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Congrats! At this point in the customer discovery process, you've gathered enough feedback from your customer discovery interviews.
This next round of customer discovery activities will put you well on the way to transforming those interviews into insights that will inform an effective go-to-market push within your target niche.
Haven't talked to a single prospective customer about your business idea yet? You should check out our Part 1 blog on this topic so you can better approach the interview aspect of customer discovery.
For other founders, this might be your second or third pass at the customer discovery process. You are on the path of continuous customer discovery and perhaps you're looking for new ways to think about the analysis and interpretation of the data collected within your interviews.
No matter what kind of founder you are, customer discovery will be the launchpad for your business success.
Whether this is your first time analyzing prospective customer data into powerful business insights or you're a customer discovery veteran, this blog will take you through the strategies that our experts use when we help pre-seed through Series B startup founders transform customer feedback into powerful business insights.
Pre-Analysis: Cleaning & Organizing Customer Discovery Data
Before you can pinpoint insights within your customer interview data, it's essential to prepare the data for analysis.
All pre-analysis work assumes that you actually recorded your customer discovery interviews. As mentioned at the end of our last article on this topic, it's absolutely essential to record your interviews.
If you don't record your interviews, you'll have no reliable data to pull insights from.
Assuming you have data, it will be in a state of chaos until you start doing this pre-analysis work. This work is absolutely critical.
Looking for insights within an unstructured dataset is like looking for treasure in a junkyard.
You might find something valuable in this scenario, but it's more likely that you'll just end up feeling discouraged and like you're drowning in garbage. Properly cleaning and organizing the data from your potential customers will help you avoid this experience.
Here are two steps for preparing customer discovery data that will set your analysis up for success:
Transfer & compile interview notes
Categorize data fields by topic/theme
Let's tackle these steps one-by-one.
Pre-Analysis: Transfer & Compile Customer Discovery Interview Notes
As the first bridge between your interviews and analysis, you should transfer all your recorded interview data into a digital format. If you had virtual customer discovery interviews alongside a digital transcription software, most of this work will already be done for you.
If your interviews happened in person, there will likely be some amount of transfer from either a notebook or recording file on your smartphone to a digital destination (like Excel or Google Sheets).
Once your data is transferred, it's time to start categorizing responses according to how they reveal customer needs.
Pre-Analysis: Categorize Data Fields by Topic/Theme
Before even diving into the data, you can create relevant data fields according to the intuitive categories that would follow from your pre-arranged questions. For example, in our last article, we considered what an aspiring founder of a premium skincare company might ask members of their target market.
Even without a defined product, we determined 10 questions questions that would support their search for a product concept that could eventually find market fit. Here were two of those questions:
How much money do you usually spend when you stock up on skincare products
What kinds of ingredients do you look for when considering a skincare purchase?
Given the nature of these questions, the entrepreneur can naturally categorize eventual data fields for stated budgets and preferred ingredients. For a refresher on the most value-rich kinds of questions to ask in a customer discovery interview, feel free to check our Part 1 blog on this topic.
With well-designed questions, you can expedite the data categorization process so that organizing the unstructured data can happen as smoothly as possible.
Once you structure the data according to how it fits within the categories of information that your question is designed to discover, you can begin analyzing and interpreting the data.
Analyzing and Interpreting Customer Feedback
Now that you've organized your data into categories, it's much easier to reliably pinpoint trends within the data.
Reliable analysis and trend interpretation is where the holy grail of market research is found or lost. For customer discovery research, this where you'll either pinpoint a possible customer base for your products or discover that no feasible customer base exists.
When done correctly, this movement toward a customer base is fueled by signals in the data that point toward a product idea or business model that fits a dominant market need within the collected customer interviews.
These same founders are rewarded with the elimination of weak business assumptions as they move forward with their product development roadmap.
You don't need to be an Excel power user or Tableau wizard in order to do this work. Once your data is correctly categorized, you can start uncovering key insights.
