time-consuming. Customer data gets old fast, leading to errors, duplicates, and outdated information that can wreck representativeness.
• How AI helps: AI-powered data quality tools work wonders here. They can rapidly scan huge databases to identify and merge duplicate contacts, standardize formatting (like addresses or job titles), validate email addresses, and flag potentially inactive records based on engagement patterns, far faster than manual checks. Some tools can even assist with data enrichment, where appropriate and ethical, of course.
• Implementation and expert insight: This typically involved integrating specialized data cleansing tools or leveraging features increasingly built into CRMs.

The key, as data quality expert Thomas Redman emphasizes, is that while AI automates cleansing, human oversight on the rules and validation is crucial to avoid the “Garbage In, Garbage Out” trap. You set the parameters, let the AI do the heavy lifting on list hygiene, and ensure a much more reliable starting point for drawing your sample, saving significant manual effort.

Use Case 2: Discovering nuanced segments for smarter stratification.

• The challenge: Stratified sampling is powerful, but we often rely on obvious segments (like plan type or demographics). What if there are hidden, behavior-based groups within our customer base whose experiences differ significantly, but aren’t immediately apparent?
• How AI helps: This is where machine learning shines. Clustering algorithms can analyze vast amounts of behavioral data (like product usage clicks, feature adoption sequences, support interaction types, and content engagement) to uncover these “hidden” micro-segments. Maybe it finds a distinct group of “occasional users who are highly influential networkers” or “new users who skip onboarding but heavily used advanced features.”
• Implementation and expert insight: This usually requires data science expertise and certain tools to run clustering analyses on relevant customer data. The resulting segments need human interpretation to determine if they’re meaningful for stratification in a specific survey.

Recent expert analysis confirms that advanced AI clustering not only uncovers hidden micro-segments but also enables agile, real-time segmentation adjustments, leading to more adaptive survey designs.

Use Case 3: Proactively mitigating non-response bias.

• The challenge: Getting enough people, and the right people, to respond is a constant battle. Survey response rates continue to be a challenge across many channels. If the non-responders are systematically different from responders (e.g, less satisfied customers responding less often), it introduces significant bias.
• How AI helps: AI models can be trained on past survey data and customer profiles to predict the likelihood that certain individuals or segments won’t respond to an upcoming survey. It might learn, for example, that customers who haven’t logged in for 90 days are 3x more likely to respond than active users.
• Implementation and expert insight: Using these predictions (generated via custom models or potentially features in advanced survey tools), you can move from simply hoping people respond to proactively managing non-response risk. Some strategies could include offering tailored incentives specifically to predicted low-responders, testing different communication channels or follow-up cadences for these groups, or adjusting messaging to better resonate.

Recent research by the Nuremberg Institute for Market Decisions even explored using AI-generated “digital twins” to simulate responses from underrepresented groups, offering a novel way to both understand and fill gaps caused by nonresponse.

Using AI Wisely

Now, as much as I wish it was, implementing AI isn’t just plug-and-play. It requires a thoughtful approach. Here are some things I like to keep in mind when integrating AI into existing processes.

• Good foundational data. I can’t say this enough – AI runs on data. If your underlying customer data is messy, incomplete, or biased, the AI’s output will inherit those flaws. Data quality is job one.
• Human oversight and critical thinking. AI is a tool, not a decision-maker. We still need to define the goals, select the right AI approach for the specific problem, critically evaluate the AI’s output (does the segmentation really make sense for our business?), ensure ethical use (privacy, fairness, avoiding algorithmic bias), and interpret the results in context.

As tech ethicist Tristan Harris often implies, tools shape our choices – we need to understand how AI is shaping our sampling choices and ensure it aligns with our research integrity.

• Transparency. Whenever possible, understand how the AI is reaching its conclusions. “Black box” algorithms can be risky if you can’t explain or validate their reasoning. Look for tools or methods that offer some level of transparency.
• Integration. The most effective AI tools will likely be those that integrate smoothly into your existing workflows – connecting with your CRM data, survey platforms, and analysis tools, rather than requiring completely separate, manual processes.

My take? AI isn’t here to automate away the need for a smart sampling strategy in a representative sample, but it offers some genuinely exciting ways to make executing those strategies more efficient, potentially more accurate, and maybe even more insightful.

It’s about using these powerful tools as leverage, guided by sound research principles and human judgment.

My Final Thoughts: Listening to the Right Voices

Building a representative sample takes deliberate effort. It takes clear definitions, careful calculations, thoughtful method selection, clean lists, and critical evaluation. It’s more involved than just sending a mass email.

But the confidence it brings in business is invaluable. It’s the difference between guessing and knowing (with statistical confidence, at least!). It’s the foundation for making smarter investments, building better products, and creating experiences that genuinely connect with the diverse needs across your customer base.

Companies that truly listen – and representative sampling is fundamental to how you listen effectively – are the ones that build stronger relationships and lasting success. Consider that increasing customer retention rates by just 5% can increase profits by 25% to 95%.

Understanding and acting on feedback from a representative sample is key to achieving that retention.

For me, striving for representative samples isn’t just about better data — it’s about respecting our customers enough to hear them fairly. When you commit to that, you move beyond just collecting feedback to building real understanding. And that understanding, rooted in reality, is probably the most valuable asset any business focused on its customers can have.

Net Promoter, Net Promoter System, Net Promoter Score, NPS and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld and Satmetrix Systems, Inc.

If you’re creating a survey, you may be wondering whether to use open-ended or close-ended questions. While both question types offer their own benefits, the right question type for your survey will largely depend on the type of data and outcomes you’re looking for.

I’ve found that while many surveys include a combination of both question types, surveys today tend to mostly lean on close-ended questions (and for good reason!)

Are you ready to dig into how close-ended questions shape your survey results? Grab your free survey templates, and let’s go.

