From “Founding Sales: Sales for founders (and others) in first-time sales roles” by Pete Kazanjy founder of Atrium Sales Analytics. Follow Pete on Twitter and LinkedIn.
Consider checking out How to Use This Book and Who This Book Is For sections to start.
Chapter Overview
Ideal Customer Profile: What Does Your Prospect Look Like? 5 minute read
So Who Does Have My Pain Point? 12 minute read
Account Sourcing: Putting It into Practice 7 minute read
Point(s)-of-Contact Discovery: Who Will Be Excited about Your Solution? 8 minute read
Introduction
Now that you have your materials pulled together and are ready to engage with some prospects, you need some prospects to engage with! And that means prospecting, going out and looking for some relevant potential customers. Later on we’ll be talking about how to do this at scale, and we’ll also be covering how to get prospects to come to you—what’s known as “inbound marketing,” which has been heavily popularized by folks like HubSpot.
Those more advanced forms of customer acquisition can be challenging for early-stage startups. Your customers may not realize that there is a solution to their business pain—or that they even have the pain in question. And if that’s the case, it can be tough to drive them inbound to you, at least to start.
Which is why, for now, your goal is to proactively find 50–100 potential clients that have the distinct pain point that your solution resolves, and to get the product in front of them for a commercial conversation about their business pains and how your solution can potentially help. Rest assured, we will definitely be iterating this approach, and you will learn things from your initial set of “supposed prospects” that help you tune your definition of whom you should actually be selling to. But this is your first shot.
Ideal Customer Profile: What Does Your Prospect Look Like?
One of the biggest issues founders and first-time salespeople have is trying to sell to people who don’t have the problem their solution solves. Instead they prioritize other characteristics, “availability” being the biggest temptress, when identifying prospects. You see this when founders sell to their incubator-mates or people they know from prior companies, or even friends and family.
The whole purpose of B2B product development is to identify a business pain in the market and build a product or service that resolves that business pain. And the purpose of B2B sales is to identify companies and individuals that have that business pain, propose the product as a means by which to resolve it, and eventually come to a mutually beneficial agreement to exchange money for that product—which then resolves the business pain, as promised. That’s how it’s supposed to work.
The opposite of this approach is trying to sell your solution to anything with a heartbeat, regardless of whether or not they or their company has the pain point the solution resolves. It’s approaches like this that give sales a slimy name. But what’s worse (aside from peeing in the pool for all the other people who are doing B2B sales, and just generally irritating people), it’s actually terrible for your business. All you have in B2B sales is your time. And when you spend your time on prospects who don’t need your product, they won’t close. So you’re spending your scarce time (and salary expense, and runway) on prospects who are unlikely to buy. Think about that. Your goal in sales is to scalably acquire customers and revenue. So spending time on people who won’t close is the equivalent of setting revenue on fire.
Running the numbers quickly, you see how bad this can be. Imagine you’re aimless in your outreach targeting and qualification because your ideal customer profile is undefined or sloppy. Maybe you do twenty demos for people who don’t have your pain point. Maybe each of those demos requires, on average, thirty minutes of prep, an hour of execution, and another thirty minutes of follow-up immediately after the fact. To say nothing of further down-funnel follow-up, chasing the opportunity. That’s a full week of your time flushed down the drain, where it will never turn into revenue. You could have spent that time selling to people who might actually buy. Or working on your sales materials. Or doing a better job servicing your existing customers and making sure they are successful.
Even worse, if you do somehow magically convince a bad prospect to buy (because of your extreme charisma and perfect hair), they won’t get value out of your offering because they don’t have the pain (or a sufficient amount of the pain) your solution alleviates. That supposed “good trade” we talked about above will actually be a bum deal. And the customer will not be happy. They won’t buy more and more of your product. They won’t renew your product (they’ll “churn out” in SaaS talk). They’ll tell their friends. But even worse than that, they may try to be “helpful,” and in the process actually hurt you. They’ll offer you feedback on how to make the offering better for them—but they’re not who the product is designed for. They’ll try to give you a second chance, and in the process they’ll consume all of your customer success and support time and resources (which, at this early stage, is probably you). And when they do, they’ll be stealing those resources from customers who actually will get value from and might buy more of your product.
