SY Alum Decissio Uses AI to Accurately Predict StartupYard Investments

August 31, 2017/in Startups, StartupYard News/by StartupYard

You may remember Decissio, a Batch 7 StartupYard alum that has been working on the “Jarvis for Investment Decision Making.” Earlier this year, the company announced its kick-off product, an intelligent dashboard for VC investors and Accelerators to evaluate and monitor companies they invest in.

Decissio aims to go beyond a typical investment dashboard by combining up-to-date company data with complex big-data based probability models and machine learning algorithms, helping investors to continuously evaluate their investment decisions.

As Decissio and founder Dite Gashi continues to gather data and build the company’s flagship SaaS product, they have focused on piloting their approach with small controlled experiments.

One such pilot has been in partnership with StartupYard. Decissio’s Mission: to process all of StartupYard’s applications for Batch 8, our latest batch starting next week, and deliver predictions on their success based on a variety of factors, including written applications, founder profiles, founder/market fit, and the current state of the company.

Dite Gashi: Founder and CEO at Decissio

The numbers are in on this pilot, and they’re very promising. We’re not ready to stop reading applications or doing our own research just yet, but we’re now confident that Decissio can be a big part of making our application process better, fairer, and more efficient.

The following case-study is a co-production of Decissio and StartupYard, written by Dite Gashi, and Lloyd Waldo. A more detailed write up and analysis will appear shortly after publication at Decissio.com. For more info on the technology and related work, please visit Decissio.com.

Warning: This post is long and contains big words. Skip to the bottom for a bulleted Tl;Dr

Good Small Decisions = Big Positive Outcomes

The StartupYard application process doesn’t happen all at once. It involves a long series of smaller decisions. Does a startup have a unique idea? Does it fit into our mentor group and experience? Do the founders have enough experience? Is there strong competition in the market?

Some decisions are even more granular: did the founder answer questions thoroughly and clearly? Were they responsive in detail?

Small details often reveal big trends. But a human mind isn’t set up to think in that direction. We aren’t programmed to carefully add up small decisions to make big ones. Enter Decissio, whose mission was to apply a machine-learning approach to small decisions we make in the application process, not to override the judgement and experience of our evaluators, but rather to augment it with important insights.

StartupYard Alum Decissio.com uses #AI to accurately predict future StartupYard startup investments... Click To Tweet

The Framework

An application to an accelerator consists of a relatively small data set. We have a written application, founder profiles (on LinkedIn), sometimes a website, and whatever has been written about the company online.

Rarely do we have hard financial data on the companies, in some cases because there is no company in existence, and so the founding team has no financial data to look at. Nor do we have much access to the IP teams are working on. We have to rely on what founders say, and what they have done in the past.

But a bunch of small data sets together make up a bigger data set. Decissio examined over 1300 previous applications to StartupYard, along with the rankings our evaluation committee has generated, and used that data as a benchmark for incoming applications.

They found a number of statistically significant trends in that data. Startups that were successful as applicants to StartupYard could be ranked point-by-point, according to the following framework:

A Completeness Score: how thoroughly the application is filled in, and with how much quality information.
Effort Score: The quality of the writing in the application, particularly the responsiveness of answers, and the scope and variety of detail provided.
Relatedness Score: how closely a founder’s profile and experience matches the content of the application
Founder Linkedin Score: The completeness and quality of a founder’s LinkedIn profile
Media Mentions: The number, quality, and sources of mentions of the company or product online, along with sentiment analysis
Money/Work/Revenue Generated: The ratio of previous investments and time spent on the project to real revenues (if any).
Spell Check

Believe it or not, Spell Check is powerfully predictive of application quality. Note to founders: always use Spell Check.

The Analysis

This is where the historical data from previous StartupYard applications comes in. While it’s not very useful to directly compare older applications to newer ones, because the topics and ideas in them are often so different, it is useful to weight the importance of the different factors in the framework according to their impact on previous decisions.

Furthermore, the final analysis includes proprietary algorithms by Decissio that can dynamically weight the outcomes for individual teams, based on cross-referencing between different data sets. For example: Decissio’s AI can adjust its expectations for the Effort Score, if the founders are experienced in marketing and sales, or have no such experience. Thus each team is examined according to its own merits, and not an evaluator’s less informed expectations.