In this article, I’ll discuss:

• What are close-ended questions?
• Why use close-ended questions in surveys?
• When to Avoid Close-Ended Questions in Surveys
• Close-Ended Questions vs. Open-Ended Questions
• Types of Close-Ended Questions
• When to Add Close-Ended Questions to Your Survey
• Building a Well-Rounded Survey

Why use close-ended questions in surveys?

There’s a direct correlation between the type of questions used in surveys and the different kinds of data that they generate.

While there are multiple benefits to using close-ended questions in your surveys, let’s take a look at the top three reasons you’d want to consider using them.

1. They’re easier to answer.

Close-ended questions are likely to be quicker and easier for participants to answer, resulting in higher survey response rates and quicker data collection. If you’re looking to run a large-scale survey, using close-ended questions can help you collect large swaths of data at once.

One study that focused on collecting demographic data reported a 67% completion rate for surveys with closed-ended questions compared to 44.7% for open-ended questions. The researchers determined they could obtain higher survey response rates for demographic data by using close-ended questions versus open-ended questions.

2. They offer straightforward data analysis.

Since close-ended questions use a limited set of structured answer choices, it’s easier to sort the data into categories and analyze it to look for trends. When it’s time to present this data to stakeholders, you can easily pull this data into graphs and charts.

Additionally, you can use this data in real time by pulling it into business intelligence (BI) tools or reporting dashboards. As participants complete the survey, the reporting dashboards can update in real time — versus needing a researcher to analyze open-text responses and sort them manually.

Voice of the Customer Program Manager at AuditBoard, Ting Lai, told me that, “Close-ended questions are definitely easier to analyze and build reporting around. The time delay is really important, because a lot of our surveys are feeding live BI dashboards, so data from close-ended questions is easier to plug into those dashboards live.”

This immediate access to results also empowers you to make decisions around this feedback more quickly.

3. They generate consistent responses.

Since close-ended questions have a predefined set of answers, you’re going to get a consistent and comparable dataset to work with. This removes ambiguity and creates standardized responses, helping you quickly confirm or deny hypotheses.

Since the answers are predefined, analyzing the data is much easier than open-ended questions. It also creates more reliable data due to consistent responses.

When to Avoid Close-Ended Questions in Surveys

While using close-ended questions can help you focus on specific data validation, it often misses the nuance provided by open-ended questions.

In other words, if you’re curious about the “why” behind a choice or behavior, you’re unlikely to get it with a close-ended question.

If you’re looking for deeper customer insights, close-ended questions could be too limiting for getting feedback on things like new product ideas, areas for improvement, or other nuanced insights.

I sat down with Mike Christopher, customer experience manager at AuditBoard, who has been in the CX industry for the last 13 years. I asked him when he would opt for an open-ended question over a close-ended question, and here’s what he said:

“When you build a survey, you kind of work backwards from the intended outcome … Leaving [a question] open-ended gives the respondent the chance to kind of tell you anything.”

On the other hand, Christopher says, “Anytime I want to get more meaningful detail from the participant beyond just them checking a box, I’d include an open-ended question.”

My conclusion? Because open-ended questions are better at getting people talking (and result in more unexpected insights), they tend to be a better choice than close-ended questions if you want to explore a topic or understand the reasoning behind something like customer behavior.

Close-Ended Questions vs. Open-Ended Questions

Close-Ended Questions

As I mentioned earlier, close-ended questions are quantitative questions designed to gather specific responses, and they include a predefined set of answers for the respondent to choose from.

Questions with options for Yes/No, a rating scale, or defined multiple choice answer options would be considered close-ended questions.

Close-ended questions are great to use when you have a pretty good idea of your intended outcomes, or you’re looking to validate a specific set of options.

Ways to Use Them

Close-ended questions are applicable to nearly every type of survey where you’re looking for quantitative feedback. Since these types of questions are focused on a specific set of responses, they can be especially useful for:

• Gathering demographic information. Asking respondents for things like age range, geographic location, etc. can be done quickly and easily with close-ended questions. (I know I’d much prefer using a pick list to answer those questions than typing it all out!)
• Measuring opinions or sentiment. NPS surveys or customer satisfaction surveys use a close-ended rating structure which makes the data easy to analyze and compare.
• Understanding customer preferences. Using close-ended questions can help you narrow down what your customer views as a priority. For example, giving them a list of potential new feature enhancements and having them choose their top priority can give you insight into what they care about the most.

Open-Ended Questions

Open-ended questions are qualitative questions that allow participants to share their thoughts freely without choosing from predefined options. This question type allows participants to decide how much detail to give in their response, and it requires the respondent to take a moment and formulate an answer.

For example, a survey question that asks “Tell me what type of beverage you start your day with” would be an open-ended question. You can imagine that asking this question would likely generate a variety of different responses, creating a vastly different dataset than our earlier question about starting the day out with coffee.

Because of the level of detail and nuance in open-ended responses, more time goes into analyzing that data and finding themes and insights.

Ways to Use Them

You often see open-ended questions in usability studies, as researchers want to understand the user’s behavior and thought process without limiting them to predefined answers. I find this to be a good rule of thumb, and suggest using open-ended questions when you’re curious about the “why” behind something.

For example, you can use open-ended questions in:

• Employee feedback surveys. Complementing the close-ended questions in your employee feedback survey with some open-ended ones ensures that your employees have ample room to express their opinions in their own words. Chances are you’ll want to understand why they gave certain ratings, and open-ended questions help you do that.
• Customer experience surveys. Including an optional open-ended question after asking the customer to rate their experience or satisfaction allows them to elaborate on the “why” behind their rating.

In my experience, customers want to feel heard and know that their feedback is getting to the right place. This method not only gives you key insights but also allows the customer to express their satisfaction (or frustration) in their own words.

