Customer research is an essential ingredient in the recipe for becoming customer-led.
The backbone of a customer-led business is the customer. Scratch that. The backbone of every business is the customer.
Why? Because everything you do, from discovering new opportunities to creating valued products to positioning them, unfolds from the aspect of satisfying the customer.
To achieve this, however, you need to know your customer. And when we say know your customer we mean know them inside-out. Only then can you build a business that can scale.
But how do you know your customer inside-out? The short answer is through customer research.
Customer or consumer research is the process of getting to know your ideal user thoroughly. It allows you to study your targeted audience and identify their:
Whether you do customer research on your current customer base or potential ones, the end goal remains the same – to uncover:
How do you gather this information? Through myriads of customer research methods. Using the data from this research, you then separate customers into segments.
What’s a customer segment? It is a group of customers with the same traits. The traits can be something as basic as gender and age. Or they can be more complex like unmet outcomes and jobs.
Although we’ll be diving deep into customer segmentation later, all you need to know for now is that it gives you a better understanding of your users.
So, to put it simply:
Customer research gives you customer segmentation. That helps you recognise all the jobs customers want to get done, every constraint they face, and what outcomes they desire.
Boiling it further down – customer research gives you clear sight on your end-user.
When you fail to do customer research, you miss opportunities, which takes away your competitive edge.
If you want to grow sustainably, you must build a product that serves your users. It could help perform a job faster, better, or more easily. Right?
Only when your product adds some value that it attracts new customers and nurtures loyal ones.
That’s where customer research swoops in with a cape on. It helps you gather information on what jobs customers want to get done and what outcomes they are trying to achieve.
By capturing these necessary data, you create a guide that helps you develop valuable products. That brings us to the biggest benefit of customer research. You don’t waste time brainstorming ideas and then implementing them only to realise that customers don’t love them.
Through customer research, you know that a product, service, or feature will be a success right from the get-go.
Let’s unpack a few more advantages of customer research.
As clichéd as it may sound, talking to customers is the best way to discover their unmet needs. We make one thing clear here. Talking to customers doesn’t imply feedback and surveys. It means in-depth and focused customer research methods built to uncover specific pain points.
What’s a pain point? It could be a simple job that needs to be done. It could be overcoming a constraint users face. It could also be a complex outcome they desire.
Once you identify a pain point, you can create a solution that users actually value!
For instance, Zapier is a solution for a simple pain point – connecting different apps that need to communicate. On the other hand, HubSpot fulfils a more complex pain point – one solution that replaces several processes.
Customer research keeps your finger on the pulse of real-world users. It prevents you from practising strategies or developing something that does not fulfil any user requirement.
By forgoing customer research, you remain ignorant of what your users truly require. You make inaccurate assumptions, which leads to costly mistakes and errors like a product with little or no value.
Let’s say you are aware of a job to be done, and you have a solution for it. Those are the first two steps. The third is positioning it to the right customers.
The target customer we dream up in a meeting room is rarely, if ever, right. And most businesses don’t get this. They sink a huge chunk of their budget selling to an audience they feel is right. They catch on, much later, that their real customers were entirely different.
Customer research saves you from this blunder. It gives you a clear, accurate picture of who your real user is because it is based on factual data.
“I love data, but it’s like driving a car using only the rear-view mirror. It tells you what happened, not what is going to happen.”
– David Cancel, CEO of Drift
Stats like sales data, paint half a picture. You get to know how many people are buying your product or what services they are choosing. In other words, it gives you clarity on what has happened, i.e., a rear-view.
What a business really needs is the ‘why.’ Why do users buy your product? Why do you need to build a specific feature?
To understand the ‘why,’ you need to capture the true voice of the customers. By the voice of the customer, we mean jobs, outcomes, and constraints.
Customer research helps you do it. It ensures that every step you take should reflect your users’ requirements.
When you get down to the brass tacks of customer research, there are two types – primary and secondary. Let’s make one thing clear. While they are different, and each has its pros and cons, they are complementary in the true sense.