As “calibration,” or maintaining consistency and fairness of scoring across a large number of applications is a significant problem with humans, Decissio can re-calibrate an evaluator’s judgement to keep them from penalizing teams for the wrong reasons. As the standardized testing field has long known, human scoring can be so inconsistent that a significant amount of scoring time (even up to half) must be devoted to calibration in some cases.

Since our evaluations involve multiple rounds with a Pass/Fail outcome, each examining more and more detailed information, highly predictive models can be built for an application that will make it through round 1. A less predictive but still strong model can be built for round 2, and a much less accurate, but still useful model can be built for round 3, and so on.

The chart below shows overall predictiveness of the approach over multiple rounds. StartupYard uses a “first past the post” system of ranking, where the ranking cutoff for each round is smaller. This means that in round one, 70-80% of applicants are rejected. In round two, just over 50% of the remaining applicants are rejected, and in round 3 (which are day-long in person interviews), only 20-30% are rejected.

None of Decissio’s bottom-ranked 63 startups were ultimately selected, meaning that virtually all of the first round of evaluations could be handed over to the AI, leaving a much smaller pool of applicants to evaluate, and allowing the human evaluators to use a much lower cutoff, in a smaller, better initial pool. In this scenario, only 20% of human evaluated startups would need to be rejected in the first round.

We would expect false negatives to rise, as Decissio gets only one pass at the data, and with each round, human evaluators gather more data, which causes their behavior to diverge from the model.

For example, if use of Spell Check is 90% predictive of the Pass/Fail rate for round 1, it may be only slightly predictive of the success rate of round 2, and by round 3, it may lose its predictive power altogether. By the time an application involves a detailed look at a founder’s CV, and personal interviews with that person, other factors can arise that vastly outweigh any minor inattention to detail, like spelling.

Or the predictiveness curve can go in the other direction as well, with certain data only gaining predictive power in later rounds. Media mentions may have a low predictive power in the earlier rounds, and become more powerful later on. This can be because a company with a low early round score for Relatedness or very high Money/Work/Revenue ratios, can have many mentions in the media, but also fatal problems in their business, team, or technology. Thus, hype is not strongly predictive in Round 1, but by Round 3, it becomes a major asset to an applicant. Once all other factors are examined, media exposure becomes an affirmation of market fit, demand, or interest.

How Well Does This Work?

Decissio’s Success rate in the first round of applications (the on-paper evaluations), was 73%, far exceeding random chance. The accuracy dropped as expected in subsequent rounds where evaluations focused on personal interviews, from 50% in the 2nd round, to 20% in the final round. Still, this means that exactly half the time, a startup that passed the first interview with our selection committee was predicted to do so by Decissio, based only on their written application and profile.

There are two ways in which this kind of analysis can be useful. Either it can be used to identify applications that have a high likelihood of success, or it can be used to filter out those with the lowest likelihood of success.

Decissio Picked the Top 2 Ranking Finalists

We don’t have enough data to be able to confidently say that an application will definitely fail. However, on the opposite side of the scale, the results from Decissio’s analysis did correctly identify StartupYard’s two highest human-ranked finalists, and placed both in its own independent top ten prediction.

Decissio Picked the 100 lowest-rated applications with 89% Accuracy.

Still, the most immediate benefit of Decissio’s approach is in the earliest rounds, where pass/fail decisions are by design based on less human-focused information than the pass/fail decisions in later rounds.

This theory holds up with Decissio’s results: their bottom 100 applicants in this pool of applications (out of around 130), was 89% accurate, meaning that only 11% of the time, we determined a startup to be worth advancing, while Decissio did not. Clearly, in terms of identifying a lack of potential, Decissio’s approach is already very effective.

Further mining of the available data could produce a much more precise prediction. For example, by analysing co-founder and founder/investor fit according to the work histories and digital footprints of both can theoretically yield very reliable predictions of compatibility, which in turn raises the chances of success or failure for a startup.

These factors would require a different kind of data to solve; a kind of data we don’t collect systematically right now. But this kind of approach, which treats people as nodes in a system that has its own features beyond those of individuals, has been deeply developed already, particularly on the level of enterprise management consulting involving things like the Meyers-Briggs Type Indicator Test.

It may prove true in the future that a set of personality tests of some kind are more predictive of success in a particular accelerator program or industry, than the content of an application, though we don’t know what that test would look like, or how it would be used.

