How AI in B2B Marketing Can Be a Game Changer for Marketers

In today's competitive business landscape, it is crucial for companies to incorporate AI B2B marketing strategies. By leveraging AI-powered tools and techniques, businesses can give direction to their marketing efforts. Marketers can gain valuable insights into the B2B audiences and create targeted campaigns resulting in higher ROI.

Let’s explore how AI can be the game changer in customer segmentation, organic marketing, and inorganic marketing and also understand how it will shape the future of B2B marketing.

Table of contents

• Impact of AI B2B marketing on customer segmentation

  • 1. Data analysis & grouping
    
  • 2. Predictive analytics
    
  • 3. Extracting insights
    
  • 4. CLV analysis
    
  • 5. Collaborative filtering
    
  • 6. Lookalike modeling
    
  • 7. Performance analysis and strategic optimization
    

• Impact of AI on organic marketing

  • 1. Email marketing
  • 2. Social media marketing
  • 3. Search Engine Optimization

• Impact of AI on inorganic marketing

  • 1. Paid ads
  • 2. Affiliate marketing
  • 3. Influencer marketing

• Wrap up

Impact of AI B2B marketing on customer segmentation

Customer segmentation mainly refers to dividing customers into groups based on specific attributes such as age, country, etc.

In B2B marketing, the target audience consists of other businesses or companies; hence the segment represents the group of people or companies with common attributes.

AI-driven tools leverage machine learning algorithms that can efficiently analyze vast amounts of data. Henceforth, managers can identify distinct customer segments and send tailored messages accordingly. It ensures precision in targeting, thereby resulting in effective marketing campaigns.

A good example is using a machine learning algorithm, the 'K-Means clustering algorithm,' for solving customer segmentation problems. It can discover the patterns in the unlabelled dataset and further group data points into different (non-overlapping) sub-groups according to similar attributes like industry, number of employees, location, purchase history, and consumer behavior. It is considered an unsupervised learning algorithm that helps obtain a better understanding of the customers.

Many AI tools are available for customer segmentation. One such tool is Peak.ai, a cloud-based AI company offering data analysis services to support decision intelligence. It can aid B2B marketers by simplifying the customer segmentation process. Furthermore, the tool can be easily linked with existing communication channels & platforms like CDP, CRM, or other social media platforms.

Key features

1. Segmentation: It provides a granular but digestible view of the customer database.
2. AI attributes: It allows marketers to choose from 35+ AI-driven attributes for segmenting customers according to their predicted behavior, including churn rate, the propensity to purchase & also, and lifetime value (LTV).
3. Organized data: It combines huge amounts of data from different sources to create a rich database.

Source: B2B Segmentation

Let's have a look at some of the AI-driven strategies for customer segmentation in B2B marketing:

1. Data analysis & grouping

AI algorithms can analyze voluminous customer data such as firmographics, customer transaction history, behavioral data, etc., in a few minutes. Furthermore, it can group data points with similar attributes into distinct segments using the clustering algorithms like the k-means algorithm (as described in the above example) or others.

2. Predictive analytics

AI aids marketers by analyzing past purchase patterns, customer engagement levels, and available demographic data. Based upon the analysis of historical data, AI can forecast which customers have more likelihood of conversion or engagement, thereby enabling tailored marketing strategies.

3. Extracting insights

Natural Language Processing (NLP) is a subfield of artificial intelligence bridging the gap between computers and human language. NLP techniques help in extracting valuable insights from unstructured data across different channels like customer reviews, social media posts, etc. It analyzes the topic, language, and sentiment as being discussed in those posts and provides useful information for refining customer segmentation.

4. CLV analysis

CLV (Customer Lifetime Value) is a metric that is used to show the value/profit expected to generate from a customer throughout their entire relationship with the company.

CLV Formula: CLV = Average Purchase Value × Purchase Frequency × Customer Lifespan

Cohort-Based CLV Formula: CLV = (Average Revenue Per User × Gross Margin Percentage) ÷ (1 + Discount Rate - Retention Rate)

It involves a complex calculation based on specific business needs. However, AI tools can automatically calculate CLV after undertaking various factors such as purchase history, average order value, customer acquisition cost, and customer retention rate.

After this, businesses can identify high-value customers by categorizing customers based on the respective CLV and prioritizing retention efforts to ensure maximum returns.