It’s a disaster.
The impulse to prioritize prospects you know, even if they may not have a need for your solution, is understandable. If you’ve never done sales before, you’re not used to the potential rejection associated with approaching people you don’t know and engaging them in a commercial conversation. We touched on this in Sales Mindset Changes. The directness that sales demands is foreign and feels frightening, maybe even presumptuous and unwelcome. And it’s so easy to get in touch with people you already know. How could this approach not be better than engaging people who don’t know you from Adam? This apprehension goes back to the age-old vision of sales guy as slickster—trying to shove product onto someone who doesn’t necessarily need it—and probably a fair amount of bad sales experiences you’ve had yourself. But you need to change your mindset and start seeing yourself as “bringer of solutions to those who have a problem.” Prospecting is about finding those who have your problem.
Think about it this way: If my window is broken and a repairman knocks on my door, do I care that I don't know him? Or that he knocked unsolicited? No! He can fix my window! Even better if I didn't realize it was broken. I'm just venting air-conditioning and heat into the world. And leaving my house open to wildlife and potential intruders. This guy is my savior!
Conversely, I can be great friends with a window repairman. Went to college together. Same fraternity. But if I don't have a broken window, regardless of how great our friendship, is it a useful commercial conversation for me or him? I might be his kid's godfather, and might buy anyway, but am I going to get value? Tell others about how great my repair job was? No way.
Targeting based on relationship rather than need is ineffective and a waste of time.
So let’s avoid it! The absolute best way to stop selling to people who don’t have your pain point is to be able to identify, based on some number of key characteristics, accounts and contacts who do have your pain point. Prospecting is about finding those people in a repeatable fashion. By knowing your ideal customer profile, you’ll be able to identify more prospects in the wild. And then scalably engage them and present your product as the solution to their problem. Additionally, you’ll be able to easily “qualify” prospects that come to you—whether through your website’s lead capture form or referral by a friend, or even at a cocktail party. If they fit that ideal customer profile, great! Run with it. And if they don’t, also great! Don’t spend any time on it. Instead, spend that time on something more useful—like finding and supporting customers that do match that profile. Or going to the gym. Or taking a nap. Anything other than selling to someone who is not qualified!
You’ll know you’ve nailed your ideal customer profile when you’re discussing what your company does with a friend, and she says something along the lines of “Oh, that sounds interesting, I could use it”—but instead of immediately selling her on it, you pause and take a step back. You ask her a couple quick questions that determine if, yes, she actually could use it, or if you should reply with “We’re not really set up to do that yet, but maybe in the future! If you have friends that need X, Y, and Z, though, we’d definitely be relevant to them.” You just saved yourself from potentially destroying hours of your time, you did your friend a solid by being respectful of her time, you underscored your trustworthiness by being candid, and you set up a word-of-mouth helper. Wins all around!
So Who Does Have My Pain Point?
Remember your sales narrative? Which in turn was based on hypotheses about how your offering solves the pain points of potential customers? Parts of your ideal customer profile were defined in there. But now, instead of expressing this in a narrative format, you want to boil it down to abstracted characteristics. You want to get to the point that you can rattle off a set of metadata characteristics that describe the relative level of attractiveness of a prospect.
For instance, in TalentBin’s case, this would be something like “This account has five technical recruiters and twenty open technical hires, including iOS, Java, and Android roles, and uses LinkedIn Recruiter.” What are the characteristics in there? First, we have the existence of technical recruiters—without at least one recruiter or sourcer to actively use the tool, customers are unlikely to have success. Passive-candidate recruiting is too time-consuming and challenging to be done “on the side” by an HR generalist or a hiring manager. In this case, the fact that there are five recruiters indicates that this is a juicier-looking account, where there is an opportunity to sell up to five seats of TalentBin. And when it comes to business pain, there’s a lot: twenty engineering hires is a tall order. Even better, iOS, Java, and Android roles are the kind that TalentBin does particularly well at compared to LinkedIn (versus, say, .NET or C#). Lastly, if we can figure out whether the account has multiple seats of LinkedIn Recruiter—a market alternative to TalentBin—we’ll know how much they’re willing to spend on passive-recruiting tooling to make those five recruiters more effective. If they all have seats, for example, we know that there’s around $50k of budget already allocated for LinkedIn Recruiter, which we can take a bite of.
