Author: Jason Widup

Disclaimer: I’m not here to bash account-based marketing. 

ABM is a proven way to connect with your target accounts in a personalized way. Most B2B companies should be doing ABM.

Yet IMHO, the pendulum has swung too far to an all account-based mentality at the expense of proven demand gen activities.

But before I delve into ABM’s overuse, I need to address an elephant in the room.

Is there an agreed-upon definition of ABM? You’ll get 10 different answers if you ask 10 different marketers.

This may sound cynical, but I blame software vendors for “ABM” being ill-defined. 

For the past 15-plus years, we’ve simply let ABM vendors define the ABM category. And guess how they defined it? Based on what their technologies could do.

It didn’t start with ABM as a strategy first, followed by defining the technology — it was the other way around. And they did such a good job at it that marketers and analysts alike just bought into it.

You can see this when you look at the inclusion criteria for a Forrester or Gartner report on ABM Platforms — it reads like a Demandbase features list. 

When “ABM platforms” showed up 10 years ago from Demandbase, Terminus, and others ABM was accepted as a “technology.” 

Not an approach or a strategy, but a quick-fix, silver bullet “tech platform.”

B2B companies went all-in on ABM without understanding if they had the sales-marketing alignment and budget to run ABM campaigns and actually make them work. 

Not surprisingly, ABM platforms never did fix B2B marketing.

Why? Because ABM is NOT A TECHNOLOGY. 

But more on that later. 

In this post, I’ll set the record straight on what ABM isn’t…and then define what it is and how you can integrate it into your marketing strategy.

To start, here’s what ABM isn’t….

ABM isn’t…B2B

In the real world, ABM is just one piece of a B2B marketing strategy. But it’s not the entire thing. You’ve heard things like “ABM is B2B” — it’s not. 

Intuitive marketers blend ABM with demand gen tactics such as direct response and performance marketing. They leverage a double-funnel approach, figuring out the right balance between ABM and demand gen.

This way, you’re not leaving any part of your market untouched — you leverage ABM programs for your top accounts and pair that with demand capture activities for 2nd-3rd tier accounts who are in-market.

B2B is all about balance.

ABM isn’t…for everyone 

Not every B2B company needs to leverage ABM as a strategy. 

For example, if your ASP is less than $50k/year — you may not need ABM (probably still a good idea, but you could grow with just demand capture). 

There are other reasons as well, for example:

1. Your business model is based on free trials and micro-transactions 

If you sell to individuals within a company and rely on free trials and monthly payments, you’ll be almost all demand capture. Most of your business is from 25 individuals doing $10 per month deals. You don’t need ABM.

2. You’re strapped for resources 

ABM is expensive. If you don’t have the budget and people yet for content creation, personalization, direct mail efforts, and proper communication with the sales team, you’ll put out shoddy, poorly coordinated ABM. 

ABM isn’t…running display ads

Display advertising has been the primary activation channel for ABM. But this is a flawed approach, for a few reasons:

1. Display ads broadly target a whole account 

When you’re spending ad dollars, you need to be sure they’re only getting in front of the relevant people within an organization. Display ads have limited ability to target specific job titles — they just blanket the entire company based on IP address and other factors. 

Why display to everyone at a company when ABM depends on targeting small, specific groups?

2. Display ads aren’t very performative

Display is historically good at driving website traffic, but most of these visitors are not qualified and your ads are not relevant to them. Is it the company’s office manager or the CMO? And then if they click, their engagement on your website is low. 

This is why it usually takes several additional retargeting touches before you get a conversion. At the same time, you can get close enough to a display-level cost-per-click in paid social, if your ads and offers are good enough — especially when you consider the fewer touches and higher engagement you get.

3. ABM platforms love display ads because they take a cut

Hmmmm, so why would ABM platforms push for display ads? 

Because they charge a percent of the ad spend. 

Meanwhile, their core metrics are impressions and engagement, which are misleading because revenue is never tracked. You’re basically stopping at “cost per impression” or maybe “cost per click”, which today’s B2B marketers understand is just not good enough.

ABM isn’t…a technology

Smart B2B marketers don’t depend on watered-down, all-in-one tech. They understand the gaps and strengths of their go-to-market approach and plug in point technologies.

For instance:

• You know your TAM is small, so you need a technology for targeting.
  
• You’re exploring new ad formats and messaging, so you need a tech platform for experimentation.
  
• You’re struggling to optimize to revenue so you need an analytics platform to improve closed loop feedback. 

Because ABM isn’t a technology, it also shouldn’t be a category line item in a budget. 

Spend on ABM should spread across supporting techs like those mentioned above. Additionally, companies should only spend money on technologies they need for a competitive advantage.
What ABM is…and where it fits into marketing plans

ABM is something you do, not something you buy. You can do ABM without an “ABM platform”. You still need technologies to run ABM, but every company’s GTM strategy is unique to them and the technologies will vary.

The resources you’ll need for ABM vs. demand generation will depend on your business model and customer base.

Here are two companies at opposite ends of the ABM spectrum.

Example 1: Loom 

Loom is a product-led growth company that provides video messaging, relies on small ($10) monthly payments from individual people in a company. 

Ninety percent of Loom’s marketing goes to demand gen tactics to get people to sign up for a free trial and then convert them when the trial ends. Loom may still have a few white whale enterprise accounts that require ABM drips. But that’ll likely be only 10% of its budget. 

Example 2: Zendrive 

Zendrive sells mapping/routing data and software to major mobile carriers. Zendrive’s TAM is small, but all the potential customers are huge companies with about five internal buyers to market to. 

Each Zendrive deal is roughly $5M. Therefore, ALL their activity is ABM because they’ll spend $75K in marketing to get one opportunity from one company. 

Those are two extreme cases. 

Most companies are in the middle where an ABM/demand gen hybrid is the right approach.

It takes experimentation and a deep understanding of your resources, product, and TAM to strike the right balance and do ABM. 

But you’ll come away with a versatile plan that merges ABM (account engagement) with demand gen (direct activation) to get your message in front of the right people and generate more revenue.

ABM this. Demand gen that.

We’re here to tell you there’s a time and a place for both. And the best B2B marketers are actually running both.