When I'm concluding a research project for clients, two categories of feedback guide my initial search for valuable market signals:
Existing allocations of financial budget
Existing allocations of attention
Let's look into these areas of signal further.
Finding Signal in Discovery Data: Existing Allocations of Financial Budget
How someone spends money signals their interest in putting skin into the pursuit of relevant solutions. Within the journey of creating a new solution, it's absolutely critical to understand how someone is currently spending their cash.
Once that info is uncovered, you can then explore where these spenders are dissatisfied with the current options they're paying for. Making this link between existing budget allocation and ongoing sources of dissatisfaction is a critical connection along the path toward developing a product that can obtain market fit.
Most founders understand they have to identify a common problem in the market, but they fail to consider whether the most common problem has profitable demand around it.
Some common problems don't have profitable demand because there's actually no way to feasibly deliver the solution or the way in which consumers currently talk about a problem is not as straightforward as their common description.
The most common example of this is the experience of time shortage among consumers. On the one hand, it's impossible to add a 25th hour to the day.
No matter what you do as an entrepreneur, there will only ever be 24 hours in a day.
Okay, so what do target market customers actually mean when they say they don't have enough time?
Well, they mean that they don't see an easy way to prioritize their most important tasks within their normal lives. Time management is a complicated consumer problem that can lead an entrepreneur in many different directions.
Not all of these different directions are worthwhile to pursue. This is especially true if a consumer doesn't see any need to allocate budget toward an underlying cause of time loss or if the entrepreneur is not immediately qualified to solve some underlying causes of time loss.
Instead of taking the most common problem ("I don't have enough time") at face value, a diligent entrepreneur must dig in by asking their interview subjects about what part of their current routine feels like the biggest leech on their time.
From there, the entrepreneur can ask how they are currently trying to solve this problem. This is often done by exploring how much someone currently spends on related solutions and what those solutions are.
While budget allocation usually offers the greatest signal about the feasibility of a new product, it doesn't account for the trust-building factors that actually help a new business owner win over the hearts and wallets of potential customers.
This is why considering the existing attention allocation within your target audience also pays massive dividends for founders that run customer discovery interviews.
Finding Signal in Discovery Data: Existing Allocations of Attention
This saying is becoming trite, but it's still as true as ever: Attention is the new oil.
While attention alone won't immediately create revenue, the long-tail benefits of reliably attracting attention will fuel word-of-mouth marketing that can drive down ongoing customer acquisition costs. As a benefit, when your ability to attract attention also generates trust, you will the increase lifetime value of your audience members due to their loyalty to you.
Both of these outcomes are massively beneficial as your customer development transitions into product development. In most cases, the path toward winning a wallet is paved by repeatedly winning positive attention.
The most cost-effective path toward attracting this attention is through content creation.
In its own way, your ongoing content efforts are their own form of applied market research. Your posts, videos, reels, carousels, etc. will allow you to consistently test which topics and content formats resonate the most with your target customer.
By turning your initial customer discovery interviews into various forms of content, you'll be creating pipelines for your long-term customer validation efforts.
As your content efforts hit their stride and result in meaningful amounts of followers or subscribers, you can also monetize the attention you've attracted by partnering with advertisers. This advertising model describes every free-to-use or freemium business model across the world's leading social media platforms.
When your company is pre-product, these advertising partnerships can also help extend your customer validation efforts. If you have affiliate links within your content to promote these partners, you'll be able to track which partner products or services convert into sales the most often within your audience.
If you don't plan to immediately charge for your product, you will can use engagement data from your content to understand how your target audience currently invests its attention into relevant topics, problems and solutions.
Based on this information, you can start connecting with this audience by creating content that addresses the largest areas of consumer interest and market need that appears within your discovery data.
As you find your voice in covering these topics, you'll naturally build a following that can lead to advertising partnerships and an eventual product launch.
These two categories of feedback drive powerful market signals because they indicate where a monetizable problem exists within the market.