In the case of Immediately, makers of a really cool sales-focused mobile email client and CRM tool for Gmail and Salesforce, it might look something like this: “This account uses Gmail, Salesforce, and Marketo. They have fifty sales reps scattered across the United States, selling software that costs on average $50k. And it looks like the Vice President of Sales has a Sales Operations Manager reporting to her.” What are the characteristics Immediately is looking for? Well, as of 2015, the software needs Gmail and Salesforce to work. So if an organization doesn’t use Salesforce and Gmail, the conversation is dead in the water. Those are required characteristics. This account also uses Marketo, which indicates a level of sophistication within the sales and marketing organization, and a willingness to pay for expensive tooling that accelerates revenue acquisition. There are fifty reps, each of whom represents a potential user, so this seems like a potentially valuable opportunity. Moreover, because the reps are spread all over the United States—rather than co-located in a single area, indicating a call center—it would seem that these are outside sales reps. And outside sales reps are less frequently in front of a laptop, which makes a compelling sales-first mobile email client and CRM all the more important for them. Beyond that, the software this prospect sells is expensive; incremental wins are very valuable to them. A solution like Immediately, which helps reps handle more deals and avoid dropped balls while mobile, is all the more important when each of those potential dropped balls represents $50k of revenue. Lastly, because someone is managing sales operations, we know there is someone who is specifically charged with making those fifty sales reps more effective, and for maintaining a clean and effective CRM. He will likely be very interested in something that not only makes those reps more effective, but also helps him with the pain point of getting fifty distributed professionals to enter information into the CRM to help with data cleanliness, forecasting, and so forth.
Take a quick second to think about what your qualifying characteristics could look like.
The shared pattern here is a set of characteristics that indicate potential demand for your solution on the part of the account in question. Some of these characteristics will be outwardly identifiable, while others will be more difficult to identify before engaging with an account. The latter are things that will have to be surfaced via what’s known as “discovery”—essentially asking questions about the prospect’s business to better understand their pain points (or lack thereof). More on that at a later juncture. But just know that even if you can’t sniff out all of your ideal customer characteristics for a given prospect ahead of time, that doesn’t mean you’re out of luck. It’s a rare situation where all of those characteristics are outwardly visible—but we’ll do our best to find them!
Importantly, while we’ll be talking about a number of tools that support this prospecting effort below, over time, tools may change, but the core concepts of identifying prospects based on their outward facing characteristics remains the same and is the key to success here.
Finding Outwardly Available Customer Data
Finding Outwardly Available Data
When it comes to looking for potential accounts, you can start with people or you can start with companies, and you should figure out what the right approach is for your solution. You’ll look in different places for this information depending on whether you’re talking about contacts (people) or accounts (companies), and depending on the kind of characteristics you’re assessing.
For account sourcing based on people who work for a given organization, it’s really tough to beat LinkedIn—specifically their premium talent solutions. Much of the time, prospect identification will hinge on job title, and LinkedIn is pretty much the best place to find that information. Before LinkedIn, resume databases and contact databases like Salesforce’s Data.com and Dun & Bradstreet were the go-tos, but they all suffer from being out-of-date and incomplete. Right now, LinkedIn is your best bet. There are industries whose professionals are less likely to be on LinkedIn, and that can present more of a challenge; in those cases, you may have to revert to more traditional sources. But for the purposes of finding 50–100 prospects, I would be very surprised if LinkedIn didn’t handle this for you (though products like Apollo and Reevo that have an integrated sales engagement offering are also helpful).
Do you sell an analytics tool that only someone with a title that includes “analytics” would use? LinkedIn is helpful for identifying those people, and thus the companies they work for.