What’s the right mix for your company? 

That’s where the double funnel from TOPO, now Gartner, comes in. 

Let’s take a quick spin through some fundamentals, then we’ll explain what the double funnel is and how you can use it to find — and continually improve — an optimal mix of account-based marketing and demand generation.

• What is account-based marketing?
• What is demand generation?
• What the heck is account-based strategy?
• Which tactics are which?
• Align ABM and demand generation with a double funnel
• A real look at the double funnel
• Dig deeper to improve performance across the board
• How to find the right balance 
• How to address the obstacles

Rather watch the video?

Busting out some buzzwords

Marketers have never been known to create buzzwords, right?

Haha, jk, jk.

Here’s the thing, friends. There’s no shared definition for these terms:

• Account-based marketing
• Demand generation
• Account-based strategy

Ask eight different marketers, and you’ll probably get eight different explanations for each of them. But there are some generally accepted principles that we can use to frame this guide and our recommendations.

What is account-based marketing?

ABM is all about identifying and marketing to a specific number of high-value accounts based on their likelihood to have success with your product. In other words, you create a list of target accounts where you have an unfair advantage with your technology differentiation — and therefore a much higher rate of success. 

Identifying these top accounts goes beyond outlining your ideal customer profile. You need to incorporate all kinds of data and intelligence so you really understand who your absolute best prospects are.

Then you take these prospects on a full-funnel journey from high-level thought leadership content (TOFU, anyone?) down to low-level, reasons-to-buy content (the meaty BOFU content). 

ABM is particularly helpful when you have a more complicated sale or set of problems you’re solving, and your average sale price (ASP) is high — which means you’re taking these accounts through the entire journey over months to years.

What is demand generation?

Demand generation is an umbrella term that includes many tactics and activities designed to create demand for your product or category. “Demand” is a desire for a particular commodity. Generating demand is therefore about making your product or category desirable to potential buyers.

With this in mind, we think of demand gen as basically everything you do as a marketer—from making your product better than the competition to making it clear how people can get value from that product.

But the generally accepted definition of demand generation is that it’s the higher-volume, lower-ASP counterpart to account-based marketing.

And what on earth is account-based experience?

Wanna make things even hairier? We’ve got a third term for you: “account-based experience”. 

Account-based experience (ABX) is the coordination of valuable, relevant experiences, delivered across all functions, to drive engagement and conversion at a targeted set of accounts.

This term has evolved out of the misconception that ABM is primarily a top-of-funnel strategy focused on targeted advertising. If you want to be successful with ABM, you must take a full-funnel approach and stretch right on through to sales. 

So, “account-based experience” is a bit of a dramatic overcorrection to make sure we’re talking about an integrated approach between sales and marketing that includes the entire funnel.

ABM vs. demand generation — which tactics are which?

This is sort of a trick question.

There are some tactics that fit clearly into the demand generation bucket or the ABM bucket:

• Activities that are geared towards understanding account intent, scoring and prioritizing accounts, and taking individual buyer prospects through a journey skew toward the ABM category.

• Activities that allow you to capture existing demand using broader, volume-based advertising tactics using your first-party data skew toward the demand gen category.

But what about the rest? 

Most of the activities you’ll do in marketing can be applied to both ABM and demand gen, depending on how they’re used. 

• Take events, for example. They can be used to build new relationships (demand gen). But sales may be required to book a certain number of meetings with key accounts during the event (ABM).

• How about podcasts? Sharing thought leadership through a podcast is generating demand. You can also use your podcast as a way to break into key accounts (invite them on the show!).

• ROI calculators. If you have the right data to offer people a decent read-out on what it might be like to work with your company (no one wants to hear you’ll save them $8 bajillion per year), this can be a great demand gen tactic. But you can also do some deep research on target accounts to start working through the calculator for them (a personalized cost-savings report is a great conversation starter).

• Guides, reports, yadda yadda. Got internal data? Publish it a la Gong for demand generation (Gong produces phenomenal, data-driven guides all about improving sales but using AI to weed through real sales calls). You can also pitch a compelling point or two from this amazing guide into a 30-minute conversation with key accounts. 

At the end of the day, you can make almost anything you do in marketing work for any go-to-market — it’s all about how you execute.

Speaking of execution…

Align ABM and demand generation with a double funnel

Okay, so you’ve been doing a bunch of traditional demand generation stuff, and your team has decided to add some account-based marketing.

You nail that account list and start going to town — and then you measure everything the same way you always have. You use the same benchmarks you’re used to from demand gen (e.g. MQLs) to judge how well your ABM programs are going.

Womp, womp.

You’re not alone. Most marketers running account-based programs try to measure them using a traditional lead model.

The truth is that your demand gen efforts and your ABM efforts will probably look wildly different from your demand gen programs in terms of:

• Volumes
• Conversion rates
• Behaviors

So, benchmarking ABM and demand programs against the same metrics doesn’t make any sense. But measuring your ABM against an entirely new set of metrics means you can’t easily compare your performance apples to apples.

This makes it tough to tell a connected story about your go-to-market strategy.

What’s a marketer to do? Never fear, the double funnel is here.

The double funnel can be represented by a pretty simple graphic, but that’s all it took to inspire an epiphany for us.

Thinking about your marketing efforts in terms of the double funnel gets your head in the right place to:

• Align all of your go-to-markets
• Measure everything accurately throughout the entire funnel

It’s not sexy. It’s not rocket science. More like simple brilliance.

The double funnel allows you to visualize your ABM and demand gen efforts side-by-side, aligning the account-based funnel with the traditional marketing funnel. Then you can see progress through each side of the funnel stage by stage and easily compare performance.

Note: Your funnel doesn’t have to end at closed-won (in fact, it probably shouldn’t). Closed-won just served as an easy benchmark for us in this double funnel visualization. The best ABM strategies go beyond acquisition right through the rest of the funnel.

A real look at the double funnel

Let’s take a look at how this double funnel comes together with some actual data. These are real benchmarks for high-growth SaaS companies from Gartner, placing ABM performance metrics alongside demand gen metrics. 

Of course, your own data will always be more valuable than benchmarks. These are some of the fastest-growing companies in the world, so don’t get discouraged if your numbers don’t look quite so rosy.

