AI for B2B personalization | AI in business #60

How to write a good B2B personalization offer?

B2B offers are aimed at other companies or businesses rather than individual customers. They can be about selling products or services, outsourcing, or consulting. To create an effective and engaging offer for a business customer, it is useful to follow a few rules:

• Use simple, easy-to-understand language – avoid industry jargon and complicated wording to make the content clear to any customer,
• Bet on specifics and numbers — provide hard data to back up the benefits, for example, how much can be saved or gained with your service. This will let you avoid unmeasurable generalities,
• Write from the customer’s perspective — focus on the benefits a specific business will get thanks to your solution. Answer the question: “Why is this offer attractive to my company?”
• Match form and tone — email, presentation, or phone call – each communication channel may require a slightly different style to achieve the desired effectiveness, and finally,
• Personalize — if possible, add elements that are personalized to the specific customer, showing that you know them well.

To personalize B2B offers, we need to have the right data about the customer. What industry do they operate in, how many years have they been on the market, and at what stage of development is their company? The list of questions here depends not only on the specifics of the services or products offered but also on the possibility of obtaining them.

The role of artificial intelligence in B2B personalization

Artificial intelligence lets you personalize B2B offers in several ways. The starting point, however, is always customer data. But what if the only source of information about the customer is the invoice? Even basic data can be a great way to start Account Based Marketing (ABM). If you don’t have an extensive database, consider building one. The more information you can get about your target audience, the better the B2B personalization results will be.

First, AI identifies customer preferences and behavioral patterns by automatically analyzing customer data. For example, the AI system can track a particular customer’s purchase history to identify the most frequently ordered products and make a personalized discount offer.

This can be done by leveraging information gathered by the sales team, who interact directly with buyers. Dedicated customer relationship management (CRM) platforms will work well here – including those that use AI to automatically transcribe conversations. These will allow you to capture data on who and what you’re talking to during a particular conversation, as well as what purchase is being discussed.

Another key functionality of AI is generating tailored service recommendations. Based on the collected data, artificial intelligence helps prepare a personalized B2B offer, precisely indicating the most suitable options for the customer.

AI is also useful for creating dynamic, personalized content as part of the offers sent to customers. It tailors the message to the defined preferences and interests of the recipient, increasing the attractiveness and relevance of the prepared offer. For example, Fabriq, a tool developed by the Boston Consulting Group (BCG) can work with any digital personalization system or platform via an API. It comes with a rich library of B2B offer templates.

Source: BCG ((https://www.bcg.com/beyond-consulting/bcg-gamma/fabriq)

In addition, AI enables precise segmentation of the customer base and targeted sales activities. AI systems analyze customers’ buying behavior, segment them into groups, and then target them with personalized marketing communications.

Finally, artificial intelligence can revolutionize the entire shopping experience for business customers. By integrating with CRM and e-commerce platforms, it creates personalized customer journeys and delivers tailored recommendations and solutions at every step.

Benefits of using artificial intelligence to personalize B2B offers

Employing AI brings several benefits. The most tangible of these are:

• Increased conversions – more relevant, tailored offers translate into more sales,
• Increased loyalty – customers appreciate that the company is learning about their needs, so they stay with the company longer,
• Lower costs – automating marketing and sales activities, such as the use of chatbots, means lower operating costs,
• Reaching decision makers faster – using AI to personalize B2B offers means better, more precise targeting.

Practical applications of AI in B2B personalization

Specific examples of how AI can be used to personalize B2B offerings are primarily:

• Generating personalized content in emails – it’s not just about using first names, it’s about taking into account the real needs and interests of customers,
• Automatically selecting products and services that match a particular customer’s profile, such as those displayed in your online store’s search window,
• Suggesting additional options or features based on the customer’s purchase history,
• Analyzing customer sentiment in conversations to improve service.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Challenges of using AI in B2B personalization

Implementing AI also presents many challenges. The most important of these is the need to capture and integrate customer data from multiple sources, such as CRM, website analytics, and social media. This is where tools like Salesforce and Hubspot come into play.

However, collecting and organizing data is not enough. The company must also develop effective, repeatable processes that use artificial intelligence to create personalized B2B offers. This will also require:

• training employees in the use of AI technology,
• ensuring compliance with personal data security standards such as GDPR, and
• verifying the accuracy of the content of offers automatically generated by AI algorithms.

It is important to remember that artificial intelligence can support the process of creating personalized B2B offers. However, the responsibility for the content sent to customers rests with humans. Therefore, to avoid errors and misunderstandings, it is crucial to thoroughly test the implemented processes, monitor their performance, and – at least randomly – check the correctness of the generated content.

It can also be a challenge to get some of the more conservative customers to accept AI-driven solutions. Therefore, the decision to implement AI-powered B2B personalization must be based on in-depth knowledge of the target audience.

Trends and future of artificial intelligence in B2B personalization

According to McKinsey analysts, 71% of customers already expect personalized interactions from companies, and 76% are frustrated when it doesn’t happen. Soon, the lack of a personalized offer will mean unpleasant surprises for every customer. As a result, experts predict that the evolution of AI in B2B personalization will go in the following directions:

• The development of voice assistants and chatbots that communicate directly with the customer – thanks to them, the B2B customer will get a personal shopping advisor who will provide a personalized offer,
• Using algorithms to analyze customer emotions expressed in conversations or emails – sentiment analysis in writing and speech is already highly developed and will be widely used in consumer solutions in the coming years,
• In-depth, multidimensional segmentation of the customer base using AI models – allowing for hyperpersonalization.

It will also be possible to include not only the customer’s company data but also the preferences of their employees.

B2B personalization – summary

AI offers great potential for personalizing offers and communicating with business customers. Thanks to automation based on artificial intelligence, companies can better understand and more accurately respond to customer needs. This builds lasting business relationships, loyalty, and customer satisfaction.

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Author: Robert Whitney

JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.

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