Remember, though, that when you think about your ideal customer profile, you should be thinking about the organization you’re selling to. While you may approach account sourcing by looking for the job titles you need, you shouldn’t be focused on targeting a particular person or people just yet. Unless you’re in a situation where there is literally no difference between the individual and the organization that is purchasing the solution (an organization of one, or an individual within a larger organization buying the solution on his own), your ideal customer is still an account. That account will certainly include key people (e.g., recruiters, sales reps, sales ops staff, data scientists, whatever), and you’ll eventually be seeking out the right point of contact, or multiple points of contact and decision-makers, to engage with. But your customer profile is the description of an organization.
As for account sourcing based on the characteristics of the company itself, because companies have a tendency to stick around longer than individuals stay in a specific role, information latency is less of an issue. LinkedIn is still a great resource here, in that you can find accounts based on the number of people in a specific role at those organizations. But there are a host of other providers as well. More traditional ones include Dun & Bradstreet, Hoover’s, and Salesforce’s Data.com, which are well suited to account sourcing based on size, geography, and industry, with DiscoverOrg and ZoomInfo being more modern variants of these. For small-business account sourcing, Yelp can be a great place to go looking for local businesses, bucketed by industry, along with contact information (but generally lacking the specific point of contact you would seek to engage with). Other helpful data sources for small and local businesses include Radius and InfoUSA’s Salesgenie. A pure Google Maps search can work here as well. You can often use products you compete with for account sourcing too. For instance, if you’re selling into restaurants, OpenTable’s index is a great way to find restaurants that care about revenue management, and GrubHub and Seamless are great places to find restaurants that care about delivery. If you’re selling into doctors’ offices, Healthgrades, Doximity, and even state license databases are good places to look.
Moreover, some of these data sources can tell you if the prospect is paying for technology and services. The profile of a small business that is paying for Yelp business services looks visually different than one for a business that’s not, showing you that they’re paying for the service. And willingness to pay for marketing services can be a helpful signifier in prioritizing a given business as a prospect.
For example, a search on Yelp for dentists yields a bunch of dentists in Orange County to target (if we sort them by number of ratings - we probably get the busiest ones at the top!):
And with Google Maps, we find auto repair shops in San Francisco to target:
More modern account-sourcing services reveal the technologies that run on an organization’s website, which can be indicative of their business pains and willingness to pay for solutions. An organization that has a Salesforce Web-to-Lead form on its home page clearly pays for CRM and could be a fit for a solution that extends Salesforce, or replaces it. An organization that runs Optimizely on its website might be a target for a solution that makes better A/B testing software. BuiltWith, Datanyze, Datafox, SimilarWeb, HGData, and Wappalyzer are examples of this type of account-sourcing service. There are also those that rely on self-reported information, like DiscoverOrg, Siftery, and RainKing, which can be helpful if the technology that is installed isn’t visible to web crawling. And then there are services like Spiceworks that provides free network-monitoring software, and allows marketers access to see which accounts use what type of solutions for marketing purposes.
For example, using Apollo to find accounts that use Salesforce, in the sweet spot of 100–200 staff, located in the San Francisco Bay Area we get ~300 accounts to target:
You can also use hiring information for account sourcing. This is most directly applicable when your solution is also hiring-related. For instance, the number of open hires listed on an organization’s website would be a good leading demand indicator for a SaaS solution that reduces time to onboard new employees, or a leading indicator of willingness to pay for recruitment-branding services like LifeGuides or Glassdoor. The type of open roles can also be revealing. If an organization is hiring for engineering staff, that indicates demand for a recruiting agency or candidate database that focuses on engineering. You can even tell if they’re a current customer of companies that provide these solutions. For instance, if you’re looking at Indeed, Glassdoor, Monster, or LinkedIn for hiring information, you can often see if the prospect has a paid account or just the free version based on the appearance of the company’s profile.
