Market List Creation – Know Your Enemy.
Explaining the decision to employ the newly developed and yet far-from-perfect radar system used to protect England from the stifling Nazi blitz in World War II, the esteemed scientist Robert Alexander Watson-Watt said, “Always strive to give the military the third-best because the best is impossible and second best is always too late.” This attitude of being good enough, and not perfect, has been dubbed ‘the cult of the imperfect.’ The French philosopher Voltaire summed this attitude nearly two hundred years earlier when he wrote, “The best is the enemy of the good.”
Certainly, when creating a call campaign or lead list, trying for perfection in our initial query is also very much our enemy. In this episode of Market Dominance Guys, Chris explains why what you are doing when you create a list is already wrong! This is Market List Creation…Know your enemy!
Corey Frank (00:57):
Explaining the decision to employ the newly developed and yet far from perfect radar system used to protect England from the stifling Nazi blitz in World War II, the esteemed scientist Robert Alexander Watt said, “Always strive to give the military the third-best because the best is impossible. And the second-best is always too late.” This attitude of being good enough and not perfect has been dubbed the cult of the imperfect. The French philosopher, Voltaire, summed this attitude up nearly 200 years earlier when he wrote, “The best is the enemy of the good.” And certainly when creating a call campaign or lead list, trying for perfection in our initial query is also very much our enemy. And in this episode, Chris explains why what you’re doing when you create a list is already wrong. This is market list creation, know your enemy.
Your advice then if I may, we have a hypothetical software company that it’s an HR persona software that helps me identify unhappy employees and it calls their social media and their credit score and their kid’s college tuition and puts this all into an algorithm that spits out a score that says, “Even though Chris Beall is a good employee today, he has all these external pressures, positive or negative, that would make him an unhappy employee.” Let’s just say we have a product like that.
And I want to focus on certain people in a company. The traditional way is to say, “Hey, listen, this fits HR people in an organization.” What I hear you saying is, “It’s more or less of who has the most, where is a potential area of pain from an organization that is suffering from turnover that needs to stem that turnover or that the cost of acquisition is so high in getting an employee that you want to make sure that when they find an employee, they keep that employee.” And so as I’m going through this thought process of creating this list for this fictitious HR software company, what are some of the steps I should think about when I create my list from how most people do it, to how it should be done? Because I have a product that fits every HR person in America and every organization that has employees should care about this product because you don’t want those employees to leave.
Chris Beall (03:43):
So the trick to all of this is actually pretty simple. It is first to recognize that it’s a hypothesis. Your list is a hypothesis about the market. It’s not the definition of a market. And as a hypothesis, it’s worth about as much effort and time that it would take to come up with a hypothesis for, gosh, I wonder if this pork chop would taste better with more salt on it? Really, you don’t want to sit around for four or five days thinking about this, arguing about it. So thing number one is, cut it with the internal meetings that are full of everybody’s opinions. All you’re going to do is reduce the darn thing to a list anyway. Until you’re actually talking to folks you don’t know very much, get a cycle time for building a list down to as fast as possible because the cycle time to discovering if the list is any good is about a week. Done right, it’s about a week.
So don’t spend five weeks talking about something that’s going to take one week to determine if it made any sense, because I guarantee you you’re wrong. So you missed, number one, admit you’re wrong in advance, and then be bold in your hypothesis. Second, be very, very specific. So whatever your hypothesis is, don’t hedge within the hypothesis. The goal isn’t to see whether you can settle at a meeting. The goal is to see whether you’ll learn something from having set those meetings. So be specific. Target a role, target an industry. If you’re really in a frisky mood, target geography for a funny reason, which is there are always differences in geographies that are not manifested in the data.
Corey Frank (05:19):
Really?
Chris Beall (05:20):
All of it, everything in the world has got local influences. It’s like in every business there’s seasonality, but until you’ve run it for two years, you don’t know what the seasonality is.
