How Jasper won AI marketing by rushing to find and acquire users

Jasper’s moat from rushing to the lead

Typically in any market, the category leader gets the lion’s share of the revenue and profits.

This becomes even more true when there is little differentiation in the functionality of products. People go to the brand they know or the company comes up first on Google.

It makes sense, most people want to risk minimize and there is nothing less risky than going with the biggest player in the industry.

And with tools to build products becoming cheaper, better, and faster. Tech is becoming more and more homogenized.

You don’t even have to be technical to build a robust product anymore. Largely due to the rapid advancements in AI.

This has only increased the importance of being the category leader. Yes there is more competition, but it will often be seen as noise by potential customers. Causing them feel overwhelmed and more likely to go with the “safe” bet of the leader.

Jasper understood this and took advantage of it.

As we discussed in last week’s episode. Jasper got early access to Open AI’s model through a YC-in.

So unlike many of their competitors at the time, they focused on building on top of an existing model, instead of building their own. Instead focusing on distribution.

A decision that would ultimately help them win their category of AI marketing (at least for now).

🔬 How this looks practically:

3 actionable insights from the tactic.

🎯 1. Narrow entry point

Jasper entered the market with two very specific use cases.

Copy for paid ads (Facebook and Google) and for landing pages.

Nothing more. Nothing less.

This became their wedge in the market. Solving a real problem in a tangible way. Super intuitively and visually.

Why is this important for your wedge? It makes it a whole lot easier to explain and for the value to click.

Demos became more about people throwing money at Dave rather than Dave trying to convince people on Jasper.

Oh Dave. He’s one of the founders. In case you’re new here, last week we spoke about how finding Founder-Market Fit became a key growth lever for Jasper.

But back to this week’s content.

Marketers started rushing to Jasper to try it out. So to make this journey easier for them, Jasper was constantly adding templates to remove the blank canvas problem and helping specify use cases even more.

The key lesson here:

Get specific.

The narrower your target customer and their job to be done is the more appealing you can make your MVP and (often-overlooked) demos to them.

More users, more revenue, and quicker learning and iterations cycles. A faster path into the larger market you’re after. In Jasper’s case: Marketing.

💵 2. High spend

Jasper had a great starting point with their background, existing customers, and networks in the industry. But they felt that AI marketing would be commoditized and the winner would be the first to lead.

So they weren’t afraid to spend money.

Getting in front from a distribution stand point was the most important differentiator, especially given the underlying model was not proprietary (people could make identical products).

Jasper wanted to be the first name people thought about in AI marketing. The first result when Googling.

It’s how user growth snowballs in relatively undifferentiated industries.

And importantly, it’s crucial when trying to go upmarket into enterprises. They are even more risk-averse than the typical person. The decision maker does not want to lose their job for picking an unheard of company that doesn’t work out.

But if they go with Jasper and it doesn’t work out. It’s fine. They can show they picked the biggest player in the market and the problem wasn’t their decision making.

So how did Jasper spend this money?

Two main categories:

1. Facebook ads

2. Influencers (with an affiliate community)

They had some money from their current business Proof, which they had gone through YC with, and could afford to invest in speeding up their GTM.

What I love about the influencer play for Jasper is that their product value is easy to realize. You can see the transformation happen.

AI was also completely new to interact with (knowingly at least) for the majority of people. So it helped to have influencers people trust walk them through it.

Within 3 months. Jasper were the clear market leader.

Sometimes you need to speed ahead of the market. When this is the case don’t be afraid to spend money. But first, make sure you have some validation of your product or else you will never be able to turn this growth into profit.

🏗️ 3. Data consolidation

This is when things start to get really fun.

I touched on it last week and honestly couldn’t wait to discuss it more.

I’ve mentioned a lot about the lack of differentiation in the market for Jasper. But that’s not 100% true.

Yes given an equal start there is little to no differentiation. But as you start to pull ahead of your competition this becomes less of the case for AI markets.

Why?

Well as great as the underlying models are (Open AI, Anthropic, Llama, etc.). They are generalized. They solve every use case.

And that’s the purpose of them. Yes they may solve some use cases better than others, but they are by no means specialized.

You see, what happens in an AI market is you get three layers developing:

1. The underlying infrastructure (the underlying LLMs)

2. The specialized models (fine-tuned models)

3. The application layer (the tools you and I use)

I could try and explain this better, but my fellow South African Jaryd does an awesome job on his Jasper deep dive. And here’s a sweet graphic he made to explain it better:

Jasper, becoming the market leader, transitioned from being mainly L3, to having a solid foot in both L2 and L3.

They had more data for marketing use cases than anyone else in the market. They knew:

• What worked.

• What didn’t.

• What prompts performed best.

• What users liked the most.

• And a whole bunch more.

Using this data as local knowledge on top of the L1 models they use.

What this means is that they can give users better results, which leads to more users, which leads to more data, which leads to even better results.

And we get this awesome data-driven flywheel:

Jasper understood their users and market better than anyone else. They had the most data. The most efficient feedback loop. And now a moat that new entrants would struggle to get past.

Don’t let this dishearten you. You don’t need ALL the data to start.

When Jasper started they already started dabbling in L2 at launch - Using their previous marketing experience to fine tune Open AI for their users:

• Copy they knew worked.

• Ads they knew performed.

• Structures and frameworks.

And that’s how you should start. Spend time at the beginning to use any propeitary knowledge, or access to knowledge to create a specialized version of what is already awesome tech.

It’s also important to remember that for most people, generative AI will never be fully understood. They just want an easy and intuitive way to use it. But I’ve said too much.

Tune in next time to The Zero to One. Same place. Same time. To have a chat about how Jasper used approachability to win users.

It’s been your host,

Sheldon

• Vinod Khosla on why listening to VCs is not always the right thing to do (link)

• Why taste is eating Silicon Valley (link)

• How to grow with strategic acquisition (link)

Tools of the week 🔨

• Pendo: Breakthroughs for better products.

• Scout: All-In-One Sales Prospecting Platform.

• Passionfroot: Where Creators do Business and brand deals.