Every day, our inbox and LinkedIn feeds are stacked with emails, ads and videos from agencies and gurus over-promising to ‘skyrocket, ‘turbocharge’ or ‘10x’ our growth. It’s hard to believe anyone takes any notice, but given the unrelenting barrage of this crap, you get the feeling they must be fooling some people.
The message is always the same. They have discovered a revolutionary new system, model or framework that has finally cracked the lead generation code. For anyone with a weak pipeline, they have found the solution.
While the outcome on offer is the same, what always changes are the methods they (let’s call them the ‘10xers’), use to deliver these life-changing results. Rather than old-fashioned strategic marketing, they are all based on whatever the hottest trend is at the time. Once it was inbound marketing, then growth-hacking and more recently, account-based marketing (ABM). But now all their attention has turned to potentially the greatest hype-wagon ever - Generative Artificial Intelligence (Gen AI).
AI-powered lead generation is now everywhere you look. And the firms making the wildest claims about their capabilities are often those that are covering up the biggest shortcomings. They target businesses they believe will be won over by the need for instant results, and therefore reek of desperation. Then, the same old story plays out. Frustration, wasted investment, a feeling of being duped - all because you were seduced by the prospect of shortcutting lead generation. Then, new innovations like AI quickly get a bad name. As does the marketing industry as a whole.
But look a bit deeper into how GenAI can actually be applied for lead generation in particular, and you’ll see that there are plenty of positives to gain from it. But only if you make the right decisions.
AI use cases for lead generation
The potential use cases for AI in lead generation are many. The fact is it can do some things better than humans, and it can do them much faster and more consistently. AI can also reveal new insights and spot patterns far better than we ever could. Here’s a few examples:
- Email personalisation: generating content that is tailored to individual customer preferences, with targeted subject lines and content to maximise the chances of a positive response
- Lead segmentation and scoring: by identifying promising leads and segmenting them, advanced and detailed lead scoring models can be built on historical data, to enable greater granularity in targeting
- Virtual assistants and chatbots: real-time interactions with customers and end–users means they can get fast and relevant responses to common queries, while also enabling further lead management
- Predictive analytics: AI can work out which leads represent the highest buying intent and best prospect of conversion, meaning that the efforts made by sales and marketing teams can be better focused on the more fruitful opportunities
- Data enrichments: AI can plug any gaps in lead information, as well as adding insights and further contact details, to support better engagement and understanding of leads
- Pipeline forecasting: Based on historical performance data such as lead velocity and conversion rates, AI can look ahead to forecast future pipeline health
As you can see, there’s plenty of potential for AI to transform lead generation. But there is one thing that is vital to make any of these use cases work, and it’s the one thing that most client-side organisations really struggle with - sales and marketing data maturity.
Data maturity is the prerequisite for AI-driven lead generation
Data maturity means the level of capability your business has to manage, govern, analyse and make use of all the data at your disposal. In marketing terms, it means possessing high quality, accurate data to a granular level covering your entire addressable market (suspects, prospects and customers) - governed in a way that is fully compliant with the GDPR.
Without it, Generative AI cannot produce results of sufficient quality to give you the outcomes you need, or make your investment worthwhile. Ultimately, AI is only as good as the data it can work with.
Client-side organisations with a high level of data maturity will already be able to easily leverage data to run effective lead generation and nurturing programmes. They will also be able to deploy strong data governance and compliance measures and ensure that advanced analytical tools can access all the data they need. They are in the best shape to benefit from AI for lead gen.
The problem is most businesses have low sales and marketing data maturity. Much of their prospect and customer contact data is out-of-date with no process for maintaining its accuracy. Their addressable market coverage is patchy. The data points needed for AI to identify ideal buyer traits, such as contract end dates or software usage, will also be very limited.
These are not data gaps that can be plugged by an independent 10x agency or guru offering a ground-breaking, AI-driven lead generation solution. If they are focused on a particular industry niche or utilise 3rd party data sources, then maybe they could bridge some of the data gap. But most agencies in this bracket don’t have a niche. 10xers target everyone and anyone, in the hope someone will fall for the bullshit story they are peddling. They cannot be trusted.
The true promise of AI will be ‘built-in’
The other major reason these 10x agencies can’t be trusted is because of their lack of credibility and expertise in artificial intelligence. Do these organisations have a proven track record of developing relevant software applications? Do they possess a history in data science and artificial intelligence? Have they really built their own AI algorithms and applications to support the use cases outlined earlier? Even if they have, do they actually work?
Just because there are now hundreds of use cases for AI in marketing, it doesn’t mean AI solutions are ready to deliver them all effectively. Many of these use cases are still theoretical, simply proposing the art of the possible. And just because some clever B2B marketers have figured out how to use prompts in ChatGPT, it doesn’t mean the marketing industry has been transformed overnight.
We’ve all heard the cliche, ‘AI won’t replace marketers, marketers using AI will replace marketers who don’t.’ While this will undoubtedly become true, it doesn’t mean we have to immediately turn to the first agency offering an AI solution or all become masters of AI before the year is out.
All the major martech vendors are making significant investments in artificial intelligence. In June 2023, Salesforce announced a $500m investment into Generative AI. At Hubspot’s Inbound conference in September 2023, the company announced its commitment to generative AI, and shared around 200 new features that would be powered by Generative AI. This means AI will be at the core of their applications - it will all be ‘built-in’, meaning AI will be easy to adopt and work with as part of every marketers’ working day.
So if you’re going to back anyone, back the major players. If their applications are still very much in the development stage, it’s highly unlikely that a small 10x agency will have got there ahead of them.
Asking the right questions
If you’re still not convinced, then see if your 10x agency can walk the walk, as much as they can talk the talk. We recommend testing their offering in a few key areas:
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Background: what is their history and track record with data science and artificial intelligence? Do they have any case studies, references or testimonials to back up their claims? Can they demonstrate experience in other related fields?
- Results: can they show examples where they have supported businesses with successful GenAI deployments for lead generation? What improvements in key metrics can be quantified? What tangible outcomes have they achieved?
- Transparency: are they both able and willing to fully explain their AI processes, data sources and methodologies? If they are working with 3rd parties, are they open about that?
- Tailoring: can they demonstrate how the GenAI deployment can be shaped to fit your specific needs, target segments, customer base and intended business goals?
- Data privacy: can they show how they can collect, use and manage good quality data, and do so in full compliance with regulations like GDPR?
- Industry-specific expertise: can they demonstrate specific experience and expertise in your industry, and show how they have dealt with particular challenges within it?
- Human skills: can they back up their technology with a good number of human AI experts, who can guide and oversee processes across sales and marketing?
- Trial period opportunities: are they open to working with you over a trial period, so that you can see how well they work without making any major financial commitment?
In a world inundated with overhyped promises from AI-driven lead generation companies, it's crucial to navigate this landscape with caution. These '10xers' repeatedly change their tactics, jumping onto the latest marketing trend, and right now, they're touting Generative Artificial Intelligence (AI) as the holy grail of lead generation. While AI indeed has massive potential, it's not a magic bullet.
The key to AI-driven lead generation lies in data maturity. So if you feel your data is lacking, then this is the first place to start. And, rather than blindly trusting 10xers, stay close to the major players. Understand how they are integrating AI into their applications, because this is where the real opportunities for improving your lead generation outcomes will materialise.
References:
- Salesforce Ventures Double Down on Generative AI Fund, Salesforce, 2023
- HubSpot unveils HubSpot AI and new Sales Hub at INBOUND 2023, HubSpot, 2023
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