• Product validation. Gathering deep insights like customer expectations and pain points from open-ended questions is critical to building a product that works for your users. You can create a tagging framework to help better sort the open-ended feedback into categories that will help you later on.
• Follow-up questions. Including an open-ended question after a close-ended one can help you better understand a participant’s answer choice. Try to limit the number of these you include in a survey, though, so it doesn’t start to feel tedious for the participant. (Survey fatigue is real!)

While both types of questions are very different in their structure and output, they each add their own unique value to a survey.

Next, let’s look at a few different types of close-ended questions that can help you on your journey to gather structured data.

Types of Close-Ended Questions

1. Dichotomous Questions

Dichotomous questions offer only two possible answers — usually “Yes/No” or “True/False.” As you might imagine, these types of questions are typically quick to respond to and easy to analyze.

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2. Multiple-Choice Questions

Anyone who’s taken a test before is familiar with the structure of multiple choice questions. These questions provide a list of options where respondents can choose one or more answers.

Multiple choice questions can be quick and easy to answer for respondents while giving them a bit more freedom in how they respond.

3. Rating Scale Questions

Rating scale questions ask the participant to choose a number or descriptor that aligns with their rating, usually concerning their satisfaction or experience. CSAT, NPS, and other relational surveys typically include a rating scale to gauge a customer’s sentiment or satisfaction.

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4. Likert Scale Questions

Likert scale questions measure a participant’s opinions on a series of statements. Participants consider the prompt and then choose from a selection of answers that range from opposite extremities.

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Pro tip: Christopher told me that while not everyone loves a Likert scale, he finds a lot of value in them when used in the right way. “These types of questions can add value to the analysis, especially if your goal is to look for correlations. Likert scales can help you get more nuance, but you have to make sure you label the scale in a way that your participants understand,” he says.

5. Ranking Questions

Ranking questions can help a researcher understand participants’ priorities or preferences by having them make direct comparisons. These types of questions can help identify top and bottom choices within a set, giving researchers insight into necessary focus areas.

Pro tip: Christopher told me that he’s not a big fan of these. “People can have a hard time stack ranking, especially if some of the options are equally important to them,” he says. “I like to try to force people to prioritize by only allowing one or a few selections, depending on the question. It makes it easier to analyze the data when you’re encouraging prioritization.”

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6. Checklist Questions

Checklist questions are a type of multiple-choice question where participants can choose multiple options from a predefined list. This style of question can require more analysis, but it could be helpful in capturing broader insights while still using a close-ended question format.

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When to Add Close-Ended Questions to Your Survey

“Both close-ended and open-ended question types are key when building a well-rounded survey,” says Christopher.

“Using them together keeps the target of your survey well defined with close-ended questions but also offers respondents the chance to add context and detail with open-ended questions. It‘s important to make sure you not only consider the outcome of the survey, but the effort you’re asking your respondents to put in to help you get insights.”

Here’s a summary of when you’ll want to consider close-ended questions over open-ended ones:

• If you know the specific outcomes you’re looking to validate.
• If you need to easily gather large sets of quantifiable data.
• If you’re gathering demographic information, asking participants to prioritize or rate something, or identifying specific answers to a question you have.

Building a Well-Rounded Survey

Now that we’ve explored close-ended questions and their benefits, I understand why most surveys utilize them. They’re easier for participants to answer, and the data is easier for researchers to analyze, which is a win-win.

However, based on my research, I’d say if you’re looking for deeper customer insights, there’s definitely a use case for including both close-ended and open-ended questions in a survey — especially if your open-ended question serves as a follow-up to a close-ended one.

When I encounter a tech issue, my first move is to search Google for a knowledge base or other self-service resources. In studying dozens of knowledge base examples, I’ve discovered that I’m not alone. Over 67% of customers prefer using a knowledge base rather than talking to a customer service representative.

As a former support rep at HubSpot, you might think I’d advocate for phone support. But the reality is that I can solve my issues much faster by referencing a knowledge base instead of calling in, explaining my problem, and waiting on hold.

A well-structured help desk knowledge base empowers your customers to solve issues independently, reducing frustration and increasing satisfaction while cutting costs and easing the load on your service team.

So, stick around if you want to build a knowledge base that improves customer experience and streamlines support. I’ve compiled 20 of my favorite knowledge base examples so you can learn from the best.

Help Desk Knowledge Base Examples

1. HubSpot

Since you’re on the HubSpot website (welcome!), you probably know a little about what we do. If you arrived here from a Google search, here’s the quick rundown: HubSpot is a customer relationship management (CRM) platform that provides a suite of tools to help businesses grow by putting customers first. Whatever tool or hub (e.g., marketing, sales, or operations) you’re interested in, the HubSpot Knowledge Base is the best place to learn about it.

It’s packed with step-by-step guides, documentation, and troubleshooting articles to ensure your success with the platform.

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What I like: HubSpot Knowledge Base makes finding the information I need easy. I love exploring the handy search bar, the quick list of all product/service categories, and even the glossary. I especially like that each article features detailed screenshots and instructions, so I never get lost.

I also like how the AI HubBot assists in summarizing answers and linking to more detailed documentation to solve customer queries.

Pro tip: HubSpot’s knowledge base software can help you quickly create a searchable knowledge base that empowers your customers to find the answers they need.

2. Slack

Slack is a workplace messaging app used by 77 Fortune 100 companies, but it’s not just for big corporations. My band, Juice, uses Slack daily to communicate, share ideas, and plan everything from rehearsals to tours. If I ever run into an issue with Slack, there’s only one place I’m going: the Slack knowledge base.

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Slack’s knowledge base features a prominent search bar and direct links to common troubleshooting topics. It also offers shortcuts that help users save time. For example, I just discovered that pressing the “Up” key allows me to edit my previous message.