When you blend them, you get the best of both worlds. So, don’t assume that primary customer research is better than secondary market research. They are stronger together.
Any customer research that you self-conduct to gather information is primary. In this case, you go directly to the source, i.e., existing and potential users of your product or service.
Primary customer research uses different methods such as one-on-one interviews, user research, focus groups, company visits, online surveys, mail surveys, etc.
Any customer research that’s already been gathered and organised by someone else is secondary. This includes studies by market research firms or data aggregators and reports by government agencies and businesses.
Another form of secondary research is the internet. Information that you gather from magazines, news sites, industry publications, online libraries, etc. all comes within it.
Both can be qualitative or quantitative research. In the first case, you get statistical data like:
In the second case, you compile information like the attitudes of your user and their motivations. Surveys are the standard tool for quantitative research, while interviews are used for qualitative.
That said, there are a few differences between the two:
As we said before, it is not about choosing one type of research. If you want a better and comprehensive understanding of your user, blend them in a single initiative.
We’ve discussed the basic aspects of customer research. We’ve told you why it is a vital step to become customer-led, and you have a fair idea of the types of customer research now.
This brings us to what information you must gather during customer research. But before we jump on to that, you need to understand where most businesses err.
Customer research allows you to gather user requirements. Yet, even after decades, there’s no set definition of requirements. This is the first issue dogging the requirement gathering process. Most businesses believe that customer requirements are:
They are not.
Why? Because they don’t help you gain information essential to create breakthrough products or even position and market them. Correct user requirements are defined as:
The second issue that plagues the customer research process is thinking that you are obtaining accurate data that you need from your users when you’re actually not.
It’s a blind leading the blind kind of situation. You have customers more than happy to share their thoughts on your product or service. But they are blind to the kind of inputs you require to make the product better.
Development teams translate these imprecise, and more often, useless inputs into insights, making them even more questionable.
And that’s why so many companies fail to grow.
They are unable to capture the relevant data necessary to develop successful products or services. They assume the inputs customers give are accurate and can be translated into “actionable” insights.
They are dead wrong.
This leads us to the third issue. Don’t spend hours thinking over which customer research method fits best. Focus on gathering the right type of information.
It doesn’t matter whether you conduct focus group research or phone interviews. What matters is the kind of inputs you gather during the process. And that can happen only when you know what information you should be looking for: jobs, outcomes, and constraints!
Solution, specification, need, and benefit are the four types of data most companies gather during customer research.
Right off the bat, we clarify that these are not the inputs you should be gathering. Good customer inputs are concise, actionable, and measurable, which these are not.
We’ll tackle the right inputs in the next segment, for now, let’s understand the data companies commonly gather:
This could be a product, service, or feature idea that the customers give. But since your users are not qualified enough to present a revolutionary solution, you should not be limited to it.
Instead of sticking to their idea, look for the criteria customers use to measure value, i.e., the means by which an outcome is satisfied.
This is a design parameter or characteristic. In the case of a physical product, a specification can be the size, the look, the colour, etc. Most users focus on specifications during customer research.
Because users are incapable of considering all the design trade-offs, accepting their specification as input is dangerous and sets you up for failure.
This is classically a description of the quality of a product or service. For instance, users may say they need a reliable smartphone. While such a statement gives you some indication of what to create, it is open to interpretation.
And therein lies the drawback. The adjectives customers use to describe needs are vague and ambiguous. It doesn’t give you a fair idea of what customers truly value.
This is a value that customers would like in a new product (or service or feature) to deliver. A perfect example of a benefit input is ‘easy to use.’ Although very useful for marketing teams, such inputs are useless to your product people.
Why? Because they are imprecise. They don’t tell you the desired outcome customers expect from your product.
Even though we’ve been repeating it, we will say it once more. The customer inputs you should be gathering are:
When you use these as your primary data source, you create products of value because:
Let’s unravel each customer input further.
