AI has the power to change B2B market research forever

We’ve really, really, really tried to resist writing about AI.

But in the end, we couldn’t help it.

And no, this article was not written by AI. 01010001010101. Just kidding. Or are we? Yes.

The consensus is that artificial intelligence will disrupt creative output. But remember that producing creativity is only part of the job of B2B marketing. The first and most important responsibility of the B2B marketer is diagnosis: understanding the customer.

So we’re here to give you a contrarian view of AI.

The biggest disruption will not be creative. The biggest disruption will be the diagnosis.

AI has the potential to change market research forever. And for the better.

Welcome to the world’s largest B2B panel

If diagnosis is so important, then why aren’t B2B marketers investing more in market research?

We’ll give you a simple answer: because most market research is slow, expensive, and flawed. We ask the wrong questions and spend over $100,000 waiting six months for answers. In B2B, market research is doubly difficult. While it’s easy to recruit a group of people with teeth for your floss brand, it’s difficult to recruit a group of IT decision makers for your ERP brand. The cost alone makes market research inaccessible to most B2B brands.

But there has been a quiet revolution in market research in recent years. Professor Jenni Romaniuk has published two seminal books that help marketers ask B2B buyers better questions. In terms of AI, Professor Romaniuk’s ‘Better Questions’ helped solve the ‘quick problem’. She is teaching marketers what questions to ask. But how to answer these questions quickly has remained an unsolved problem…until now.

Artificial intelligence is starting to solve this problem.

So why is AI so good at quick and accessible market research?

Because GPT is essentially trained on a copy of the internet, including trillions of collective sites, links and reviews. AI, like GPT, can “survey” the world’s largest online panel – the Internet – to make brand performance ratings. And it can return preliminary responses much faster than traditional market research surveys, and at a fraction of the cost.

We recognize that speed and cost aren’t everything: data quality matters hugely, and humans have yet to review and verify AI results. But when it comes to brand research, we’re in the “all models are wrong, but some are good” camp. In our first touch-ups, we ran two tests to analyze the usefulness of AI as a market research “co-pilot.”

And now we want to share what we have learned with you, our beloved readers.

B2B brands need not fear rejection, but be unknown

ChatGPT on category entry points

We believe that Category Entry Points (CEPs) should form the basis of your B2B brand positioning.

Buying situations are what make 95% of prospective buyers finally enter the market. To thrive, your brand needs to be remembered in as many of these situations as possible. But before you can make that link, you need to understand what all the different shopping situations look like in your category.

So, on a sunny Friday morning in New York City, we decided to ask ChatGPT to generate a list of 32 distinct reasons buyers might buy CRM. In about 20 seconds ChatGPT gave us 32 replies. Answers like “give support teams a 360-degree view of customer information, leading to personalized and proactive support.” See an example below.

These are all doable CEPs for CRM marketers, generated in 20 seconds at a cost of $0.00.

It’s important to note that “arousing” buying situations is only the first stage of proper CEP research, as described by the Ehrenberg-Bass Institute. Prioritizing the right situations based on the “3Cs” (common, competitive, credible) still requires a follow-up survey to assess the relative value of the different CEPs to your brand.

But meanwhile, ChatGPT can also write a survey to determine which brands come to mind in which situations. This is the type of survey you should use to measure and optimize your mental readiness:

Dall-E on the brand’s distinctive assets

Professor Romaniuk coined the term ‘distinctive brand asset’ (DBA) to describe branding devices such as logos, slogans and lettering. But our foul-mouthed mentor, Professor Ritson, has a competing term called “brand codes.”

We’ve always preferred the word resources, as it’s a financial concept that resonates with CFOs, who covertly run most marketing departments. But recent developments in artificial intelligence may propel Ritson forward in this rhetorical race.

Why brand codes on distinctive assets? Because brand codes are no longer theoretical concepts. Brand codes are now technical requirements. If your brand can’t be translated into code, it will be impossible to harness the power of AI like Dall-E.

Most B2B categories are a sea of ​​uniformity with few distinctive brand codes.

We first realized this after seeing an instrument built by our friend, Noah Brier. Noah has a combined marketing and coding background and has created a tool called CollXbs, which uses AI to generate collaborations between popular brands. He explained the connection between AI and branding in extremely simple terms:

“In some ways, the goal of branding – to create recognizable patterns – is perfect for the machine learning tool, which is, in effect, recognizing patterns in large datasets. The AI ​​seemed to figure out which brands were strong and which were weak. When you’re running an in-system collaboration with Hermès, for example, the other brand needs to have a strong aesthetic, or else Hermès will cover it. As a rule, strong brands seemed to come out with better and more realistic results.”

In other words, brands with clear codes will soon have a clear edge in creative development.

The best way to test the strength of your brand codes today is with Professor Romaniuk’s “Distinctive Asset Matrix”. But again, this is a survey-based methodology that costs money and takes time. Instead, you can pre-test your brand codes by asking Dall-E to generate ads for you.

When we asked Dall-E to generate a Guinness ad for LinkedIn’s mobile app, we got the image below.

This announcement will not win a Cannes Lion and imminently lay off any artistic directors. But the ad tells you that Guinness has three very strong distinctive assets, the harp, the font, and the black and white color scheme. And it tells you that the Guinness brand has been managed so faithfully and consistently over the centuries that even an AI can create a recognizable ad. This makes Dall-E a fast, cheap and useful supplement for distinctive asset testing.

We tried to replicate the same experiment for our B2B clients and the resulting ads were generic and could be attributed to any brand. Most B2B categories are a sea of ​​uniformity with few distinctive brand codes.

Again, it’s easy to imagine a future where AI can scan thousands of ads in a category and generate a list of the strongest brand codes for use in marketing communications. And Dall-E can keep us busy while we wait.

AI is made for B2B market research

We’ll end this column with some final thoughts on the word “prompt.”

There is a lot of talk about the need for a future job called “prompt engineer”. But to a large extent we already have these kinds of professionals within organizations… they are called marketers. After all, AI uses “tips” to return responses just like marketers use “tips” to measure metrics like required awareness.

AI will broaden the market for marketers and market researchers. Every B2B (and B2C) brand needs to understand their customers at scale to build better marketing and make better products. Diagnosis is often slow, expensive, and optional. It’s about to become fast, accessible and essential. And that’s what disruption looks like.

Peter Weinberg and Jon Lombardo are the heads of research and development of the B2B Institute, a LinkedIn think tank that studies the laws of growth in B2B. You can follow Peter AND Jon on Linkedin.