With ABM, our first performance metric is engaged accounts. What counts as engagement?

That’s subjective, but it must be some form of meaningful interest in your products or services.

Impressions, clicks, website visits — those don’t count as meaningful engagement. Instead, look for things like visits to the pricing page, pricing requests, demo requests, etc.

Basically, you want to ask yourself what metrics tell you an account wants to talk to your sales team. Once an account shows engagement, it’s time to start focusing on contacts instead of accounts (you know, the real people that work there). 

Conversely, on the demand gen side, the first performance metric is marketing qualified leads (MQLs). 

At this point, SDRs must qualify both engaged accounts and MQLs. This is the unification point where our double funnel merges into a single funnel — with the same metrics for both ABM and demand gen.

There are a couple of key learnings from this benchmark data:

1. Stick it out beyond engagement. Using ABM is supposed to come with lower volumes, higher conversion rates and higher ASP. However, these benchmarks show conversion to an engaged account, our first ABM performance metric, is actually lower for ABM than demand gen. Some companies see this, get spooked and pull away from ABM before they’re able to realize the benefits. 

But conversion rates to SQL, opportunities, and closed-won deals increase significantly with ABM versus demand gen. The lesson? It’s harder to get engagement with ABM, but once you do, it will pay off.

2. Your balance of ABM vs. demand gen has a big impact on your SDR team. The team only has to work 300 accounts on the ABM side to get to the same amount of revenue as working 2,300 on the demand gen side of the funnel. That’s some serious efficiency, which impacts your budget and ROI. It also means that these SDRs can get really specialized and sophisticated when it comes to bringing these accounts through the funnel.

Dig deeper to improve performance across the board

It’s the side-by-side comparison of the double funnel that provides the real magic — and you can use it to dig even deeper into your performance data than ABM vs. demand gen.

The goal is to directionally understand which levers are working for you within each framework and at each stage of the funnel, then make incremental improvements. 

For example, the following data (examples only, not benchmarks) breaks down volume-based demand gen performance across channels. With this kind of side-by-side breakdown, it’s easy to see where to invest next, depending on what you’re trying to accomplish.

Say you are totally loaded up on MQLs, but you need to encourage more of them along into the SQL stage. Based on this data, it’s easy to see that you’d want to double down on website and events while ditching the syndication. 

Look at as many slices of your data as possible:

• Which channels are performing?
• Are any campaigns knocking it out of the park (or tanking)?
• What gated content is working best?

Highlight the good stuff, and highlight the bad stuff.

The more slices you can examine, the more mastery you’ll have over your funnel, understanding how everything fits into and contributes to your performance.

But don’t get so hung up on measurement that you neglect the things that can’t be measured. 

Podcasts. Word of mouth. Personal brands.

There are plenty of things that are important to building relationships and trust, but they’re nearly impossible to get a clear read on, performance-wise.

Use measurement as a guide, but make sure you still have the confidence to work on the meaningful things that aren’t measurable.

How to find the right ABM-demand gen balance

Most companies will need to find their own sweet spot when it comes to ABM vs. demand generation. Swinging too far one way or the other can completely tank your performance — and trust within your organization. 

We’ve got a few tips for you if you find your pendulum too far off-center. 

Too heavy on demand?

1. Use as much data, intelligence and signals as possible to develop a strong ICP.
2. Develop a small list of target accounts with definite reasons to buy.
3. Build an MVP ABM program that spans teams, channels and activities to prove out and refine the use case.
4. Use the double funnel to start hashing out what your split between ABM and demand gen should be.

Too heavy on ABM?

1. Identify the channels and tactics that your buyers are most likely to respond to.
2. Get a deep understanding of your prospects’ pain points, and develop messaging that creates urgency around those pain points.
3. Create content that addresses the pain points.
4. Test these messages and content by running high volumes of experiments to broad audiences. Determine which drive the highest engagement.
5. Plug these findings into the double funnel to get a feel for what your ABM-demand gen split should be.

Infighting, anyone? How to address the obstacles

The double funnel is a simple concept, but it can still be difficult to align around. You’re going to face some obstacles. Maybe your boss wants you to go all in 100% on ABM. Maybe the sales and marketing teams are fighting over who gets credit for what. 

The answer is:

• Get really good at measurement. Measure as many slices of data as possible throughout your funnel, and you’ll be able to offer data-driven responses to everyone’s crazy requests (and demands).

• Remember that good > perfect. Finding the right mix of ABM and demand gen — as well as all of the other tactics and channels beneath them — will be an iterative process. Create MVPs, get them out there, and learn from them fast. You should be looking for directional improvements rather than the perfect program out of the gates.

• Unify measures of success. Sales and marketing are on the same team, which means it doesn’t matter who gets credit for what. Unify measures of success between the groups to get everyone working together.

• Be wary of drastic swings. Letting your pendulum swing too far one way or the other will probably hurt your overall performance. Use the double funnel to keep an eye on balanced efforts and outcomes.

Let’s end with a marketing dad joke:

Why were the B2B marketer’s holiday gifts boring to look at?

Because he only used white paper.

Ba-dum, ching! 😆

If you’re a marketer, you need to be experimenting. 

There’s no way around it.

When a company discourages (or doesn’t actively encourage) experimentation it leaves marketing vulnerable. 

I’ve seen it happen a few times in my career. 

Marketing is afraid to test new ideas or ask for more budget, so they repeat once-successful campaigns that have long reached the point of diminishing returns. 

And here’s what happens:

• KPIs get watered down
• Leads are weak
• Marketing slowly loses credibility within the organization

Does that sound familiar?

I’ve written recently about experimentation as a key way to inspire new ideas, prove marketing ROI and generally avoid being a mediocre company.

A lack of experimentation within an organization usually goes hand-in-hand with a fear of failure. Company cultures that don’t view failure as an opportunity to learn will frown upon experimentation or even forbid it.

If this sounds like your company, my advice is to run like hell. Because if there’s one trait innovative companies share —including Google, Facebook, and Amazon—it’s that they embrace experimentation. 

No marketer I know wants to work in this type of environment. 

Get an experimentation framework that works

Even if you work in a culture of experimentation, you still need to find the time and creativity to test new ideas. 

