CustomerIQ Practice

We believe teams can harness the power of AI to universally align on customer needs.
These are the principles CustomerIQ is built on.
This is our practice.

The old way vs the new way

Unstructured data, especially audio, video, and text, is notoriously hard for customer research teams to manage and analyze. It takes a long time to read through, pull out relevant snippets, and organize those into something meaningful.

We call this the "unstructured data problem." This problem historically led teams to collecting only what they deemed most important, exactly when they needed.

The old way: manual, transactional research

In the old way, customer research is done manually, transactionally, and in silos.

Whole teams scour product reviews, interview transcripts, support tickets, and bullet points from notes to map out themes and pinpoint customer pains and desires.

If there are no answers to be found, teams take to surveys, customer interviews, and in-app feedback to fill research gaps.

Due to a lack of organization and communication, teams duplicate research, or worse, exhaust customers from repeated discovery on the same topics.

Eventually pains and desires are communicated to solution teams like engineers and designers in the form of slide presentations or PDF reports.

This type of research is done transactionally, it's not continuous. After all, it takes a ton of time and effort. Time and effort not spent doing things like building and marketing.

The problem is: deep knowledge of the customer is built over time, not through one-off transactions, and manual processes limit the breadth and depth of knowledge we can build.

A better way would be to continuously discover customer needs and make that discovery available to every team, at any time.

Thankfully, that's what we have in CustomerIQ.

The new way: automated, collaborative, continuous research

Now, with CustomerIQ, we can automate the synthesis of unstructured data. This doesn't just save us time, it also opens up a world of possibility around where we find insights and how we socialize them with our team.

In the new way, product, marketing, and sales teams can use CustomerIQ to analyze product reviews, interview transcripts, support tickets, and notes, but also mine other existing stores of customer intelligence: support calls, sales discovery calls, competitor reviews, deep research reports, and more. Automatically and in minutes rather than days.

We can ask more open-ended questions to uncover rich insights we wouldn't have otherwise found.

We can shift from collecting only small amounts of unstructured data, to collecting all of it.

The problem shifts from, "How do we analyze this?" to "What do we have to be analyzed?"

We shift from thinking about research projects transactionally, to continuously: building deep knowledge for us to leverage in developing and marketing solutions customers love.

The CustomerIQ Practice

This paradigm shift is the result of what we've built with CustomerIQ: a place to centralize, analyze, and socialize customer feedback with blazing speed using AI.

With this new technology, we're able to think about customer research and discovery in new ways.

Because of this, we've outlined a number of principles we think teams should follow to get the most out of AI in their customer research. We call this our practice:

  • Capture more data: With AI we can do more with more, so we should explore and aggregate all the feedback sources we have available, identify gaps we need to fill in our feedback collection, then organize them all in one place.
  • Get specific in analysis: With AI, analysis that used to take days now takes seconds. So we can do more analysis to inform more work. What exactly do we need to know to build and market our solutions? What customer segment are we interested in exploring? Are we exploring problems or desires? What are those problems? How have they changed as a result of our efforts? Expand your depth and breadth of understanding with specific queries of a larger data set.
  • Keep a customer wiki: Provide every team access to customer research. Build and record institutional knowledge so that we all solve for the customer.
  • Write effective documentation: Share what you learn in a format that the whole company can understand. Consider new hires in any customer-facing department: how have you organized what you've learned so that they can quickly contribute to serving your customer?
  • Ask better questions: Now that we can capture more data it's important that we're capturing good data. We want unbiased feedback. We need to learn how to ask questions that result in facts and truths about our customers lives. Good data breeds great insights.
  • Research continuously: Just as solutions change, problems change. Build workflows to continuously generate, aggregate, and analyze customer feedback.

Let's dive in.

Capture more data

Solving our unstructured data problem means we can shift our focus from capturing specific bits of feedback transactionally, to capturing all the data continuously.

We need to shift our mindset from cherrypicking to stockpiling. We need to expand the scope of what we gather.

Explore sources of customer feedback.

Start by aggregating traditional sources of customer feedback. What has your team already generated? What are we generating now?

Traditional sources

  • Product reviews
  • Surveys
  • In-depth interviews
  • Support tickets
  • Idea submissions

But now with CustomerIQ, we can analyze more. What sources of customer feedback do we have in the company that could contain insights but were previously too cumbersome to analyze?

