Is the AI ​​search and AI research in the B2B area more dominant and more important than social media such as LinkedIn?

Ki instead of LinkedIn? Why B2B is now on intelligent search - strategy change in B2B?

In the B2B area, AI-supported search and research gain in importance and are already viewed in many industries as dominant and strategically more important than classic social media platforms such as LinkedIn. Nevertheless, social media-especially LinkedIn-remains a central element of B2B communication.

Passdemone:

• Between 67% and 90% | B2B prefers the web search with AI tools instead of classic search engines

AI search and AI research in B2B:

High usage rate and efficiency

Between 67 % and 90 % of B2B companies now prefer web search with AI tools compared to classic search engines. AI-supported research and search tools such as Microsoft Copilot, Chatgpt and Perplexity AI are used in particular to get faster and relevant to specialist information, market analyzes and basis for decision-making.

Competition advantage through AI

AI technologies enable efficient automation, data-based decision-making and personalized customer approach. They accelerate processes such as keyword research, content optimization and competitive analyzes, which leads to a clear competitive advantage in the B2B area.

Strategic importance

In technically demanding industries (e.g. mechanical engineering, large plant construction), AI-based research for product development, market observation and lead generation has become essential. The ability to analyze large amounts of data and derive trends or risks makes AI tools indispensable for B2B companies.

Social media (LinkedIn) in B2B:

Very high distribution

The use of social media in B2B communication is on an all-time high: 97.4 % of companies in the roof area use social networks, with LinkedIn clearly the dominant platform.

Role of AI in social media

AI-based tools are also increasingly being used on LinkedIn and other platforms for content creation, target group address and performance analysis. Over 82.6 % of companies regularly use AI tools in social media communication.

Lead generation & image maintenance

LinkedIn remains important for networking, employer branding and the targeted address of decision -makers. The platform is particularly suitable for building relationships, sharing specialist knowledge and positioning as a thought leader.

Comparison: AI search/research vs. Social Media (LinkedIn) in B2B

A comparison between AI search/research and social media (LinkedIn) in the B2B area shows significant differences in the possible uses and strengths of the two approaches. The AI ​​search and research enables quick and relevant information acquisition, supports data -based decisions and promotes process automation. In contrast, the primary benefit of social media, especially LinkedIn, in networking, brand structure, lead generation and positioning as a thought leader. While the distribution of AI tools in B2B companies increases with 67–90 %, LinkedIn is already widespread with a use of 97.4 %. Strategically, AI search is particularly relevant to areas such as research, development and sales, while LinkedIn primarily supports communication, branding and recruiting. AI plays a central role in the search and analysis, whereas you are mainly supported on social media for content, analysis and targeting. With regard to the industry focus, AI search is primarily prioritized in industry, technology and complex B2B markets, while LinkedIn is used universally, especially for service providers, consultants and Mr. Trends indicate that AI tools are becoming more and more standard in the future, while social media remains significant, but is increasingly being supplemented by AI-based search and research solutions.

Efficient and visible: How AI and Social Media should work together

In the B2B area, AI-based search and research is now at least as important, in many cases even more dominant than classic social media platforms such as LinkedIn when it comes to targeted information procurement, market observation and data-driven decisions. However, social media, especially LinkedIn, remains indispensable for the development of relationships, brand positioning and lead generation. The future lies in the intelligent combination of both approaches: AI-based research for efficiency and precision, social media for visibility and network care.

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Automation and increased efficiency

AI clearly exceeds LinkedIn in the field of process automation. While LinkedIn is a platform that mainly requires manual engagement, Ki repetitive tasks can fully automate.

Resource optimization by AI

AI systems work independently and can significantly accelerate marketing and sales processes. They automate repetitive tasks such as data analysis or content creation and significantly reduce manual workloads. In contrast, LinkedIn requires continuous support from employees, which binds resources.

Suitable for:

• The sales paradox-forget the Sales Funnel: The Customer Journey is dead-despite AI, Automation and CRM!

