Data-Driven B2B Marketing Themes for 2024

During 2023 there was the obvious rise of using AI (and other forms of LLMs) to assist marketing’s efficiency and effectiveness.  However, most of the uses of AI I’ve seen were in the areas of (a) content generation, and (b) CS and chatbots. 

Entering 2024, I’m hoping that we see deeper use of AI/ML to enhance the marketing domains of understanding customers, enriching marketing datasets, and better analysis of customer journeys.

In short, I believe – and hope to see – the following B2B marketing trends progress… and hope that B2B Marketing leaders begin to invest in the following: 

• Use of Mass-Customization in Marketing
• Turn to Data-Driven Customer Targeting
• The Death of Traditional Lead Attribution  

I’ve also added a few resources below each observation if you’d like to look more deeply.  Please leave comments, notes and other insights so that others can learn from this! 

1. Use of Mass-Customization in Marketing: 


AI is now being applied to “mass customization” (personalization-at-scale) of content, marketing outreach, web experience, and even product experience.  As marketing departments mature from using LLMs/AI for simple content creation, they’ll find that combining AI with customer data enrichment will open the floodgates to creating even better Ideal Customer Profiles (ICP).  

AI tools are being increasingly used to enrich customer data; not just to add missing contact information and addresses, but to uncover individual customer use-cases, jobs-to-be-done, existing tech-stacks, previous purchase history, social network engagement and more. This data can be assembled for much more precise targeting, outreach, product recommendations, and of course content.

In 2024 I hope to see marketing leaders decrease their “spray-and-pray” outbound marketing and demand generation – in favor of using AI-based tools that will both harvest/enrich contact information (see below) to generate more relevant, ICP-based outreach.  The results could be an order-of-magnitude improvement in outreach response and click-through rates.

For deeper insight: 

• The Intersection Of AI & ABM: Transforming B2B Content Syndication (Mark Nachlis)
• The Power of Personalization in Mass Customization (Faster Capital) 

2. Data-Driven Customer Targeting: 

Building Ideal Customer Profiles has largely resided in the area of assembling personas - and doing so has largely been qualitative research, experimentation, and a pinch of guesswork.

In 2024, I see top-of-funnel ICP creation becoming far more precise and data-driven, leveraging new AI-driven data enrichment, correlation, and analysis. This will be guided by targeting the highest lifetime-value customers, as well as those with the lowest acquisition costs.  

As the trend continues (I hope) we’ll also see a shift to augmenting existing Customer Relationship (CRM) systems with newer Customer Data Platforms (CDPs).  CDPs collect/amass more information about customers than what is simply entered by sales and marketing teams.  I see CDPs leveraging publicly-available 3rd-party data sources, social, etc.  to add to customer profiles.  The results will (a) help better understand existing customers and needs, as well as (b) help predict sources and ways to ID net new customers. 

Source: Alignicp.com

For deeper insight: 

• What is an ideal customer profile? (Unusual Ventures) 
• The Framework for Ideal Customer Profile Development (Gartner)
• Best Customer Data Platforms (Forbes Advisor)

3. The Death of Lead Attribution  

In the very recent past, Lead Attribution and last-touch tracking was used to determine what resources “caused” customers to convert (and what org got the credit). In my opinion, this approach has been simplistic and short-sighted. 

In addition, cookie restrictions and web tracking limitations are making these approaches even more difficult to implement. 

In response, marketers will begin to use more holistic (and privacy-centric) approaches to finding the sources of marketing and sales leads. Strategies like adopting first-party data collection, data enrichment tools, investing in predictive analytics, leveraging AI-driven models, and emphasizing contextual targeting will gain traction.  

Plus, advanced analytics and ML algorithms enables a deeper understanding of customer behavior and allows for more detailed attribution models that take into account various touchpoints and interactions. One of the key benefits of using ML in attribution modeling is its ability to identify the most significant touchpoints in the customer journey, even when those touchpoints may not be obvious.

