Imagine that you are searching for a new potential product or for a new functionality for your existing product that will be successful on the market. You are convinced that the market you’re targeting is in development, you talk to people in the industry, you research online, and you finally find the information needed to support your belief that the market is on an ascending trend and that your product will be a successful one.
You decide to invest time and energy in the product and, soon enough, you are launching it backed up by a major marketing campaign. But your product fails. Nothing went as you have expected it to go: the market didn’t develop fast enough, you have fewer and fewer customers than you have initially estimated, you can’t cover the product costs, and you are working now in losses.
In 1972, two psychologists, Tversky and Kahneman, were demonstrating over and over again, by experimenting, that the idea of a rational human being is not completely true and that we, as humans, are prone to making decisions based on distorted and deformed reasoning biases. These psychological effects, which are the results of our mind trying to create shortcuts in dealing with a high volume of information, are called ‘cognitive biases’.
One of these biases is the ‘bandwagon effect’ and is the ‘sin’ in which we fall when we make decisions about our product because it’s trending or because it’s supported by a large group of people. And indeed, it’s reassuring for a client, or an investor to know that they made a decision as many others did; but later wonders or even ask themselves “what went wrong and where did we go wrong”?
The ‘bandwagon effect’ prevents us from an objective analysis of our product or functionality, and from investing first and foremost in researching the problem, not the solution, with our end-users, even if, at this stage, they are only potential end-users.
For a complete vision and an informed decision we need two sets of data: qualitative and quantitative.
The qualitative data will have a descriptive nature and will help us peek in terms of customer perception. This is the information which we’ll use to connect, for example, what a “user-friendly” app means, or what a “simple” order form with an actual response time entails, or what the amount of information required on our order form is.
The quantitative data, on the other hand, will help us validate that this information is not purely subjective, that it is not a matter of perception of one single end-user or two, but that it is applicable to a larger group and that it is indeed a problem that we need to consider during the product or functionality development stage.
There is a variety of techniques used for quantitative data collection. Most of the times, these will establish only a preamble, a ground base, for your quantitative research.
Social Media, Forums, & App Ratings Analysis. These entail an analysis of the conversations and issues raised by your target group about your product, or about your competitors’ product on Social Media channels, on app stores, or on dedicated forums. Usually, these are the channels where end-users raise concerns or questions, and you can use these for a better understanding of your target group’s major needs and challenges.
Focus Group. This represents a discussion on a specific topic with a group of 8 to 10 participants, for about 1-2 hours’ time. The goal of this technique is to profoundly understand your customers’ motivations, behavior, and perception about your product. You can use this technique for testing messages, functionalities, advertising materials, forms, etc. You can use this technique for exploratory reasons for a better understanding of your customers’ perception about an idea or solution you came up with based on your previous research. You can identify the characteristics that stand out in a product for your end-users. You can have an insight into their decision-making process during product acquisition. You can reach a better understanding of you target group’s aspirations, wishes, and set of values. And ultimately, you can determine your product idea’s strengths and weaknesses.
Use the qualitative data to validate the ideas collected during the qualitative analysis.
A/B Testing or MVT Testing. It involves testing a product before and after its final state. You can test one or several variants of a product with the end-users. This type of testing enables you to decide which of the design options are more efficient and which generate higher conversions; for example the scrollable design or the non-scrollable one. You can decide which of the flows drives the most expected results, the 2-step one, or the 3-step one. The advantage of this testing technique is that you’ll have a better understanding of a product or functionality’s effect on a more limited sample of end-users before rolling it out to all your users.
Google Analytics or Internal Data Analytics is another series of techniques that we can use to measure the current state or our product, which provides us with an excellent basis for report creation tailored for the kind of information we’re after or for segmenting our end-users and their challenges. Use the data analysis for a better understanding of how end-users interact with your pages, what the most common steps they take to buy your product are, what the most common errors they meet actually are, etc. Use analytics to understand what’s happening with your product now.
If you approach your product, or your functionality, correctly, your end-users will tell you themselves everything you need to know about a successful product, and will lead you to the gold mine you were searching for. The most important aspect though is to get involved and to listen to them in order to avoid the bandwagon effect and the “majority knows best” sin.
Invest in your customers’ idea, not in your own.
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