New sources

  • Sales discovery calls
  • Customer interviews from product and marketing
  • Customer success check-ins
  • Customer support calls
  • Social media posts
  • Industry forums
  • Research papers

Make a list of all recorded customer connections around your company and who the "owner" of that data source is. Reach out to each owner to understand how you can get a CSV of that data to upload to folders.

Remember: AI will do the heavy lifting, don't worry about only finding data where customers explicitly state needs or requests. Data sources just need to include mentions of customer needs or desires. CustomerIQ will extract these insights and filter them to fit your research goals.

CustomerIQ View

Managing your feedback in CustomerIQ

Give Folders specific names

We built folders in CustomerIQ to give you a home for all of these data sources. We recommend naming these specifically what you intend to store in them, and here's why: While you're probably familiar with the concept of a folder, you may not be as familiar with our "Views".

Views are how you will actually work with and analyze your insights. Views help you filter and analyze the body of insights you amass through folders.

Important note: Views can filter views across multiple folders. This means that when you go about naming your folders, it's okay to get specific. You can always combine the data from two folders together in a view.

For example, you may want to use folders to store customer support calls that results in a "Good" outcome, then create another folder to store customer support calls that resulted in a "Bad" outcome. This way you can compare and contrast the two in views.

Use CSV imports to start your stockpile

Work with the owner of the data source you'd like to add to the folder and get a CSV export to upload. The format of the CSV is simple: you just want the first row to contain column headers, and have the data populate each underlying row.

CSV Upload at CustomerIQ

Add integrations to automatically update folders

You'll hear a common phrase in our docs, "deep knowledge is built in lines, not dots." This means the best teams research continuously, not in one-off transactions (dots).  So it's important to build those lines to your folders using integrations.

Use our suite of native integrations or our Zapier integration to connect the sources of feedback in your company to your folders.

With this set, it's time to start analyzing the data you've aggregated.

Get specific in analysis

With our stockpile of feedback in place and organized, we're ready to move onto analysis. Depending on what you're researching, you're going to want to filter for specific customer/user segments, classifications of insights, or attributes before running an analysis.

For example, if you're trying to build a new solution, you likely want to analyze customer problems (so you can solve them). It probably doesn't make sense to include comments about competitors or little snippets of praise about existing features. In this case, you should classify insights in view by those describing problems vs preferences vs requests vs others. You can classify by whatever makes sense. The point is to classify, then search or discover.

Analyzing data in CustomerIQ

With CustomerIQ's Views, you can accomplish all of this and more using a handful of features:

  • Filters
  • Topic search
  • Classification
  • Discovery


CustomerIQ Filters

Filters are how you narrow down the data in view. They allow you to add and remove insights in view based on attributes like:

  • What folder they came from
  • Who they came from (accounts or contacts)
  • What tags they have (from you or others in your workspace)
  • When they were created
  • How they've previously been classified

Since it now takes seconds to discover themes among your data, it makes sense to get specific with your filters. For example, build two views to compare customer problems between enterprise clients vs SMB. Or promoters vs detractors.

Topic search

CustomerIQ Semantic Search

Think of topic search like a Google search for customer insights. If you know what it is you're looking for, or what question you're trying to answer, just type it here.

And just like a Google search, you don't need to search by specific keyword, you just need to know your query. For example, if you want to quickly see what customers are saying about the mobile application, search, "What are customers saying about the mobile application?"

Yes, it's that simple and yes, it's that powerful.


CustomerIQ Classification

Every view can either classify or discover. Classify will sort every insight in view by whatever tags you have provided. Discover will sort every insight in view by those that are related to each other, then give them a tag.

Think of classification like sorting hat, where the AI will sort all insights by the categories you give it. For example, you might want to sort all insights in view by customer problems, pains, preferences, needs, UX issues, and other.

To do so, just add tags for: customer problems, pains, preferences, needs, UX issues, and other and click Classify.


CustomerIQ Discovery

Let's say you just classified all views by problem, pain, preference, etc and the "pain" group has 300 insights.

A good strategy would be to now build a view filtered by insights tagged as "pain" and then discover themes among those 300 insights.

A view in discovery mode will run a cluster analysis to identify which insights in view are most closely related, then tag those clusters with their related theme.

This is a quick way for you to understand what a large body of customer problems might entail.

You can repeat this process as many times as needed to get to the lowest level of synthesis. Rather than relying on specific research reports and jumping to conclusions we can build hyper-specific reports on the fly.

