Any B2B marketer who hasn’t been stuck under a rock in recent months will be considering how and where AI will impact what they do.
Across the latest developments — whether it’s ChatGPT and Claude for text, Mid-Journey and DALL-E for images or HeyGen and the upcoming Sora for video — things are changing fast.
Simultaneously, we’re seeing massive opportunities facing off against significant risks.
Generative AI offers opportunities to do what we do faster and better, replacing the need for a lot of manual work and complex number crunching. On the risk side of the equation we’re seeing issues around copyright and data privacy — not to mention Google de-indexing sites that have gone all-in on bulk AI-generated (spam) content.
A key question for many B2B marketers (and agencies like Considered) is if, when and how to use AI to create compelling content.
For our part, we’re experimenting with all the major platforms. Some are undoubtedly impressive. Some less so right now.
The main issue we’ve encountered so far is that an AI-generated image tends to look like most other AI-generated images. And AI-generated text often reads fine but says little and, again, feels just like every other piece of copy we’ve seen.
This flies in the face of the critical importance of being distinctive which delivers such a significant positive impact in our B2B Effectiveness Engine data (you can learn about this in more detail in our deep-dive article).
This limitation is understandable given how these models are trained. They are an average of their inputs. But it does mean that the out-of-the-box results fall far short of anything we’d be happy putting in front of a client.
So does that mean we shouldn’t use AI?
No. We just need to use it for what it’s good for.
In this article, we’ll look at the difference between how many marketers are hoping to use generative AI for copywriting (the big red write-it-for-me button) and how we’d suggest using it instead.
With all the hype around AI and people’s early experiments (write me a song, write me a poem in the style of…) there’s been an expectation that, given the right prompt, the likes of ChatGPT will do the job for you.
It will, but not well.
So, let’s try an experiment. For the following copy I’ll be using ChatGPT 4 (the paid version). I’m going to cap the copy length at ~300 words so you don’t have to read a ton of text. (In reality, the word length would likely be more in the 1,500+ region for an article.)
We’ll start with a lazy prompt:
Write a 300-word article on using ABM in B2B marketing. Use UK English and sentence case for headlines and subheads. Optimise for the SEO term “B2B ABM”. Give it a catchy headline.
Is this a great prompt? No.
Is it the kind of thing that many might use? Sadly, probably.
The result:
Revolutionising B2B marketing with ABM: A strategic approach
In the ever-evolving world of business-to-business (B2B) marketing, Account-Based Marketing (ABM) has emerged as a key strategy for companies aiming to navigate the complexities of engaging high-value accounts. By focusing on targeted accounts with personalised marketing campaigns, B2B ABM offers a more efficient and effective approach to driving growth and building long-term relationships.
Understanding the core of B2B ABM
At its heart, B2B ABM is about precision and personalisation. Unlike traditional marketing strategies that cast a wide net to attract a broad audience, ABM targets specific accounts that are identified as key prospects. This targeted approach ensures that marketing efforts are concentrated on potential clients who are most likely to convert, thereby optimising resource allocation and maximising return on investment.
The strategic advantage of ABM in B2B marketing
One of the main advantages of implementing ABM in a B2B context is its ability to align marketing and sales teams. By focusing on a shared set of target accounts, both departments work towards a common goal, leading to a more cohesive strategy and improved communication. This alignment not only enhances efficiency but also fosters a deeper understanding of the customer’s needs and preferences, enabling the delivery of highly personalised marketing messages.
Moreover, B2B ABM allows for the measurement of marketing efforts in a more meaningful way. By concentrating on a smaller, more defined group of accounts, businesses can more accurately track the impact of their marketing activities. This data-driven approach facilitates continuous improvement and optimisation of strategies, ensuring that they remain relevant and effective in the face of changing market dynamics.
