It’s no surprise that Entrepreneur magazine lists consumer research as one of the most important practices of successful startups. Indeed, careful, focused research can be the difference between failure and success. Research is one of the most powerful tools for determining where you will play in the market and how you are going to win.
You may already know you need to do consumer research, but the question still remains: “When is the right time to engage with the consumer?” This article answers this question by exploring the following topics:
1. Where are you today
2. The Product/Solution Fit
3. Making Your Research Data Actionable
4. Build, Measure and Learn
1. Where are you today?
Startup Genome has completed excellent work around what it defines as the “Internet startup’s stages of growth.” Best practice tells us that if you know where you are, you will have a better idea about which way you should travel. So your first step is to understand where you are relative to the stages of business growth. For the purposes of this article, I’ll assume you are in the Discovery phase, but in order to place this important step in context, I will also touch on the Validation phase (Figure 1).
Figure 1 - Internet Startup Stages of Growth
Discovery Phase (First Base)
After you have identified an important underserved need in the market, you enter the Discovery phase: the first step to gathering empirical evidence that the consumer has an interest in your nascent solution. You haven’t made a significant investment at this point; you’re just floating a trial balloon. Key characteristics that identify you are in Discovery phase are:
- Some customer interviews have been conducted
- Your startup has one or two employees
- A Minimal Viable Product (MVP) has been created
Testing and validating your value proposition is what gets you to first base. The output of the customer interviews are intended to create a near-complete picture of your consumers and the problems they are wrestling. But most importantly, these interviews can help you better understand if your proposed value proposition and associated solution solve one or more of your customers’ problems. It’s called the Problem/Solution fit. You accomplish this by analyzing both explicit and implicit customer responses, then organizing the information so themes, patterns, and causes become more visible. This way, you're not saturated with opportunities (refer to section 3 for more details).
Validation Phase (Second Base)
The Validation phase helps you confirm that there is demand for your product in the marketplace. You’re in Validation when:
- Your startup has 2 – 4 employees
- You have secured funding from sources other than friends and family
- You have refined core features of your offering based on output from customer interviews
- You have your first paying customers
Quantitative research should be deployed to examine if your product is a good fit across your target market (Product/Market Fit); this enables generalizability with the data.
But qualitative research isn’t enough. In fact, buoyed by a successful qualitative intervention, entrepreneurs often flirt with, and are often lured into the “greedy zone.” With their vision clouded by the fantasy of instant success, they misinterpret the distance they have to go and the obstacles in their way and make a mad dash to steal second base - and are invariably thrown out.
This stage requires you to validate your proposed solution, with a much larger sample size, before scaling your operation.
2. The Product/Solution Fit
When you’re in the Discovery phase - i.e., when you’re safe at first base - allow yourself to catch your breath. There’s no rush. The best players are not reckless tacticians; they’re patient strategists. The point is to get to home plate, not to get to home plate now.
At this point, you are still at first base - Discovery phase - and your MVP is, in essence, your proposed solution. Your second customer interview should be designed to test and validate your proposed solution; it needs to focus on your targeted opportunities and how the minimum set of features address the customers’ problem. Additional questions to be addressed should revolve around the following topics:
- Will your target customers pay for the proposed solution?
- What will they pay for the proposed solution?
I assume that at this point, a prototype of the solution is available for you to demonstrate how you intend to solve your customer’s problem. The prototype needs to be as close to reality as possible, otherwise you risk creating expectations that you may not be able to meet.
3. Making your research data actionable
Within the context of the consumer’s journey, well-structured and executed research provides a clear lens through which to view customer needs and determine potential solutions. These problems may be partially addressed, they may be over-served, or they may not be addressed at all. To illustrate the point, lets take a simple everyday example.
I recently visited my local pharmacy to buy some eye drops during my lunch-break. We can break down the overall process into a number of steps:
- Locating the eye drops
- Product selection and price
- Checkout.
For me, getting in and out of the shop quickly is essential. Signs were well placed and I was able to locate the shelf quickly. When I arrived at the shelf, there was a bewildering array of eye drops at different prices. After some deliberation, I made my choice and moved to the checkout. There was a huge line of customers waiting to pay. Figure 2 breaks down my analysis of the process. Importance and performance data is captured using a simple survey, which uses a scale of 1 - 10. 1 = Very Poor and 10 = Excellent. Each step in my journey is then ranked in terms of opportunity, based on importance and performance.
Figure 2 - Identifying the Most Critical Needs
My example shows that locating the product and pricing were satisfactory. There is little room for improvement here. However, at the final step, while rated as very important, the actual performance was very low, resulting in a relatively high opportunity score. A simple formula design by Anthony Ulwick enables the prioritization of opportunities. Opportunity scores can range from 0 to 20 – the closer to twenty, the greater the opportunity. The opportunity rating is a measure of the discrepancy between the perceived importance and actual performance.
This example illustrates under-served needs; here, the opportunity score is high. Customers are over-served when satisfaction ratings surpass their respective customer importance rating – ripe for cost reduction or disruption. (I didn’t feel over-served at any point in my journey to purchase eye drops; I don’t predict any disruptive innovation coming out of that pharmacy any time soon).
Now that you have identified what matters most to your customers, you can build a data-model for delivering solutions and hard-wire it to your product-development roadmap. Your data model is fundamental to generating insights and should be considered native to the application or eco-system. Now you also have a baseline from which to measure the performance of your proposed solution. But of equal importance, you can separate the wheat from the chaff and identify the most important customer needs – I need to be able to checkout, fast.
4. Build, Validate and Learn
Best practice indicates that the Discovery phase helps the entrepreneur to validate her approach before scaling to address a proposed solution. Why scale and waste time and money if you have not already confirmed that the market is ready to embrace your solution?
Once you have identified viable growth opportunities, you need to determine the types of data that you will gather and analyze to validate your MVP. This is why successful startups do not begin building the MVP before establishing a clear view of their opportunities. If you make that mad dash to second base, you’re almost guaranteeing yourself a great deal of rework and non value-added costs!
Targeting the right data sets, along with accelerating data turns will change the game for you. In particular, the data your app generates can be used to measure customer engagement, along with evaluating technology acceptance, usage and adoption. For example, my Polar Loop tracks my physical inactivity and notifies me that I need to move to keep active. It’s a gentle nudge, but the data has a significant influence on my behavior, thus creating information for me that is immediately actionable.
Need some help? Give SWBR a call.
What makes startups succeed - Entrepreneur
Startup Geneome stages of growth