Remember the early days of b2b lead nurturing when a content download would commonly be followed up with an awareness stage blog post, a case study and an offer in quick succession? Like we could convince a business buyer to make a critical purchase with three pieces of (not so) tailored content.
Just the idea seems laughable now, but if we’re being completely honest it was considered best practice for a little while. Luckily, b2b marketers have got a lot more sophisticated with their content marketing and automation since then.
But, if we’re being honest, we’re still pretty bad at predicting what content might be relevant at any given point. B2b buying is simply too complex to try to manually program our website content and nurture workflows based on pretty basic data like traffic source and lifecycle stage, plus a healthy handful of assumptions.
However, AI analytics is showing a lot of promise in helping us become more targeted and effective in our efforts. Here’s how we think it will transform b2b buying.
1. Personalised content delivery
One of the most significant advantages of AI website analytics is its ability to deliver personalised content in real-time. AI algorithms analyse user behaviour, such as pages visited, time spent on site, and interaction with different types of content. They then use this data to predict what type of content a visitor is likely to engage with next.
For example, if a visitor has shown interest in case studies and detailed product comparisons, AI can suggest more in-depth content like white papers or webinars that align with their research phase. This level of personalisation not only improves user experience but also increases the likelihood of moving prospects further down the sales funnel.
2. Predictive lead scoring
AI website analytics can also enhance lead scoring processes. Traditional lead scoring models often rely on static criteria like company size or job title, but these don't capture the nuances of buyer intent. AI, on the other hand, can assess a wide range of behavioural signals to provide a more accurate picture of a lead's readiness to buy.
By analysing patterns such as frequency of visits, content consumption habits, and engagement with high-value assets, AI can predict which leads are more likely to convert. This allows b2b marketers to focus their efforts on the most promising leads, improving conversion rates and shortening the sales cycle.
3. Optimising content strategy
With AI website analytics, marketers can gain deeper insights into what content resonates most with their target audience. AI can identify trends and patterns that may not be immediately apparent, such as which topics generate the most engagement or which content formats drive the most conversions.
These insights can be invaluable for refining content strategies. For instance, if AI analytics reveal that interactive content like quizzes or calculators is particularly effective at engaging prospects, marketers can invest more resources in creating similar assets. This data-driven approach ensures that content strategies are aligned with audience preferences, leading to better outcomes.
4. Dynamic website optimisation
AI website analytics doesn't just stop at content recommendations; it can also be used to optimise the entire user experience dynamically. AI can analyse user interactions in real-time and adjust website elements accordingly to enhance engagement.
For example, if a visitor is showing signs of disengagement, such as rapid page scrolling or short dwell time, AI can trigger personalised pop-ups or suggest relevant content to re-engage them. Similarly, AI can adjust navigation menus or call-to-action placements based on user behaviour, making it easier for visitors to find what they're looking for and increasing the chances of conversion.
5. Advanced a/b testing
AI website analytics also brings a new level of sophistication to a/b testing. Traditional a/b testing can be time-consuming and limited in scope, often requiring manual analysis to determine the winning variant. AI, however, can run multiple tests simultaneously, analysing vast amounts of data in real-time to quickly identify the most effective variations.
Moreover, AI can go beyond simple a/b tests by creating personalised experiences for different segments of your audience. For example, while one variant may work best for first-time visitors, another might be more effective for returning users. AI can automatically adjust the website experience based on these insights, ensuring that every visitor receives the most relevant content and experience.
Conclusion: Embracing AI for better b2b outcomes
The potential of AI website analytics in b2b marketing is vast, offering more precise, data-driven approaches to content personalisation, lead scoring, and overall website optimisation. By embracing these these technologies, b2b marketers can move beyond the limitations of manual processes and basic data points, creating more effective strategies that resonate with their target audience.
As AI continues to evolve, its role in shaping the future of b2b marketing will only grow. Those who adopt AI website analytics early will have a significant advantage, driving more meaningful engagements and ultimately, better business outcomes.
To learn more about how to incorporate AI into your marketing operations and production, get in touch with us here.
Brand chemistry are experts in b2b website design and development. Our tried-and-tested approach will ensure your website drives more qualified traffic and improve your site conversion rates.