But most importantly, you need a systematic plan to run effective experiments and learn from them.

With a plan you can confidently and effectively:

• Test new creative ideas.
• Find new demand.
• Verify that new audiences are receptive.
• Maximize the value you get from every campaign.

You can finally bring a method to the madness.

Here’s how we do it.

Prefer to watch the video?

Develop your own framework

At Metadata, we’ve been leveraging a framework (originally built by Guillaume Cabane) for a few months to create and prioritize experiments and measure the results. One downside is that it’s, admittedly, fairly sophisticated. 

So, if you’re just formulating an experimentation plan, walk before you run.

Create a spreadsheet where the only inputs are: 

1. Impact
2. Effort
3. Confidence

With the categories:

1. High
2. Medium
3. Low

If you want to expand your experimentation framework further, here’s an Airtable framework template to get you started.

And each section below will give you tips and strategies for creating each part successfully.

1. Focus on your budget

Before you’re off to the races with experimentation ideas, you should understand the budget you have to experiment with and the demand and performance gaps you need to fill.

Your experimentation budget will depend on: 

1. Your total working budget.
2. The percent of your goals your working budget will deliver.

Let’s say you know you can deliver a lead for $100 from your current channels and tactics. Last month your goal was 100 leads, and your budget was $10,000. You had enough budget to meet your goals using tried-and-true tactics. 

However, this month your goals have changed to 120 leads, but your budget only went up by $1,000 to $11,000. 

Now there’s a gap of 10 leads to make up.

You no longer can meet your goals using past tactics and performance. In this case, you would only spend, say, $9,000 on the traditional tactic to drive 90 leads, leaving a gap of 30 leads and $2,000. This is where experimentation comes in—you need to try new things to get your CPL down to $67 for those last 30 leads.

However, even if you have enough budget to meet your goals using tried-and-true tactics, you should still be experimenting. Because at any moment those tried-and-true tactics could bomb. 

Try and optimize your current campaigns and use the dollars saved to do additional experimentation. 

If you’re in this situation, try and reserve at least 10% of your budget for new experiments.

2. Gather your experimentation ideas 

Start by brainstorming ideas and then consistently add new ideas to your list as they come up. You should also give others within your organization access to the list so they can add their own ideas.

If you’re looking for a few ideas to get started, try these out:

1. Test new landing page headlines.
2. Try out a brand new marketing channel (TikTok, YouTube, LinkedIn).
3. Run a new set of Facebook Ads.
4. Throw a digital event for your users/prospects.
5. Sponsor a newsletter or podcast.

Ideas can be big as running a user event. Or as small as testing some ad copy and creative. The goal is to get in the mode of experimentation. After you have enough solid ideas down on paper, add data to help prioritize the experiments. 

After you have your ideas, start to include data points such as: 

• Effort – The difficulty level to build the experiment. 
• Time – How long it will take to build and run.
• Impact – What the potential impact will be (in terms of dollars or primary KPIs).
• Confidence – Your confidence level of the experiment working.
• Revenue possibility – The revenue estimate for the experiment.
• Surface area – What part of the lifecycle it will affect: Acquisition? Pipeline? Retention?

When you do the math properly, the ideas that have the best mix of effort, impact, and confidence will float to the top— i.e. your “low-hanging fruit”. 

From these data points, you’ll be able to create an ordered list of the experiments you should run.

3. Set up experiment timelines and KPIs

Next, assign the top priority experiments to sprints and begin building. When you build, build the most basic MVP (minimum viable product) possible so you can test and iterate the experiment without wasting time. 

One of the biggest mistakes in experimentation is to try and build the perfect version the first time out. 

Here’s the reality: a large percentage of your experiments may fail.

So it’s important to reach the right balance of quality and speed. Use your MVP to learn and decide if it’s an idea you want to formally expand.

Provide the budget and timeline so you know from the start how long you want the experiment to run before you have enough data to be satisfied. 

You should also assign success and failure KPIs so you know if the experiment is beating or missing expectations. For instance, metrics to watch for an MVP would be CTR, CPL, and lead conversion. 

While these are not the normal metrics for measuring marketing success, they’re a good indication that the experiment is resonating and should be rolled out more formally. 

4. Assess the impact and what’s next

After an experiment has run its course, do a complete analysis of its impact and make a decision, usually one of the following: 

• It performed great and should be an evergreen campaign that we build out even further and continue to optimize.
• It needs some tweaks and a retest. 
• It just didn’t work…let’s get rid of it.

Make sure to absorb and track these learnings so you can build on what you learned and not repeat the same experiment twice. And keep a running list of insights you’ve learned through experimentation. 

Start experimenting

Take the ideas with low effort and high impact and confidence and run them as your first experiments. 

As you generate more ideas (and you will), add inputs such as the metrics it will affect and how long it will take to build and run. Keep building on it.

Sooner than you think, you’ll have enough data and ideas to run a well-oiled experimentation engine that’ll keep you a step ahead of your always-evolving audience. 

In this special episode of By Marketers For Marketers, Jason discusses ABM and demand generation with Chris Walker, Founder and CEO at Refine Labs.

In this special episode of By Marketers For Marketers, Jason discusses ABM and demand generation with Chris Walker, Founder and CEO at Refine Labs.

Additional panelists for this episode include:

• Alex Mann, Director of Growth and Marketing at Capchase
• Blake Cohlan, Director of Growth Marketing at SupportLogic
• Brandee Sanders, VP of Marketing, Motive Retail

What is the definition of ABM and demand gen?

ABM and demand generation are two of the most common strategies that B2B organizations use to generate leads.

The problem is no one agrees on a standard definition of the two.

Most marketers define demand generation and ABM differently depending on how their company or software vendors define it. And that’s a big problem because it distorts the reality of what’s involved in demand generation and ABM.

The truth is, the definitions of demand generation and ABM are the same regardless of what technologies you use.

Think of demand gen and ABM as subsets of each other rather than two separate things. You can’t have one without the other.

Demand generation involves using paid ads, organic search, or other channels to reach your target audience. Once you’ve done this, ABM allows you to go after the most valuable accounts on your list. It helps you personalize your messaging.