SY Alum Decissio.com predicts first round StartupYard application decisions with 89% accuracy, picks two finalists. Click To Tweet

Potential Applications:

Time Saving

Decissio was able to predict with strong accuracy (73%), the likelihood that a startup would make it through the first round. This means that evaluator’s mental resources can be focused more on rounds in which more human-level data is being examined, particularly personal interviews and meetings.

An evaluator can spend relatively less time making early-round decisions, because Decissio can compare cursory evaluator consensus to its own scores, and “call out” the circumstances in which these do not match for further study. There is less of a chance that a good application will be “overlooked” in this way– a constant fear among startup investors dealing with many applications.

Bias Reduction

While a human with experience can “skim” an application and be able to tell it isn’t strong, that subjective evaluation is highly prone to error and internal biases. Very poor spelling could cause a human evaluator to give up on an application, whereas an algorithm might see past this issue and find more value in the startup than a person would look for.

This process could also serve as a check against more latent biases, such as gender, age, nationality, and sexual orientation. While it’s difficult for a human to differentiate between their instinctive reactions to people based on conditioning, and their objective evaluations of people in a professional context, an algorithm can demonstrate more consistency in that regard. Biases can’t be eliminated even this way, but they can be better controlled.

Thus, Decissio can be a check against the human decision making process, enhancing it without replacing it.

Fighting the “Best Horse” Problem

Decissio’s approach can also serve to fight the “best horse” problem, whereby a candidate with a strong outward appearance can advance well into the selection process without revealing sometimes severe deficiencies.

The best horse problem is one of reinforced selection bias. Imagine you have 10 horses, and you send them all running around a track. Then judging by the outcome of the test, you give special care and attention to the fastest horse, believing that it above the others has greater potential as a champion.

In this way we sometimes pick winners for all the wrong reasons. The horse to finish first can finish first for a number of reasons not having to do with potential as a racehorse. Cheating for example, or luck. Likewise, the last horse around the track can be the one with the most future potential.

In our application process, a very strong written application or interview performance can mask a basic weakness in the founding team’s experience or ability. It’s only much later that these weaknesses reveal themselves in a lack of tangible results from the company.

Startups can and do advance very far in accelerator programs while still lacking the core abilities and disposition needed to thrive. It can take a long time to recognize a fraud or a fish out of water.

Creating More Useful Feedback

Another thing this big data approach can solve is the information problem. What happens frequently with accelerator applications, as we suspect happens in many fields, is that successful written applications contain a near-perfect mix of description and data. Something like the “golden ratio” often described in mathematical analyses of artworks and natural proportionality.

The human mind likes a certain level of balance in the information it receives. When a person writes, they tend to favor either information or analysis, but only experienced writers know how to mix the two into pleasing and easy to read narratives. It’s a problem even good writers frequently struggle with.

Too much writing about ideas, and the application seems too “light.” Too much data, and it seems too dense or too technical. In formal writing analysis, this formula is often used to describe balance between facts and ideas, where the value a is descriptive and creative writing, while b is supporting data and factual information. Those familiar with the classic “5 paragraph essay” often taught in schools, will recall the same proportionality. About 3 parts of persuasive writing, for every 1 part of factual basis.

This type of training is not universal even among professionals, which sets up an arbitrary test of writing skill that may not be as relevant to the outcome as we tend to believe. If our job is to train people how to be better entrepreneurs, then we fail at that mission from the beginning if we can’t differentiate between someone who deserves our help, and someone who doesn’t.

By offering feedback on the strength of an application according to the above mentioned metrics (Completeness, Effort, Spelling, etc), Decissio could potentially improve the chances of failing applications where the main problem is poor writing.

An opportunity to improve an application is also an opportunity for us to see value where it is hard to spot. Telling an applicant that their application is failing because of style and substance can help those applicants to better express themselves, and thus deliver us more opportunities to find quality teams.

Conclusions

StartupYard and Decissio pilot project shows that AI assisted investing can improve results quickly. Click To Tweet

The results of this pilot clearly show that there is great potential in enhancing our decision making process with machine learning and data analysis.

We are not at the point where we’re ready to let a machine determine our investment strategies on its own- the way machines already do some forms of investing without human inputs.

Unlike an investor in securities, or a high-frequency bond trader, an accelerator’s main advantages are as a first mover. We invest in companies that don’t exist yet, have limited information on their markets, and have a limited history, or no history. So we invest in people – and people are inherently hard to quantify.

Our anecdotal experience of meeting teams in person *before* evaluating their applications, consistently reveals that the application process cannot identify many important personality traits. For an accelerator, success comes only when we are right about a trend, and a particular person, at just the right time.