Corey Frank (05:31):
So for instance, if we had this hypothetical HR software, obviously where there’s a high amount of employees moving back and forth for attrition. I tested in the SFO Palo Alto area, and I may get a different result of the same product in Mission, Kansas, where there are only four big employers and people work at these organizations for 10, 15, 20 years. And so if I would validate it in Mission, Kansas versus SFO, I would potentially get some false positives if I never tested it outside of particular geo.
Chris Beall (06:10):
Exactly, exactly. It’s always good to be more specific if there’s enough volume to support the experiment. Unfortunately, these experiments don’t take that much volume, the smaller this debt that you’re going, if you think, okay, it’s possible that the San Francisco Bay area is a market for us. That means it’s self-referencing. And that’s a great example you gave because that’s a great example of companies that share a bond, a background that you wouldn’t have elsewhere. They share the same venture capitalists, the same board members, and they’re under very similar pressure. So I would go as specific as you’re going to go to the SaaS companies in the Bay area that are funded, they have to be funded, maybe even going to the ones that are series B and beyond because they have this particular talent management problem, in my head. And I just go over to some combination of ZoomInfo and LinkedIn Sales Navigator.
And maybe if I’m really looking for funding information, CB insights, I make my list. How long does it take to make that list? 15 minutes, 20 minutes, not very hard. I put in some criteria, then I inspect the list. How do I inspect the list? Well, list-making always brings false positives in terms of titles, mapping personas to titles is very, very hard. Don’t worry about it. Make the list with a broader set of keywords, because that’s all you really have to work with or concepts and attributes. As you know, this used to be my business at one point in my life was the world of catalogs, all that kind of detail in them. Don’t worry about it too much. Just make sure your list has false positives in it. Then pull the list into Excel, pivot the list on the title, sort descending, that’s from the biggest, the highest count to the lowest count, on the counts of the titles.
And there are two things you’re looking for. One is you want the title with the biggest count to be a drop-dead obvious, this is who I want on the list. And you want titles that have high counts that are obviously not possibilities. In the classic case, I’m looking for CEOs and I got assistants to CEOs.
Corey Frank (08:15):
Yes.
Chris Beall (08:16):
Well, I pivot the list and now I’ve aggregated the assistant to CEO titles with a number next to them, there’s 22 of these, 11 of these, and sort. I just strike those as a chunk. And then I got my list. It’s a very simple two-step process. And even with the big list, a list of say 5,000, it takes about another 15 to 20 minutes to go and pivot the list and say, “Oh, there’s a title that I never want. And there’s too many of them.” If there’s a title you never want, and there’s not very many of them, below some level of count, just ignore it, go ahead and call them and talk to them. You might learn something. Now you’ve got a list. That’s it.
Corey Frank (08:50):
That should take about a week to run through with the proper support mechanism, the proper technology. I should be able to use that week cycle to validate to vet out the success of that list. And we can talk about next time since we’re up against the clock here, talk about what is the definition of success, whether this list should continue or whether we should pivot, change the selects and go with a different list. And then AB tested week to week before we quote-unquote scale or get a larger list.
Chris Beall (09:23):
Exactly. Actually what we’re going to pivot on is the message first. The message is more nimble than the list. And we always modify the design based on the cycle time of getting to a new design. So the new design is a new message, which is a new product remembering that our original problem was not to find the people, they’re always out there.
The companies are always out there, but to find a product. It’s a search process looking for a product, and we’re going to look for the product that resonates in a list. Once we do that, we lockdown on the list and we lock down the message, we expand it, and we immediately go to scale. I’m hearing you say, Chris, they’re just so overly rigid in their messaging and what they perceive as their market, after maybe a cycle or two, either in seed round or early A round where they never come off of that and then they just throw boatloads of money at trying to fit the square peg in this round hole. And I think that’s what we can talk a little bit about next time here since we’re up against the clock.