Because Lattice has a “Company Updates” feature, and a list of “Jobs You May Like,” you know that they pay for Glassdoor:
And thanks to their Careers page, we know that Rippling is hiring lots of engineers:
The kinds of people an organization has hired in the past can also be helpful. Even if an organization is not currently hiring for data scientists, if they previously had a posting up for that role, it’s likely an organization that employs data scientists. So if you sell a solution that makes data scientists more successful and efficient, you know, at a minimum, that the account likely has data scientists in-house, and might be interested in your solution. Account intelligence providers like Apollo and ZoomInfo typically include this information, or even just job boards like Indeed, Monster, Glassdoor, and LinkedIn can show you who’s hiring for that role now (and has probably hired for them in the past).
If you sell to organizations that employ data scientists, these accounts would be good ones to address.
While these services will often be able to show you the single piece of information that you’re looking for—like whether an organization uses Salesforce or not—other times you can use them for finding information that is correlated with the actual demand signifier you’re looking for. For example, organizations often replace the default Salesforce lead capture form with specialized marketing automation lead capture forms from Marketo or Eloqua. And while the existence of a Marketo form on an organization’s website isn’t 100 percent correlated with Salesforce use, it’s usually a pretty good leading indicator. And it shows that the company is willing to pay for a more evolved solution.
Remember, you aren’t wedded to one data source when you’re fleshing out ideal customer accounts and contacts. For instance, if you were Immediately, makers of that really cool sales-focused mobile email client and CRM tool, you might use BuiltWith or Datanyze to find organizations that use Salesforce and Gmail. Then, if you wanted to know how many salespeople there are in each of those organizations, you might run a title search on LinkedIn for “Account” or “Sales” (catching people with titles like Account Executive, Sales Consultant, Sales Director, Account Manager, etc.) to get that demand magnitude information. And that’s before you would turn to finding the individual contacts you would like to engage, which are likely to be on LinkedIn.
We can see there are around forty recruiters at Rippling—so say we sourced Rippling as an account based on their Glassdoor company page, and wanted to see how many potential users there were, we now know they have a goodly amount of recruiters:
However, while you might use multiple data sources to flesh out the demand characteristics of accounts that you’ve already found, using multiple sources to drive sourcing is typically a bad idea. If you can reliably find promising new accounts with a specific source (e.g., Datanyze or LinkedIn or Yelp), and there is a good quantity of them, you’re kidding yourself if you are trying to get much more out of other sources, at least to start. Usually this is a sign of prospecting ADHD more than anything else. Feel free to iterate and see if there is a more effective tool for account sourcing, but don’t pretend that prospecting across things like Twitter and Meetup and Facebook is actually anything more than poor discipline.
Finally, a cautionary note. There are also lots of marketing list providers, but I typically take a pretty dim view of those. They’re generally extremely out-of-date compared to information on LinkedIn, and they’re generally poorly modeled. That is, they typically include very sparse metadata outside of name, title, contact information, and lightweight company geography, size, and industry information. So your targeting will be poor. Moreover, you don’t want to waste your time calling numbers that go to nowhere and emailing email addresses that no longer exist. And for now, since you’re just looking to get your first hundred targets, you can do it manually. Not to mention that manual prospecting is a very good exercise to go through to get a more concrete sense of what these accounts and individuals look like. As you go, you may discover that a characteristic you thought was important actually isn’t, or discover another characteristic that is even more important for sourcing or qualifying potential prospects. So stay away from lists.
Getting Data That Isn't Outwardly Discoverable
Just because a given characteristic isn’t outwardly discoverable doesn’t mean that you shouldn’t include it in your ideal customer profile. It could still be extremely important in determining whether or not your solution is relevant for a prospect. For instance, with TalentBin, whether an organization used passive-candidate recruiting databases, like LinkedIn Recruiter, was a fantastic indicator of whether TalentBin could help them with their recruiting efforts. The problem was, that information was not publicly available. There were correlating pieces of information: if an organization paid for a premium LinkedIn Company Page, or had job postings on LinkedIn’s job search engine (by paying for a “job slot”), and also had recruiters in-house, they usually had one or more LinkedIn Recruiter seats. Still, we couldn’t definitively identify this characteristic from publicly available information—but it was an important one to include in our ideal customer profile.
