Chapter 7 – What Does Good Research Look Like?

John and Alex were starting to see the fruits of their hard work. Their ads were live, and data was finally trickling in. But with every report Alex shared, John felt a mix of excitement and confusion.

“Alex, how do we know if these results are good enough to act on? And what if we’re missing something?”

“Good question, John. Good research doesn’t just tell us what is happening; it shows us why and how to move forward. Most of all, it helps us avoid costly mistakes. 

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In this chapter, I am not going to teach you how to do research, but it is very important to understand what good research looks like. I’ve seen too many teams think that they’re doing a great job because of all the templates and examples they see online, but in reality, that is all really sub-par work.

When we do research at Spear Growth, we create at least 12 spreadsheets worth of research. You don’t have to do it at the level that we do, but I want to show you a couple of examples of our work and how we use it just so you can get a sense of how to judge your processes in comparison.

Not-so-humble brag: Spear Growth has an AI Dev team, which means we are able to do extremely deep research for our clients, and it takes us less than 10 mins, vs doing it manually would have taken 2-3 weeks at least, and you still wouldn’t get to the depth we do.

Stories of Bad Research

Every quarter of poor research could cost you $30,000 in failed experiments and up to 25% of your team’s productivity. 

Let me share a story from my early career days. A lot of our ads simply weren’t working, and at first, I couldn’t figure out why. We were following all the standard advice—talking about pain points and benefits rather than features. 

Our copy would say things like “save money,” “save time,” or “increase productivity by 3x.” 

Sound familiar?

Do you see where we went wrong?

The problem was that every SaaS company was saying exactly the same thing. We weren’t standing out because we weren’t speaking our audience’s language. 

So, when you’re talking to different industries, use the exact terminology your audience uses. They’ll instantly recognize that you understand their world. E.g., instead of talking about ROAS, talk about the pipeline. They think, “This agency gets me better than the others.” And that trust comes solely from using the words they’re familiar with. You only get these insights when you do thorough research.

Here are some repercussions of poor research:

1. Expensive Experiments That Should Never Have Happened

I’ve seen teams waste thousands of dollars on experiments that could have been avoided. An unnecessary experiment may cost around  $4,000, and teams often run three or more of these parallelly. That’s $36,000 down the drain every quarter simply because they didn’t do their homework first.

2. Never-Ending Review Cycles

When your initial work isn’t well-researched, each review round can delay your campaigns by days or even weeks. Over a year, these delays can put you an entire quarter behind schedule. Think about it – one-fourth of your entire team’s salary cost could be wasted just because of inadequate research.

3. Targeting the Wrong Audience

Recently, when we asked a marketing team about their buyer persona, they gave us one answer. But when we dug deeper and asked the sales team, “Who is really the champion of the deal?” we discovered something completely different. In some cases, especially in dev industries, while marketing was targeting CTOs, it was actually the developers who were making the buying decisions. This misalignment means your ads are speaking to the wrong person entirely. 

So, what does good research actually look like in practice? Let’s find out. 

Spear Growth’s Competitor Research

Example

When doing competitor analysis, the first thing you want to understand is what type of competitor they are. There are four types of competitors:

• Direct Competitor: That’s the simplest one
• Subset Competitor: They do a small part of what you do
• Superset Competitor: They do everything you do, and they do more
• Indirect Competitor: This could be another type of software that solves the same problem, but it could also be consultants or agencies, or just hiring employees

Then, we look at understanding how big they are. Are they bigger or smaller than us? This gives us a good sense of how many people already know about them, their market share, and mind share. Because whatever you want to say in marketing, a larger company with a higher market share usually has a higher mind share, too.

Pricing is another important aspect. If you’re running ads against a competitor and the pricing is very different (much lower or much higher), you probably have different ICPs. 

For example, one of our customers is a Shopify competitor – to work with them, you need to spend about $50,000 a month, while for Shopify, you can start at literally $10. This makes a big difference in how we approach the market and run our campaigns.

Spear Growth’s Persona Research

Example

When analyzing target audience data, we focus on a lot of aspects:

First, we look at demographics to understand which age groups we’re dealing with, as different age groups think very differently about things. This impacts how we need to approach our messaging.

(P.S. – We have a chapter coming on messaging soon.)

We then look at who really influences the deals. Often, clients say they want to target certain people, but when we ask the sales team, we discover different champions. For example, why target CTOs when the developer is going to make the sales decision? This is a very common issue we face.

Our research includes analyzing how our audience actually talks. When they say they do “strategic planning” – we need to understand and use their language. For instance, they say their role involves “Problem-solving”. Now, this might seem vague to us, but if that’s how they talk about it, we need to remove our biases and adapt.

We also collect topics that they’ve actually said in their profiles and posts. Sometimes, we find gems like “change management” or specific industry terms like “mining fertilizer.” This can transform our copy from generic efficiency claims to highly specific, relevant messaging. 

For instance, instead of saying, “increase team productivity by 60%”, say, “reduce fertilizer mining time from seven days to three days.”

TL;DR

Don’t half-ass the research. Yes, doing it for the first time will take a couple of weeks, but you’ll save yourself:

• Time spent getting approvals and fixing misalignment issues.
• Money on experiments you didn’t need to run.
• Resources spent on your team implementing the wrong things.