5. Collaborative filtering

AI enables customer segmentation based upon similar preferences and helps to recommend relevant products or services accordingly. This approach is useful for delivering personalized suggestions and messages.

Also, AI supports dynamic segmentation, which means AI tools can consistently perform data analysis in real time. Consequently, businesses can monitor customer interactions, adjust segments and modify marketing strategies accordingly.

6. Lookalike modeling

Lookalike modeling is the process of identifying those individuals or prospects that share similar characteristics with the target audience.

The emerging AI tools dig deeper to support marketers in lead generation and further conversions. These tools can analyze existing customer segments and reveal the "lookalike" prospects, the ones that share similar attributes with the high-value segments.

This approach can be beneficial while expanding the customer base. Marketers can timely deliver personalized content that resonates with specific segments hence deriving better engagement & conversion rates with effective marketing campaigns.

7. Performance analysis and strategic optimization

The constant performance monitoring, measurement, and evaluation defines the progress of a business. AI smoothens the process by analyzing the performance of different customer segments, thereby shedding light on the success of marketing campaigns. It allows the managers to grasp a better understanding of the conversion rates or engagement metrics and optimize strategies for improving campaign effectiveness.

These AI B2B marketing strategies for customer segmentation can take the growth of a business to another level by enhancing customer engagement and improving return on investment (ROI).

Related guide: How to Use AI for Small Business Marketing (With Examples)

Impact of AI on organic marketing

Organic marketing (inbound marketing) refers to building an audience base and generating leads naturally by non-paid methods.

AI technology has impacted every area of B2B marketing, including how organic marketing was conducted before. Now, manual tasks are being automated, and marketers are leveraging various AI tools to minimize time and maximize productivity. Let's see how AI transforms three main dimensions of B2B marketing - email marketing, social media marketing, and search engine optimization.

1. Email marketing

With AI technology, marketers can improve the quality of emails and run effective campaigns.

Let's take, for example, a manager running an email marketing campaign for a B2B software company. Then, instead of brainstorming for subject lines or email content, they can turn to AI tools and platforms like Phrasee. It uses generative AI to create engaging subject lines, email content, and messages across the digital customer journey.

Phrasee analyzes audience personas, brands' product lines, and guidelines for providing optimized copies accordingly. The language insights reflect upon the words, emojis, syntax, and even sentiments that resonate with the brand's audience and enable marketers to test message variants in real time.

We can say that AI supports intelligent decision-making backed by research, factual data, and thorough analysis.

In email marketing, AI algorithms can analyze data, identify hidden patterns, and segment email lists according to specific criteria for sending relevant messages only. Predictive analytics can help managers to identify which is the optimal time to send emails, what are the trending subject lines and convertible email copies, what is the likelihood of email recipients being converted into customers etc.

AI tools optimize subject lines and email content hence resulting in higher email open rates, click-through rates, and conversion rates.

Apart from these, AI-powered spam filters have become increasingly sophisticated these days. These filters pick out and remove unwanted or malicious emails by detecting patterns or characteristics related to spam emails, phishing, or malware. Therefore, these AI tools with such spam filters ensure that only legitimate B2B marketing emails reach the intended recipients' inboxes.

Related guide: 17 Best AI Email Marketing Tools for All Use Cases

2. Social media marketing

AI has influenced social media marketing in a number of ways. Some of these are explained below:

1. Content Curation

AI tools assist marketers in curating content for different social media platforms in a few clicks. The advanced algorithms can scan through the huge database and generate compelling content copies while taking into account specific keywords, trends, or user preferences. This leaves B2B marketers with more time to focus on other strategic activities. They can maintain a consistent presence on social media, interact with industry experts, explore growth opportunities, and engage with their audience simultaneously.

Despite a few limitations, ChatGPT, developed by OpenAI, has become a popular choice for research and content development. It is designed on the GPT (Generative Pre-trained Transformer) architecture. It aims to generate human-like responses in the form of natural language conversations.

ChatGPT is a user-friendly application and can be used for multiple purposes like content generation, language translation, e-learning, creative writing, storytelling, personal recommendations, etc.

Related guide: A Deep-Dive Into Best Practices of AI in Content Marketing

2. Performance monitoring

AI goes far beyond the scope of content marketing. AI-powered social media marketing tools can monitor performance and track brand mentions, relevant industry conversations, and customers' feedback in real-time. Marketers can dig deeper into these insights to understand customer needs, address concerns (if any), and tailor their social media strategies accordingly.

