Lean Startup for big brands: how market research can help, not hinder

  27 / 08 / 2015 


By Eve Bayer
Marketing Director at Winkle

A common question in large companies today: how can we adopt Lean Startup into our innovation process?

lean-startupLean Startup is exciting because it offers a structure for large corporations to adopt the strengths that startups have over them: agility, speed, and a mindset for learning and improving from failure.

In reality, though, it’s hard for companies to shed the legacy processes and culture in order to embrace Lean Startup. We see this is a particular challenge when it comes to market research and data.

How can the market research function help–not hinder–large companies in achieving the Lean Startup promise?

First, let’s clear up some common misunderstandings about Lean Startup, and how market research plays a role in it.

  1. Lean Startup is not about taking wild risks, acting on gut or throwing ideas at the wall to see what sticks. On the contrary it’s a risk-reducing, data-driven process for increasing innovation success through iteration and structured experimentation.
  2. Lean Startup is not anti-market research. In fact its basic structure–build, measure, learn–has research at its core. However, it tends to look to alternative, more agile and authentic solutions rather than traditional research.

Indeed that’s where much of traditional research falls short: when it fails to provide genuinely useful learning that advances progress. Market research departments of large companies are laden with trackers, U&A behemoths, and–the worst offenders–costly screening tools tied to ‘gates’ that ideas have to pass in order to see the light of day. The effect is maintenance of status quo, with budgets tied up in bulky studies that take months to conduct.

Imagine if small upstarts had a fraction of the budget that corporations spend on these large, outdated studies? Would they invest in a single six-digit screening study to try and predict the market reaction to a product after they’d spent the better part of the year developing it behind closed doors? Or would they invest in iterative and agile studies along the development journey, to have the luxury of consumer feedback to act on, evolving and pivoting to ensure that by the end, there’s little chance of surprise at how consumers react?

Here’s a proposed set of principles to reframe market research in large companies. The aim is to support modern innovation processes which are designed to reduce risk and improve success rates. These principles hold whether your company is adopting the Lean Startup approach itself, or merely modernizing to meet the constraints of tightened budgets, increased competition for shelf space, or threats of commoditization. The principles are as follows:

Iterative, modular system of research.
Use checkpoints along the way to collect validated feedback and improve or pivot. Each stage of research will show tangible improvement (which ties back to the “validated learning” premise of Lean Startup) to increase confidence of in-market success.

Agility, not norms.
Breakthroughs don’t come by comparing new products to the past. Many companies still rely on normative scoring systems in which new product or services ideas are scored against a composite score of products launched in the past—sometimes in irrelevant categories or based on completely different target consumer profile. Better to test the right metrics for your business goals and use reliable, real-time benchmarks. This more transparent route ensures you can trust the results that come out, and feel equipped to make crucial decisions to advance.

With agility, no compromise on rigor or robustness.
Market research has to be board room ready if large companies are to succeed in adopting lean principles. It’s true that small startups have fewer layers when it comes to decision making, and that’s not going to change. Innovation at large companies can be highly visible, expensive, and the burden of decision making lies on senior managers who have shareholders to answer to. Those shareholders’ tolerance for risk or—perish the thought—failure, is quite low. So research has to create flexibility in the innovation process, but it must maintain the rigor and validation to give senior management confidence in the data to make decisions.

Engage – but don’t pander – to consumers.
On this front it’s important to take a critical look at 1) who you’re engaging in innovation research and 2) how you’re interpreting their feedback.

First: the question of who. To uncover untapped needs, or to gain a fresh view of long-standing problems in your category, do you want a representative sample of mainstream consumers who are non-rejecters of your category today (meaning they accept the status quo)? Or do you want the leading edge, the fringe who are engaged, frustrated, and opinionated about what needs changing? For innovation, such Lead Users, as they’re called, become a more reliable source for insight and truly useful, actionable feedback as you iterate on your concept, particularly in the early stages as ideas are taking shape.

Second: interpreting results (emphasis on interpreting). In Lean Startup, author Eric Ries explains, “We adopted the view that our job was to find a synthesis between our vision and what customers would accept; it wasn’t to capitulate to what customers thought they wanted…” So if your research results stop at the observational level (as in, they said it should be bigger / red / cheaper) it’s a job half done. Interrogate their feedback until you get past what they’re saying on the surface until you understand what implications lie beneath it that you should take away for your concept.

Use data to understand what consumers mean, not just what they say.
Traditional research asks consumers questions in very rational ways while our brains work highly irrationally when it comes to making decisions about products we buy in real life. Advancements in technology are allowing researchers to ask consumers questions using natural language. Such questions get richer and more unfiltered reactions. Then, using sensitive analytics, we can to decode the real meaning and opportunity they’re subconsciously providing.

At Winkle, we’re applying proprietary IP in this area of emotion analytics to spot untapped growth potential and get deeper into consumer sentiment. We’ll be sharing learning and progress of development of this methodology as we go – in true Lean Startup fashion.

Category: Best Practice

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