Leadership

Prime Future 42: Doomed from the start: how bias distorts

Prime Future 42: Doomed from the start: how bias distorts

A few years ago I set out to tackle the problem of wildly opaque price discovery in poultry, focusing on the $120 billion US chicken market and the ~40% of chicken priced & traded in the spot market. I talked with executives and sales leaders up and down the chicken industry and received widespread affirmation that yes, of course everyone would like a more transparent market with more accurate price discovery and yes, of course, current price discovery is broken. I interpreted the vigorously nodding heads as sufficient evidence I was on the right track so I raised capital, recruited a team, and built out a product. It was then that I realized that when my prospective customers said “yes, of course I’d like to see a more transparent market with more accurate price discovery” what they actually meant was “yes, of course I’d like better transparency into the rest of the market but not at the risk of giving others better transparency into our pricing”.

With the benefit of hindsight, I see every cringe-worthy mistake I made in the earliest steps of that company, in the core assumptions that laid the foundation for everything else.

There are a lot of reasons new companies die and new products fail, but I'm increasingly convinced that the art & science of customer discovery & validation can be de-risking superpowers to new products & new ventures.

Luckily, these are skills that can absolutely be learned and cultivated. Let’s start with definitions:

  • Customer discovery is the process of identifying the core hypotheses wrapped up in a new product, whether about the problem being solved, the target market, the value proposition or any other core element.
  • Customer validation is the process of testing to prove or disprove those hypotheses. (Let’s use the generous definition of product to mean any new product from a hardware or software tech product to an innovative business model.)

Customer discovery & validation can make the difference between success and failure for early stage companies. It can determine the trajectory of a new product for corporates. It can unlock an insight for a new market for a producer or packer. Whether implicitly or explicitly, a thousand decisions about the product itself and the market will be determined by the outcomes of customer discovery & validation. Think of this as the foundation that entire products/ventures are built upon. (That’s why it’s so risky to outsource this process.)

Sometimes the best products are created by those who know the problem & market from firsthand experience, sometimes the best products are created by those who approach the problem with fresh eyes from the outside. Since we can point to examples of each, I’d suggest that (aside from the role of luck & timing & uncontrollables) the 2 best predictors of a product creator’s success are:

  1. Curiosity & humility. These 2 usually go together. Curiosity leads to asking new questions in new ways to get new insights which lead to new ways to solve old problems. Humility spurs curiosity.
  2. Self awareness to resist cognitive biases. More on this in a moment.

Here are some common traps of customer discovery:

  • Strong convictions, loosely held. I observed one startup lose several years building a product the market said it did not want. Despite the market’s continual feedback that the founders’ core hypotheses were inaccurate, the founders resisted updating their mental models which, naturally, had negative implications on everything from product design to sales. Having “strong convictions, loosely held” allows you to move faster and positively impact the trajectory of a product’s adoption. Update your mental models, listen to the market.
  • And yet, the market can’t evaluate what the market doesn’t know. What will you pay for this, how will you use it, when will you use it? People aren’t great at projecting their future behavior so they usually don’t know the answer to hypothetical questions when you’re still in the product concept stage. We all know the Henry Ford saying that if he’d asked people what they wanted, they would have said faster horses. Bill Gates, Steve Jobs, Jeff Bezos, & Elon Musk would presumably all agree with that sentiment. When it comes to product concepts, customers are better at describing what they don’t want than knowing what they do want; insights from the “don’t wants” usually make highly effective constraints for product design. For example, a smart barn product for poultry has to withstand xyz environmental conditions, function without cell signal, and cover x sq feet. Those constraints create a great sandbox to play in.
  • Sometimes its hard for “nice” people to be brutally honest. Nobody wants to say “that’s a dumb idea & here’s why” so instead they say “well, that’s interesting.” The best way to discern whether someone is merely being polite or if they really want a solution like you are describing, is to get cold, hard pre-orders. Someone saying they might purchase something is a very different thing from issuing a purchase order or swiping a credit card. There is an enormous time cost to not recognizing a proverbial pat on the head for what it is.
  • N of 1. Just because 1 prospective customer says something, doesn’t mean there’s a meaningful market that shares that view. Look for trends, not individual data points.