Why does anyone look for a service or product? Because it helps them get a specific job done. So, knowing what job or jobs your product performs is fundamental to your growth and success.
That’s why you need customer inputs on jobs to be done. It is a key factor in your growth.
There are primary jobs, and then there are ancillary jobs.
Let’s take the iPod as an example. Its primary job was to act as a portable device for listening to music, but it also performed a secondary job – download new music and organise it.
Irrespective of whether the job is primary or ancillary, it could be of three different types:
Both personal and social jobs are also known as emotional jobs.
Every single job creates value, so they all should be part of your customer research.
People don’t just buy products because they want to get a job done. They also buy a product or service because it helps them perform a task well, which could mean:
To create a product that fulfils any of these three (or more) needs, you should be able to measure:
These metrics are what we call desired outcomes, and they drive innovation.
Outcomes are not just one or two. There are hundreds of them, and as a business, you should be aware of all of them. Why? Because you don’t know which of the many outcomes is underserved!
The rub with desired outcomes is that your customers define them, and they rarely want to share them. So, how do you extract these metrics? By dissecting the job to be done.
The last customer inputs you should gather are constraints. Think of them as roadblocks to your success. For your users, constraints are anything that prevents them from getting a job done.
When you create a feature, product, or service that overcomes a constraint, you uncover a growth opportunity.
Most often, customers face three types of constraints:
To recap, a customer-led company gathers three types of requirements during their customer research process. One, what jobs they need to get done. Two, what outcomes do they expect. Three, what constraints do they face.
Without these inputs, innovation and growth will remain elusive.
We briefly talked about customer segmentation before. It’s the practice of grouping your audience based on shared characteristics such as demographics.
It’s been a common practice for decades, yet it is riddled with issues.
Why? Because companies tend to use classification methods that are convenient for them. As a result, they end up targeting phantom segments – groups of users that don’t actually exist.
Being customer-led, takes care of this, often prohibitive, a mistake.
When you are customer-led, you segment your audience based on unique and underserved jobs and outcomes.
These groups, called ‘segments of opportunity,’ help you find:
The journey to customer-led segmentation began long back. Here’s a brief timeline of it.
About 70 years ago, the only data businesses had was demographic:
So, they classified their customers based solely on these characteristics, giving rise to demographic customer segmentation.
Around the 1950s, information technology came into use. It gave businesses the ability to gain more insight into their users.
Consequently, they used not only demographic segments but also psychographic segments. Users with shared attitudes and traits were grouped together, including:
The birth of databases brought another sea-change in customer segmentation. A business could not only capture ample information about their customers but also do it in real-time.
This gave rise to consumer behaviour segments:
When the ’80s rolled around, a business could use clustering techniques to classify their target audience based on customer needs:
Although needs-based segmentation gave helpful insights, in the end, they failed.
Why? Because ‘needs’ are intangible. It makes understanding and targeting these segments near impossible.
Most organisations currently segment their customers with a blend of demographic, psychographic, behavioural, and needs-based data. A better idea and method is customer-led segmentation.
Most people have ‘jobs’ that need to be done. When a person becomes aware of a ‘job,’ they start hunting for a product (or service) to get the job done and achieve the desired outcome.
Customer-led segmentation relies on this foundation. The division of your customers is based on:
Customer-led segmentation gives you a more predictable target customer base, and you deliver significant value to your users. Therefore, the chances of success of your product, feature, or services increase!
Customer-led segmentation doesn’t mean you have to change the methods through which you gather data. It merely implies that you alter what information you collect.
As we have said before, companies pick a customer segmentation method that’s convenient for them. Therein lies the rub.
Let’s say a firm uses demographics to divide their users into small and medium businesses. They will expect all small business owners to have the same set of requirements.
Why? Because traditional segmentation methods say that a group is:
But we all know that is not the case. Not all small businesses have the same set of requirements.
Another reason old segmentation practices fail is the definition of a requirement. We spoke about this during ‘issues that plague the customer research process.’
