The idea of going from zero leads to closed-won with nothing but a phone – and actually scaling a startup that way – is central to the lore of the tenacious pipeline building and to “if I could do that then, imagine what we can do now” pep talks everywhere. The problem? It’s rapidly becoming obsolete. Unless you price your offering at upward of around $200K per year, hiring live salespeople to find and close deals is simply unaffordable. Scaling a startup simply doesn’t work that way anymore, especially cloud software products whose average deal prices don’t economically support more then short tele-sales calls, at best.

Let’s talk about why that is, and why, today, success for growth-minded startup CXOs now hinges around one key objective: building an automated lead generation outreach system with verticalized messaging – and executing it at scale.

COVID changes the calculus on scaling startups

A few years ago, if you asked a startup sales rep what their highest-return lead gen methods were, two likely answers would have been:

• Lone-wolf prospecting culminating in picking up the phone for a smile-and-dial
• Hitting the conference circuit and engaging face-to-face

Then, in 2020…well, you know what happened. “Post-COVID New Normal” discussions are nothing new, but the pandemic’s impact on startup lead gen was so massive we can’t ignore it here. In February of 2020, conferences were a blast, and a wellspring of leads; by April, they’d been slapped with the much-maligned label of “superspreader event.” And with everyone working from home, business-appropriate phone numbers aren’t always available, making cold-calling even more of an uphill battle than it already was. In the cloud software space, all of this converged with a downward trend in price points, which have now gotten so low that one-prospect-at-a-time cold calling no longer scales profitably.

In the aftermath of this shift, the B2B customer journey has migrated almost entirely to the internet – and it will probably stay there regardless of the virology outlook going forward. After all, following the customer journey is much faster and more efficient when it happens online. For one thing, it can be automated; for another, without the time, money, and effort required to host and travel to conferences, it’s much cheaper and less intensive. We’ve talked to several CROs who say their days on the road are over, as they’ve become so much more productive closing business by digital means.

This represents a fundamental change in the way startups will scale from here on out, and there will certainly be some growing pains (more on that in a moment). But it’s not necessarily as daunting as it sounds. Yes, the internet is a big place – but when it comes to B2B lead generation, the good news is that a plethora of on- and offline lead sources converge on only a few relevant online touchpoints – and it’s at these key touchpoints where most B2B customer journeys will either start or end:

• Landing pages fed by SEO and nurture tracks
• LinkedIn ads, connection requests, and / or InMails
• Cold and nurture emails
• Paid and/or organic search results

Figure 1 is a detailed breakdown of most common B2B lead sources to be leveraged in the company’s verticalized messaging, with all ending up in the same handful of digital channels (more below).

With just about all B2B traffic funneling into this handful of online channels, it’s not too hard for startups to make their digital lead generation efforts both effective and efficient. Which brings us to…

Verticalized messaging, 2022 style

Verticalized messaging is, of course, not a new concept – but the way companies typically approach it has two major flaws that won’t stand in the fully digital post-COVID era. First, its deployment often relies on live, initial contact, which isn’t viable anymore, especially for lower-priced offerings. Second, companies too often treat it as an afterthought – marketing teams might draw up some rough notes about how to message to different verticals, but they often don’t take the time to build out a meticulous verticalized messaging framework in advance.

With digital marketing and selling, starting a customer’s journey without tuning your messaging to their precise needs will lead to lower click-through and conversion rates and thus a lower volume of good quality leads coming off the Internet. If you move forward without a scalable plan in place to verticalize your messaging, you’ll end up underwater. But if you take the time to build a robust plan up front, you’ll start scaling in no time.

So why is verticalized messaging so pivotal today? It’s really quite simple: In a fully online lead-gen paradigm, a one-size-fits-all approach to messaging will not work. In the pre-COVID days, back when it was easy to phone a lead directly or chat one-on-one at a conference, there was no need for the messaging deployed in those conversations to be particularly flexible. The audience was n-of-1 – as long as you spoke to the needs of the person you were talking to, you’d be alright. And a good sales person could work that out on the fly.

Today, lead generation is no longer a one-on-one conversation. It’s search engine ads, LinkedIn ads or invites, email blasts, and other digital channels – all seen by hundreds or thousands of people. That larger audience will consist of diverse segments of buyer personas, and you’ll need to have the tools, assets, and mechanisms in place to speak to all of them – with specific messaging and value propositions that relate to them. To successfully scale pipelines and grow startups today, then, you’ll need to automate the verticalization of your messaging.