Time savings in the sales process

A significant advantage of the AI ​​lies in their ability to relieve sales employees: "The AI ​​accompanies and supports the sales employees, so that they have more time for the essentials: selling". This support includes automated reports, protocols and collecting and analyzing data, while LinkedIn activities such as networks and content creation require active participation.

More comprehensive data analysis and personalization

KI offers significant advantages in the profound analysis and use of customer data.

Deeper data analysis

With AI, B2B marketers have tools "that enable huge amounts of data to analyze in the shortest possible time and gain deeper insights". In comparison, LinkedIn is limited to the profile information of its users and their interactions.

More precise personalization

AI enables much more precise personalization than the Targeting options of LinkedIn. It analyzes the behavior of the customers and provides “precisely precisely accommodated offers and speech, which strengthens customer loyalty”. This goes well beyond the demographic and job-based targeting options from LinkedIn.

Predictive skills and forward -looking analysis

A major difference between AI and LinkedIn lies in the ability to predict.

Final probabilities and potential analysis

AI systems in B2B sales are based on “historical transaction and interaction data and-based on these-precise predictions for future customer behavior”. These predictive skills are missing from LinkedIn, which mainly works with current and past data.

Identification of promising leads

A concrete example of the added value of KI: "Predictive Analytics tools analyze the behavior of website visitors, evaluate historical data and most likely predict which leads are particularly promising". LinkedIn offers targeting options for campaigns, but no comparable forward-looking analyzes.

Scalability and resource efficiency

AI solutions offer significant advantages in scaling business processes.

Flexible growth

AI systems can easily scale with growing amounts of data and customer requirements, while LinkedIn activities require more resources with increasing reach. The “optimization of marketing processes and precise target group addressing at lower costs” should be emphasized.

Cost efficiency

In the long term, AI can reduce the operating costs: "Automated workflows and data -based decisions reduce the need for manual interventions and create freedom for strategic tasks". LinkedIn, on the other hand, causes continuous costs through premium subscriptions and advertising expenditure, whereby the disadvantages of Sales Navigator are that “the subscription fees can be high, especially for small companies or individual users with a tight budget”.

Wider uses

AI can be used in significantly more business areas than LinkedIn.

Versatile areas of application

While LinkedIn is primarily a platform for networking, marketing and recruiting, KI can be used in almost all areas of business processes, including marketing, finance, human resources, manufacturing and supply chain management ”. This versatility makes AI a more comprehensive tool for B2B companies.

Content creation and management

KI offers special strengths in the content area: "I see the greatest potential for B2B marketing where it is about content: research, aggregation, validation and multimodal production". LinkedIn serves as a platform for content distribution, but does not offer integrated tools for content creating.

Suitable for:

• Forget keywords! This is how B2B SEO works in the age of Google Gemini-Eeat: The secret for top rankings

Synergetic potential: combine AI and LinkedIn

Despite the advantages shown, B2B companies should keep in mind that AI and LinkedIn cannot be regarded as competing, but as complementary technologies.

Optimization of LinkedIn activities by AI

AI can be used to optimize LinkedIn activities-for example through automated content creation for LinkedIn posts, intelligent analysis of network connections or the identification of the best posting times. The “Process acceleration for campaign planning, execution and optimization” by AI can significantly increase the effectiveness of LinkedIn marketing.

Data acquisition from LinkedIn for AI systems

LinkedIn remains a valuable source of professional data with “direct access to decision-makers and managing directors”, which in turn can serve as an input for AI systems. This combination enables companies to use the strengths of both technologies.

Use complementary strengths strategically

Compared to LinkedIn, KI offers considerable advantages in the areas of automation, data analysis, personalization, scalability and variety of application. While LinkedIn remains an indispensable platform for B2B networking and targeted addressing of decision-makers, AI can provide deeper insights, automate processes and enable forward-looking analyzes.

A strategic approach that combines the strengths of both technologies is therefore recommended for B2B companies: LinkedIn as a primary platform for professional networks and AI as an engine for automation, personalization and data-based decisions. This combination promises the greatest chances of success in the increasingly digitized B2B landscape.

