editione1.0.1
Updated August 22, 2022You’re reading an excerpt of Founding Sales: The Early-Stage Go-To-Market Handbook, a book by Pete Kazanjy. The most in-depth, tactical handbook ever written for early-stage B2B sales, it distills early sales first principles and teaches the skills required, from being a founder selling to being an early salesperson and a sales leader. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, commentary and future updates, and a high-quality PDF download.
Now that we’ve discussed the various places where you could go and look for accounts, let’s get very specific about doing this in practice, shooting for that goal of 50–100 targets.
Later we’ll get into CRM and where to house your list of accounts and contacts to attack, but my recommendation at this stage is to just use a Google Sheet as your initial repository of prospects. This doesn’t mean that you’ll use this spreadsheet as your CRM (though you probably could for this limited scale of engagement), but you do want a place to house the structured prospect data. You can use a spreadsheet template, with both role-specific prospecting and hiring-specific prospecting.
You’ll note that there are typically distinct columns for pieces of information that we might eventually want to query on or use in a mail merge campaign.
exampleFor a company like LifeGuides, makers of awesome recruitment-branding solutions, it would be useful to have a target prospect’s Glassdoor information, as Glassdoor is a big indicator of recruitment-branding business pain and spend. So in this case, we’d not only capture their Glassdoor star rating (our messaging might change if their score is low versus high), but also the Glassdoor profile link, and maybe a link to a particularly bad review. Not only is this good for future reference (before you got on a call with a prospect, you’d want to check it out), it would also be useful in an initial outreach email.
So if you’d done a good job of structuring that metadata, you’d be able to send awesome mail merges like:
exampleSubject: Hi {{FIRST_NAME}}! We can help {{COMPANY}} with that {{STAR_AVERAGE}} Glassdoor average!
Hi there, {{FIRST_NAME}}! I saw that, like many companies out there, your Glassdoor ratings ({{STAR_AVERAGE}}) are probably not where you’d love them to be. And like a lot of companies, you have reviews that are probably not representative of the true employee experience at {{COMPANY_NAME}}. This one was a good example: {{REVIEW_LINK}}. Those are never fun.
The good news is that we’ve been working on something to help you tell your authentic employment experience story. And tell it in a way that isn’t held hostage by an organization that wants to charge you to influence those reviews. And we can help you take back the top Google search results for “working at {{COMPANY_NAME}}.” (Have you looked at that query lately? Glassdoor is in the first few results. Here’s a LINK to it.)
You can see an overview video of how we help out with that here: {{VIDEO_LINK}}.
Would you be interested in hearing more?
How great an outreach email is that compared to your typical mail-merged garbage? Furthermore, you can see why creating your own custom prospect list is so much better than buying ready-made marketing lists. The better your own prospect metadata, the better your appeals to prospects can be, which leads to higher demo rates.
Diligently capturing those pieces of metadata in the prospecting process not only ensures you’re targeting relevant accounts, with the right points of contact, it also puts you in a strong position to leverage automation when you start your outreach process.
While you know to target accounts that have the business pain your solution is built to solve, there are varying levels of this business pain. Moreover, you also need to consider an organization’s ability to react to a potential new solution to that pain. The traditional way that this is described is in terms of hunting various size animals. Whether it’s “minnows, dolphins, and whales,” or “rabbits, deer, and elephants,” the point is that you will encounter accounts of varying sizes and magnitude of business pain. And while it might seem attractive to go elephant hunting, given that those deals could be potentially the largest, you’ll want to think twice there. Large organizations have existing legacy systems and workflow and are less reactive; even if you do end up closing them, you may have trouble onboarding them and supporting them effectively. And if a single elephant ends up being a disproportionate amount of your revenue, you may end up beholden to them to build features that they demand. You might end up a professional services company for this particular elephant. The elephant might fall and crush you just as you’re doing your victory dance.
Similarly, rabbits might seem attractive, in that you can get buy-in from a senior decision-maker quickly, and there won’t be a lot of legacy process they need to modify to adopt your solution. Unfortunately, the size of their deals may not be much to write home about. And the lack of business process may mean that they aren’t all that good at doing the thing your solution enables, which means that they’re more likely to churn out.
Targeting deer is usually a good initial approach. They’re large enough to have a goodly amount of the business pain—sufficient to entertain a new solution—and likely have business processes that can ingest new technologies. But they’re small enough that they can make purchasing decisions quickly, and their existing business systems are probably not so entrenched that adopting a new solution would require substantial change management.
exampleThat said, you certainly want accounts that trend larger—bigger deer, let’s say! With TalentBin, this might be a ~100-person organization with three recruiters and 20 open engineering requisitions. That would be far more attractive than a similarly sized organization with only three open engineering hiring requisitions. Or for Immediately, this might be a 50-person company with ten field sales reps, selling software with an average contract value of ~$100K, as compared to maybe a 100-person company that has 30 inside sales reps, selling software with an average contract value of ~$10K. In these cases, all the accounts might be considered deer, but we want to target the most attractive ones to enhance our chances of winning.
To start, I find it most effective to look for accounts in your own geography. Even if the potential deal sizes for your solution are such that they will require an inside sales approach, being in the same time zone, and even being able to go on-site to visit potential customers, will be very helpful. Unless your solution is extremely specialized, or you are based somewhere with few potential accounts, you should certainly be able to find 50–100 juicy deer that meet your ideal customer profile. And if you can’t, that might indicate that you should think about relocating to somewhere with more economic activity to help your chances of success.
When it comes to finding potential accounts, as noted above, you can start with people or you can start with the company, and you need to figure out what the right approach is for your solution. Then, once you’ve started with one data source, you’ll likely move to the other type—from company-centric to people-centric research, or vice versa—to flesh out more information about the account.