And when done correctly, it leads to better alignment between sales and marketing and improved return on investment.

Are display ads for B2B worth it?

As a B2B marketer, you’ll need to continually experiment with different ABM channels to get the results you want.

Many software vendors recommend using display advertising for ABM, but it’s not a requirement.

The problem with display ads is that it can be challenging to prove their ROI. Yes, they can help you with brand awareness.

They can keep your brand top of mind. But if your goal is to increase conversions among your target accounts, you might be disappointed by the results you see when using display advertising.

ABM lists are typically smaller compared to broader audiences you would use for brand awareness display ads. As a result, an ABM display ad campaign will more often than not have lower click-to-conversion and higher-than-average costs.

Why bloated sales teams make ABM harder

ABM is especially challenging to implement in large companies. The larger the sales team, the more importance is placed on quantity over quality of leads.

This forces marketing teams to focus most of their effort on launching demand generation campaigns at the expense of ABM.

The difference between volume and quality is often the difference between demand generation and ABM. You can only generate a large volume of leads by using a one-to-many demand generation approach.

It’s not possible to do that with ABM, which requires you to focus on lead quality instead of quantity.

Prioritizing a large volume of leads means that marketing teams won’t always be able to put resources towards an ABM strategy. Similarly, focusing too much on ABM might create a situation where the sales team doesn’t have enough leads to hit their goals at the end of the quarter.

A compromise between sales and marketing is always required when developing an ABM program.

Marketers aren’t experimenting enough.

I’ve seen it as a B2B marketer and research confirms it.

Too many companies underuse marketing experimentation even though it’s the most effective way to maximize campaign results, generate new ideas and prove marketing ROI.

At Metadata, we think of experimentation as putting out combinations of new creative and offer types to new and different audiences and channels to discover which have the most positive impact on revenue, brand, or whatever the metric is we’re trying to influence.

Experimentation is necessary because audience behaviors change all the time.

Whether you’re improving website conversion or ad performance, you must continually experiment with new messaging and design to understand what works for your evolving audience.

If you never experiment, your company will only be mediocre. Sure, you may get lucky with a few campaigns that resonate, but “luck” is never a good marketing strategy.

The most common experimentation challenges — cough, excuses — are lack of time and resources. These are both legitimate barriers, but both are resolvable with better planning.

Much of the blame for not experimenting enough go, to that most human of emotions: fear of failure.

And this is where company culture holds sway.

Marketers understand that some experiments will fail, but at least they’ll learn something about audience expectations. If the C-suite doesn’t share this point of view, they’ll see experimentation as a waste of time and budget.

The experimentation engine breaks down quickly in this type of culture. And so does innovation.

The danger of company cultures that don’t support experimentation

Put simply, innovative companies embrace experimentation.

Google runs thousands of tests and experiments per year. Amazon, Microsoft, Facebook, Expedia, and even companies without digital roots such as FedEx and State Farm credit experimentation for maintaining successful products.

In my experience, the “fear of failure” mindset is more common at bigger, bureaucratic companies that tend to be adverse to change.

I know, I know. All the companies I just mentioned are BIG, but I consider them outliers regarding experimentation.

When an intimidating company culture doesn’t support experimentation, it’s human nature for marketers to play it safe.

They become afraid to experiment or ask for more budget. They run once-successful campaigns repeatedly and then see ad fatigue set in.

Goals are based on activity, not performance. Vanity metrics, not revenue.

You end up with a complacent marketing team cranking out weak leads that’ll inevitably create a rift between marketing and sales.

Trust me. Once marketing gets a reputation for repeating itself, it does not get taken seriously. None of this sounds like fun, right?

Well, take comfort knowing there are still many companies that weave experimentation into the fabric of their culture.

And they share the following characteristics:

• They value marketing in general – The size of the marketing team is relative to the overall company size. If there are 200 employees and only a five-person marketing team, that’s a red flag. When a company invests in the appropriate amount of marketing staff, it’s usually open to letting marketing experiment and try new ideas.

• They celebrate failures as huge learning opportunities – If the company supports experimentation and is ok with failure as a way to learn, they tend to be unambiguously upfront about it.

• Trust, relationships and communication across the company are strong – As a general rule, when people trust each other and communicate clearly, that’s a good sign they support experimentation.

Get over the fear of failure with an experimentation strategy

If you’re fortunate enough to be at a company that supports robust experimentation, you need to maximize the opportunity.

One way of reducing your fear of failure while maximizing the opportunity is to actually have a strategy and plan for experimentation.

At Metadata, we’ve been using a framework inside an Airtable base for prioritizing experiments and measuring results.

In a follow-up post, I’ll publish our experimentation framework and explain it in more detail. But for now, here are the main features:

• It starts with the budget – Figure out how much discretionary marketing budget you have for experimentation.

• Collect ideas internally – Anyone at the company should be able to contribute an experimentation idea.

• Each idea gets data points – What metric will it impact? How long will it take to build? What’s a revenue estimate for the idea?

• Each idea gets a status – To keep the workflow organized, add statuses for experiments such as building, running, awaiting results, etc.

• Assign each experiment to an owner – A specific person runs the experiment for a set period and returns with data.

• Assign a next step to the experiment – Promote it to evergreen status or toss it out because it didn’t meet the initial criteria.

Your first experimentation framework can be simpler than this. Track the most critical inputs such as “impact” and “effort” and add data points as the framework grows.

If your company views experimentation — and failure! — as opportunities to learn rather than costly mistakes, be thankful. But also, get to work!

A framework like ours is a good start. It’ll help you follow through on the company’s faith in marketing to understand audience expectations and help build better products.

We’re exposed to word-of-mouth marketing all the time. We take recommendations from friends for Netflix shows, restaurants and plumbers. Trust is a huge factor. We trust our friends and family, so we’ll take their word over an advertisement.

In fact, people are 90% more likely to trust and buy from a brand recommended by a friend.

Trust is at the heart of WOMM (word-of-mouth marketing). Despite living in a world obsessed with inbound marketing data, 64% of marketing executives believe word of mouth is the most effective form of marketing.

For a B2B tech company, having great products and helpful customer success people is not enough to move the word-of-mouth needle. Building brand trust requires a united company willing to play the long game with customer relationships.