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AI to optimize the target group address in B2B: methods, advantages and practical examples

Artificial intelligence transforms the way B2B companies identify, segment and address their target groups. By analyzing extensive amounts of data, automated processes and personalized communication, AI enables more precise, more efficient and effective target group address. The technology supports companies in better understanding customer needs, optimally using resources and ultimately increasing sales through more targeted marketing measures. The combination of data-based knowledge, automated processes and personalized customer experiences makes AI an indispensable tool in modern B2B marketing.

Suitable for:

• Breaking through traffic through Google AI overviews: The new challenge for website operators and their traffic development

Personalization on a new level: AI in B2B marketing

The personalization of marketing measures is no longer an optional extra, but a fundamental expectation of the B2B customers. AI raise this personalization to a new level that goes far beyond the simple salutation with first names.

AI enables tailor -made offers and personalized address, which demonstrably strengthens customer loyalty. This technology analyzes customer data such as browser history, buying behavior, social media interactions and demographic information to determine specific needs and preferences. Based on these findings, product recommendations or tailor -made content can be delivered in real time.

The results speak for themselves: Personalized experiences can double the conversion rates for booked meetings, and action requests in personalized content have a 68% higher interaction rate than generic content. Particularly impressive: Personalized experiences lead to almost four times as many demo requests as non-personalized interactions.

Modern B2B personalization goes beyond individual touchpoints and includes a cross-system, context-sensitive address-including individual pricing, relevant product suggestions or tailor-made content based on the customer segment, industry or business history.

Hyper-personalization as the next step

The AI-based hyper-personalization goes one step further: Companies can contact customers at a lower level by tailoring messages, content, offers and product recommendations to individual persons. A study by McKinsey shows that rapidly growing companies achieve 40% more sales than slower growing competitors through hyper-personalization.

AI-supported customer segmentation: to be behavioral from demography

Precise customer segmentation is the key to the effective target group address. AI revolutionizes this area by going beyond traditional demographic criteria and recognizing complex behavior patterns.

Traditional vs. AI-based segmentation

The traditional customer segmentation is mainly based on demographic data such as age, gender, income and location. Although these criteria offer important insights, but are often not enough to fully understand the complex behavior and the motivations of customers.

In contrast, AI enables multi-dimensional B2B customer segmentation, in which several criteria are also taken into account for the grouping. Through machine learning, huge amounts of data can be automatically processed and analyzed in order to create more precise and more specific customer profiles.

Advantages of AI-based customer segmentation

A B2B customer segmentation with AI offers numerous advantages:

• Identification of sales potential and cross-selling options: The AI ​​segmentation is open which sales potential may have customers
• Individual content: The appropriate content can be planned and presented for each segment
• Resource savings: Targeted communication is avoided through target losses
• Demand -oriented product development: Products and services can be better geared towards customer needs
• Individual pricing: prices can be adjusted according to the segments

Intelligent recommendation systems and lead prioritization

A key area in which AI revolutionizes the target group address is intelligent recommendation systems and automated lead scoring.

AI recommendation systems in B2B context

AI recommendation systems are intelligent algorithms that analyze the behavior and preferences of customers in order to generate personalized recommendations in real time. These systems increase the conversion rate and maximize the Customer Lifetime Value using various methods:

• Collaborative filtering: recommendations based on similar user behavior and preferences
• Content-based filtering: Analysis of product properties for tailor-made recommendations
• Context -based recommendations: Consideration of context such as time of day, location and device

AI-based Lead Scoring

Ki Lead Scoring is an automated system for evaluating sales contacts. The AI ​​analyzes customer data such as buying behavior and interactions in order to evaluate and prioritize potential leads based on predefined criteria.

