Six Scenarios for AI-Powered B2B Marketing

6 AI Application Scenarios

1. Lead Scoring

In B2B marketing, determining the quality of leads is extremely important.

Scoring, which identifies targets to approach from accumulated leads and existing customers, is already implemented in many Marketing Automation (MA) platforms.

By using AI for this scoring, you can determine whether a customer is likely to have purchase intent based on all available data. This automates the scoring process, allowing you to pass high-priority leads to inside sales and enabling sales representatives to focus on the most important leads.

2. Streamlining Sales Processes

In B2B products and solutions where the unit price per deal is high and the sales cycle is long, sales representatives spend a significant amount of time handling individual tasks.

Consequently, they often become occupied with existing customers and ongoing deals, leaving little time for the essential task of prospecting for new customers.

Against this backdrop, SFA (Sales Force Automation) systems are also evolving through the integration of AI.
By using AI in SFA, several tasks can be automated. You can automate many processes, such as lead classification (identifying whether a deal or transaction exists), follow-up automation, and responding to customer inquiries. Streamlining sales processes is a critical challenge to ensure that leads acquired through marketing are not wasted.

3. Targeting

Especially when the target market is limited, identifying the right targets is crucial for efficiently acquiring leads with limited sales resources.

By using AI, you can analyze data from existing customers to identify optimal targets with high sales potential.
Compared to traditional methods that rely on the experience and intuition of sales representatives or complex, manual marketing analysis, the advantage of AI is its ability to identify who to approach in real time.

Sales representatives can allocate their time to high-priority customers rather than spending it on leads with unclear potential.

4. Optimizing Marketing ROI

For marketing professionals, optimizing the ROI of advertisements and various campaigns is a key responsibility.
Recently, the range of online initiatives has expanded, making the visualization of ROI increasingly complex and difficult.

Here too, AI can be used to constantly track the performance of advertising campaigns and identify the optimal advertising platforms, effective creatives, and messaging.
For companies managing their own advertising in-house, as well as for advertising agencies, utilizing AI-powered advertising analysis tools will be effective in optimizing marketing ROI.

5. Enhancing Chatbots

For marketing professionals, optimizing the ROI of advertisements and various campaigns is a key responsibility.
Recently, the range of online initiatives has expanded, making the visualization of ROI increasingly complex and difficult.

Here too, AI can be used to constantly track the performance of advertising campaigns and identify the optimal advertising platforms, effective creatives, and messaging.
For companies managing their own advertising in-house, as well as for advertising agencies, utilizing AI-powered advertising analysis tools will be effective in optimizing marketing ROI.

6. Strengthening Marketing Automation (MA)

MA (Marketing Automation) is an essential tool for marketers that allows for the automated, mass distribution of emails to customers and the visualization of customer behavior online.
It enables the automation of marketing processes, such as automatically following up with customers or sending follow-up emails when a customer performs a specific action online.

AI-driven functional enhancements are already underway. AI can set accurate targets, determine the optimal timing for follow-ups, and realize effective one-to-one messaging not only via email but also through social media and pop-ups.
AI features that constantly analyze lead behavior data and website access history accumulated daily in MA systems serve as powerful partners in sales activities as they become increasingly digitized.

These are some concrete ways to utilize AI in B2B marketing.

By leveraging AI, you can automate complex marketing processes and enhance the effectiveness of your initiatives.

AI Requires Accurate Data

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However, accurate and abundant data is essential to utilize AI effectively.
AI learns from relevant data and makes predictions or inferences based on it. Therefore, conducting AI-driven marketing activities requires a large volume of accurate data.

Consequently, if the training data is inaccurate, you cannot expect sufficient results.

Marketing and Data

For example, when determining a customer's purchase probability, complex data is required, including what products or services the customer is interested in, their typical behavioral patterns, and even past sales history.
By collecting this information, AI can accurately grasp customer needs and trends, enabling more targeted marketing activities.

Furthermore, data obtained through actual marketing activities can be used as material to further improve the AI.
If the inference result made by the AI is correct, it is judged that the AI's prediction accuracy is high, and the same logic can be applied to future predictions.

Conversely, if the inference result is incorrect, the cause can be analyzed to identify points for improvement.

As described above, collecting accurate data is essential for utilizing AI, and that data also leads to the improvement of the AI itself.
To conduct more accurate marketing activities, you must first review the environment in which your company utilizes data.

Reviewing Your Company's Data Utilization Environment

To utilize AI and take your marketing to the next level, let's re-examine what data your company has and how it is being utilized.

uSonar supports the construction of a foundation for data utilization, such as the automation of data organization, supplementation, and maintenance, for data-driven marketing.