So employing an AI powered decision-making approach cannot mean abandoning the unique advantages we have: the ability to see things others don’t see. Expertise (and hard work) is still the core of sound early-stage investing, but AI can help us to focus that expertise on the “creme de la creme” of potential investments.

It can save us from becoming jaded by the junk applications that routinely swamp our inboxes.

A startup is not an individual, it’s a team. And it is not in our interest to arbitrarily eliminate applicants who are not good at writing applications, or have other deficiencies more visible on an application than in real life. However, it is in our interest to conserve and spend our resources (including our time and energy), where the potential for gain is highest.

This approach can benefit higher-dollar investors too: later stage investors have many of the same problems accelerators have, but on a different scale. A Seed or Series A investor makes decisions involving 10-50x more money than any single investment from an accelerator, and they also receive more requests, on average, than a small accelerator does.

Currently the most obvious and most immediate advantage of using Decissio’s AI is for very early stage investors with many applicants, such as government innovation programs, and big accelerators like TechStars, Y-Combinator, and 500 Startups.

Tl;dr:

StartupYard alum Decissio analyzed our past applications over a 6 year period.
Decissio used this data and their own AI to predict which applications to StartupYard would succeed.
Two of their top 10 picks were also StartupYard finalists
They accurately predicted the bottom ranked half of applicants.
This approach can be used by accelerators to:
- Improve applications overall
- Save time on the poorest applications
- Reduce systemic biases
- Get better information on applicants
Decissio’s AI could be applied to other early stage investors, such as Series A and Seed Investors, or to large accelerators, particularly Tech Stars, Y-C, and 500 Startups.
At the end of the day, AI will help early-stage investors to get better information, and spend more time focusing on the human-focused side of their work.

The Positioning Statement: Finding a Window Into the Mind

August 30, 2017/in Life at an Accelerator, Starting a Business, Startup Tools/by StartupYard

Originally published on our blog way back in 2014, this post has been one of our most enduringly popular. According to Google Analytics, the average reader has spent over 20 minutes studying it. It is also our most popular piece on Medium. Since that time, we’ve shared this post with scores of startups, and used the methodology detailed here over and over again. This post is updated to reflect all that we’ve learned in the past 3+ years.

What is Positioning?

“Positioning” has often been described as “the organized system for finding a window in the mind.” That’s how Al Ries and Jack Trout described it in their book: Positioning, a Battle for Your Mind, a groundbreaking work from 1981.

Al Ries is often credited with coming up with the term “positioning,” and he describes it as a way of using a customer’s own experience of the world (including with other brands and products) as a way of communicating with that customer. Rather than communicate in a vacuum, companies that use effective positioning target customers who are already familiar with competing products and brands, and use that familiarity to differentiate themselves.

In the book, Ries highlights perhaps the most famous example of brand positioning in the 20th century: that of Avis, which in 1962 premiered the tagline: “At No. 2, We Try Harder.” Avis was at the time the market runner up in rental cars, and the company used that fact to imply that they were more accountable than their competitors, because they had to be.

Positioning is Everywhere

When we stop to think about positioning as a promotional tool, we begin to see that it is everywhere.

Brands use their competitors as foils for their own messaging constantly. Remember those “I’m a PC, I’m a Mac” adverts.

Brands for the the past half century have often focused less on defining what their products are, and chosen rather to define what they are not. Another striking example comes from 7-up, which in the 1970’s sought to gain market share by telling customers that their clear soda was “the un-cola,” explicitly defining themselves as essentially “Not Coke.”

Whereas in the past, consumers may have seen their range of choice as: “drink Coke or don’t drink Coke,” 7-up presented a different scenario: “drink 7-up when you don’t want Coke.”

In presenting consumers with a new choice: either drink Coke or drink 7-up, the brand found a window into consumers’ minds. It suggested that there were many people who would prefer an alternative to Coke that was not available.

By framing 7-up as an alternative to a popular drink, the brand convinced retailers and consumers alike to buy 7-up along with Coke, in order to fill the demand implied by the advert. In 7-up’s ideal scenario, customers would not stop buying Coke, but would buy 7-up in addition to Coke.

In the 1970s, 7-Up promoted the idea of a citrus-flavored soda as an “un-cola,” to break down consumer expectations that carbonated sodas are dark in color.