Here’s what a high-level action plan for verticalized messaging should look like:

1. Build out value props and precision segmentation
   Your first steps are to establish your value propositions, break your ideal customer profiles (ICPs) into precise segments, and map them all together. If you’re a healthcare software company, for example, which ICPs will resonate with your product’s ability to ensure regulatory compliance? Which will instead resonate with other benefits like accelerated data entry?
   Pro tip: A 2D grid, with value props on the X axis and ICPs on the Y axis, is a great way to lay this out visually.
2. Develop and incorporate assets
   Now that you’ve mapped your value props to your ICPs, it’s time to identify the channels you’ll leverage to deploy the right messages to the right people. The biggest lift here is up-front content production. How many landing pages will you need to produce? How many LinkedIn ads? Your 2D grid is now 3D, with each cell identifying a verticalized asset that incorporates the three considerations of lead channel, value prop, and ICP: A LinkedIn ad focused on regulatory benefits for Compliance personas, a landing page focused on accelerated data entry for end users, and so on.
3. Execute at scale
   You now have everything you need to deploy your messaging in a robust, verticalized, and automated way – to a much larger audience than your sales reps would ever have been able to reach via lone-wolf prospecting. Once your message is out in the world, don’t forget about essential post-launch tasks like campaign management and performance metrics, both of which are essential to making optimizations based on early results.

There’s no denying it: This will take some planning and preparatory work. But it will also empower you to maximize both the effectiveness and the efficiency of your messaging, leaving little doubt that the investment will pay off. As Anna Courval, V.P. of Marketing at Afterburner, puts it, building a scalable and automated verticalized messaging plan “has been a game-changer for us. It’s allowed us to find a clear path to our revenue goals for the year.”

Modern verticalized messaging in action 

How this plays out in real-world scenarios will, of course, vary – and may get a bit more complicated depending on the needs and realities of the company in question. Below is an example of how scalable and automated verticalized messaging played out for a startup in the crypto space. As with any company operating in a still-burgeoning space, views of their business ranged from enthusiasm to skepticism to confusion. This made for a wide array of buyer personas, requiring the company to develop a wide array of tailored messages to kick off the customer journey.

Step 1

The company started by mapping out the various buyer personas they’d target (Figure 1). They took a hierarchical approach, first breaking down their market into Existing Customers and Net-New Bookings, then further segmenting the buyers within each category. Within the Existing Customers category, the segmentation was pretty simple – but for Net-New Bookings, the company leveraged demographic, firmographic, and psychographic attributes to create much more detailed and precise personas.

Once that was done, the company mapped each persona to the value props they’d developed, creating a 2D matrix indicating which personas would receive which message or messages.

Figure 2: Ideal customer profiles (ICPs) broken out into precise segments with matching value props (Example shown)

Step 2

It was then time to add the third dimension to the matrix by identifying their digital messaging channels (Figure 1 above). Here, too, they took a hierarchical approach, first dividing their lead gen into Inbound and Outbound channels, and then assigning specific channels to each of those categories – SEM, LinkedIn, and email for Outbound; SEO, chat boxes, and “Contact Us” forms for Inbound. And because traditional, pre-COVID methods haven’t lost all of their value, they also made space for Sales-driven lead gen.

Step 3

Finally, it was time to think about content. The company identified the assets that would need to be produced for each persona (Figure 3 below) and got to work developing those assets, making sure each one messaged the right value props to the right personas. Figure 3 is about mapping customer touchpoints to ICPs, while Figure 4 is about the verticalized messaging component of an automated lead gen outreach system is mapping value props to ICPs, and Figure 5 highlights a planning matrix of content assets to be developed for the targeted ICPs:

Figure 3: Mapping digital touchpoints to ICPs is a critical aspect of verticalized messaging (Example shown)

Figure 4: The verticalized messaging component of an automated lead gen outreach system maps value props to ICPs (Example shown)

Figure 5: Part of the inventory of content assets to be developed for various ICPs (Example shown)

When they were done, the company had everything they needed to deploy precise verticalized messaging at scale. For each ICP, they knew which value props to message, and the content assets and digital channels they’d leverage to do so.