There is no set definition of requirements, even until now. Thinking of them as needs, benefits, solutions, or specifications doesn’t give you reliable segments because they are wrong or unreliable customer inputs.
Customers purchase a product to get a job done. Their desired outcomes are measures of value that explain what it takes to get the job done perfectly.
The base of customer-led segmentation is that different customers have different jobs, and they may expect different outcomes.
So, when you create a segment, you do so on three fundamental factors:
That’s what makes customer-led segmentation unique from all other segmentation methodology.
Customer-led segmentation is based on finding ‘segments of opportunity.’ These are groups of customers who believe that a set of outcomes is not only important but also unsatisfied.
To discover these segments of opportunity, you need to follow 4 steps:
The data you need for customer-led segmentation are the desired outcomes of your users. Once you have those outcomes, create a survey method like a questionnaire and administer it to a sample user group.
Through the survey, you will be able to identify:
Collecting both data points is necessary.
Why? Because it gives you the opportunity score for each outcome, and that helps you find out which desired outcomes make the best segmentation criteria.
Successful customer segmentation happens when you identify what makes your users different. You don’t utilise all the desired outcomes of your users (which can be in 100s).
You use only those that have the most variation. Meaning, look for outcomes that are important and unsatisfied for some members of the sample group, but not to others.
It is in these outcomes that your customers wish to see improvements.
How do you determine which desired outcomes are both important and unsatisfied? By calculating the opportunity score, which is [importance + (importance -minus- satisfaction)].
In short, to select the segmentation criteria, you pick the desired outcomes with the best opportunity score – outcomes that explain differences in what customers want to achieve when getting the job done.
Typical cluster analysis looks at differences in the importance users place on an attribute. Customer-led cluster analysis takes a unique approach.
We capture differences in the opportunity for improvement in the achievement of the desired outcome.
The cluster analysis focuses on the opportunity score given to the selected segmentation criteria (the chosen desired outcomes). Then the sample user group is divided into segments based on the responses.
Let’s say you conduct a customer-led segmentation. After gathering data, you pick 10 desired outcomes, i.e., the segmentation criteria.
After performing cluster analysis, you realise a group in your sample users rates desired outcomes 1, 5, and 8 as important and unsatisfied. This group of users becomes your 1st segment of opportunity.
In another group, your sample users rate desired outcome 2, 4, 9, and 10 as important and unsatisfied. They become your 2nd segment of opportunity.
And so forth.
Creating segments of opportunity doesn’t tell you what type of users it has. Therefore, the last step of the customer-led segmentation process is profiling the segments.
It helps you understand the demographic and psychographic traits of the segments (think: buyer personas). So, when you conduct a consumer survey besides asking outcome-related questions, include questions such as:
The answers will be instrumental in comprehending the segment content of each cluster. And then you can create:
There are loads of customer research methods, and each has its own merits and demerits. Some are useful in understanding your current users, and some are excellent at creating a map of your potential customers.
A few of these barely cost anything, while others require investment in time and/or money. The trade-off you opt for depends upon what kind of information you want.
For instance, Google Analytics lets you capture what your users do instead of what they say. This customer research method is perfect when your users don’t know what they want. The drawback of analytics is that it doesn’t tell you why.
Another valuable tool is review mining, mainly for SaaS firms. The method allows you to gather your competitors’ reviews and amass qualitative information on how to improve your service.
From user testing to heat maps, from customer panels to sales data, there are N number of ways to conduct customer research. But as SaaS experts, we put the spotlight on 4 methods that are more common and appropriate for SaaS businesses.
These are precisely what the name says – a conversation between you and your users.
Customer interviews are great when you want to:
Customer interviews give you rich insights. More than that, you can extract specific details and the exact information you are looking for because you can ask pinpoint questions. Plus, they don’t cost a penny.
The data you gather from customer interviews is usable across the business. It is why SaaS teams who conduct ten or more interviews every 30 days show 2-3 times faster growth.
Customer interviews can get biased if too many leading questions are asked. Converting the information you gain from users into actionable insights is not easy and takes expertise.