My word-of-mouth wake up call

Even if prospects knock on your door unattributed, they heard about you somewhere.

We recently ran an experiment that opened my eyes to how much of our demand is from word of mouth. I was seeing a surprising amount of unattributed website leads for demo requests and I couldn’t figure out where they came from (Direct / Organic Search). But wherever they came from, they were converting. They were good hand raisers.

To get to the bottom of it, I cold emailed them to ask how they found us. It turns out every one of these unattributed leads that responded to me was a recommendation from somebody.

Some came from an agency partner that has a large social media following. Some were friends or former colleagues of our existing customers who were told to check out Metadata.

When I realized what % of these unattributed leads were word of mouth, it really hit home. I was reminded again that people buy a product because someone they trust recommended it. It’s not a clever display ad or a cool logo that seals the deal. It’s their friends telling them.

The challenge for B2B marketers is that you can’t hack or track word of mouth. There’s no quantifiable silver bullet.

At the same time, you can’t just hope people sing your praises. You can be proactive about WOMM. But it’s more about steady relationship building than any demand gen tactic.

Methods for enhancing WOMM

In my experience, time and patience are required to turn consumers into vocal brand lovers. It’s a long game. Do not expect short-term fixes.

Get the whole company involved in building trust

Companies that are out of sync internally will struggle with positive word of mouth. For WOMM to grow organically, all the major groups in a company need to nurture relationships and help build trust in the brand.

Engineering: Engineers are often on calls with prospects and customers discussing product roadmaps. That’s an opportunity to engage with people sincerely and directly. Be open to feedback and feel comfortable with being honest about the state of the product – good prospects and customers will understand that product development is hard.

Customer success: Customer success helps customers get wins with your product. You are usually one of the main points of contact with customers after the sale. If you’re personable on top of helpful, customers are more likely to spread the word about their successes and your brand (and you!).

Marketing: You should always be genuine in your marketing to establish real, trusting relationships with customers and prospects. Avoid bait-and-switch, flash sale schemes and be honest and authentic. And humor never hurts.

Sales: A salesperson is usually a prospect’s first interaction with a company. If you come off as aggressive, you may not only blow a deal but do damage to word of mouth. It’s important not to value closing a deal over the prospect’s situation. If they don’t have the budget, try to figure out a way to find that budget, maybe not until next quarter. Be flexible and thoughtful to gain a prospect’s trust and it will pay off later.

The C suite: To help word of mouth, the CEO and leadership team should be accessible and in tune with customers. That applies to the content they post on LinkedIn and how they present themselves at conferences and with customers. If the CEO seems aloof, people will assume that cascades down to the rest of the company.

Help customers help you

In addition to consistently cultivating relationships, companies can initiate WOMM by letting customers have a say in the product.

• Before and during your product development, elicit feedback from customers about what new features they want in the next version. This will be personally fulfilling for them. They helped build your product. They’ll brag about it.
• Tap an industry influencer to bang the drum for your company on social media. It always helps to have a well-known figure humanizing your brand to a large audience.
• You can ask customers to leave product reviews on sites like G2, on your own website, or on social media. User reviews often give people the reassurance they need before making a purchase.

Stay positive and don’t burn bridges

Having strong customer relationships is the foundation for WOMM, but even good relationships end.

So keep in mind that even when business relationships are over, they can still pay dividends. You want relationships to be so solid that customers still recommend you when they stop being customers.

It’s no fun when a customer churns or ends up being a bad fit for your product. There will be revenue loss and word-of-mouth marketing will be the last thing on your mind. But always let go of customers with respect and professionalism.

Despite the disappointment, they’ll remember how gracefully you handled the parting of ways and will spread that good word to friends and colleagues.

What do you get when you cross a website chatbot with a LinkedIn Sponsored Message?

A Conversation Ad!

B2B marketers have been using LinkedIn Conversation Ads for over a year to connect with prospects and generate leads and revenue.

At Metadata, we discovered this ad type last year when we had to adjust to pandemic-related budget cuts. I chronicled Metadata’s game-changing experience with conversation ads in a recent post. But long story short: The total closed/won revenue from conversation ads for us from April 2020 to April 2021 was $1.3 million. That’s a 5X ROI!

LinkedIn Conversation Ads were such a win for Metadata that we decided to offer them as a product feature. The feedback from customers so far has been positive and I’ll share customer quotes and performance metrics later in this post. But first, here’s a rundown of the key characteristics of LinkedIn Conversation Ads.

They’re flexible and cost effective

One of the main benefits of conversation ads is their flexibility. You can use them for demo request campaigns, but also for content downloads and webinar registrations.

They’re also cost effective: You can deliver conversation ads at a low cost per send (under $1.00 in most cases) with consistently high open rates of 60% or more.

The sender needs to be the right person

It’s critical that the message comes from someone the target recipient would take advice from.
If the target is the VP of marketing, the message should come from your VP of marketing. Note: A conversation ad should NOT come from a salesperson.

Conversation ads use CTA buttons for responses. Therefore, targets can’t actually respond to the sender, which will prevent you (the sender) from getting inundated with questions. However, in my experience, targets may reach out to you on the side via InMail or directly if you’re already a LinkedIn connection.

CTA buttons create a ‘choose your own adventure’ path

Messages typically have two CTA response buttons, giving conversation ads the feel of a “choose your own adventure”. Instead of just a single response, a new message is sent to targets based on the response button they select.

The CTA buttons usually say something like “Save my spot” and “Tell me more”. “Save my spot” goes to a landing page containing a form to sign up for the demo or webinar, followed by a Thank You page once the form is completed.

“Tell me more”, however, takes a circuitous route where the target is directed to a message describing the demo/webinar in more detail with CTA buttons “Not this time” and “Yes please”. “Yes please” goes to the form page. “Not this time” goes to a message offering more info about the product or an invitation to view an on-demand version of the webinar later. This message will also include a link to visit the website to learn more about the company. And all of this is infinitely configurable.

Placeholders allow for personalization

When setting up conversation ads, use placeholders for information such as first and last name, job title, company and industry. These placeholders are data points that LinkedIn already knows about your targets. So when the ad gets served in real time those placeholders are replaced with the actual values to help you better personalize the ad.