What I like: I find Slack intuitive, so I rarely use the knowledge base. When I do, I appreciate the powerful search function and high-quality video guides.

3. Confluence

As a collaboration and knowledge-sharing tool for companies, you’d imagine that Confluence’s knowledge base would be a masterclass in knowledge organization. Spoiler alert: It is.

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If you’re considering using Confluence to create your knowledge base, you must visit this Resource Center. You’ll see great examples and learn the steps and best practices for building a knowledge base.

Beyond the clear and helpful main page, you’ll find detailed documentation, step-by-step guides, and plenty of high-quality GIFs and images to make the technical details more digestible. I enjoy the wealth of video examples that Confluence has for topics like team collaboration and getting started, as well as demos for all the products’ different use cases.

What I like: Confluence’s knowledge base is an excellent example of how marketing efforts can fit into an information site. It adds customer success stories alongside easy-to-access demo videos and product guides to reel you in.

4. Apple

When something goes wrong with my iPhone, my first stop is Apple’s knowledge base. It features extensive step-by-step troubleshooting guides, detailed FAQs, and YouTube video tutorials with subtitles for enhanced accessibility. If I can’t find what I’m looking for, the knowledge base conveniently directs me to human support.

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What I like: Apple offers a dedicated support app, which is super convenient for me as an iPhone user and mobile app enthusiast. The app provides helpful videos related to all Apple products and automatically links all your Apple devices so you can quickly file support claims.

5. Amplitude

Amplitude aims to make digital analytics accessible to every business, and its knowledge base plays a prominent role in making that a reality.

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Amplitude’s help desk knowledge base has a user-friendly layout that’s easy to navigate, providing a great customer experience. If you’re new to this platform, the “Start Here” section is the ultimate guide to get you up and running. They also offer a helpful “Popular Content” section on their homepage that intelligently displays the most viewed articles.

What I like: The search bar on Amplitude’s knowledge base is more than just your average search bar. It’s AI-powered and remarkably fast. I like that it displays search results in a scrollable pop-up window instead of bringing you to a new page to display the results.

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6. Whale

Whale is an AI-powered knowledge base and training tool designed for scaling companies. Its AI assistant helps you create your knowledge base in seconds, and its complete suite of process documentation tools enables you to easily develop high-quality documentation.

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Beyond the intuitive and user-friendly web and mobile app, when you dive in, you’ll find the Whale Universe containing all the information you need to guide you through setting up your knowledge base. It has automated workflows so you can easily share information with colleagues.

I love their Chrome extension, which surfaces knowledge directly within the apps you are using, saving you valuable time.

What I like: Whale’s knowledge base is a stellar example of how seamless integration of AI can enhance information management. The ease of use is remarkable — you can get started in seconds. Additionally, Whale provides easy-to-use templates, comprehensive product guides and training, and engaging customer success stories to keep you informed and motivated.

7. Airbnb

Love it or hate it, Airbnb is a powerhouse in the travel industry, and its knowledge base reflects that dominance. Using Airbnb’s knowledge base, you can find answers to common questions for both guests and hosts.

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I played around with the knowledge base and was impressed with how personalized it is. When I clicked on the search bar, top articles appeared as suggestions, and the list changed depending on whether I was a guest or a host.

What I like: Airbnb prominently displays in-depth content related to anti-discrimination and accessibility policies, tips for avoiding scams, and advice on what to do in an emergency. I like Airbnb’s focus on safety and inclusion because it reflects the company’s core values while remaining a helpful support resource.

8. 1Password

1Password is one of the industry’s most trusted password managers, with over 100,000 businesses and millions of customers relying on it to keep their data secure. If you’re just starting, you’ll find everything you need once you hit the “Start Here” button.

For more experienced users, the knowledge base offers valuable tips on getting the most from 1Password, like using apps, browser extensions, and vaults.

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What I like: 1Password offers a variety of support tools. They have a comprehensive knowledge base, a friendly AI assistant, and a customer-driven, professionally moderated support community where users can connect and discuss support issues and best practices. They even give one another a heads-up about potential phishing scams.

9. Canva

With over 170 million active users, Canva is one of the most popular design platforms in the world, and its knowledge base supports that. With so many users, a comprehensive knowledge base and extensive documentation are necessary. Otherwise, their support team would be completely inundated.

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Using Canva’s knowledge base, I could quickly browse documentation by topic. Comprehensive, well-organized documentation supports everything from account settings to downloading and saving projects and using Canva’s editing and design tools.

While Canva’s product is simple to use, it’s actually quite complex regarding available features, so I was impressed by the thoroughness of its documentation.

What I like: Canva’s Design School provides thousands of tutorials to help customers learn design fundamentals, significantly enhancing customer success and making great design accessible to everyone.

10. Google

Google’s extensive knowledge base supports the company’s various products and services.

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Instead of a table of contents, the knowledge base displays icons for each Google product and service (e.g., Chrome, Gmail, YouTube, etc). I poked around the knowledge base and found it intuitive. Clicking on an icon brings you to a list of the most popular articles, followed by more neatly organized help topics in drop-down lists.

As you’d expect from Google, all their help documentation is searchable via a high-powered search bar that immediately populates with relevant articles based on your search terms. If you can’t find what you’re looking for, Google also offers robust community support where users can ask questions directly and receive replies from Google team members and other customers.

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What I like: Google’s support homepage is on-brand with its minimal, uncluttered, yet whimsical design. Despite Google’s extensive number of products, I could hone in on whatever I was looking for in just a few clicks.

11. Netflix

I usually have no issues with Netflix, so I rarely need to check their knowledge base. If my TV binge session is interrupted by technical issues, I can typically fix it by refreshing my browser or turning my TV off and then on again.

When I do need to look up Netflix support, it’s usually because I suspect someone is sharing my password or using Netflix on too many devices (I’m innocent, Netflix!).