The product itself also emphasized its differences from traditional sodas. It was not caffeinated, it was sour, and it mixed well with the more popular alcoholic drinks of the time, including gin and vodka, which were gaining market share in the 1970s. “7 and 7″ was a popular drink choice by 1970, a mix of Seagram’s 7 Crown Gin, and 7-up.

The brand thus further differentiated itself from Coke, which had traditionally focused its brand on taste and tradition, using the tagline “It’s the Real Thing.” Whereas Coke was a conservative choice, enjoyed by families and older generations, 7-up was a young brand- enjoyed at night in bars and in cocktails, rather than on sunny afternoons at baseball stadiums or at restaurants.

Thanks to these ads, 7-up rose in the 1970s to 3rd place among sodas, only losing its market share with the rise of diet sodas in the 1980s and 90s, and the decline in popularity of mixed drinks in favor of bottled drinks and beer.

What a Product Positioning Statement Looks Like

Here we’ll focus on a sub-discipline of positioning as a whole: Product Positioning. It’s the same general philosophy, but with its own specific methodology.

When a startup team joins StartupYard, one of the first things we ask them to do is to sit down and write our a “positioning statement.” The format is deceptively simple, and it looks like this:

Product Positioning Statement:

(Our Product) is for (target customers):

Who (have the following problem):

Our product is a (describe the product or solution):

That provides (cite the breakthrough capability):

Unlike (reference competition):

Our product/solution (describe the key point of competitive differentiation):

Why A Positioning Statement Is Important

The positioning statement contains the core elements not only of a product, but also of its marketing and sales strategy. And while most of our teams have worked primarily on ways of describing their ideas, a positioning statement does more than this: it also justifies the notion of that idea becoming a business.

It’s important for a startup to have the concepts of saleability and market differentiation baked into the essence of the product. Writing a positioning statement, like writing a SWOT analysis, can reveal basic strengths and weaknesses in a product while it is still in the “idea” phase.

A Starting Point

Even more importantly, a positioning statement can serve as the basis for validation of a product. If you can’t describe what your company does in this compact format, it’s possible that you aren’t sure yet what your company actually does. You may be sure of what you are doing on a technical level, but what that means in business terms might not yet be clear.

The positioning statement is a conversation starter, particularly with early mentors and core team members, to facilitate early discussions about core strategy, and how the team sees itself in the bigger picture, what market it is really addressing, and what its real competition is in that market.

And a positioning statement, well-executed, can be transformed virtually complete into the core marketing message for a product, once it is developed. Take this copy from Nest’s webpage:

“Our mission is to keep people comfortable in their homes while helping them save energy, and with the next-generation Nest Learning Thermostat, we’re able to spread that comfort and savings to even more homes — and to help higher-efficiency systems perform the way they were meant to.”

Here are all the elements of a positioning statement. If the Nest founders filled in our form, it would look something like this:

Our Product is

For: Upper-middle class and wealthy people

Who: Own homes and spend a lot of money on energy costs and heating/cooling systems

Our product is a: Smart Thermostat and related products

That provides: Savings and increased comfort by improving efficiency of existing systems.

Unlike: manufacturer provided systems

Our product/solution: Learns and intelligently adapts to the inhabitants to increase comfort at all times, while saving money

A Positioning Statement Tells the Truth

The above “translation” of the Nest positioning statement doesn’t say exactly what their marketing copy says of course. They don’t mention wealthy clientele for one thing. But at $130 for a smoke detector, and $250 for a thermostat, that is surely the market they are targeting.

Their products are priced high enough to be clearly exclusive, but low enough not to seem extravagant or make a money-wise customer feel foolish for purchasing. And anyway, that messaging is not only found in the price, but in mention of “homeowners,” and of “higher-efficiency systems.” These subtle cues indicate to customers that the product is made for people who value performance, and are willing to pay to get it.

Features ≠ Differentiation

Notice too that none of the positioning statement deals with the exact features of the product. It’s all about the outcomes the product promises.

This is key: their competitive differentiation is not on a feature-by-feature basis, but holistic. They frame their competition as not only out of date, but barely worth mentioning at all. They indicate that their competitors (the providers of the systems), are not even in the same business as they are, and that therefore competing products are not even worth comparing in a more granular way.

These are all elements of Nest’s marketing that are informed by the market segment they have chosen to address, from the quality of the products, to the design, to the sales language and the pricing. And so the marketing message that says: “this product is for you,” when speaking to its target client, is backed up by a product that is built with that person in mind. The mission is clear: this is not a product for anyone, but for someone very specific, so that when the customer comes across the product and thinks about buying it, he or she can immediately see that it is made for them.