3. Sentiment analysis

The tools incorporating such algorithms can analyze social media data spanning various channels and pinpoint positive, negative, or neutral sentiments. Using this analysis, marketers can gauge the effectiveness of their organic marketing efforts and evaluate whether they'll be able to achieve the marketing goals within the stipulated time or need to work on the strategies again.

4. Customer support

Nowadays, AI-driven chatbots have automated customer interactions at different levels. B2B marketers employ chatbots & virtual assistants that provide instant responses to customer inquiries, offer support, and gather required feedback.

These conversational AI tools increase responsiveness and enhance the customer experience altogether. Apart from ChatGPT, as explained above, there is 'Zia,' the AI-powered assistant developed by Zoho. It assists businesses in collecting customer data, writing documents, analyzing sales numbers, etc.

Source: Social media marketing with AI

For instance, let's take the case study of 'Neuroflash.' It specifically offers AI text and image generators. As shown above, it can generate tweets for Twitter, captions for Instagram posts, posts for LinkedIn, video descriptions for YouTube, etc.

Professionals can employ these tools to create customized social media posts, maintaining momentum and driving huge traffic.

Overall, it shortens the ideation and writing time while maintaining the quality of content and images.

Related guide: 6 AI Marketing Trends You Should Know as a Marketer

3. Search Engine Optimization

As per social media strategies, "ClickUp Uses NLP AI to Boost Blog Traffic By 85%. There’s been no shortage of opinions (and controversy) about AI-powered content writing."

Search Engine Optimization (SEO) plays an equally important role in content creation for acing the competition in the market. Even if you write regular blogs (over 1500 words or so) but if it's not ranking on Google and deriving enough traffic, all efforts may go in vain.

For each optimization, I would say that Surfer's Audit tool saves me about an hour. I always start out my optimizations with the Audit feature to get an overview of how the content is stacking up compared to competitors. The content score is really useful because it can help me identify if a page has content or topical issues or if I need to look elsewhere.

• Adele Payant, ClickUp’s SEO Specialist

Many companies, including the project management solution company, 'ClickUp' and others, are turning to natural language processing AI tools like 'SurferSEO.' It helps identify specific keywords, optimize content, and gain insights into the structure, like the number of images to be included, the optimal length of subheadings, etc.

“For example, if our content score is significantly lower than the other pages in the SERP, I know off the bat that the content needs improvement, whereas if the content score is in-line with the other players in the SERP, I know to start looking for other things, such as missing common backlinks, internal link gaps, or images.”

• Adele Payant, ClickUp’s SEO Specialist

Another great example is MarketMuse's AI, the capabilities of which include natural language processing and semantic analysis. Using this, it can understand the context being referred to and the relationships between words, phrases, topics and segments for devising the most comprehensive content strategy that improves search rankings.

Impact of AI on inorganic marketing

Moving forward, let's explain how AI is bringing change in inorganic marketing. Inorganic marketing is a paid form of marketing.

Here we shall study another 3 dimensions of B2B marketing - paid advertising, affiliate marketing, and influencer marketing.

1. Paid ads

Let's take up a case study of how artificial intelligence benefited a company in optimizing pay-per-click advertising for higher returns.

Interactive Investor is an online investment service company. They were struggling with acquiring new customers. The challenge was to increase account sign-ups while reducing the customer acquisition cost through paid search campaigns.

Consequently, it approached Albert.ai, an AI tool for bringing new sign-ups at a cost that matched or was lower than the previous one.

Albert.ai is used for planning, building, and optimizing marketing campaigns. Furthermore, it enhances the effectiveness of digital advertising. In the case of Interactive Investor company, the tool performed extensive keyword testing, faster analysis, and real-time improvements to demographics, ad copy, converted keywords, etc., during the pay-per-click campaigns.

As per the strategies summit, "The tool created nearly 10,000 different text ads and delivered a 286% increase in keywords, helping Interactive Investor gain an impressive 89% share of voice across the top 10 branded terms. This, in turn, resulted in a significant drop in cost per acquisition."

“Albert has helped us acquire new customers and grow our existing account sign ups. Albert’s ability to quickly and autonomously generate and optimize keywords has driven more relevant traffic to us, and the keyword universe created by Albert is far richer and diverse than what we previously had.”

• Anthony Wong, Digital Performance Director

Ultimately, the AI tool albert.ai boosted campaign performance by making rapid optimizations and sharing holistic insights for the teams to work upon. It managed to reap the positive returns for the company.

