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What Donald Trump’s Success and Your Product Failure Have in Common


Two of the biggest political events of 2016, the British exit from the EU and the U.S. elections, have put a big question mark on the value of big data. Pollsters predicted a more than 71% chance that Hillary Clinton would win the elections, versus a 28.6% probability of a Trump victory. Across the Atlantic, the markets had predicted on the day of the referendum an 85% likelihood that Britain would remain in the EU. So what went wrong?


According to the Economist the explanation of the spectacular failure probably lies in the cognitive biases people have. These biases affect the survey respondents as well as analysts, prompting the former to express opinions they don’t actually hold and the latter to interpret data based on faulty assumptions.

The LA times, which was almost the only publication that predicted Trump’s victory, said their polls were successful because they identified and removed the social desirability bias, which was causing Trump supporters to be less comfortable about revealing their vote to telephone pollsters. The analysts’ judgment was probably clouded by confirmation bias, which causes researches to form hypothesis and beliefs and give more importance to facts that confirm their preconceived ideas.

How this Applies to Your Product Failure

It appears pollsters and pundits were guilty of making the same mistake that businesses and startups make too often—not looking beyond the cold hard data. This should raise serious concerns for businesses conducting research prior to launching new products, because they don’t have nearly the same amount of resources and capabilities as the companies that exist for the sole purpose of making predictions.

The vast majority of technology products and apps sink without a trace because their makers fail to identify the customers’ needs and translate those needs into a mouthwatering value proposition. Nine out of 10 tech startups crash and burn within the first two years, primarily because they fail at customer discovery.

As humans, we are emotional beings. Our opinions and buying decisions are overwhelmingly based on emotions rather than objectivity. That’s why it’s essential to incorporate the human element in user research.

The LA Times and the Economist suggest that polls may place too much trust in the data and analytic tools while almost completely missing the human factor. For a startup business or the launch of a new product, ignoring human emotions is the biggest pitfall it can step into. Emotions are where truth is to be found.

Your internet research does not predict human behavior, nor do online questionnaires, or the number of people that attended your webinar attendance. Nor do sample sizes of four or five, or discussions with friends and family. You cannot count on cold, hard data to always predict human behavior. That’s why it’s critical to consider emotions and biases (your own and your customers) when you’re doing user research.

Four Ways to Incorporate the Human Factor in Your Customer Research

Here are four ways to make sure that your customer discovery incorporates the human element, so you can build the product that your customers readily adopt.

1. Stop Being the Center of the World
Many entrepreneurs fall in love with their “billion dollar idea” and ignore all evidence that suggests otherwise. “I feel that my app is better than other apps, therefore other people will feel the same.” When you think you’re the center of the world, you select only favorable feedback and reject the sobering facts. This creates an illusion of demand, which lasts only until the reality hits. Remember that regardless of how amazing you might feel your idea or app is, it’s worth nothing until the people who use it also feel the same way.

2. Interact with Real People in the Real World
Many app developers and technology entrepreneurs feel uncomfortable at the prospect of going out into the real world and interacting with real people. It’s not because they are afraid of people, but often that they are afraid that they might learn their idea is not marketable. By nature, software developers and marketers are proactive.

Connecting with prospective customers for product fit interviews takes time. They are inclined to rely on online sources and interactions to gain insights into their users’ minds.

This research often falls prey to a confirmation bias which creates a distorted picture of what the customers really want. If they get it wrong, it is believed that product/market fit problems can be fixed with better/more marketing after launch. This is an expensive fallacy.

The road to failure is paved with findings from online research. The reason is that 93% of all communication is nonverbal, comprising of body language (55%) and tone of voice (38%). It means research tools like online forms or questionnaires only glean about 7% of what’s in your customers’ minds.

3. Design and Sample Carefully
You have to ask the right people the right questions in order to get the right answers. Go back to your segmentation and targeting data and customer personas before you choose your sample group. Design your survey questions in a way that it incorporates the emotional feedback. Listen much more than you speak. Customer discovery interviews are not the time to test out your sales deck. Nor is it a time to demo your product. It’s a good idea to start with a few interviews, learn and adjust. Your customer discovery should be ongoing.

4. Outsource Customer Research
Designing, sampling, and managing customer discovery in not a job that’s right for everyone. Your core team is preoccupied by the need to rush the product to the market and can ignore some key aspects of the research. Also, it may be difficult for the people who have conceived an idea or product to emotionally detach themselves from the research. While doing research, conduct at least a dozen interviews on your own. While experts can help you conduct more interviews, with better outcomes, it is important to experience your interviewees responses yourself. By outsourcing prospect research, engagement, questions and scheduling you are free to keep your product development on track. An experienced research team will have access to more user groups and resources and can deliver dependable results in less time.

As we’ve recently experienced with the surprise election results in the U.S. and the UK over-reliance on cold hard data may deliver nasty surprises. “The most important thing in communication is to hear what isn’t being said,” says Peter Drucker.

Today’s successful products appeal to their customers’ emotions to inspire the “aha factor.” For this, you need customer discovery that goes beyond the facts to incorporate the emotions of your future customers.

Pam Swingley in the founder of Savvy, a marketing agency for B2B technology companies. Savvy’s marketing services connect product marketers to customers for market validation. Quickly fills sales pipelines with qualified leads. And, supercharges anemic social media accounts. Results are backed by decades of success with Fortune 1000 companies (ADP and Oracle), startups, and mid-sized software firms. Get savvy about your marketing here.