Conclusion: The future of B2B marketing
As the B2B landscape continues to evolve, ABM stands out as a strategic approach that can adapt to the changing needs of businesses and their clients. By focusing on building strong, personalised connections with key accounts, B2B ABM not only drives immediate sales but also lays the foundation for sustainable growth and long-term success. In an increasingly competitive environment, the ability to effectively implement ABM will be a significant differentiator for B2B companies.
It reads like English — well, English from someone with a basic understanding of the topic and an over-fondness for the kind of words that get used in academic essays (I’m looking at you ‘moreover’).
The tone is generic business-speak. Structurally, again it’s got the whole school essay thing going on.
The most important issue is that it is basically exactly the same as the thousands of other pieces that litter the internet about ABM. It is the average of the average. You could replace ABM with any other business topic, expand the piece to any length and the result would be the same.
The bland leading the bland.
Now, of course it would be improved by human editing. But that would only get you so far if you stick to the core approach suggested by ChatGPT.
Ok, enough bitching about the lazy approach. What’s a better way?
Let’s start with a truism that applies to everything we do in B2B marketing: get the thinking right.
Whether we’re talking high-level strategy and messaging or down-in-the-weeds technical copy, putting the right foundations in place matters. A lot.
This means being clear about what success looks like. Why are we creating this piece? Who is it for? How will it stand out among the thousands of other pieces?
So, in our abbreviated example, let’s approach the task differently.
We’ll start by using ChatGPT for what it excels at, crunching vast amounts of data and bringing it together in an easy-to-understand synopsis. The prompt:
I am writing an article on account-based marketing (ABM) for a knowledgeable audience of B2B marketers. I don’t want to waste their time covering the same things as every other article they have read. What are some interesting challenges around delivering a successful ABM campaign that don’t get covered in many other articles?
The result:
When crafting an article on Account-Based Marketing (ABM) for a B2B marketing audience well-versed in the basics, focusing on lesser-discussed challenges can provide fresh insights. Here are several nuanced topics that could captivate your audience:
- Integration with Traditional Marketing Campaigns: How to effectively blend ABM strategies with broader marketing initiatives to avoid siloed efforts and ensure a cohesive brand experience across all customer touchpoints.
- Data Privacy and Compliance: With increasing data privacy regulations (e.g., GDPR, CCPA), addressing the challenge of executing personalized ABM campaigns while fully complying with legal standards is crucial. This aspect includes acquiring consent, managing data ethically, and navigating the complexities of global regulations.
- Cross-Departmental Collaboration: Highlight the importance of breaking down silos between sales, marketing, and customer success teams to ensure a unified approach to ABM. Discuss strategies for fostering collaboration and overcoming resistance to change.
- Scalability of Personalized Content: Creating highly personalized content for each account is resource-intensive. Explore innovative solutions for scaling content personalization without compromising quality, such as leveraging AI and machine learning technologies.
- Measuring ABM Success: Beyond traditional metrics, delve into advanced measurement strategies that accurately reflect the impact of ABM on long-term customer value, pipeline acceleration, and deal size. Discuss how to attribute revenue to ABM efforts accurately and the challenges of quantifying ABM’s indirect benefits.
- Technological Integration and Utilization: Address the complexities of integrating ABM platforms with existing CRM and marketing automation tools. Explore the challenge of leveraging these technologies to their fullest potential, including data synchronization, workflow automation, and the use of AI for predictive analytics.
- ABM in a Changing Market Environment: Examine how ABM strategies need to adapt to rapidly changing market conditions, such as shifts in buyer behavior, industry disruptions, and economic fluctuations. Offer insights into agile ABM strategies that can pivot in response to market signals.
- Account Selection and Insights: Discuss the intricacies of selecting the right accounts for ABM campaigns, including the use of predictive analytics and AI to identify high-value targets. Also, delve into the challenge of continuously gathering and updating insights on selected accounts to maintain campaign relevance.
- Global and Cultural Considerations: When executing ABM on a global scale, consider the cultural nuances that affect communication styles, content preferences, and decision-making processes. Offer strategies for customizing ABM campaigns to fit diverse global markets.