You can no longer accelerate your growth without planning

People like to think of the early days of a startup as scrappy: a small team, with few resources but a lot of fire under their feet, getting on the phone and making things happen. Taking the time to think things through in detail and making sure all the pieces are in place to achieve your goal doesn’t exactly jibe with the ethos of moving fast and breaking things. But today, it’s truly the only way to scale.

To be clear: This is good news. Once those pieces are in place, things will move faster than ever. Startups who try to scale the old way might think they’re moving fast, but they’ll hit a wall before too long. Startups who build the necessary infrastructure to launch a scalable verticalized messaging plan – and automate it – are the ones who will see accelerated growth in 2022 and beyond.

The post Verticalized Messaging at Scale – Key to Effective Digital Lead Generation appeared first on Premonio.

How did we get here? Why have one-year revenue forecasts proved as unreliable as one-week weather forecasts? And what can companies do to be more confident in their ability to hit their numbers?

When a sales team falls short of its quota goal, the common reflex is to assign the blame to the sales reps and their managers. But in our experience, the underlying problem just as often turns out to be overly optimistic, imprecisely formulated revenue growth forecasts.

With this in mind, it’s important for companies experiencing quota shortfalls to consider a fundamental question:

Is your team underperforming, or is someone over-forecasting?

This report will drill down into this question, diagnose some outdated norms that have led to such high rates of quota shortfalls, and show how teams can create a roadmap to consistent alignment between forecasted revenue and actual revenue using a process we call Growth Architecting^TM. This will empower teams to:

• Improve relations among C-Suite leaders, especially sales and marketing
• Keep the company in good standing with the board
• Validate their growth strategy without spending three or four quarters on trial and error – leading to accelerated time-to-revenue.

Underperforming vs. Over-Forecasting: A Case Study 

A good place to start exploring this issue is with a recent case involving an information security software company. When the company’s CEO announced the coming year’s forecast in Q4, the Revenue Operations teams took it at face value. The CRO mapped out a plan, and everyone was off to the races.

The Sales and Marketing teams were able to deliver expected growth numbers quarter after quarter – but they were one quarter delayed compared to the CEOs original Q4 forecast – and by the end of the year, they had fallen short of the CEO’s projection. Instead of rewarding the best quarter-to-quarter revenue growth the company had experienced in its history, despite the one quarter delay, finger-pointing ensued as the board looked for someone upon whom to hang the failure. In this case, that person ended up not the CEO but the Head of Sales. That VP lost their job – only to be replaced by someone far less competent, who led the team head-first into a series of losing years before being let go later, as well. When all was said and done, the company had gone through multiple executives, experienced turnover in the sales and marketing ranks due to a culture that had become unpleasantly political and failed to create any real value for the investors: Seven years later they were sold at a lower valuation than at the time when the CEO had made that original, fateful forecast.

As for the original VP of Sales and other key members of the Sales and Marketing team? They went on to have stellar careers, successfully scaling multiple startups. This suggests quite clearly that the problem isn’t always poor leadership or performance in the Sales and Marketing teams – more often than one might think, it can also be a case of unrealistic forecasting.

Why Forecasts and Actuals Get Disconnected

The case study above begs the question: What if the CEO had been able to see from the outset, using scientific and data-driven insights, that the projection was unrealistic – and understand the changes necessary to make it feasible?

Growth forecasting makes this possible – and it starts with understanding why over-forecasting occurs in the first place. As we survey common practices today, certain aging norms emerge as primary drivers of the problem:

1) Disconnect between decision makers and executors

The revenue figure that the CEO demands typically comes from a board of investors with one goal in mind: to achieve a corporate valuation that will yield large payouts. The growth pattern seen as the best way to achieve this goal might look something like this:

• Year 1: 3X revenue growth
• Year 2: 3X revenue growth
• Year 3: 2X revenue growth
• Year 4: 2X revenue growth

By Year 5, the common idea is to have created enough value to get acquired or go public.

The problem? These edicts delivered from on high are often completely divorced from realities on the ground, where CEOs or CROs, as well as other sales and marketing leaders, struggle to define a path to hitting their board’s revenue projections – or to determine whether such a path even exists with the resources at their disposal. Making these goals feasible requires an interplay between those making the revenue forecast and those responsible for hitting it, whereby leaders on the ground can either:

• Push back: Convey to the board, using data, what revenue attainment is actually feasible given the resources they have.
• Prescribe: Show what resources and practices will be necessary to hit the number that has been assigned.