And now that we can increase our breadth and depth of knowledge, it's imperative we share it with the whole team. We do that in docs.

Keep a customer wiki

Prior to building CustomerIQ our founder had a realization: as companies grow and employees move from generalist roles to specialist roles, they start to lose the serendipity that comes from combined responsibilities like product/sales or engineering/support.

You can no longer use what you learned in a sales call to inform the product roadmap because you weren't on the sales call.

You can't learn about interesting ideas that came up in a support call because support is a dedicated role now.

It just gets harder to stay close to the customer. And it gets harder to stay informed with what other teams are doing or what other teams are learning.

So how do you manufacture that serendipity while scaling your organization? You have to make what we know about the customer available to everyone.

You have to keep a customer wiki.

Your customer wiki is an evolving set of documents where employees across different departments contribute information they learn about customers, their needs, and their interactions with the company.

To make this initiative successful it's important that everyone in your company has access to the wiki, knows how to add feedback to the database, knows how to analyze feedback, knows how to write good documentation, and is encouraged to generate more feedback for future analysis.

Everyone contributes

Invite teams to explore the stockpile of customer feedback you have aggregated. Encourage teams to learn how to explore insights using views, create their own docs, and share with their teams.

We have resources available to get teams started:


Training: Team training available on pro, plus, and enterprise plans.

Write effective documentation

Your customer wiki will lose its value over time unless team members know how to write good documentation.

Good documentation is informative but easy to read. It's comprehensive but easy to navigate.

Here are a few guidelines for writing good documentation:

  • Follow the pyramid principle: a communication strategy formulated by Barbara Minto at McKinsey & Company, the pyramid principle helps you present learnings coherently. The main idea is to start with the main points or conclusions, then support them with hierarchical, logically ordered data points (Views in our case). The structure resembles a pyramid, with main conclusions at the top supported by a broader base of data underneath.
  • Make it clear not clever: don't muddy your documentation with jargon or elaborate summaries. Leave the speaking up to the customer, using as many verbatim quotes and figures as possible. Write concisely and clearly to inform your team of what you've learned.
  • Regularly publish updates: Bring attention to changes so that everyone stays up-to-date with the customer.
  • Provide open access for improvement: Knowledge should be shared. Share documents with everyone in your company. This is a main principle for why we make viewers free in CustomerIQ.
CustomerIQ Docs

Ask better questions

Good questions are open-ended, clear, relevant, and thought-provoking, allowing the respondent to provide detailed, insightful answers rather than simple affirmatives or negatives.

Here are a few tips to asking better questions:

  • Ask "What would that help you do?" when a customer makes a request. Instead of diving deep into a random solution, try to dive deep into the problem. Customers are in-tune with their needs but unaware of possible solutions. Ask questions that uncover facts and truths about your customer's life, not opinions.
  • Talk about their life instead of your idea: Ask about specific instances in the past to uncover real problems and needs, instead of vague future scenarios.
  • Ask open-ended questions: Since we've automated the synthesis of unstructured data, we want to get people talking freely. Avoid yes/no questions or questions that lead the person to a particular answer. Get them thinking outloud.
  • Ask about specifics in the past instead of opinions: People's predictions about their future behavior are usually incorrect, so focus on what they've already done.
  • Listen more than you talk: The goal is to gather data for analysis, not pitch new ideas.

Asking good questions to generate better data is a bit of an artform. It takes practice and it takes focus.

Remember: CustomerIQ will do all the note-taking and analysis for you, so focus on building an environment, whether in person or in writing, for honest and open responses.

Afterward, you can use Folders and Views to extract insights and find themes in the data you've gathered.

Research continuously

As our company grows, it also changes.

As we build new solutions, we will identify new problems, which lead to new solutions. Solutions and problems evolve together.

This means that we should do customer research continuously.

By building continuous sources of feedback we are always learning without disrupting our processes of building and marketing.

Work with your team to identify continuous sources of feedback

Some examples of continuous feedback sources mights be:

  • In-app surveys
  • Sales call recordings
  • Customer support recordings
  • Regularly scheduled customer interviews

Use CustomerIQ to connect feedback sources

As you establish pipelines of customer feedback, aggregate them in CustomerIQ automatically with integrations. With data refreshing automatically, your teams can update their views and docs with the click of a button.

Get Started with CustomerIQ for Free