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I noticed that the account and billing section is high on the knowledge base, which makes sense; it’s the only section I need to read. Once you click a section heading, you’ll find a drop-down list of helpful explainer articles.

Personally, I’d like to see snippets of these articles displayed here, but I don’t have any significant issues with the bare-bones layout. It makes sense, considering the straightforward nature of Netflix’s product.

What I like: I appreciate that a section of Netflix’s knowledge base lets me suggest TV shows or movies. It means I can hold out a tiny bit of hope that they’ll take my suggestions and add my favorite content to their library.

12. OpenAI

Open AI, the company behind ChatGPT, has a solid knowledge base primarily dedicated to account information and using ChatGPT effectively. As you might expect, OpenAI integrates an AI chatbot into its knowledge base to provide quick answers.

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When I browsed some knowledge base articles, I was impressed to see that they had recently been updated. The knowledge base intelligently suggests related articles to the one being viewed, which is helpful in quickly browsing the documentation.

What I like: OpenAI’s knowledge base is transparent about ChatGPT’s limitations, including its Western bias and the fact that it can be very convincing while providing incorrect information. I think new users should study ChatGPT’s knowledge base to ensure they’re using the tool effectively.

13. Asana

Asana is a work management platform with workflow automation and project management features. Its knowledge base has a clean layout with lots of whitespace and offers extensive video tutorials to help you learn the basics of Asana in minutes.

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Asana provides helpful use cases, screenshot-assisted help articles (I love these!), and even courses on the product, including self-paced, pre-recorded, and live training. Asana also features a helpful AI chatbot to assist in surfacing appropriate knowledge base articles. Unfortunately, it didn’t seem enthused that I was writing an article about it:

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What I like: The explainer videos in Asana’s knowledge base balance a funny and informative tone. Even if you don’t plan on using Asana, check out these videos to see how knowledge bases can have real entertainment value.

14. Dropbox

If you’re among the 700+ million people who use Dropbox, you might have used its knowledge base to solve common issues like syncing, sharing, and organizing your files.

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Like many of the best knowledge bases, Dropbox features a prominent search bar at the top of the page. When I clicked into the search bar, it popped up with what I can only assume are the most popular support inquiries.

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What I like: This knowledge base prominently features the Dropbox Community Forum, which I’m a big fan of. It has specific groups for beginners, photographers, and musicians — shout out to Community Manager Graham for some great posts on music-related topics.

15. Notion

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Notion’s knowledge base provides extensive resources for getting the most out of the platform’s features. I like their Guides section, which contains many helpful articles for learning new ways to use Notion, replete with detailed screenshots and GIFs that break down step-by-step guides into easily digestible snippets.

I found Notion’s help center well-organized, attractive, and intuitive. I like how the content is separated into three blocks at the top of the page for Reference, Guides, and API documentation, making it easy to dive right into whatever you need help with.

What I like: I like the Notion Academy, which offers curated learning pathways for getting started with Notion and taking advantage of custom solutions for everyday use cases. If I used Notion for my business, I would first devour all the content in the Notion Academy to ensure I was familiar with the product and its various capabilities.

16. Nuclino

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Nuclino is a collaborative knowledge management platform that helps teams manage and organize information in real time. Their knowledge base reflects the product’s ethos by providing clear, well-organized guides in a modern, minimalist interface.

I like how each product category is represented by a card on the home page of the Nuclino knowledge base. I also tested out the search bar and found it to be quick and powerful:

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I like how articles related to your search term immediately pop up when you start typing. You can even scroll down to reveal the complete list of help documentation containing your search terms without leaving the page you’re on.

What I like: Nuclino’s knowledge base is clean and easy to navigate. I love the “Get Started” card on the homepage, which brings you to a comprehensive document with video tutorials, screenshots, and templates so you can hit the ground running with their product.

17. Shopify

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Shopify is a dominant ecommerce platform with a knowledge base to back it up. Shopify’s help center prominently features an AI-powered assistant that excels at processing natural language inquiries and providing appropriate help articles in response.

It also features a search function, but unfortunately, it doesn’t automatically populate with articles related to your search term. You’ll need to type in your search term and press “Enter” to view your search results. Crazy, I know.

What I like: The resources tab at the top of the knowledge base lets users browse by the type of support content they’d like to view. Shopify offers webinars, business courses, a community forum, and even a Shopify Academy YouTube channel with extensive video tutorials and how-to guides.

18. Intercom

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Intercom is a customer communication platform that offers extensive live chat and AI bot functionalities. Intercom’s knowledge base is pleasing to look at and features a prominent search bar at the top of the page.

When I started typing in the search bar, related articles immediately popped up, which I liked. I also liked that the search bar prompted me to ask Fin, their AI agent, the question I was typing up:

I think that is a helpful feature of the knowledge base that could encourage AI adoption amongst users who aren’t as inclined to click on the little message bubble in the bottom right corner of the screen.

What I like: Intercom’s knowledge base is well-organized and comprehensive. I like that it features a “Get the most out of Intercom” section at the top with links to popular help articles, and I’m always a fan of breaking down help topics into enticingly clickable cards.

19. Squarespace

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Squarespace Help Center is a top-notch knowledge base with detailed guides, videos, and documentation for all of Squarespace’s features and products. I like the highly functional search bar at the top of the page and the cards below that break down popular articles by product.

I use Squarespace for multiple websites and am familiar with their support resources. Specifically, I’ve found the Squarespace community forum to be an invaluable resource.

I remember when I was setting up my personal website, I had some kind of issue with my SSL certificates not being available. I honestly don’t remember the problem, but I solved it by referencing a community forum post where someone else overcame the same issue.

What I like: Squarespace’s community forum is highly active. Its dedicated base of super users constantly posts and helps one another out. That’s especially true for the “Customize with code” section of the forum, where users can discuss advanced editing with HTML, CSS, and JavaScript. It’s teeming with helpful troubleshooting threads and code examples. I also love its leaderboard of top forum contributors (shout out, paul2009).