Read more about tips for delivering quality LinkedIn Conversation Ads.

Customer feedback on conversation ads

Here’s how a few of our customers have been using conversation ads so far.

Upkeep

Maintenance management software company Upkeep uses Metadata’s conversation ad feature to drive lower funnel conversions such as demo requests.

How have conversation ads performed so far?

Silvio Perez, Paid Demand Gen Manager, UpKeep: “We’ve generated 300+ demo requests and the percentage of new leads generated from conversation ads that convert into SQO’s are among our highest converting channels.”

What have you learned so far from trying conversation ads?

Perez: “You should build a solid exclusion list, especially when offering gift cards as an incentive. Also, look beyond LinkedIn vanity metrics and collaborate closely with sales to make sure the right opportunities are coming through and closing into revenue. Use CTA labels such as ‘how do I qualify’ to make sure the right people take you up on your offer.

“I recommend you don’t stop at a demo request. Create conversation flows that downsell prospects into your next most relevant offers such as a free trial or gated content.

“Finally, triple-down on your first sentence. It’s the first thing the prospect sees in their inbox. We’ve found a difference between 50-77% open rates just from testing the first sentence.”

Labelbox

Labelbox, a training data company, uses Metadata’s conversation ads to offer downloadable content pieces and demo sign ups.

How have conversation ads performed so far?

Chris Ebhogiaye, Growth Marketing Manager: “We’ve seen conversation ads drive 60% lower CPLs than our typical LinkedIn ads with strong engagement and conversion rates helping offset higher than average CPMs.”

What have you learned so far from trying conversation ads?

Ebhogiaye: “The shorter and snappier your messages are the better. So far, conversation ads are more successful for us at driving top of funnel interest than lower funnel activity.”

LaunchDarkly

LaunchDarkly, a feature management platform, has been using conversation ads to offer demos using a Doordash gift card as an incentive.

How have conversation ads performed so far?

Maurice Maxwell, Sr. Digital Marketing Manager, LaunchDarkly: “Performance has been stellar. We spent only $7K and drove 50 leads and those automatically became SALs [sales accepted leads]. Conversation ads have influenced $60K in pipeline.

“We asked people a qualifying question and gave some background to make sure this was something they did or managed. This helped weed out people who wouldn’t benefit from our offer.”

What have you learned so far from trying conversation ads?

Maxwell: “Pinpoint your audiences, but also exclude anything you can to improve segments, job titles and functions. You really need to know your ICP to make sure the audiences you want are what you’re targeting.”

Click here to learn more about running LinkedIn Conversation Ads with Metadata.

Like the rest of the marketing world, we had to make significant budget cuts when the pandemic hit last year. 

Except these budget cuts actually led to a game-changing discovery for us.

We discovered LinkedIn Conversation Ads, a new ad type that’s generated more revenue for us than we could have ever imagined. And it didn’t take very long to see initial results.

Our monthly advertising budget was cut by 60% so we only had $17,000 to play with each month.

We weren’t seeing a whole lot of results from our email campaigns and our website wasn’t converting. We needed to find new (and cheaper) channels to generate qualified demos for our Sales team. 

I came across LinkedIn Conversation Ads within my own network and decided to try them out.

If you don’t know much about conversation ads – think Drift chatbot meets LinkedIn InMail. They’re short, sponsored messages that can be sent to a targeted inbox on LinkedIn.

After a quick introduction, each message usually offers a demo, webinar or case study with call-to-action buttons (“Yes I’m interested”, “Tell me more”) that link to your landing pages. As well as pre-populated responses that you create.

In April 2020, we delivered our highest number of demo requests ever (by 230%), and spent 60% less than the prior month. 

This was NOT beginner’s luck. Here we are a year later and I can tell you our success with conversation ads has only grown. 

Conversation ads are a viable long-term strategy for B2B marketers. And you’re missing out by not including them in your demand gen strategy.

LinkedIn Conversation Ads ROI: By the numbers

The dangling carrot of our initial conversation ad campaign was a $100 DoorDash gift card to get people to take a demo with us.

We’ve kept the $100 gift consistent over the past year and tried out several different offers.

Given we’ve needed to account for the advertising spend and the gift card for each demo request, we made sure our audiences were laser focused.

We target primary buyers in very tight industries and company sizes, ending up with an audience size of about 50,000.

With that in mind, here are performance metrics for our conversation ads, from April 2020 through April 2021:

• Total spend: $266,000
• Average cost for each message sent: $0.30 to $0.70
• Total demo requests: 2,089
• Total pipeline generated: $5.3M
• Total closed won revenue: $1.3M
• 5X ROI to cash … That’s true ROI, not just ROI to pipeline
• 85% of marketing’s total ad spend has gone to LinkedIn Conversation Ads

That 5X ROI on our campaign has been so strong that our sales team asked us to slow down inbound marketing because they were inundated with demo requests. 

How often does that happen???

Six tips for delivering quality LinkedIn Conversation Ads

The message should come from the right person

Conversation ad messages are “sponsored.” 

Because there’s a stigma with the word “sponsored”, make sure the message has a conversational tone and comes from someone the target relates to and would take advice from. 

If the target is the VP of marketing the message should come from your VP of marketing. 

Note: A conversation ad should NOT come from a salesperson.

Make your offer visible in the pre-header

You only have one message to get the target’s attention, so the first sentence is crucial. 

The key detail — in our case the $100 — must be clearly seen. 

The pre-header shows most of your first sentence so get the $100, or whatever your hook is, in the pre-header. As in: “Hi Jason, I’d like to send you a $100 gift card.”

Show social proof in your intro

Prove your worth in your introduction by listing well-known companies using your product. 

Winning over the recipient’s trust is half the battle, especially if you are a startup. 

They may say to themselves, ‘I’ve never heard of this company. But these two other companies I like are working with them, so I’ll give it a chance.’

Establish you understand their problem and how to solve it

LinkedIn Conversation Ads only allow 500 characters per message. 

So indicate as soon as possible that you empathize with the recipient. This part is the heart of your message. 

Let them know you’ve been in their shoes. Something like: “I’m a marketer like you, with the same pressures and goals.” Then quickly describe how you’ll help them increase ROI.