Who, Not What

There’s a reason the positioning statement starts with “who.” Over the years, we’ve consistently observed that the first thing most startup founders do is try to talk about the product before talking about the customer.

But here’s why that’s a mistake, and why the positioning statement doesn’t do that: understanding the target market is the first hurdle in actually validating a new product. Features are a distant second consideration to clearly articulating who the customer is, and what their problem is.

A laundry list of features doesn’t really address the problem of “who” the product is for, but only “what” it is for. And that “what” that a feature describes doesn’t necessarily give any indication of what problem is being solved. Startups that are dealing with complex technologies can easily skip over the core user benefits of the technology, in favor of describing the technology itself.

Common is the startup that pitches “a revolutionary new method of transforming leavened wheat products into crispy squares by employing concentrated on-demand heat conduction derived from electrical coil technology,” instead of pitching: “toast whenever you need it,” or even “a less boring version of bread.”

People Buy Outcomes, Not Features

Customers ultimately buy solutions to their problems, not technical specifications. And those problems are not always the same as the ones that the feature list actually addresses.

Consider this, when thinking about buying a car, what are the first things you’re likely to check?

Probably it isn’t technical specifications. Most people will answer one of two ways: they will check either prices, or reviews.

That indicates that the customer is very aware of what their problem is. They need a car, and they need it at a certain price, or at a certain minimum level of comfort and safety, or both. Car companies rarely list their prices up front on their websites precisely because they know that this is what customers are looking for, and so they are able to ask for customer information in exchange for information on their pricing.

Cars rely heavily on marketing to differentiate themselves, but the marketing is typically not focused on what the cars actually do. And that’s because cars all pretty much do the same things. So the problem being solved for the customer is not “I need a car,” but “I need a car that fits my personality/lifestyle/class/status and/or specific needs.”

Look carefully at a car commercial, and you’ll be assaulted with subtle and unsubtle cues about price, lifestyle, class, education, and culture, but not much about fuel injection, or anti-lock brakes, or all-wheel drive. These things may get a mention, but the whole object is to present the car as being a great value, in consideration of all that it offers for the price being asked.

The goal of a typical car commercial is to convince a customer that they are buying the status and the culture that is associated with the car; that their decision is not motivated by price, even when it usually is.

That is how powerful positioning is. By showing a very clear understanding of who their customers are, car companies can turn a price-motivated decision into a statement about who the customer is, and about their place in society as a whole.

Try this: go and ask someone why they bought the phone they own, or the car they drive, or the computer they use. Whatever it is, ask them why they chose it.

The majority of people you speak to will probably not say: “it’s the best I can afford.” Instead they were answer the question in terms of what the phone or car or computer represents to them; what it says about them and their values.

For example, if the person has a cheap phone, they’ll say something like: “I just use the one that came with the plan. I don’t need anything fancy.”

That’s often code for: “I’m too cheap to buy a nicer one.”

On the flip side, ask a latest model, hi-tech phone owner why they bought their tech toy, and they’ll say it’s because they value the design, the features, or the amazing convenience of using it. They won’t say: “I bought this because I want to signal that I am wealthy and can afford luxuries.”

This dedication to explaining our motivations in personal terms doesn’t extend only from a marketing strategy for high end consumer products – it derives from the way those products are made as well. The design and build of a product must subtly betray its role in social signaling for the owner. Cheap cars are “humble,” while super-expensive cars are “subtle.” It is the cars in between that are most ostentatious.

When you see a fancy paint job on a cheap little economy car, you cringe because it is a confused communication of values by the owner. It’s pig dressed as a lady.

Consumer products can also be designed to signal their utilitarian nature, in order to make customers more comfortable with their purchase. For every €20 bottle of wine, there is a €5 bottle of wine that looks somehow less pretentious, and more sensible.

The Position and the Pitch

The main difference between a positioning statement and a full blown pitch is that the positioning statement says in plain words, what is really true about who your product is for, and what you believe its market fit to be.

This will help you to stay away from visions of (and talk about) your product changing the world, even if it doesn’t really have the capacity or the capability to be a real world changing idea. Not all products have to be for everyone, and many of the best products aren’t.

It will also keep you honest and focused; force you to make clear the needs of the market you are targeting, and force you to live in their shoes instead of your own.