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.
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.
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.
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.
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:
Let's dive in.
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.
Start by aggregating traditional sources of customer feedback. What has your team already generated? What are we generating now?
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?
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.
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.
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.
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.
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.
With CustomerIQ's Views, you can accomplish all of this and more using a handful of features:
Filters are how you narrow down the data in view. They allow you to add and remove insights in view based on attributes like:
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.
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.
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.
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.
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.
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:
Templates: https://www.getcustomeriq.com/templates
Training: Team training available on pro, plus, and enterprise plans.
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:
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:
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.
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.
Some examples of continuous feedback sources mights be:
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.