The gift card should be $100, or more

I believe $100 — not $50 or $75 — is the right offer amount for a low-funnel offer like a demo.

A hundred dollars is affordable for you but still enough to convince the recipient you’re serious about targeting them. After all, you wouldn’t just offer $100 to anyone.

Have a plan for people agreeing to the demo just to get the $100

To combat money grabbers, I stress in the ad message to only book the demo if:

1. You work for a B2B company
2. You spend at least $10K/month in paid social ads and have a genuine interest in learning about Metadata
3. You are involved in martech purchasing decisions

You can now run LinkedIn Conversation Ads with Metadata 

LinkedIn Conversation Ads have been so successful for us that we’re adding them as a new feature in our own platform. 

That’s right, Metadata is one of the first LinkedIn partners to offer conversation ads in its own product! 

To learn more about how you can use Metadata to run LinkedIn Conversation Ads, click here.

I recently wrote about workplace anxiety and how data can be a great remedy. For this post, I’d like to expand on the topic of data, specifically how I’ve built and used “demand models”.

Demand models use various data inputs and then work backwards, using historical conversion rates and costs, to identify the budgets and scenarios you need to meet the demand in a given period.

A true demand model is tied to revenue, not leads.

It’s a bottom-up calculation where you start with your planned ending revenue number and you work your way up the ladder to figure out how much demand (in dollars) marketing needs to generate. 

By distilling marketing and sales activities down to accessible numbers, your company leadership will have confidence that the goals are at least somewhat achievable, and what rates and values will need to hold true in order for the goals to be met.

The CMO can communicate more accurately with the CEO about how many actual opportunities and deals marketing will drive this quarter to support growth.

Key ingredients for your demand model

Here are the major data points I include in Metadata’s model:

Estimated start-of-quarter ARR – The amount of revenue you predict you’ll have at the start of the period you’re forecasting. For the sake of argument, let’s assume we’re measuring quarters.

Estimated end-of-quarter ARR – The revenue you want to get to by the end of the quarter you’re forecasting.

The net new ARR needed (the growth delta) – The difference between the start-of-quarter and end-of-quarter numbers, i.e. how much new ARR you need.

Net churn – Customers leave, new ones sign on. This number could be positive or negative. But it should be based on the actual $’s that are at stake for a given quarter, not just an average % for the year.

Most SaaS businesses sign on more people in the 2nd and 4th quarters than the 1st and 3rd, so straightlining an average won’t work.

Total ARR goal – The growth delta plus (or minus) net churn, giving you the total ARR goal for the quarter. 

Let’s say that the total ARR goal number is $1.7 million. Marketing is not on the hook for sourcing and closing all of that in the quarter. The majority will come from existing sales pipeline you’ve been building up over the quarters. 

Expected pipeline revenue – The next section of the demand model includes estimated revenue from current quarter and next quarter pipeline.

At Metadata, we have 6 opportunity stages, so I break this section down by each stage. Each stage includes the total revenue in that stage, the expected close rate for that stage, and then the expected revenue by multiplying these together.

I use historical close rates by opportunity stage to plug into the model and may either increase or decrease based on our current trajectory.

Total ARR to Source and Close in the period

Add together the expected closed/won pipeline revenue from the existing quarter and next quarter and this is the revenue you’re forecasting will close from existing opportunities. Subtract this number from the Total ARR Goal above and you have the amount of revenue that needs to be sourced and closed between now and the end of the period you’re forecasting.

We now start to work this revenue back to the activities we need to drive in Marketing and Sales to get to that number.

Average ARR selling price – In order to turn the revenue you need into the total number of new customers, you need to divide the total ARR goal above by the average ARR of each new customer.

Conversion rates – I then use stage-to-stage historical conversion rates to work the total number of new deals back to the number of early-stage opportunities I need to deliver. I then work that back into the number of demo requests I need to drive through Marketing.

How many demo requests then? I need to know my demo request to opportunity conversion rate, which will tell me how many demo requests I need to drive to turn into the early stage opportunities that will turn into closed/won deals.

Cost per demo request – As long as I understand my average cost to drive a demo request, I can now figure out how much marketing budget I need to drive the # of demos needed. If there is a delta (meaning I can’t afford all of them), then I get into a war room and start figuring out how to get additional lift on conversion rates, lower cost per demo, or places to find earned demand.

Note: I’ve included an ungated demand model template you can use to input your own data points and update regularly.

The biggest challenge for your demand model: sales cycles

The one thing we haven’t discussed are sales cycles.

Sales cycles can be 30 days, 90 days, 120 days or more. This means the marketing I do today will likely not turn into company revenue for a quarter or more.

Metadata’s sales cycle is short enough that we usually have the pipeline/churn/revenue data within a quarter to feed a demand model. However, as we move upmarket our sales cycles will extend.

If your cycle is 90 days or less, then you’re within a quarter and don’t need to worry too much about this.

If it’s longer than that, you’ll want to do an analysis of how many deals are sourced and close within whatever timeframe you’re working from and use that to adjust the number of demo requests you need to drive so that enough of them close within that time frame.

Start with basic inputs and let it grow 

For a basic demand model, these data points are a good start:

• Revenue now
• Revenue goal for end of the quarter
• Average product selling price 
• Conversion rate from opportunity to closed/won
• Conversion rate from lead to opportunity

For a quarter or two, your model may only include 10 data points.

It’s fine in the beginning to just use estimates for churn and pipeline and then feed in more accurate inputs as you generate more sales data. Before long you’ll have 40 or 50 data inputs. 

Don’t obsess over the demand model

I do not recommend updating the demand model every day. One day it will say you need 100 demo requests, the next day it will be 85. Daily incremental data changes are not worth obsessing over.

So stick to doing updates once or twice a month. If there are discrepancies in your data, bake those into the next iteration and keep evolving. If your model says you have more than enough budget to pay for the demand this quarter, that’s great!

But don’t rest on your laurels. Spend the downtime finding that next round of demand for your product.

Whether your model delivers good news or bad about the demand/budget ratio, it serves a supremely important purpose: it reduces the dread of uncertainty, allowing you to plan ahead and make the best marketing decisions for your business.