- The Role of Customer Advocacy in ABM: Explore how leveraging customer advocates within targeted accounts can amplify ABM efforts. Discuss strategies for identifying and nurturing customer advocates to enhance trust and credibility.
Focusing on these areas can provide your audience with valuable insights that go beyond the basics of ABM, addressing real-world challenges and offering innovative solutions.
We’re already into more interesting territory here. In reality, we could write a separate piece on each of these.
As it stands, the answer above runs to just under 500 words. For our 300-word mini-article, we need to make some decisions about what to focus on.
Of course, we can do this ourselves based on our knowledge, experience and understanding of our audience. But, we can also use ChatGPT as a thinking partner to help inform our approach.
So, we can add a follow-up question:
What would you expect to be the top 3 topics that would be most interesting to an experienced, knowledgeable B2B marketer? Give justifications for your choices.
ChatGPT replies:
For an experienced, knowledgeable B2B marketer, the most compelling topics would likely be those that address current pain points, leverage emerging technologies, and offer strategic insights for gaining competitive advantages. Considering these criteria, the top three topics might include:
- Integration of ABM with Emerging Technologies (AI and Machine Learning):
- Justification: The integration of AI and machine learning with ABM strategies represents a frontier in personalized marketing, offering unprecedented capabilities in predicting buyer behaviors, automating content personalization at scale, and identifying high-value accounts with precision. For seasoned marketers, understanding how to harness these technologies to refine and scale ABM efforts can unlock new levels of efficiency and effectiveness, pushing beyond traditional limits.
- Advanced Measurement Strategies and Attribution in ABM:
- Justification: Proving the ROI of ABM initiatives remains a challenge due to its focus on long-term engagement and multiple touchpoints across the buyer’s journey. Experienced marketers are likely to be interested in advanced measurement techniques that go beyond conventional metrics, enabling a deeper understanding of ABM’s impact on sales acceleration, deal size, and long-term customer value. Topics like multi-touch attribution, customer lifetime value analysis, and predictive modeling for forecasting ABM success are crucial for justifying investments and optimizing strategies.
- Adapting ABM Strategies in Response to Rapid Market Changes and Global Disruptions:
- Justification: The ability to quickly adapt and pivot ABM strategies in response to global market disruptions, economic volatility, and shifts in buyer behavior is increasingly important. This topic is particularly relevant in the current business climate, where agility and responsiveness can distinguish between market leaders and followers. Experienced B2B marketers would be keen on strategies that ensure their ABM programs remain effective and resilient, incorporating real-time data analysis, agile methodology, and scenario planning to stay ahead of market trends.
These topics stand out because they address advanced challenges and opportunities in the realm of ABM, offering experienced marketers actionable insights and strategies for leveraging ABM to its fullest potential while navigating the complexities of today’s business environment.
Personally, I broadly buy the first two but the third feels weak. It’s a little too generic business-y — things are always changing, you need to keep up, be agile, blah, blah, blah.
I would probably ditch that and go with ‘Integration with Traditional Marketing Campaigns‘ as one of the three. Your mileage may vary.
But, despite the fact that I disagree with one of the options, I’m still in a better place for having AI interrogate the approach and give its perspective based on what it understands to be happening in the wider world.
But, what if it’s wrong?
Generative AI is somewhat infamous for making stuff up. It’s like a child who is so eager to please they’ll simply tell you what they think you want to hear. Bless.
Sadly, for anyone creating content for a savvy audience, this way lies failure.
There’s no way around this: we need to get to some trusted sources to verify the approach.
We could, of course, dive back into that old workhorse Google. But, if your experience is anything like mine, you’ll have seen Google’s search results getting worse and worse as SEO-optimised rubbish has taken over so many areas. We’re trying to use AI to accelerate and improve the process, not simply abandon us to trawl through one link after another.
Time to switch tools. We’ll now fire up Perplexity, an AI-based ‘answer engine’. Again, I’m using the Pro version which gives me a bit more scope and the ability to use the latest LLMs (I’ve got this set to Claude 3 Opus to help balance a possible over-reliance on ChatGPT 4). The free version of Perplexity is still very good though.