Lastly, customer interview information remains relevant only for 12 months. You have to repeat the entire process after that because both your user base and service would have changed.
Personal interviews are easy to conduct. You can do these either over phone or face to face; the key is that they are always 1 to 1. A good practice is to interview users in different stages of utilising your product/service.
These are small groups of people who discuss a feature, service, product or even an idea you are testing out.
Focus groups are the best customer research method when:
Focus groups are an excellent way to gain more sense of your customer’s preferences, desired outcomes, and pains. Besides verbal reactions, you can collate data on non-verbal cues like facial expressions, etc.
Different participants can bounce off ideas with each other, giving you richer insights.
Groupthink is the chief disadvantage of focus groups. People inherently want to fit in. This can cause some members to change their opinion of your product or not even share it.
Another issue with focus groups is the small sample size. If you want to see true patterns emerge, you have to conduct several focus groups.
Focus group research is conducted in-person. A group of 5 to 8 people is asked to sit together in a room and discuss their opinions on a product, service, or idea. They are led by a moderator, who ensures that everyone shares their thoughts freely. Moderates also keep the conversation on track and facilitate the discussion.
Surveys are a series of questions that you ask your customers – potential and current. You can conduct them on your own or take help from a 3rd party.
Customer surveys are the best when:
A survey is the most commonly applied customer research tool for good reasons. It is quick. It is easy. And it is inexpensive.
You can research a large sample size because customers are more ready to participate. Since the response rate is high, you can amass a lot of statistical data.
The drawback of surveys is that you need a skilled person to create the questions. Too many or inaccurate questions will give you muddied results. Moreover, you can’t ask long rambling questions. Your customers will either get confused or simply drop the survey.
Sometimes, surveys will give you surface-level responses and not deep insights that help in breakthrough innovations.
Surveys can be conducted on the phone, via mail, in person, on chat, through websites, or even text messages. You create a list of questions and simply ask your users to answer them.
These can be done online. Think: social media accounts or websites.
Or they can be done in person. Think: observing someone while they use your product.
The aim is to contrast between what a user says versus what they do.
User observation is the right research tool when:
Online user observations are perfect when you don’t have the budget for other research methods and need real Voice of the Customer phrases and vocabulary.
With user observation, you can gather a lot of data. With online observations, you can do it from anywhere and anytime.
Another advantage of user observation is during the early phases of a product, feature, or service, such as testing if a new function has any real value for users.
This is possible because you can literally observe their behaviour and activities instead of relying on a written survey, which might be untrue.
Online user observations give you only surface-level information. With in-person user observation, the trouble is that analysing the data is tough.
But the most consequential disadvantage with controlled user observations is the Hawthorne Effect. The phenomenon says:
‘the action of observing someone in research affects the way that they behave.’
In other words, when you focus on what your customers are doing, they will change their behaviour to suit your expectations. Ultimately, you end up with skewed, meaningless data.
Online user observation is possible through social media accounts, emails they send you, chats, or conversations they have with your customer service agents. Or you can use tools like Hotjar and FullStory for it.
Hotjar, a behavior analytics and user feedback tool, allows you to observe what your users see / click on your webpages, understand where visitors drop off, and even get to know your users better through surveys, polls, and heatmaps.
FullStory lets you observe when, where, and how your users struggle. It discovers, analyzes, and ranks poor experiences data, uncovering hidden opportunities for better revenue and retention.
In-person user observation is when you can watch a user react to your service or see them go through routine activities. These can be of two types: controlled and naturalistic.
Controlled user observation is conducted in a pre-arranged setup. A naturalistic user observation happens in the natural environment of the customer, like the office or at home.
Don’t take this as an exhaustive list of customer research methods. While there are several ways to capture information, these four make a good starting point.
We leave you with a food for thought:
As a SaaS business that mostly sells a product repeatedly to the same user base, can you afford not to do customer research?
If you aim to be customer-led, you need to glean better and more in-depth insight into your users. And that happens only through customer research!