In this edition

• 📊 What types of companies are Google Ads best for?
• 📈 Using data to validate ideas, test new marketing channels, and educate your team
• 🙌 Target your ICP, not your TAM

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📊 Trends & Insights (from Benchmark Groups)

Can most businesses see good ROI on Google Ads?

Should you try Google Ads this year? Can they actually help you hit your pipeline or revenue goals?

If you ask your peers, you’ll probably get mixed responses from one of two camps. One camp says they’re competitive, expensive, and unsustainable. The other swears by them and finds they’re an integral part of their marketing plan.

We looked at our January benchmark data on Google Ads and were surprised to see that, on average, Cost Per Click and Cost Per Conversion wasn’t as ridiculously high as some people make them out to be.

In January 2022, the average cost per click for B2B companies was $1.52 (see image below) and the average cost per conversion was $39.65. 

Looking at the data, it seems like most B2B companies could see a great ROI from Google Ads, potentially in a much faster timeline than say, starting an SEO campaign from scratch. But ultimately, the success of a search ad campaign could come down to one or all of these factors:

1. Product-market fit: how well you solve a big enough pain, for the right audience, at the right price point.

2. Targeting: getting in front of the right people, for the right searches, with the right intent.

3. Copy: making the ad copy as compelling and clear as possible.

4. Landing page: what happens when they click (where they land, what they see, what you ask them to do, what you communicate to them there).

5. Budget

6. Sales ability to close prospects or drive signups

7. Your financials: and obviously, the cost of your product, profit, CAC Payback, etc.

To explore this topic deeper, you can check out our latest article where we surveyed 96 companies (46% agencies/consultants, 28% B2C, 25% B2B) to learn:

• How many consider Google ads effective
• Why they prefer Google ads over other platforms
• What they wish they could improve about the platform
• And more

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📈 Drive Predictable Performance (from Metrics & Chill)

Using data to validate ideas, test new marketing channels, and educate your team.

Here are three things most companies need to do each year:

1. Launch a new service, product, or service

2. Share your team’s performance with the rest of the company

3. Invest in new marketing channels

Ryan O’Hara thinks data can help you do those things better, but only if you use it the right way. 

Ryan was the former VP of Marketing & Growth at LeadIQ, and he’s now the founder of Request For Meeting. And he recently came on the podcast to share how he uses data to validate ideas, evaluate new marketing channels, and share performance with the team.

Use data to validate and refine an idea.

In most cases, you should validate an idea before you ship it. Ideally, you want to know there’s some demand for what you’re creating: that people are willing to hand you money for it once it’s out in the world. 

Sure, there are exceptions. 

For example, you’ve probably heard the famous quote attributed to Henry Ford (founder of Ford Motor Company), “If I had asked people what they wanted, they would have said faster horses.”

Or take the iPhone. In his pre-iPhone revolution university days, Ryan was part of a group that did market research for what consumers wanted out of a new phone. Respondents highlighted features, like a better signal or a flashlight. None of them could’ve imagined the modern smartphone.

But most of us aren’t building the first car or iPhone. These examples are the exception, not the rule. We’re trying to build new features, products, or services that solve our customers’ articulated pains.

And Ryan feels that before we invest too much time, resources, money, we should validate to ensure we’re building the right thing. To do this, he recommends using a blend of qualitative and quantitative data. Here’s the framework he recommends:

First, perform qualitative research by interviewing 30~ people for each job title or role you’re looking to serve. After you complete those calls, distill the insights and move on to the next role you need to survey.

Next, conduct quantitative research. You can use a tool like SurveyMonkey, Typeform, or Google Forms to survey a much larger sample size. Once you’re done with that research, you can distill the insights and let them inform the next feature, product or service you build. 

Ryan takes it a step further, and circles back with people he interviewed to get immediate feedback on what he built.

In other words: draw insights > build > get feedback > repeat.

Use data to ensure you’re headed in the right direction (while avoiding indecision).

Ryan noted that many marketers are overly reliant on data. They either get stuck in “analysis paralysis” (trying to deduce what actions to take) or feel like they have to track every single thing and wait until they have a perfectly clear picture of what their next steps should be. 

Their intention is pure. They want to make data-informed decisions. They don’t want to make the wrong decision and end up wasting time or budget on things that won’t drive results. If this is you, Ryan’s advice is to spend less time analyzing data and more time on output. 

