Ethegra Data Miniseries pt 6: Data is Accessible, use it.

Data is accessible to every business.

When we are running our (small business) operations one phrase that is often repeated is [i will worry about data later] right now we need to get this plan going, we need to get started, we need to make some money then we can worry about long term things like data and keeping track/ measure outcomes. Let’s save that for the end to see how good we are doing.

Well, let me tell you something. Waiting until after you start making money/ start your operations to implement a data plan is like saying we’ll get the foreplay stuff — but first, we have to orgasm!

The goal in front you may be very potent and have the ability to change your business, but often times the jump to get to that goal is very perilous and steep. Gathering data and using it properly provides a set of baby steps that make transitioning and growing your business easier and more manageable.

Case Study:

Nasir, Kiera, and Bob want to run a new product line for their respective businesses. Each person is really in need of growing their business. If they do not grow their business interests will be in jeopardy ie they will miss payroll, not have enough capital to keep running a business, miss rent

Nasir believes he knows exactly what product his customers want. He’s been around them and every time he hears them speak he hears them say the words plastic sweater. He has had a bunch of success in the past and he will just continue to trust his gut. He just knows that if he manufactures a whole line of plastic sweaters he is going to make a killing so he sinks in profit from the last 3 quarters into developing a line of plastic sweaters and sends them straight to the market.

Kiera has just raised startup capital,has a remote team of loyal people who work contract when there is something to be done and she knows she cannot afford to make a mistake on her money. She hears her customers talking about plastic sweaters so before she invests any money, she makes a mock webpage with a buy button and a photoshop rendition of a plastic sweater. She has analytics running in the background and has a comment section attached. She asks a few of her blogger friends to post the mock webpage on their sites to see what results she gets.

Bob has very little money. He doesn’t know much about plastic sweaters but he loves video games and he notices that when he goes to tournaments, people often end up buying new video game controllers because the joysticks are all worn out. Bob decides to create and market a whole new line of joystick protectors and sells them at his video game outlet before he puts them on eBay for pro gamers to use.

What do you think happens to all of their businesses?

The results:

Nasir, who was so sure that plastic sweaters were the thing, went for the shot. He invested 3.5 million dollars into creating, marketing and distributing the plastic sweaters. He had a team of designers make the most fashionable lines of sweaters made from various plastics. When they hit the stores, a few hipsters purchased a couple for their art projects, but otherwise they did not sell. Nasir was then late on payroll for the next few quarters and many of his key employees quit the organization. He did not go out of business ( because his wise chief financial advisor always made sure to keep 4 months runway in cash ) however, that move cost his business dearly and put them further behind from their original goals.

Kiera collected the data from her market testing and found out that when people said plastic sweaters, what they were really looking for were ponchos. Specifically they wanted ponchos and boots so that they could take instagram photos in the rain. Kiera then changed her webpage to show poncho and boots and asked some of her instagram friends to share the webpage. Over 3000 people a day visted the site and of the 3000 3% (90) people clicked on the buy button. Kiera did some A/B testing and saw that the best price point was at $55 which gave her a $22 profit margin on each sale (labor included). This meant that every day she could expect around $990 in total profit. Every month she would average about 30,000 She also knew from her data that these items would only sell in the rainy season. She had 4–5 months of profitabilty window so she should keep her overhead small and only sign a brief contract with the distributor. Kiera made 300,000 in profit without even optimizing her copy

Bob sold all of his joystick protectors on day one. It costs him about $2 to make and $2 to customize. He sold each one for $15 and custom ones for $24. He put them on ebay, made a small e-store and put the specs into a manufacturer. .Every day he gets about 200 orders for regular and 150 orders for custom (internationally) and on holidays and tournament season his orders spike ( its alot cheaper to buy a protector than it is to buy a new controller every 3 months ). Bob has a profit of at least $5,900 per day which came out to 177,000 per month. Bob does get to keep all that money, because he has to reinvest it into his business, but Bob is doing all right.

What did we learn from this case study.

Well in the first case we see an application of the golden rule of data which seems somewhat counterintuitive, without a defining reason past performance does not preclude future results. Nasir was confident that because he had good picks before and he thought that would continue. He was flying by the seat of his pants without any validation or data that his idea was what the customer was looking for. He did use some information from the customer, but stopped there. He went all in and he lost. That is not to say this sometimes doesnt work, however this method is equivalent to gambling and as we know, alot of people get the jackpot, but the house always wins.

Kiera, wisely saved her money and ran test. She started at the same point as Nasir, and tested her assumptions. She got rigorous data and launched when she had sufficient evidence supporting her launch. She made sure to reach out to the right people that her data told her and approached the situation reasonably. She noticed in her data that when her customers said plastic sweaters they did not literally mean plastic sweaters they meant ponchos and her end product reflected that. She made a strong profit that she could then use to further grow her business.

Now on to Bob. It would seem that in a book about how to use data, the case studies would favor those who most rigorously use the data to guide their decisions. Why is Bob then the most successful? Trick question, Bob of the three cases used the data available to him in the best way.

Data is an appoximation of an event. We take an event under a specific condition and record what happens at that date and time. Experience is its own form of data. Even though it may not be statistically rigorous, it gives us a fairly strong understanding of what we are trying to study and look at. In using business data where the goal of the data is to understand customer behavior, there is no better way to do that than by observing customers.

Bob used the data of his regular experience to understand the buying habits of pro-gamers which is itself a niche market and catered to their needs. He also understood that pro-gamers themselves operate as a brand and if he creates products for them to use there will also be plenty of people who follow those brands and want to be like them even if they do not have the functional use for them. Bob then acquired validation data cheaply at his local pro-gamers meetup and used that to spring forward and launch his international brand. By using pre-orders he paid for his manufacturing upfront and sunk in very little of his own money all while servicing a customer that he wants to work with. Of course, Bob, if he wants to keep doing this, will have to build a business structure and he cant keep his entire 2.1 million dollars a year, but if he hires an accountant and a data professional, he should be fine.Data is not only the numbers in a chart but also a regular recording of events we’ve experienced.

Now these case studies are not exact and though I made all these people up, each of these situations reflected a very real circumstance that entrepreneurs often find themselves in. The defining difference is how effectively each entrepreneur uses the information available to them. If you ever read a book like Lean Startup — about half of it is talking about how to use data effectively to make good decisions so you minimize waste and maximize impact/profit.

If you are running an organization, do yourself a favor and start learning about your data!!

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