For this experiment, let’s just focus on the first topic area above — ‘Integration of ABM with Emerging Technologies (AI and Machine Learning)‘.
Traditionally, with a Google search, we’d be limited to search terms such as ‘integrating ABM with AI and ML’. We might add on something like ‘research’ to refine the results.
With an AI-powered service such as Perplexity, we can create far richer queries. So my prompt is:
I want you to act as a knowledgeable B2B marketing consultant. How should forward-thinking B2B marketers integrate ABM with emerging technologies such as AI and machine learning? Focus primarily on results that have research-based evidence to back them up.
The results:
As a knowledgeable B2B marketing consultant, I recommend that forward-thinking B2B marketers integrate Account-Based Marketing (ABM) with emerging technologies such as AI and machine learning in the following ways:
- Use AI-powered predictive analytics and lead scoring to identify key accounts most likely to convert. By analyzing data from existing customers and conducting market research, AI algorithms can prioritize outreach efforts to the highest-value prospects, resulting in higher engagement and conversion rates (2).
- Leverage AI chatbots with natural language processing for engaging potential clients and gathering information about their needs. Chatbots can provide a seamless user experience by understanding and responding to inquiries, allowing businesses to scale their personalization efforts through automated initial interactions (2).
- Enhance personalization and targeting by using machine learning to analyze vast amounts of data, identify behavioral patterns and preferences, and gain insights into individual accounts. This data-driven approach enables delivering highly relevant messages and offers tailored to each customer’s needs (2, 3).
- Utilize AI for hyper-personalization, continuously learning from customer interactions and data to deliver highly customized content and experiences in real-time. Hyper-personalization driven by AI will elevate customer engagement, strengthen relationships, and improve business outcomes (2).
- Incorporate AI-powered content intelligence to automatically generate highly relevant content based on customer behavior and purchase history. AI algorithms can quickly create personalized messages aligned with each customer’s needs, resulting in more effective and efficient marketing campaigns (1).
- Implement AI-driven predictive analytics models to recognize patterns, uncover trends, and forecast customer behaviors. These insights allow marketers to optimize content for maximum reach, refine messaging, and adjust strategies in real-time, ensuring a tailored experience for every lead (1).
By integrating AI and machine learning technologies into ABM strategies, B2B marketers can scale personalization efforts, reach the right accounts with relevant messages, increase engagement, achieve higher conversion rates, and drive better overall marketing outcomes (2, 3). As AI continues to advance, embracing its potential will be crucial for B2B brands to stay competitive in the evolving landscape of marketing strategy (3).
This does two jobs. It gives us more depth on integrating ABM with AI/ML. And, perhaps more importantly, it shows where it got the information from (click any of the numbers in brackets).
This means we can go to the source and verify the answer.
We can then repeat this for the other topics and will quickly have all the information we need to write our article.
You were hoping I’d show you how to use AI to create standout B2B copy in just seconds weren’t you? Sorry about that.
Compared to traditional approaches, what we have above is faster — at least in the research stage. Hopefully you can see how it helps us quickly get past the same-old, same-old that would lead to a result that would quickly vanish without troubling the eyeballs of a potential customer.
More importantly, it’s an approach that will result in our ability to create something far more interesting and engaging. Something that shifts the needle for our audiences. (In the real world, we’d repeat a number of the stages to get a deeper and richer insight into the topic and our audience.)
Generative AI offers some powerful opportunities for today’s B2B marketers. It means we can do more, do it faster but, crucially, do it better than ever before.
If you’d like an initial chat about how we can use this kind of approach to help you develop higher-performing B2B content, get in touch. Simply email us at hello@consideredcontent.com.
Jason Ball is the founder and managing director at Considered. With a multi-decade career in B2B marketing, he’s worked with world-leading brands such as Adobe, Google, EY and Cisco together with niche specialists in technology, manufacturing and professional services.