Without the will or means to pursue a negotiation like this, CROs have little choice but to embrace a revenue goal that they may well lack the means to achieve, thereby setting up their CEO, their team, and themselves for a painful end of the year.

2) Failure to incorporate complexity

If hitting a projection were merely a matter of mapping out the number of leads and the conversion rate necessary for a team to hit their number, it would be easy. Rev-Ops leaders could estimate the number of reps they’d need, the CRO could toss some funding to the Marketing team to expand demand gen activities as needed, and everyone would watch the money come in.

Let’s look at an example of what such a calculation might look like. Say, the CEO forecast closed-won revenue for the year at $15 million – a not-atypical projection for a startup just beginning to stand on its own two feet. If the average deal size expected for the year – based on pricing and historical performance – were $30,000, then the Sales team would need to close 500 deals, either in new bookings or renewals. If we imagine they planned for a lead conversion rate of, say, 1 in 15 (6.7%), the team would need to generate 7,500 leads for the year.

• CEO annual forecast
  • $15 million
• Simple calculation
  • Average deal size: $30,000
  • Bookings needed: 500
  • Lead conversion rate: 1 in 15, or 6.7%
  • Leads needed: 7,500

In reality, however, things aren’t so simple. To truly understand what it would take to hit $15 million – or if that number is even feasible – the CEO and CRO would have to incorporate a lot more complexity. That simple calculation would be blown up by a plethora of variables, including, but not limited to:

• Conversion rates

A lot happens between “new lead” and “closed-won.” Leads at different stages of the sales cycle convert at different rates. How does the MQL-to-SQL conversion rate compare to SQL-to-SQO? What about SQO-to-closed-won? Conversion rates also vary by lead source, whether they belong to a cold call to an executive, an inbound demo request through the website, or something in between.

• Sales cycle

Selling a small deal to a startup flush with VC cash might take no more than two or three weeks. Selling half a million dollars to a Fortune 500 account, on the other hand, may take months of pitching, proposal writing, technical reviews, wining and dining, and negotiating. Depending on the underlying sales velocity, a deal that’s essential to hitting your revenue goal this year might not actually close until next year.

• Ramp times

If hitting a goal requires hiring new sales reps, accounting for variations in ramp time is paramount. Based on factors like industry experience and seniority, it typically takes between three and six months for a rep to be fully ramped. Hiring a rep today doesn’t necessarily mean their close rate will be tracking with a simple spreadsheet forecast by tomorrow.

Once all variables are accounted for, what was once a simple calculation might now include a lot of unknowns, and the resulting revenue growth looks something like this:

And drilling into first touch revenue contribution by lead source looks something like this:

The issue isn’t that CEOs, CROs, and sales leaders aren’t aware of all these factors. It’s that they have neither the time nor the tools or resources to incorporate them into their forecasting model and game plan, leaving them no choice but to rely on simple models – typically using ordinary spreadsheets that aren’t well-equipped or dynamic enough to handle the necessary complexity – and fall short as a result.

How to Use Growth Architecting to Make Things Right 

Aligning forecast revenue with actual revenue requires relying on an often incomplete or imperfect body of data. It requires using detailed calculations to either push back against an unfeasible revenue forecast or prescribe the changes necessary to make it feasible. It requires the flexibility to adapt when unexpected challenges emerge along your way to your goal. Growth architecting, at its core, is a methodology that incorporates all these measures.

Here are the key steps to successful growth architecting.

1) Define an integrated, detailed lead flow

As you map out your lead flow, consider the following questions:

• What lead sources will we need to tap to hit our goal, and that can be shown to target the precise segments or ICPs that we want to engage?
• What minimum lead conversion rates will we need to maintain, and how will they differ for each source?
• Given this information, and the resources at our disposal, how many leads will we need each source to deliver?
• What will the average deal size need to be?
• What additional budget and resources will we need to achieve all of this?

If, after answering these questions, the numbers look unfeasible, then either push back on the forecast or use the data to map out the necessary increases in, and reallocation of, resources and budget.

Keep in mind that when it comes to lead sources, there’s often an inverse relationship between volume and conversion rate. For example, an email campaign can pull in a healthy volume of content engagement-based leads – but few if any of them are likely to convert to opportunities; meanwhile, BDR prospecting or inbound web leads may pull in a much lower volume of leads, but at a higher conversion rate.