All of these traps lead us directly to the wonderful world of cognitive biases:

“A cognitive bias is a systematic error in thinking that occurs when people are processing and interpreting information in the world around them and affects the decisions and judgments that they make.” — VeryWellMind

Cognitive biases impact us all on the daily, but they can have an outsized impact on the customer discovery process and a new product’s subsequent trajectory. If a business/product is built on a tenuous foundation, the house of cards will fall. Either for the person testing their hypotheses OR for the person responding, here are some practical ways cognitive biases can hijack the customer discovery process:

  1. Confirmation bias leads us to hear what we want to hear and disregard information that disproves our hypotheses. “The reaction to disconfirming evidence by strengthening one’s previous beliefs.” Yiiikes.
  2. Endowment bias is “the tendency for people to demand much more to give up an object than they would be willing to pay to acquire it.”
  3. Anchoring bias is “where an individual depends too heavily on an initial piece of information offered to make subsequent judgments during decision making.” This is why the n of 1 problem is a dangerous trap.
  4. Selection bias is “the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed.” Who you go test hypotheses with matters as much as how you test them.
  5. Framing effect is “drawing different conclusions from the same information, depending on how that information is presented.”

The list goes on, but these are the cognitive biases I can link directly with my mistakes in customer discovery from the poultry scenario.

So you know why customer discovery & validation matter and you know the traps to avoid, but what are the elements of a good process?

  1. Articulate your core hypotheses about your product, the problem it solves, the market it serves, and the value proposition. PSA: this is harder to do than it sounds but it serves as an incredibly valuable forcing function to clarify.
  2. Design an experiment to prove/disprove those hypotheses.
  3. Execute the experiments.
  4. Synthesize insights and update your assumptions where necessary.

An experiment could be anything from a conversation/interview with someone in the target market to running a paid Facebook ad campaign to test demand for a product concept. Most often, a conversation is the best place to start as a way to get “clean water”. As you think about structuring a process to conduct conversations that will allow you to test core hypotheses, a few things to consider:

  • Who you talk to. You know you need to talk to more than 1 person, but what about talking to people across a range of functional roles within a company? A range of seniority levels? A range of company sizes? It’s amazing what kind of insights that 5-10 targeted, well structured conversations can yield.
  • How you structure the conversation & frame questions to maximize insight yield. This is the trickiest part, IMO. Remember you are trying to get CLEAN water, not influenced water. There is art & science here, but the quality of the question impacts the quality of the responses. Open ended questions should be the default with only an occasional but highly targeted closed ended question. Planning ahead can help you move from generic open ended questions to incredibly high value questions. Consider a few examples, which one is likely to yield more rich intel?
    1. “Do you try to reduce labor costs?” or “What are the top 3 ways you’ve tried to reduce labor costs in the last 3 years and what did you learn from those efforts?”
    2. “Is it important to increase calving rates?” or “How does increasing calving rates fit into your top 3 priorities for the business this year?”
    3. “How much does x cost annually?” or “What % of your budget does this problem represent today and what is a reasonable target in the future?”
  • How you interpret what you heard. Did you prove or disprove your hypotheses…really?

The principles around customer discovery & validation can be game changers in almost any commercial context from an early stage venture launching software for hog farmers to publicly traded animal health companies launching feed additives to a top 5 poultry integrator offering new products to foodservice customers.

What are your tips for running great customer discovery & validation processes?


Now Available! Prime Future Volume 1 (link)

Get the first 42 editions of Prime Future in a single PDF….95 easy-to-read pages about trends impacting animal agriculture across the entire value chain from production to processing. This was fun to put together & made me all the more excited about the opportunities in this space.

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