20. Spotify

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As a musician, Spotify is close to home for me. My band’s music is on there, and it always trips me out to know people are streaming our stuff as I write this. But I digress. If you ever have an issue with the platform, Spotify’s knowledge base is the place to go.

Spotify’s product is more straightforward than the business apps and platforms discussed above. Most of the time, when I have a problem with streaming on Spotify, I just need to reset my router.

Still, Spotify’s knowledge base is an excellent example of a consumer-focused help center that gives you everything you need to know about the platform in an easily digestible format.

It’s a simple page design with a search bar and six dropdown menus for each category of support-related questions you might encounter. If you can’t find what you’re looking for there, there’s also a “Quick help” section with popular articles and a community forum where Spotify users around the globe can pitch in to help each other out.

What I like: Spotify’s community forum has an Ideas page where users can suggest new products and features directly to the Spotify team. I love that you can browse popular and trending idea suggestions and see which ones develop into real app features.

Stay Relevant With Tech Trends

In my help desk knowledge base examples, you might have noticed that many companies use AI chatbots to share knowledge — this is no coincidence. Businesses are reworking their knowledge bases in response to technological advancements, making them much easier to manage and navigate.

While I think AI is an excellent addition to knowledge bases, it’s no substitute for expert-backed content. Ideally, AI should complement rather than replace your support team. It’s super helpful in surfacing articles in your documentation, but it doesn’t replace an actual human support rep who can provide answers and empathy when customers need it most.

When creating a knowledge base of your own, consider adding these key elements that I’ve noticed amongst all my help desk knowledge base examples:

• Intuitive search functionality. Your search bar should be prominently featured on your homepage, and it should surface relevant content quickly. Consider using AI-powered search so customers can ask questions using natural language.
• Multiple content types. Your self-help content should include text guides, videos, screenshots, and GIFs wherever relevant.
• Community support. Community forums empower users to help one another and foster a sense of community and goodwill around your brand.
• Clear organization. All of the best knowledge base examples feature logically organized categories so customers know where to click depending on the type of question they have. My favorite examples use attractive card-style layouts to organize content types.
• Personalization features. Different types of customers need different types of support content. Your knowledge base should adjust content delivery based on user type and history.

Your knowledge base should reflect your company’s values while addressing your customers’ concerns. I recommend regularly analyzing your most popular articles, updating your content regularly, and implementing feedback mechanisms to ensure you are always providing customers with the information they require for success.

By learning from industry leading knowledge base examples above, you too can create self-help resources that empower your customers, increase satisfaction, and reduce support costs.

Editor’s note: This post was originally published in August 2022 and has been updated for comprehensiveness.

I’ll be honest. My use of AI for customer insights is straightforward: I use ChatGPT to understand audience pain points and create targeted content from there. But some push the boundaries and use AI to revolutionize how companies understand and serve their customers.

From product managers tweaking features based on usage data to customer experience teams creating hyper-personalized journeys, AI is becoming the secret weapon for those who know how to wield it.

In this article, we‘ll explore how cutting-edge teams are using AI. Whether you’re overwhelmed by data or just starting to explore AI, you’re about to discover how artificial intelligence can be your compass in customer success.

Table of Contents

• What Are AI Customer Insights?
• How AI Impacts Customer Insights in 2024
• How to Use AI for Customer Insights

What Are AI Customer Insights?

AI customer insights are the goldmine of user understanding that machine learning algorithms unearth from your data. They go beyond traditional analytics and highlight patterns and behaviors humans might miss.

These insights can predict a customer’s wants before they even realize it. They bridge the gap between guessing and knowing why your churn rate spiked last quarter.

AI customer insights allow businesses to move from reactive to proactive strategies, predicting customer needs before they arise.

AI connects the dots, revealing insights like how users who engage with your onboarding emails are 3x more likely to become power users or how customers using feature X are 70% less likely to churn.

The real advantage? AI insights are actionable and often real-time, offering guidance not just on what happened but on what to do next. Whether it’s tweaking your UI or personalizing outreach, AI insights guide decisions that tangibly improve your product and user experience.

How AI Impacts Customer Insights in 2024

While 2023 was the year AI became mainstream, 2024’s the year where people start figuring out how to squeeze the most value out of it.

At the same time, I think customer care leaders are in the trickiest position yet. Sure, they now have unlimited technologies at their disposal to improve their customer service operation. But if (and when) these AI technologies don’t give them the results they need amid rising customer expectations, there’s a need to step back and reevaluate entire work processes.

The promise of AI-driven customer insights is tantalizing, but the reality is proving to be a mixed bag. On one hand, AI is revolutionizing how businesses understand their customers. McKinsey reports that over 80% of companies are already investing in or planning to invest in generative AI for customer care. The potential is clear: deeper insights, faster response times, and more personalized experiences.

But here’s the rub: Only 8% of North American companies report better-than-expected satisfaction with their customer performance.

Why the disconnect?

AI implementation isn’t a plug-and-play solution. Companies face technical barriers, data security concerns, and the complex task of integrating new tools into existing workflows.

Despite these hurdles, early successes — like a European bank improving chatbot efficiency by 20% — show that persistence pays off.

Meanwhile, customer service pros are caught in the middle. With 79% viewing AI as critical to their strategy, professionals know that mastering AI is key to staying competitive — but they must balance innovation with the human touch customers still crave.

The truth is, we‘re in uncharted territory. While AI promises to parse through mountains of data, extracting golden nuggets of customer insights, it’s not without its pitfalls.

Yet, the potential benefits are too significant to ignore. Marketers are feeling the pressure, with over half admitting they fear becoming irrelevant if they don’t master AI.