2) Account for key variables

Certain aspects of your calculations will involve a range rather than a fixed number. To keep forecast revenue aligned with actual revenue, teams need to account for variations in:

• Sales velocities

If your customer profile includes companies of all sizes, you’re likely to see a wide variety of sales velocities. A lead at a small company will probably close after a relatively short sales cycle – pulling in that lead near the end of the quarter or year is probably safe. But if your plan includes enterprise leads, you may need to either increase the volume, or create a mechanism for pulling them in before the end of Q3, to account for a sales cycle that often stretches for months at that level.

• Ramp time

Just as different leads take different lengths of time to convert, some sales reps ramp up more quickly than others. If and when you enter negotiations for more resources, keep in mind that getting new reps onto your payroll does not, by itself, get you to your goal. You have to make sure they’ll be ready to start closing early enough to produce the revenue you need from them.

3) Check in periodically

After you map out, in detail, what it will take to get to the revenue you need, it’s time to switch from planning to monitoring. Check in regularly to see how you’re tracking against your goals and determine what optimizations, if any, you need to make. We suggest starting at bi-weekly or monthly intervals, as this will allow for tidy recaps of the beginning, middle, and end of each quarter.

In some ways, this is nothing new. But while most teams today check in on their progress toward goal during their weekly or monthly syncs, these check-ins are often treated as informational, with little to no talk of optimization when the data reveals a shortfall. And quite often there is no line of sight between raw lead sources – say, search engine ads or BDR cold-calling – and their resulting contributions to closed-won bookings. This needs to change.

Beyond Revenue: The Fallout from Inaccurate Revenue Projections

Everyone has an interest in making sure forecasts and actual revenues line up. The board wants a return on their investment. The CEO, by committing to a number, is laying their reputation on the line, and therefore wants confirmation that that number is achievable. The CRO wants assurance that the available resources and budget are commensurate with the revenue forecast, so they’re not walking into a buzzsaw. The CMO, meanwhile, wants a clear roadmap for bringing in the necessary quantity and quality of leads, so that a miss can’t be pinned on their demand generation engine.

The true tragedy of missed forecasts is that even though all these leaders share the same goal, the reflexive instinct for self-preservation often leads to finger pointing and strained relations instead of a problem-solving attitude. This creates more harm than just lost dollars and cents:

• C-Suite relations can sour

Everyone in the C-Suite needs to be on the same side if the company is to succeed. Infighting will only stymie progress. Unfortunately, that’s exactly what often happens when a company misses its number and finger-pointing ensues.

• Careers can suffer

As the case study with the infosec company makes clear, a missed number and the resulting finger-pointing can have a serious impact not just on the health of the company, but on individual careers. People lose their jobs. What would otherwise be career-long, mutually beneficial professional relationships are strained and broken. Especially with the heads of sales, their careers end up having to take unexpected turns – even though the problem wasn’t them missing their number, but rather that their “number” came from an unrealistic, poorly-architected company financial forecast that didn’t take feasibility into account.

• The optics are unsavory

Just as bad as these events themselves is the fact that all of it is playing out in front of an audience. Leaders bickering as revenue falls short paints a picture of instability and incompetence, causing customers to lose faith – which, of course, further imperils future revenue generation. Never mind the loss of credibility and faith with the employees.

Conclusion

Growth architecting asks a lot of C-Suite leaders. It demands a level of meticulousness and detail in revenue forecasting that busy leaders have historically found impractical. It also demands a culture change, whereby leaders and their teams exchange finger-pointing for problem solving and a stronger sense of shared purpose, even in times of trouble.

This culture change comes from committing to a data-driven approach to pipeline management. In regular intervals – every two to four weeks is a good place to start – all revenue generating teams should convene to review what worked and what didn’t, what achieved the KPI goals and what fell short. It takes people a while to pursue their work this way, both dealing with the potential discomfort of having all one’s numbers out in the open and having to discuss other people’s ideas if their area happens to be the one not hitting KPIs.

But it also provides the tools to either push back on unrealistic forecasts or prescribe the means to make them realistic. Having a truly data-driven pipeline will make leaders more confident – in their revenue projections, in their negotiations with the board, and in the overall success of the company. Then they won’t have to consider whether their Rev-Ops team is underperforming, or the CEO is over-forecasting – because the answer will be neither.