So, if a characteristic isn’t identifiable, how will you ever be able to get the information you need? Well, you’ll have to ask. That means when prospects come inbound through your lead capture forms, or you’re on the phone with them, you’ll have to specifically ask them if they have these characteristics. And if you can’t proactively identify prospects that have these hidden characteristics, how can you scalably attract them? That’s where things like content and inbound marketing come into play. More on that later. For now, for identifying a hundred potential targets, direct prospecting, coupled with discovery questions when key characteristics aren’t readily observable, will be your approach.
Rolling Up The Demand Signifiers
When you think about your ideal customer profile, you should be thinking about not just the minimum requirements for product/prospect fit, but also the magnitude of demand that prospect may have. This helps for a couple of reasons. First, knowing the size of an account’s demand can help with understanding how much of your solution you can potentially sell them, so you can focus on accounts that might buy lots of your solution. It also can help you understand the magnitude of their business pain and, as such, how motivated they will be to pay for your solution. It can also help you prioritize various opportunities, so you can spend your time on those with higher pain. (We’ll talk about small versus medium versus large accounts in a bit—but more pain usually means more money.) You’ll eventually be able to converge on a “customer attractiveness” scoring algorithm that will help you (and later, your sales staff) judge the relative attractiveness of a prospect.
For instance, with TalentBin, the score would be based on any combination of the following: number of recruiters, volume of engineering hiring, passive-candidate recruiting acumen, and ability to pay. So an account that has a single recruiter in-house, but ten open iOS and Android engineering requisitions, and that just recently raised $5 million might be as attractive as an account with four recruiters, all sharing a LinkedIn Recruiter seat, and only a couple of engineering reqs. And both would pale in comparison to an account with three recruiters—and three LinkedIn Recruiter accounts—fifteen open engineering reqs, and a history of doing manual sourcing on GitHub and Twitter. Note that all of these accounts have the minimum viable criteria that we set at TalentBin: at least one recruiter and at least three open engineering roles. Think about what this looks like for your ideal customer. What factors will you consider—perhaps it’s number of field sales reps or how bad the company’s Glassdoor reviews are or its volume of e-commerce sales—and how will you weigh them in combination?
Account Sourcing: Putting It into Practice
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.
Prospect Data Management
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. This is a rough example of a spreadsheet template you could use, with both “role-specific” prospecting and “hiring-specific” prospecting: https://docs.google.com/spreadsheets/d/1fmi04yO_5AvqgopTCCukWq5mbInLdZR25FXF_yCYYpI/edit?usp=sharing
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. For instance, for 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:
Subject: 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 crap? 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.
Rabbits, Deer, or Elephants?
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.
That 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 twenty 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 fifty-person company with ten field sales reps, selling software with an average contract value of $100k, as compared to maybe a hundred-person company that has thirty inside sales reps, selling software with an average contract value of $10k. In these examples, all the accounts might be considered “deer,” but we want to target the most attractive ones to enhance our chances of winning.
Geography
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.
Account First? Contact First?
When it comes to finding potential accounts, as noted above, you can start with people or you can start with the company, and you should 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. With 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 lead 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.
People-centric Sourcing
If you’ve decided that the best way to target accounts is based on the presence of people with a certain title (e.g., “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.
I’m using LinkedIn’s Sales Navigator in the screenshot below (their flagship sales product), but a number of their products let you achieve this type of query:
In this example, there are ~977 results for a query of the current 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 ~1000 accounts for us to target, but rather 1000-odd potential users for our solution.
The next step would be to walk through the rest of the profiles, capturing the relevant account names (e.g., Vibrant Planet, Homogeneous, Fermata Energy, etc.). 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—for instance, number of Data Scientists, size of sales staff, size of organization, and so on.
Company-centric Sourcing
As noted above, you can also use company-specific metadata to help find accounts that could be a good fit for your solution.
Say 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.
This is what that search looks like:
So we have a good 400+ 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 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?
Point(s)-of-Contact Discovery: Who Will Be Excited about Your Solution?
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?” That is, you want to find the right point of contact—ideally, the relevant decision-maker for the account. Note that this is different than 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.