There will typically be a most efficient way of doing this, and it’s usually the result of what your qualifying characteristics are and how easy they are to find from existing data sources.
exampleWith TalentBin, we started by using LinkedIn to find technical recruiters—because if an account didn’t have any recruiters, it was a nonstarter. Once we found technical recruiters, this led us to the organizations that employed them, whose current technical hiring demand we would then seek to understand. This meant we would flip to company-specific data sources to flesh out more account information. But if you’re Immediately, the sales-focused mobile email client and CRM tool, you know that without Salesforce and Gmail your product is a nonstarter. So finding sales operations and sales leadership prospects whose organizations run on Microsoft Dynamics (a competing CRM to Salesforce) and Exchange isn’t all that helpful. As such, you would probably start by using Datanyze or BuiltWith to find companies that match the required software characteristics, before pivoting to LinkedIn to sniff out how many sales reps they employ, whether those are inside reps or outside reps, and which relevant sales leadership or sales operations staff the Immediately sales team might seek to engage.
If you’ve decided that the best way to target accounts is based on the presence of people with a certain title (for example, Data Scientist), as discussed above, LinkedIn is probably the right place to start.
Do a title search for the relevant title, constrain it to the relevant geography, and then use LinkedIn’s search faceting to constrain to the appropriate size of company you’d like to target.
Source: LinkedIn
I’m using LinkedIn’s Recruiter Lite in the screenshot above, but a number of their products let you achieve this type of query. In this example, there are ~716 results for a query of the title Data Scientist in the San Francisco Bay Area where the contact’s company size falls into either the 11–50 or 51–200 buckets. Of course, that doesn’t mean there are ~700 accounts for us to target but rather 700-odd potential users for our solution.
In terms of low-hanging fruit, the Current Company facet will show you which companies have the most employees with the title in question. Of course, you’ll only get a handful of companies, but those are probably great accounts to target. They’re smallish and have a ton of potential users of your solution!
Source: LinkedIn
Beyond those accounts that show up in the Current Company facet, the next step would be to walk through the rest of the profiles, capturing the relevant account names (for example, Navera, Twitter, Dropbox, or others). Again, remember that we may not actually be selling to the data scientists in question—they’re simply potential users. Later we’ll be figuring out exactly who at the organization we’d like to target with our outreach. For now, the goal is simply to capture the account that we want to target, along with demand signifiers that we touched on above—number of data scientists, size of sales staff, size of organization, and so on.
As noted above, you can also use company-specific metadata to help find accounts that could be a good fit for your solution.
exampleSay we were HIRABL, a company that sells revenue acceleration products for staffing agencies. In their case, prospecting by company type (industry) could be helpful. LinkedIn is great for this purpose too. We could go to LinkedIn’s Company search function, select for Industry, Staffing and Recruiting, choose a Company Size in the deer range we discussed above, and constrain to the San Francisco Bay Area, because we want proximity to our initial accounts in the event we can go on-site.
Source: LinkedIn
So we have a good 200+ of these targets, and we can now pull a selection of them into our prospect spreadsheet, and then start appending the relevant demand signifiers on top of that. In HIRABL’s case, key signifiers include the number of recruiters and the type of candidates the agency places (higher-value professionals being better, as HIRABL helps recover missed fees from high-value placements).
If you were doing this for a set of targets for your solution, what would be the right way of going about it? Would you look for accounts by starting with people or company metadata? What data source would be the most relevant to you?
Now that you know how to find promising accounts, either by company-centric or people-centric demand signifiers, your next question is “Who should I be engaging at this company, and how can I reach them?” You want to find the right point of contact—ideally, the relevant decision-maker for the account. Note that this is different from people-centric sourcing of accounts. In that case, you were looking for potential users of your solution, like data scientists or field sales reps, at the account in question. That doesn’t necessarily mean that those data scientists or sales reps are the correct points of contact to sell to. This goes back to your sales narrative: you want to target and engage the person who is responsible for solving the pain your solution resolves and who has the decision-making authority, and budgetary control, to resolve that pain. You may also choose to involve people who would be users of the solution, but that is more for the purposes of marketing to them to build a groundswell of support and help convince the decision-maker in question.
How can you identify these decision-makers? Conveniently, it’s often their title that gives it away. By extension, you can use LinkedIn (or Data.com, or others) to search for those titles, constrained to a given account. You should be paying attention to what these titles look like as you are prospecting, and you’ll eventually converge on the right set. If you’re selling a recruiting solution, perhaps it’s the VP of Talent, Director of Recruiting, or Recruiting Manager. Or if you’re selling an e-commerce solution, it might be the Chief Marketing Officer, Digital Marketing Manager, and so on. If you sell sales tooling, it could be the VP of Sales, Chief Revenue Officer, VP of Sales Operations, Director of Sales Effectiveness, or Sales Operations Manager. The right title can vary based on stage—an early-stage company is less likely to have a Sales Operations Manager, so the responsibilities of sales operations might fall to the VP of Sales.
As such, I typically like to take the approach of cascading points of contact. If an account has a VP of Sales, a Director of Sales Operations, and a Sales Operations Manager, I prefer to grab all of them as potential points of contact. This can be even more scaled at a later juncture—perhaps you’ll grab not just all the decision-makers, but potentially all the users too, for later engagement via drip email marketing. LinkedIn is very helpful for finding these individuals. Just do a boolean title search, like (“account” OR “sales” OR “sales operations”) AND (“Director” OR “Vice” OR “VP”),” which will return people that have any of the words in the first set plus any of the words in the second. That will give you a good list to start with. Then look more deeply at each profile to figure out which person, or group of people, you really want to target.