The post Are you missing your number or is someone over-forecasting? appeared first on Premonio.

We can now define segments much more precisely using dozens or even hundreds of dimensions, not just the usual standby segmentation filters of vertical, geography, size, or titles, i.e., a much more precise and thus more success-prone definition of to-be-targeted segments can be obtained. And we can measure the degree of our success hitting the right targets in near real-time, i.e., getting market feedback to our targeting way more quickly. This means that “getting it right the first time” is not as necessary, and it’s more important to be able to rapidly collect lots of data on the rise of demand, process it, and adjust course if and as needed.

Why do precision segmentation at all?

Add to the top line: Ultimately, the reason to identify resonating market segments is that lead volumes, conversion and closure rates, sales velocities, and/or average deal sizes will go up, in some cases dramatically, as we showed in Figure 1. In contrast to the MBA reasoning of old, nowadays, we can measure changes to those parameters after a few weeks or months into a new go-to-market launch. And the faster we can measure them, the sooner we’ll hit those promising market segments with high resonance.

For example, underlying resonances can be established more readily: Increases in open or click-through rates in search engine ads or email campaigns into targeted segments give near-immediate feedback about the interest of a targeted sector in your offerings. In the olden days, it required live customer interviews and detailed sales analyses that maybe were available after a few months. Or decision-makers had to rely on impressionistic data and intuition. Now you’ll know days or weeks at most into a campaign.

Add to your bottom-line: Not only are there the above improvements to a company’s top-line, but the bottom-line also improves as well through the more efficient purchase of databases and online advertising. You only need to buy the leads and ads that precisely speak to those you are targeting. For many B2B businesses, the to-be-targeted population often contains only a few hundred or maybe a couple of thousands of decision-makers. What if you only had to know who they are and create ways only to reach them? Much cheaper than Super Bowl ads or blanket database purchases that telesales reps then cold call into with low closure rates.

A lot is also about messaging to allow organic traffic to your web assets to filter themselves in or out. Which means, if they’re not interested, they won’t even show up on your radar screen, and you don’t have to waste time with them.

What is different about precision segmentation in digital marketing?

So, say hi to precision segmentation in digital marketing along deep firmographic, demographic, and psychographic dimensions using machine learning techniques and in-depth analysis of CRM data, as well as a wealth of online, behavioral data. Much of that didn’t exist a few years ago, and so much of the needed know-how is still tribal knowledge shared among innovative practitioners. So, here is a summary of our practitioner experience in segmenting markets in the new age with a focus on leveraging these new approaches to create more high-quality pipeline and lower the costs of doing so.

The significant difference between segmentation in the digital age and more conventional “MBA school-type, segmentation analyses” is not that segmentation isn’t about product-market fit and finding the segments that resonate with your value proposition anymore; it still is. The difference is in the techniques and the speed with which a more significant number of circumscribing filters that define resonant segments can be established, and therefore the degree of targeting precision that is possible now.

A few illustrative examples of how segmentation can be more precise:

Accurate CRM Data:

Resonating segments might still be verticals with specific needs that match a company’s product strengths. One eHealth company discovered through a detailed analysis of their CRM system’s data (i.e., lead volumes, conversion rates from one first stage to the next, and sales velocities for different segments) that their application was tailor-made for the regulatory needs of the medical device industry and thus saw significantly increased closure rates there (see Figure 1). Two such high-resonance segments had around 4X the closure rates than the low resonance segments, all other things being equal. Such insights, impossible to obtain without detailed CRM data, can have profound implications for how much revenue a given sales and marketing team can generate.

Non-traditional Segmentation Like Social Proximity:

Other examples are from several professional services businesses that we served whose original targeting was along conventional, vertical lines. Only to discover that the segments where they had resonance weren’t associated with specific verticals at all, but with a mixture of company size (medium to lower enterprise, in most cases) and, more importantly, “social proximity.” i.e., they mostly succeeded in selling to folks that either knew them already or that had been referred to them by someone that knew or had worked with them. That’s a common theme in services: Whether it’s your doctor, car mechanic, creative agency, or a consultancy, for services, we tend to go to the folks we trust. And trust mostly comes from close social proximity. So, what worked for them was figuring out how to target new prospects in their respective networks or at most one connection away. With tools like LinkedIn, that kind of targeting is quickly done now.