If I thought that Rippling were a good account to target for a sales enablement or acceleration solution, I would go looking in these contacts, which were uncovered by the search referenced above:
There’s a concept of “complementary” decision-makers as well, who are typically internal customers of the primary decision-maker you’re seeking to target. That is, while the VP of Talent is the person responsible for solving the business pain of “hiring more engineers,” it is the VP of Engineering, or CTO, that has the downstream business pain of “ship more software.” Or even though it might be the Director of Sales Operations who is responsible for making sales reps more effective, ultimately it falls to the VP of Sales to generate more revenue. That CTO or VP of Sales definitely has a stake in solving the business pain you’re looking to address. Sometimes you can target these complementary decision-makers, with the intent of being referred to the appropriate primary decision-maker. You might even engage with these complementary decision-makers to make the case for your solution and, having convinced them, join forces with them to convince their colleagues, together.
Of course, you can take this logic to its end point and say, “Well, the CEO or founder of the company is the one who is ultimately responsible for all of these problems, so maybe I’ll just go to her.” There is a bit of truth to this. In fact, another fantastic sales-learning resource is the book Predictable Revenue by Aaron Ross and Marylou Tyler, which advocates what they term “Cold-Calling 2.0.” The upshot of this idea is that CEOs or other very senior staff are highly identifiable, attuned to receiving ROI-based arguments about budgetary spend, and used to delegating investigation to subordinates. So if you just target the CEO or founder of a firm with an email that delineates your product’s potential value to the organization in a crisp, dollars-and-cents fashion, and ask to be directed to the relevant delegate, the thinking goes that the CEO can refer you—and now you have tacit executive sponsorship, plus the name of the correct decision-maker. If it works, that sounds great. But this approach can be good and bad. It certainly made sense in the pre-LinkedIn world, when the proactive discovery of relevant decision-makers was much tougher. But the risk of this approach is that these individuals get amazing volumes of email and often have a delegate, like an executive assistant, assisting with email triage and ensuring that your outreach gets deleted promptly. So there are downsides.
I am a bigger fan of first determining who is most likely to be the decision-maker, and the individual most excited about solving this business pain, and appealing to her directly. If this doesn’t work, then you can potentially cascade back to her internal customer (her complementary decision-maker), like the CEO. This is a more advanced form of prospecting and outbound lead generation, so for our initial purposes here, I would constrain to targeting the relevant decision-maker.
There’s the opposite approach as well, in the form of “bottom up” prospecting. In this approach, you target the individual users of your solution—the sales reps, recruiters, data scientists, engineers, etc. The goal of this approach is to convince them of the validity of your solution and how it will improve their lives by making their jobs easier, making them more money, making them better at their jobs, etc. From there, you can enlist them in making the case to their management, who ultimately control the budget that would be used to purchase your solution. Again, this is another more advanced form of prospecting, and is something to consider when you’re looking to scale your prospecting and lead-gen efforts, which we’ll discuss more later. Some great examples of this approach are actually products that lend themselves to individual or team usage, where the “free” version of the product is really just a form of lead generation—the registration for which is used as a signifier to sell into an account. Box, Yammer, Slack, Yesware, and others are good examples of this; all, eventually, end in an enterprise sale to a relevant decision-maker. For now, though, we’re going to skip this.
One piece of information that can be very helpful when engaging these prospect decision-makers is to see if you potentially have an “in” to them. That is, if you’re using a professional network like LinkedIn, or any of its premium tools, you can see if you have a shared LinkedIn connection with the decision-maker in question. This is not the same as selecting the accounts you’re going to target based on who you know. In this case, we know the account has the business pain we’re trying to solve, and have identified the person(s) who should care most about this, and only then are we seeing if there’s a potential “warm intro” into that person. This can be in the form of someone who is directly connected to both of you (look on LinkedIn and Facebook—you’d be amazed who you want to college with that is friends with them), or the broader version, where you can see if anyone you know is connected to the organization. That is, say you’re trying to engage the head of Sales Operations, and you don’t have an “in” there. But you happen to be LinkedIn connected to the VP of Marketing, or an engineering manager. They likely know the other person, and even if they don’t, they can still forward along your outreach materials (more on this in the next chapter) with their commentary on how you’re a good guy, and worth paying attention to. Mark these “ins” up in your CRM or a column in your prospecting list, e.g., “Potential Intros”.