Image Source

As we forge ahead in 2024, the message is clear: AI is not just a tool — it’s a transformation. The companies that will thrive are those that can harness its power while navigating the complex human elements of this technological revolution.

How to Use AI for Customer Insights

Since adopting AI in 2021, I’ve used it to uncover emotional drivers within customer segments which helps me refine my LinkedIn content strategy with data-backed precision.

Here’s how some companies use AI for customer insights. Let’s start with my own use case.

Understand the Emotional Drivers of Each Customer Segment

AI-powered customer segmentation goes beyond traditional demographic or behavioral groupings. It uses machine learning algorithms to analyze large amounts of data, identifying subtle patterns and correlations that humans might miss. This gives me detailed, useful groups of customers I can actually work with.

Precise segmentation allows me to tailor my product, marketing, and customer experience with laser focus. It’s the difference between shouting into the void and having a personalized conversation with each customer group.

As a freelance writer for B2B SaaS companies, I’ve used my conversations with different clients to create three main segments: content managers expanding operations, startup founders needing strategy, and freelancers seeking guidance.

I’ve then used ChatGPT to uncover deeper emotional drivers for each segment which helps me create more targeted LinkedIn content. I use the Fear, Frustrations, Goals, and Aspirations framework with AI to figure out what my audience is thinking about.

Here’s an example of the prompt:

And the output I get:

While this is a basic example, you can learn how to use ChatGPT to identify micro-segments and even predict which customers are likely to churn or upgrade based on subtle behavior patterns.

To implement AI segmentation, I recommend you collect user behavior, engagement metrics, and feedback from multiple touchpoints.

Decide what you want from better customer grouping. More sales? Less churn?

Start with what you know about your customers — such as past behavior, engagement metrics, or survey feedback — then let the AI refine and reveal new insights.

Lastly, AI segmentation isn‘t a one-and-done process. Regularly update your models with new data to keep segments relevant. Even the best customer segmentation is pointless if you don’t use what it tells you.

Use NLP for Customer Feedback

AI-powered natural language processing (NLP) can quickly go through tons of feedback. It’s great for product teams swamped with customer feedback, helping them focus on what to improve and spot issues early.

Product teams can also discover pain points that usage data alone might not reveal and prioritize product improvements based on customer sentiment.

I suggest implementing NLP for customer feedback analysis by:

• Aggregating feedback from multiple sources (support tickets, app reviews, social media, surveys).
• Using an NLP tool or service to process and analyze the data.
• Setting up alerts for emerging trends or issues.
• Integrating insights into your product development process.

Heydar Naghiyev, head of demand generation at security platform Censornet, shares a real-world example:

“We implemented an AI system to analyze feedback from support tickets, app reviews, and social media. About two months in, this system flagged an issue with our task dependency feature that we thought was solid based on usage numbers.”

The AI detected numerous frustrated comments buried in longer feedback posts, revealing that users were using the feature out of necessity, not preference.

By revamping the feature based on these AI-surfaced insights, Censornet saw a 30% increase in feature usage and higher customer satisfaction scores.

Pro tip: Use NLP insights not only to enhance product development but also to fine-tune your customer support approach and create marketing messages that directly address customer pain points.

Decode User Behavior Through AI-Powered Website Analytics

AI tools can spot detailed patterns in user behavior on websites, finding insights that traditional methods might miss. This tech helps teams understand not just what users do, but why. That leads to smarter decisions about site design and content.

Edward Tian, CEO of AI Detector GPTZero, shares how his team uses AI to gain deeper insights into user behavior:

“AI helps us learn about our audience’s habits. It helps us better understand navigation patterns on our website, for example. This allows us to learn what pages people are entering first, where they go to from those pages, how long they stay on certain pages, and where they are most likely to exit out of our site.”

AI provides a full view of the user journey. It reveals pain points, preferences, and opportunities for optimization that might otherwise go unnoticed.

Pro tip: Focus on key metrics like entry points, user flow, time on page, and exit rates. Use these insights to tweak your site layout, improve where content goes, and make the user experience better.

Improve Customer Experience With Predictive Analytics

Imagine knowing what your customers want before they do.

Predictive analytics looks at past data to guess future trends. AI sifts through mountains of customer data — from social media comments to transaction histories — to spot trends, helping you send personalized offers or solve issues faster.

Knowing these trends helps you stay ahead of competitors and ensures your products stay relevant.

In the CX Pulse podcast, Robin Gareiss, CEO and Principal Analyst at research advisory firm, Metrigy, highlights how companies using conversational AI see major boosts in revenue, efficiency, and customer ratings. Simply because AI helps them solve issues faster and serve customers better.

“When we look at the overall value of AI, the metrics are incredibly compelling,” says Gareiss. “We ask companies, after implementing AI — particularly conversational AI — what impact it had on their business. Did revenue increase? Did costs go down? Did employee efficiency improve or customer ratings rise? In all of these areas, we’ve seen substantial improvements.”

When you have these insights, I’ve found you can use it in these ways:

• Send personalized product recommendations based on past behavior and preferences.
• Create tailored marketing campaigns and outreach that feel one-on-one at scale.
• Set up AI-powered chatbots for 24/7 personalized customer support.
• Implement churn prediction to identify at-risk customers and take preemptive action.
• Fix problems before they happen like detecting when a product’s popularity is dropping.

Find Out What Customers Are Feeling With Sentiment Analysis

Sentiment analysis figures out how customers feel about your product or service by analyzing their words and reactions.

In my experience, understanding customer sentiment helps you gauge satisfaction, identify pain points, and respond to issues before they escalate. It provides a pulse on how your product or service is perceived emotionally, beyond just feature functionality.

AI-powered NLP models can understand the nuances of human language, including context, sarcasm, and idiomatic expressions. This allows for more accurate sentiment classification beyond simple keyword matching.