There are different approaches to the AI ​​Lead Scoring:

• Predictive lead scoring: uses machine learning to analyze historical and demographic data and recognize patterns that indicate a high conversion potential
• Behavioral Lead Scoring: focuses on the analysis of real-time campaigns such as website visits, email engagement and content downloads
• Conversational AI Lead Scoring: Use data from chatbot interactions and messages to evaluate the potential of a lead

The advantages are clear: B2B marketing teams, which are often overwhelmed with a large number of leads, can quickly identify the most valuable leads through AI, so that sales teams can use their time efficiently.

Efficiency increase in B2B sales by AI

AI not only offers advantages in personalization and segmentation, but also increases efficiency in the entire B2B sales process.

Automation of routine tasks

One of the biggest challenges in B2B sales are time-consuming and repetitive tasks. AI can automate these tasks so that sales employees can use their time more efficiently. Examples of such tasks are:

• Data maintenance
• Lead scoring
• Implementation of email campaigns

By automating these processes, sales employees can invest more time in strategic activities, such as maintaining customer relationships and the development of sales strategies.

Optimization of resource allocation

AI enables optimized marketing processes and precise target group address at lower costs. Machine Learning can automate processes and employees receive specific recommendations for action based on data.

Practical advantages of AI in B2B marketing are:

• Personalization and customer satisfaction: AI enables precise offers and addresses, which strengthens customer loyalty
• Cross and uppers: Identification of potential for increasing sales through data-based recommendations
• Efficiency and automation: optimization of marketing processes and precise target group addressing at lower costs

Practical examples and implementation strategies

The successful integration of AI into the B2B target group approach requires a well thought-out strategy and observing proven practices.

AI architecture for B2B personalization

A modern architecture for AI-based personalization in the B2B area typically includes the following components:

• Customer Data Platform (CDP): As a central data hub, the CDP links account, user roles and interaction data across all contact points and thus enables a consolidated view of business customers and its decision-making networks
• Recommendation machines: AI-based machines identify relevant cross and uppseling options based on buying behavior, seasonal demand or industry-specific patterns
• Experience level: Shop portals, self-service tools or offer configurators access the personalization system via APIs
• AI and prediction models: These predict probabilities, sales opportunities or customer immigration and enable data -controlled interactions in real time

Best practice for AI implementation

When implementing AI for target group analysis, B2B companies should note the following steps:

1. Data adjustment: Artificial intelligence can help to recognize and correct incorrect, double or incomplete data. It can filter out irrelevant data and thus significantly increase the data quality
2. Data integration: AI can bring data from different sources and check for consistency. This enables a more complete and accurate picture of the customers
3. Data acquisition: AI tools can record and interpret a wealth of data points that would not be visible or understandable for humans

Suitable for:

• 30-50% unused digital work tools in marketing and sales-AI tools are also affected in addition to CRM and ERP

Challenges and solutions

Despite all the advantages, companies face challenges in the AI ​​implementation:

• Data protection and compliance: Data protection regulations must be observed when processing customer data
• Integration into existing systems: The AI ​​solutions must work seamlessly with existing CRM and marketing automation systems
• Qualification of employees: Teams must be trained in dealing with AI tools in order to fully exploit their potential

According to a study, 75% of the B2B marketers are planning to invest in generative AI over the next twelve months, although many do not yet actively work with it and some have a limited understanding of their functioning.

The future of AI-based target group address in B2B

The AI-based target group address in B2B marketing offers enormous opportunities for increasing efficiency, personalization and ultimately increasing sales. B2B companies that strategically use AI to improve their marketing processes can obtain a decisive competitive advantage.

The most important advantages are summarized:

• Special personalization and precise customer segmentation
• Automation of marketing processes and precise target group address
• Identification of sales potential and cross-selling opportunities
• More efficient resource allocation and reduction of wasteland

If you want to be successful in B2B-E-Commerce, you cannot avoid intelligent, data-controlled and AI-based personalization. The technology becomes increasingly mature and accessible, which also enables smaller and medium-sized B2B companies to benefit from the advantages of AI.

The future belongs to companies that are not regarded as a substitute for human intelligence, but as a powerful expansion that enables it to better understand customers, address more specifically and build long -term business relationships.

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