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Market research for digital products – how much is enough?

Eons ago, one of us (Nag) was involved with a large consumer products company that wanted to launch a new brand of salt. Naturally, they embarked on a gigantic market research (MR) project where the team carried out focus groups across the length and breadth of India, including little Padrauna in Uttar Pradesh, India. 

They undertook rigorous quantitative surveys with home makers and restaurants. They asked the respondents about their points of view on nutrition, observed them cooking and recorded their reactions to our proposed USP; healthy for the heart. By the time the test-market in Hyderabad had begun, the research team had carried out a year’s worth of research on the name, price, point-of-purchase displays and more for the product. Happily, it all worked out – two decades later the brand is still thriving.

Cut to the present, where we at ThoughtWorks meet companies led by successful ex-FMCG executives. A lot of these leaders want our help in building innovative digital products across industries like travel, eCommerce and banking. “Why not start with extensive market research,” they ask. “Let’s take uncertainty and risk out, especially since the idea is unproven,” one executive said.

However, the empirical evidence from the digital world is against their proposed strategy. 

Digital native companies favor hypothesis-driven approach

The canonical case of Instagram. There were several photo apps prior to Instagram, including Nag’s favourite, Flickr. The creators of Instagram would have struggled to accurately describe what was so unique about their proposition during a conventional and large scale MR. Focus group attendees and survey respondents would not have gotten the picture, so to speak. Certainly not well enough for their inputs to be of much value. 

What Instagram did was invest in minimal upfront MR and launch. The rest, as we know, is history.

Consumers took to Instagram because the filters made their photos look good, and sharing right from their phones was exceptionally easy. Even as a business worth billions to its parent company, Facebook, Instagram’s little to no-MR approach continues. When Instagram want to introduce a new product feature, they take it live for a sample group of their enormous user base, watch for feedback and make necessary tweaks before a global launch or going back to the drawing board. In doing this, Instagram leverages what’s popularly called the Build-Measure-Learn (BML) approach – something we shall return to it, in this article.

Which approach is right for you? The 10 dimensions to investigate.

Executives aren’t wrong in wanting to reduce risk. Millions are at stake, more so during a crisis like the one we currently find ourselves in where budgets are not easy to come by. In fact, both MR and BML (which includes user research) are ways to reduce risk and improve the odds of success.

How do you recognize the approach that’s right for your digital offering? The answer may have been clear-cut for a purely digital product like Instagram. But, many digital products do have an offline component. For instance, a peer-to-peer delivery service may have a strong digital footprint but still requires the back-end logistics of a supply chain.

Here’s our guide that covers the 10 dimensions you need to investigate when navigating the launch of your digital product –
 

If your answers to these questions largely veer towards the right or blue side (which is likely the case for most digital offerings), then chances are you are better served by getting your ideas out into consumers hands to test their responses, rather than commissioning time-consuming and expensive market studies. This is not to say that you jump in head-first with no research whatsoever. Rather, we want product owners to base the scale and value of such research on the intended product-objective. 

For instance, perhaps you have an idea that you’d like to test out with consumers. But, you’d like some insights to even begin to articulate your hypotheses and maybe decide which user segments you’d like to test your idea out with. Often, this can be achieved by secondary research – spending a short time-boxed period assimilating consumer studies and reports from reputable sources, understanding the competitive landscape (if there’s any), reasons for any past failed attempts etc. With this basic understanding in place, you can move forward, quickly validating your hypotheses with your target user segments. There could be multiple approaches to doing this – in the next section we’ll talk about one approach we like to adopt when creating new digital B2C offerings.

Most [email protected] ideas are best validated through Build-Measure-Learn

To an executive who cut their teeth in a traditional business, the Instagram approach might look like a dangerous figure it out along the way approach. But, there is a method to the madness. Underlying is an important insight; digital products are best validated through real-world use. You begin with a hypothesis about your offering, make a minimalist version of the product, put it in front of your customers for feedback and make rapid changes till you get it right. 

The method has become de rigueur in the digital world. The toolkit is rich – two examples are business model canvas and A/B testing which allow you to rapidly tweak one variable at a time in response to customer feedback. 

A method suited to digital-heavy offerings

Start with a customer insight and a vision. After six weeks of market and user research, form a hypothesis to validate. Invest the time and energy needed to make it to market with an MVP that allows you customer feedback. When your ‘formula’ is right (which will hopefully be sooner than later), scale the product. The key is to have the requisite tech infrastructure in place that can support such rapid experimentation.

An effective manner of testing and validating hypotheses for digital customer-facing experiences is, via a phase of rapid prototyping. This involves planning a time-boxed design sprint in which medium-fidelity visual designs of the proposed solutions and customer journeys are created and packaged for quick testing with identified target segments. 

Some of the key benefits of this approach are –

  1. The quick feedback on a divergent set of ideas amongst different user groups. This happens at a relatively low-cost, in a low-risk manner and before converging and investing more time and resources.
  2. Unearthing brand new pain points that allows you to refine your ideas so they resonate better with your target audience. 
  3. The validation of hypotheses and insights around the needs and demands for your solution. Some of these hypotheses might have been driven by anecdotal evidence, secondary research, gut feel etc. This phase helps you sense-check the assumptions and potential demand for your solution.
  4. Often ignored, the value of medium-fidelity prototypes that extends beyond target group feedback. They can also help you align and inspire other teams or individuals within your organization, investors, strategic partners etc. to rally behind your cause. The earlier and better alignment in the process, the higher your chances of giving your idea the impetus it needs to succeed.

By the end of this phase, you should have a fairly good view of your product in a very short period of time with minimal investment.

This allows you to define your MVP fairly quickly and then look to get working software out into the hands of your consumers. And, ensure the on-the-ground feedback loops back into your iterative development process.


Build Measure Learn a nimble and iterative approach to product innovation

Build-Measure-Learn: a nimble and iterative approach to product innovation

So, we jump ahead to building a product? Isn’t that financially risky?

We see it as just the opposite. Remember the 10 dimensions we asked you to evaluate earlier. If you found yourself on the blue side of things, more often than not, a Build-Measure-Learn approach could yield a better return on your investment. Contrast, taking all the time and money spent on extensive market research with, using it instead, to get real market-tested results to work with. 

If you’re concerned with the financial risk of what seems like a ‘leap before you look’ method, don’t think about Build-Measure-Learn as eschewing research in order to rush to market. Instead think of the investment in prototyping and building an MVP as a different approach to customer research. One which could, with comparable time and resources, give you a real-world understanding of your customer while quickly getting your product to market. In a world as increasingly digital as ours, speed is of the essence.

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