For example, the phrase “This product is sick!” could be interpreted positively or negatively depending on context. AI can discern the intended meaning.

AI systems can analyze vast amounts of textual data from multiple sources in real-time. This enables companies to gather insights from:

• Social media posts.
• Customer reviews.
• Support tickets.
• Survey responses.
• Chat logs.

All simultaneously and continuously.

Plus, instead of just categorizing text as positive, negative, or neutral, AI can detect more nuanced emotions like frustration, excitement, confusion, or satisfaction.

AI models can be trained on company-specific data, improving accuracy for your particular product or industry terminology. They also learn and adapt over time and become more precise with each analysis.

Refine Knowledge Base With Post-Case Closure Insights

Ever wish you could automatically learn from every customer interaction? AI listens to every call, reads every chat, and then tells you exactly how to improve.

In the Deloitte AI360 podcast, Howie Stein, managing director and leading AI strategist, shares how they’re using this technology:

“Post-case closure, after a case or after a conversation is complete, we’re using generative AI to summarize call reasons, resolution steps, customer sentiment. Then we’re using that information to refine the knowledge base, refine troubleshooting guides, and refine our predictive capability for future calls.”

What‘s happening here? AI is analyzing customer interactions after they’re over and picking out the important bits — why the customer called, how the problem was solved, and how the customer felt about it all.

Your customer service can then get better with every single interaction. You’re learning how to solve issues faster and better next time.

Pro tip: Look for patterns in the AI’s summaries. Use these insights to update your help guides, create targeted training sessions, and role-play real customer scenarios with your team.

With AI, you can turn every interaction into a learning opportunity and make your service better and better over time.

Synthesize and Analyze Customer Feedback at Scale

As companies grow, the volume of customer feedback can quickly become overwhelming. AI tools are proving invaluable in synthesizing this information and extracting actionable insights. Here’s a real-world example of how Peter Luba at SmartPass, a digital hall pass company, uses AI to manage and analyze customer feedback:

“As we’ve grown, we’ve consistently been getting more and more pieces of feedback and insights from customers. We’ve been using a handful of tools to collect feedback from Intercom support chats, Gong recordings with prospects/customers, survey responses, our public-facing wishlist, G2 reviews, and more. The AI tools we’ve used help synthesize the feedback and highlight important spikes/trends in the product that we need to dive into.”

Automated analysis ensures all feedback is treated equally, reducing human bias. AI can also continuously analyze incoming feedback to provide up-to-date insights.

The company found that AI-powered analysis solved a critical problem with their previous manual approach.

“Prior to having a tool like this in place, we would manually tag customer feedback to feature requests. This worked for a while, but over time we just had too much feedback to manually log,” shares Luba. “Not only was this time-consuming, but after a few months, this feature-request log became out-dated and we never ended up using it.”

Perhaps most importantly, SmartPass’s experience reveals that AI-driven insights can directly inform product development and customer retention strategies:

“One way we’ve used this so far: We’ve recently had some customers churn. In just a few clicks, we received tactical, actionable, synthesized insights from direct feedback those customers have sent us over the past few years. With AI, we’re able to quickly pick up on trends and figure out the highest impact changes we can make. We’re using these insights to develop our product roadmap over the next few months.”

I like how AI feedback analysis helps companies quickly analyze thousands of customer reviews, identifying common pain points and emerging trends that traditional methods miss. Companies can develop better products, increase customer satisfaction, and improve overall business performance.

Improve Marketing Strategies

AI is changing marketing. We can now aim ads at exactly the right people and tweak them on the fly. It’s way better than the old method of sending ads to everyone and hoping for the best.

One of the most exciting developments, in my opinion, is AI‘s ability to create dynamic content. Picture emails that update based on what you’re doing right now, or websites that show different info depending on what you like. Marketers used to dream about this stuff, but now AI makes it happen.

Stefan Chekanov, co-founder and CEO of Brosix — a secure instant messenger — shares a practical example of how AI enhances their marketing efforts:

“We use the AI capabilities of Google Analytics 4 (GA4) to give us insights into the data from our marketing efforts. GA4 uses machine learning models, which is quite useful when it comes to gaining perspective into useful user behavior. We find it helpful for detecting traffic patterns, so we can then identify new opportunities for improvement quickly. We can also get a picture of how our visitors interact with the website and improve our marketing strategy around that.”

This AI approach lets marketers understand exactly how users behave. It’s not just about knowing where your visitors come from, but understanding how different segments interact with your content and where they drop off in the conversion funnel.

Chekanov elaborates on how this insight directly impacts their strategy, “For example, we know that the majority of our visitors come from organic traffic, so we decided to see how many of those visitors actually convert. What we did was analyze the flow of organic visitors using the funnel visualization tool, and we saw where drop-offs occurred and identified key pages that contributed most to conversions. Once we had that information, we knew that we had to double down on our organic efforts on those pages.”

With this info, marketers can spend their time and money smarter. They can focus on what really works and keep improving their plans based on what the AI tells them.

Create a Single Source of Truth for Customer Data

I started this journey thinking AI for customer insights was just about chatbots and basic data crunching. Boy, was I wrong. As we’ve seen, AI is changing how we understand and serve our customers in ways I never imagined.

But here‘s the real deal — AI’s best trick is bringing all customer info together in one place.

We‘re not just guessing anymore based on bits and pieces. AI pulls everything together — what customers say online, what they buy, their questions, how they use our website. It’s like seeing the whole picture of each customer, always up-to-date.

HubSpot’s Director of Customer Support for EMEA, Noel O’Reilly, sums it up perfectly in this podcast: “Where it’s really helping us right now is in the fluidity of our answers, in understanding our customers better, and what their particular queries might relate to on a personal level.”

This complete picture helps us make things just right for each customer, guess what they’ll need next, and fix issues before they become problems.