Once you’ve decided which individuals, or set of individuals, you want to target, it’s time to find contact information so you can actually engage them. This will typically entail email addresses, and potentially phone numbers. Later, when you have market development staff, switchboards will be helpful for cold-calling, and you may do some of that yourself here. However, email addresses are typically the most beneficial, in that they allow for better automation via templating and lightweight drip marketing (more on this later), instrumentation via open and click tracking, and prospect progress tracking. So we’ll focus on email outreach to start.
Later we’ll discuss how you can use offshore resources via work marketplaces like Upwork to assist with this, but in the short term, finding emails yourself is good practice to get really intimate with how available these email addresses actually are in your vertical. If it turns out that it is nearly impossible to surface the email addresses of decision-makers in your market, you’ll have to reconsider parts of your go-to-market. Much better to know that information earlier than later! One of the best ways to surface email addresses is to just search around. Depending on the decision-maker in question, you may find a fairly substantial digital footprint. So simply Googling for their name and “email address” can sometimes provide a hit. Looking for personal websites or LinkedIn profiles can be helpful. If you’re selling to sales people or recruiters, they tend to include their email addresses and potentially desk and mobile phones on their LinkedIn profiles, so prospects and candidates can contact them easily. Let’s make use of that! This can extend to personal email addresses as well. Some people are concerned about outreach to personal email addresses. I find that concern overblown. If you are doing a good job of pre-qualifying, and have excellent outreach materials that document why you’re engaging prospects and how your solution will make their lives better, that information is still relevant delivered to a personal Gmail address. And they actually might have less email traffic in that inbox, anyway!
For even more leverage a host of modern prospecting tools that can assist you in finding email addresses tied to a given LinkedIn profile. Apollo, Lusha. and LeadIQ are popular ones that make finding an email address associated with a LinkedIn profile as easy as clicking a button.
Apollo’s Chrome Extension providing the email address for this LinkedIn profile.
For even more leverage a host of modern prospecting tools that can assist you in finding email addresses tied to a given LinkedIn profile. Apollo, Lusha. and LeadIQ are popular ones that make finding an email address associated with a LinkedIn profile as easy as clicking a button.
While email addresses are the piece of contact information you’re going to want to start with, phone numbers also have a place—either personal or desk line, potentially discoverable through LinkedIn or sources like Data.com, or just the main direct switchboard line for the company. The latter is typically very easy to find just by Googling for “company_name contact,” which will usually resolve to the company’s contact information page. Later, as you bring on market development resources and do more substantial cold-calling, switchboard lines will be more important. Capturing them at this stage is also useful, so make sure to do that as you’re building your initial list.
Phone numbers are particularly important for local businesses, which are typically an exception to our email-before-phone rule; the acquisition of email addresses for those decision-makers may be more challenging. For local business go-to-markets like Yelp, Groupon, GrubHub, Redbeacon, and more, acquiring phone numbers in order to reach out via direct dial is key.
And while grabbing this data once is the way you start out, over time, it will get more and more out of date, as decision makers move from company to company. While this is something not to be concerned about to start, later on something that refreshes those records automatically, like a LeadGenius, or otherwise, can be helpful to make sure that those contacts are always fresh.
Now Let’s Get Going
At this point, you should have a targeted list of 50–100 prospect accounts—all of which represent the demand characteristics of your ideal customer profile, are in your local geography, and are in the sweet spot of company size—along with one or more potential contacts to engage at the prospect company, and their basic profile data (name, title, and so on).
Now it’s time to go hunting! You’ve got your sales narrative, you’ve got various materials encapsulating it, and now you have a rich list of prospects for whom your narrative should resonate. So go sell.
Further Reading:
More on prospecting in Leading Sales Development, The Sales Development Playbook, Fanatical Prospecting, Predictable Prospecting, and Sales Development.