For deeper insight:

• Future of marketing attribution: How AI changes the game (Segmentstream) 
• Why It’s Time to Change Your Attribution Model (Pecan)

Final Thought

These themes are simply observations I've made as a practitioner; please leave comments, notes and other insights so that others can learn from this! 

First Steps to Find Product-Market Fit: Zero-In on Your ICP

Recently I spent 2 days at the Techstars FounderCon event in San Francisco. And during the previous week I participated in their “mentor madness” events, meeting 1:1 with dozens of founders.

What was clear in 80% of my meetings was an urgent need for early product founders to narrow-down their focus on their Ideal Customer Profile…. And to try to stop “selling to anyone”.

Indeed, so many founders - especially those with technical backgrounds - felt they were building a platform that would appeal to a huge swath of the market. The problem with this approach is that with a limited budget, their priority is to prove their concept… not to show scale (or even revenue). Investors and leadership need to know that there is actually a market for their product – one in which customers will be thrilled by the value it creates.

This is true for new products at established companies, not just startups.

Indeed, the classic product-market-fit metric, the “Sean Ellis Test”, needs to show ~ 40% of customers would be “very disappointed” if the product no longer existed. This isn’t a metric for how many people adopt your product – but rather one that shows sustainable product value.

The takeaway is first to find a narrow customer profile (perhaps uncomfortably narrow) that you can define, identify, target, and win. Worry about scaling later. The advantages of this rationale are:

• Efficiency: A narrow focus makes your outreach more cost efficient
• Data: The feedback from a narrow customer segment is statistically meaningful
• Value Focus: you’ll find the exact value you provide to a precise type of customer

A great summary from Robert Kaminski recently illustrated this – How brand-names got their start…. With an uncomfortably-narrow initial focus:

• PayPal owned payments for eBay sellers before they were in every online checkout.
• Facebook owned online social networking at Harvard, before your mom (and the rest of the world) signed up.
• Airbnb spread their concept through New York and San Francisco before they disrupted the hotel industry. (Uber did the same thing)
• Amazon dominated the book market before they owned all e-commerce.
• eBay owned the collector items market before they took over all online auctions.

Where to begin: Focus, Focus, Focus

Typically I see marketers at all sizes and types of companies default into a blurry, imprecise customer profile, believing that a firmographic customer definition is sufficient. But for an early-stage B2B company, there are many more ICP dimensions of focus you need to consider:

• Firmographics: the broadest filter to use that helps narrow-down the type of businesses you are targeting. The most common basic step to define customers.
• Psychographics: Getting to know the *buyers* and *influencers” you’ll be marketing to. Understand the language they use, the problems they’re solving, and where to find them when it comes time to market to them.
• Jobs-to-be-Done: Describes the *specific* jobs and outcomes the buyers are trying to achieve with your product. E.g. What are the specific problems, solutions, and workflows they use? How would your product fit-in and enhance their experience?
• Technical Indicators: Are there specific technologies or products that would bias the company into buying yours? Are there products in use that would *not* bias the customer to buy from you? What technology categories would they turn to find yours?

Next: Form a hypothesis, test, repeat!

Now comes hard work… manual reaching-out to, and conversations with, individuals who’ll help you validate your ICP hypothesis. These are *not* sales calls. Start with connections, colleagues, social networks, etc. and ask relatively open-ended questions. What makes sense about your product to them? How do they describe it? What do they compare it to? Do they reach an “aha!” moment where they see value? What value is it, and what got them to that point? Try to identify as many firmographic, psychographic, JTBD, and technical indicators as you can.

This process will take weeks if not months, and you should have perhaps 50 conversations – during which time you’ll find yourself constantly refining your “pitch”, challenging your value propositions, and maybe reconsidering much of the product itself. You’ll come to realize that your initial ICP might be off (or totally wrong). But the result will be invaluable learnings that you probably didn’t even anticipate when conceiving of your product.