In other words, check the data frequently enough to make sure you’re headed in the right direction and getting the right leading indicators that something will drive results. But then put your head down and get back to work. 

Imagine you’re in a rowboat in the ocean, and trying to find an island off the coast. You want to stop rowing often enough to pick your head up and make sure you’re headed in the right direction, and that all that effort isn’t in vain. But then you’ve got to get back to rowing to make progress. The longer you row, the more inputs you get about your direction, and the more decisions or adjustments you can make.

At Databox, we actually teach companies to track “output metrics” and “leading indicators” as part of their dashboards or reports. The output metrics track the work you’re shipping in an effort to hit your goals: things like blog posts published, articles optimized, or interviews produced. Leading indicators are metrics that track the early signal that that work will be successful: things like visitors to the blog, or podcast downloads.

Data is critical to help inform decisions, but it’s not going to tell you what to do. At some point, you’ve got to make a bet and start shipping work. Ryan would rather see marketing teams increase their output and get 3-4x the “at bats” than wait to test the perfect, data-validated idea.

The more output you have, the more data you’ll get about the efficacy of that strategy. And based on that data, you can decide to scrap the project and try something new, pivot and change direction, or double down.

And if this second tip feels in conflict with the first, maybe a good way to summarize it is: spend your time validating and building the right thing, not deliberating on how to market it.

Use data to educate your team, not merely report numbers.

Ryan believes you shouldn’t merely report the numbers, you should teach why they matter. In other words, you should educate your team on what they mean, and why they’re important.

Reporting the numbers looks like this: “we ran this campaign, which drove x leads and x opportunities, for $x.”

Educating on the numbers looks like this: “here are the numbers we need to grow, what we tried to make it work, why we tried this, what worked, and what didn’t.”

When you educate your team on the numbers, you’re sharing why you did what you did, the result you thought it would drive, what you learned along the way, what worked, and just as important – what didn’t. 

Let’s say you’re leading demand gen at your company, and reporting on the performance of a new paid ads initiative. Instead of reporting the impressions, average cost per click, and total leads generated, use the numbers to help your team understand why you did what you did.

Explain why you chose LinkedIn over other channels, why the messaging and creative were what they were, and why you made this bet. Then share what worked and what didn’t. Finally, share your hypothesis as to why this was.

Ryan says that educating your team like has a number of benefits: 

You’ll gain trust: when team members see you’re willing to share failures (not just successes) and understand your reasoning behind an initiative, they’ll trust you more.

You’ll help your entire company learn from failures. If other teams were to try something similar, they can learn from your mistakes and save time and money.

You’ll inspire other teams to emulate what works. The rest of the team will see what’s working, and often want to be a part of it. Ryan cited a story where they had an organic LinkedIn brand-building experiment perform really well, and had top account executives at LeadIQ offer to be a part of the next one. 

Data is powerful, but only when used the right way.

As I reflected more on my chat with Ryan, the main takeaway I got was “data is as valuable as you make it”.

It can be a crutch or something you hide behind. It can be something you merely report, with little help to the rest of your team. It can lead to over-reliance or indecision. 

Or, it can inform what you do and help you make better decisions. It can help you know if a new marketing program has potential. It can help you build something people will actually pay for, or share with their friends. And it can help educate the rest of your team and inspire them.

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🙌 Community Feature (on LinkedIn)

Target your ICP, not your TAM.

This week we’re featuring a former podcast guest and incredibly smart marketer: Jonathan Bland, co-founder of Omni Lab Consulting.

He had a great writeup on why you should be focusing on selling to your ideal customer profile (ICP), not your total addressable market (TAM).

Here’s a snippet:

🙌 Community Feature (on LinkedIn)

Target your ICP, not your TAM.
This week we’re featuring a former podcast guest and incredibly smart marketer: Jonathan Bland, co-founder of Omni Lab Consulting.

He had a great writeup on why you should be focusing on selling to your ideal customer profile (ICP), not your total addressable market (TAM).

Here’s a snippet:

“When you don’t [target your ICP] it leads to:

1.) Watered down messaging
2.) Wasted ad spend
3.) Poor quality leads

Then you are confused why:

1.) Pipeline is down month over month
2.) Sales reps continue to miss quota
3.) The product team can’t keep up with new product feature requests

Your TAM is your total addressable market…the total number of people that could be a fit for your product or service.

TAM = total # of potential customers x annual contract value (ACV)…”

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