The above case study shows that AI has begun to play a vital role in optimizing paid advertising campaigns. B2B marketers can also leverage data for enabling automation and improving targeting, brands' bidding strategies, and overall return on investment (ROI). Here's an overview of how AI enhances paid advertising:

1. Analyzing demographics, interests, browsing behavior, and past interactions
2. Targeting the most relevant and convertible segments
3. Using Real-Time Bidding (RTB) algorithms for automatically optimizing bidding strategies after considering bid data, competition, and user behavior and determining the optimal bid for each ad placement
4. Forecasting the performance of different advertising campaigns and ad variations (if any)
5. Allocating the budgets more effectively by making data-driven decisions for success in the long-run
6. Identifying the most engaging ad elements, such as images, headlines, and call-to-action (CTAs), to optimize the ad creatives

2. Affiliate marketing

AI technology is reshaping various aspects of B2B affiliate marketing by enabling marketers to harness the full potential of data.

Using this, they can predict customer behavior to a great extent and hence optimize the affiliates' commission structure accordingly.

The AI algorithms point towards ongoing trends and reveal hidden patterns. Also, the tools establish correlations within the data to provide deeper insights into customer preferences, affiliate performance, and campaign effectiveness.

The AI tools and platforms further highlight the high-value affiliates. Marketers can get real-time feedback after deploying the AI algorithms for A/B testing and continuous monitoring. This iterative approach helps in modifying the commission structures to bring optimal results over a period of time.

AI-powered tools like referral.ai are used for building meaningful affiliate contacts, generating leads, and nurturing partner relationships. The result can be witnessed in increased customer engagement, acquisition, and retention rates. The AI-driven marketing efforts lead to significant improvement in ROI.

Related Guide: 8 Best AI Tools for Affiliate Marketing in 2023

3. Influencer marketing

Like other forms of marketing, influencer marketing has challenges, especially for B2B businesses. To list a few, marketers come across multiple fraud cases, unreliable data, old content, and inaccurate analysis. Nonetheless, AI technology offers a viable solution to overcome the loopholes in the system and ensure the campaigns become a hit for the company.

Here are the benefits of using AI for B2B influencer marketing:

1. Prevent potential frauds

AI tools can detect accounts with fake followers and bots. Thus it helps to prevent influencers frauds and ensure the campaign reaches a real audience to achieve the desired results.

2. Identify relevant influencers

Marketers can conduct thorough analysis using AI across various social media platforms, websites, blogs, and other sources to identify those influencers who align with the business's target audience and the specific industry niche. The AI and machine learning algorithms consider numerous parameters, including content relevance, engagement rates, post frequency, and the number of followers, before measuring an influencer's potential impact.

For instance, let's say there is a B2B software company that intends to run an influencer marketing campaign targeting professionals in the IT industry. AI tools assist managers by analyzing the social media landscape and presenting the list of influencers specializing in technology, particularly in the B2B industry. Also, these tools would most likely take into account the respective audience and their engagement metrics.

Klear is a tool that offers smart AI and advanced filters for a focused search. It helps brands discover and connect with the right creator for multiple platforms, including YouTube, Twitter, Instagram, TikTok, etc.

3. Analyze audience demographics

The characteristics of the respective influencers' audience must align with the target audience of the company. Only then can the campaign be successful. But how to know about the audience's demographics in detail that's where the AI comes into the picture.

AI algorithms can analyze an influencer's followers deeply and determine whether they predominantly consist of decision-makers in B2B companies or not. Also, marketers can gain valuable insights into audience composition, interests, relevance, location, engagement patterns, etc.

4. Measuring campaign progress

CMOs must track the influencer marketing budget, engagement, conversions, sales, ROI, etc. AI-integrated platforms like Upfluence provide them with a personalized dashboard with which they can measure data in real-time. They can reach out to new creators and automatically track the performance of existing ones via links and sales in a precise manner.

AI-powered analytics enable them to measure the impact of influencer marketing campaigns and take data-driven decisions accordingly. Also, they can easily determine the influencers who drove the highest engagement levels or generated the maximum conversions for the B2B business.

Related guide: Trends That Will Shape AI Influencer Marketing in 2023

Wrap up

In conclusion, AI B2B marketing is a game-changer that empowers businesses to drive growth, enhance users' experience, and refine marketing strategies.