Research Approach for ICPs

My own ICP experience at a series-A big-data startup showed how many levels and iterations of ICP research, experimentation, outreach, and refocus are needed. This process took the better part of a year before clearly understanding who to initially target. And it turns out that the “traditional” firmographics (e.g. vertical industry, company size) had almost no bearing on finding an ideal customer…




Also Ask: What’s My Category?

As you do your research, you also need to uncover the *product category* you fit into. That is, how do customers think about your product, and where do they conceptually place it? This is all about letting the market *informing you* about where your product fits. And with this knowledge, you’ll better understand competitors and alternatives, as well as keywords and search terms to use.

One important thing to note: Don’t try to create a new category! (or, at least not yet). No matter how “cool” or unique your product is, categories are defined by their competition and by having a defined market. Chances are, at an early stage, you’re not defining a new category. While some products may indeed merit a new category, a challenge is that *no one* will know to ask for, or search for, a product in a category that’s new. . So go with what’s understood.

Great places to begin to discover and define your category (and associated language to use when marketing) are product review sites and industry analysts. For example, G2, Capterra and TrustRadius group products into categories for comparison. Analysts such as Gartner (the Gartner Magic Quadrant) and Forrester (the Forrester Wave), also group products by category. Follow these market-driven definitions as you bring your own product to market.




ICP in the Broader PMF Context

Now that you have a clear 1.0 ICP – which may have taken months to hone-in on – your real go-to-market work can begin: Closing your first early-adopter customers.

As you progress in your go-to-market journey and begin growth, you still should do customer research on your ICP. Why? Because (a) you’ll find that customer expectations change, (b) markets change, (c) competition changes, (d) different customer segments have different ICP requirements.

Consider embedding an “ICP Mentality” into your marketing, customer success, and sales organizations. As you mature, you’ll iterate into a “v 2.0” ICP. This is where you also begin to identify your “superfans” (high-expectation customers who’ll give you regular detailed feedback) and a regular process to poll customers and track ICPs for different solutions, verticals, etc.. And eventually, you should build a customer advisory board made up of customer advocates and high-expectation customers … choose those that will be candid, direct, honest, and representative of the various segments that you sell to.

ICP Maturity Model Stages

Embedding an ICP Mentality also serves to align your entire organization. A single, agreed-upon ICP (or set of ICPs for different products/markets) helps focus messaging, sales and even finance… and avoids that messy finger-pointing between sales and marketing (HT to Dan Sperring):

• Marketing/Sales -
• Agreed-upon GTM & campaign focus
• Consistent lead qualification & prioritization
• Sales/Finance -
• Planning & forecasting based on ICP segment targets
• Finance/Marketing -
• Computation of CLTV based on ICP segments
• Estimation retention based on ICP segments

Other thoughts? I always welcome them!

Additional ICP References

• George Leith: What is an Ideal Customer Profile and 6 Ways to Identify ICPs
• Lenny Rachitsky: How to identify your ideal customer profile
• Product Marketing Alliance: How to create an ideal customer profile
• Gartner: The Framework for Ideal Customer Profile Development

Announcing: Fountainhead Product Marketing

I’m happy to announce my newest professional chapter: Establishing Fountainhead Product Marketing, an advisory and consulting practice focused on serving early-to-mid stage engineering-led companies.

Fountainhead is based on leveraging product-market fit as the core model to help B2B SaaS companies organize teams and improve their marketing.

Full article published here on LinkedIn

What are “Team APIs” and why should product marketers use them?

The API metaphor is a powerful concept when thinking about how marketing teams and adjacent orgs need to intercommunicate. Basically, your goal is to formalize your information exchanges, frequency, and formats so that you begin to build regular bridges with other important players at your company.  It's also a great metaphor for organizing standard company interactions of any kind....

Complete article posted on Product Marketing Alliance