Segment Filters Obtainable Through Deep Web Searches:

A third example was a company with an online trade credit application that realized that segmentation along vertical lines was only partially significant. Other, less readily discernable factors determined their areas of resonance like the age of their target companies, their ability to secure lines of credit or not, and their need to compete with larger, better-funded companies. Less tangible filters like the ones just cited could not be obtained with traditional segmentation analyses. Still, they can be obtained through modern, machine learning-based techniques that can flush out filters with high closure rate correlation from scraped data off the internet or from in-depth analyses of CRM data looking first volumes, conversion rates, and sales velocities by segment.

Launching a Brand-New Segment or Technology:

How can we segment those markets? For startups that are launching disruptive products, the ability to leverage the before-mentioned increases in speed and precision by understanding new techniques and technologies is significant. Extrapolating from past data isn’t all that relevant for disruptive products, and the speed with which revenue ramps need to be scaled doesn’t allow for months-long strategic research projects anymore. Market segments and sub-segments need to be identified, tested, and expanded or discarded in a matter of a few weeks or months these days.

Where to start – how to find your high-resonance segments?

At the end, where segmentation definitions manifest themselves are in concrete decisions around the design and number of attributes of the lead databases that get purchased or enriched, the selection of targeting filters in advertising platforms like LinkedIn, Facebook or Google, as well as in the associated messaging and underlying value propositions. The latter allowing readers to self-select if they want to become prospects because they resonate with what is being said, or if they’re going to self-select out.

Setting up the data collection mechanisms to what segment attributes to filter in or filter out is vital, and at least in the beginning can be done easily. A/B testing Google or LinkedIn ads, looking at email open and click-through rates, monitoring social media engagement rates are not difficult, yet if collected regularly and mapped back against the tested variables will provide insights very quickly.

So, segmentation these days is more about what databases to buy, who to advertise to with what messaging, and about who to contact on LinkedIn or Facebook. And if we don’t get it right in the first throw, it’s good enough to get close and iterate into a resonating target segment(s) through data-driven iterations. The market might surprise us with who is interested if we just expose ourselves to them versus over-defining a target segment and exclusively focusing on it, missing out on attractive others.

So, rather than defining what the segmentation should be and then running demand generation against that definition, we now can instead define a few segments, run some cheap and early campaigns, and measure their differential uptake. Then refine quickly over the ensuing 3 to 12 months and step on the gas in the (sub)segments where market resonance has been established.

Here is a straight-forward process to identify high resonance segments:

1. Analyze the last 5 or 10 wins and losses (or early prospect discussions if your organization is too young to have sales yet), writing down answers to these questions:
   • How did you find them/they find you?
   • In their own words (not yours), why are they interested / did they buy / not buy?
   • What did they / did they not like?
   • Capture any segmentation information you might be able to ascertain like company size, geo, vertical, profitability, etc. about the company, and things like title, gender, attitudes about innovation, personal motivations, social proximity, etc. about the buyer(s)
   • Can you detect any apparent correlations that might give you a sense of what their defining commonalities are? Sometimes segment commonalities / shared filtering attributes take a while to surface and often can be counter-intuitive.
2. Armed with that information, then run regression analyses (Excel has capabilities that are good enough with their “LINEST” function) against CRM data using the above criteria as a source list of filters to start. This will allows you to see which of the measurable portions of the above filters correlate with sales volume, conversion and closure rates, sales velocities.
3. Once segmentation filters/dimensions/attributes have been identified in steps 1 and 2, then the task becomes where and how we can obtain leads that match these segmentation criteria. Some ways to do so include:
   • First comb through commercially available databases that offer leads enriched with the types of segment attributes you are looking for (e.g., gender, age, educational attainment, other vendors deployed at a prospect site, etc.) and verify they are of high enough quality
   • Second, using vendors skilled in using machine-learning techniques to do deep web searches and separate the attributes of your buyers and prospects from everyone else.
4. Validate the findings with a few live interviews to make sure they pass a qualitative acid test
5. Translate into:
   • Clear definition of prospect/customer journeys and where and how to intercept them
   • Precise definitions of database purchases
   • Launch of highly targeted ads (Google, LinkedIn, FB, ad retargeting, communities)
   • Online and collateral messaging.

When executed, there is better product-market fit, higher conversion, and closure rates, and less wasted marketing dollars i.e., more revenues and lower costs.

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