Once I see that people are investing meaningful amounts of cash or attention into a class of existing solutions, it's then time to connect those existing investments of personal resources with corresponding problems they are trying to solve.
After you label your spreadsheet columns for budget and attention related data, you can duplicate the sheet and rename one sheet "Budget Signal" and the other sheet "Attention Signal." Within each set of of signal data, you can simply sort the sheet according to whichever category reveals budgetary preferences or time spent browsing online for related products. If you do descending order (Z->A) sorting, you'll see who the highest spenders of time or money are at the top of your sheet.
You can then navigate over to their stated problems and see whether there are trends among their statements.
Whether your initial guide toward meaningful insights is based on use of budget or attention, these data sources are excellent sources of signal when correlated with problem statements within interview data.
When you pinpoint these trends, you're on track to create your customer personas and define your core value proposition(s).
Creating Customer Personas
The subsequent experiments that you run based on these trends in the data will fuel your customer development process.
With each experiment, your core business assumptions will be corrected by real market outcomes. This scientific method of company building positions your customer base as the engine for all the informed decisions behind your business strategy.
The most powerful drivers within this customer development model are reliable customer personas.
A customer persona is a fictional character representing your target customer. Creating personas helps businesses visualize who their customers are and better understand their needs, wants, and pain points. When creating customer personas, it's important to consider factors such as age, gender, income, interests, and behaviors.
Returning to the luxury skincare example, a customer persona might be "Sophia," a 35-year-old professional woman who regularly spends $500 on premium, organic skincare products. Your discovery data will help you fill in details about Sophia like whether she prefers to shop in-person or online. Perhaps she arranges DIY spa days with her friend group or maybe she likes to protect her skincare secrets.
As your sense of a customer persona like Sophia becomes more precise, your ability to consistently market, message and meet the needs of target market members like Sophia will improve. This is how new companies become dominant commercial forces.
Along the way, these same companies become increasingly confident about their key value propositions. Let's talk more about that.
Defining Your Value Proposition(s)
Your value propositions communicate the unique value that your company offers to customers.
Most founders don't talk to customers enough before they develop their company's minimum viable product. As a result, these founders are always uncertain about whether what they've built is the right product and they're always unsure if their message makes any sense among their target market audience.
When your core business assumptions are dead wrong, your key value propositions will also be dead wrong.
If you think the target market wants a spaceship, but they actually want a submarine, all of your company messaging will be upside down. Guess what, you're already on track to avoid these problems.
The key questions you asked during the customer discovery portion of your customer development model will be your north star toward the messages that resonate most with your key customer personas.
If we imagine again that our fictional skincare founder has brought some sample products to relevant trade shows and perhaps spun up some landing pages that test online conversion rates, it's easy to imagine their path toward homing in on both the right product and the right messaging for their "Sophia" customer.
Perhaps these experiments highlighted that the customers who align with the Sophia persona value fresh ingredients, flawless skin and being first among their friend group to find premium skincare products. Those qualities will appear across their marketing efforts.
By understanding your target market, creating customer personas, and defining your value proposition, you'll be better equipped to meet the needs and preferences of your customers and stand out from competitors.
Final Thoughts
As you can see, customer discovery is a crucial process that every business should take seriously. Industry trends are not reliable enough to guide your company building process. This is especially true if you're establishing a startup as 9 out of 10 startups eventually fold.
The alternative to building products based on instinct and holding your thumb to the wind of industry trends is following the lean startup approach to business development.
After reading our Part 1 and Part 2, you're now prepared to confidently approach, interview and analyze your target market more effectively.
By understanding your customers' needs and wants, you will create products and services that are truly valuable and relevant. As a result, you are far more likely to be an outlier founder who can launch a sustainable and astoundingly lucrative business.
Need more advice? Feel free to book an intro call with one of our experts here or read on about the tools our team uses to predict product-market fit for new products and services.
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