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Generative CRM in 2024: Benefits, 5 Use Cases & Real-Life Examples

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Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Generative CRM in 2024: Benefits, 5 Use Cases & Real-Life ExamplesGenerative CRM in 2024: Benefits, 5 Use Cases & Real-Life Examples

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Customer Relationship Management (CRM) tools have become essential for companies to enhance their customer interactions. With ~80% of CRM customers seeking AI or machine learning capabilities in software when making a purchase, the integration of Generative AI in CRM systems is emerging as a transformative trend.1

This article is a guide for businesses to help them leverage Generative AI in CRM, as we explain its multifaceted benefits, use cases, and real-life examples.

Source: Gartner

Figure 1. Percentage of CRM buyers looking for AI and machine learning capabilities in a CRM tool.

What is generative AI CRM?

In today’s technologically driven business world, Customer Relationship Management (CRM) systems have undergone significant transformations. From being just databases to store customer interactions, they’ve evolved into intelligent systems that offer insights and strategies for businesses. Among the most innovative evolutions in this domain is the concept of Generative CRM.

At its core, generative AI CRM integrates the power of generative artificial intelligence (AI) with traditional CRM functionalities, allowing businesses to:

  • Analyze customer data
  • Derive new insights
  • Generate forecasts
  • Personalize interactions with the customers.

For those interested in, here is our article on the leading vendors that offer AI-powered CRM features in their platform.

What are the benefits of generative AI in CRM?

Key benefits of Generative Artificial Intelligence for CRM data

Figure 2. Key benefits of Generative Artificial Intelligence for CRM data.

Ensure data security

Generative CRM solutions prioritize this by ensuring that both publicly available data and the user-provided data are safely stored. While they utilize a mixture of public and private data to generate valuable insights, private customer data remains securely stashed in the cloud. This dual approach ensures optimal data utilization without compromising data privacy.

Free up employees for high-value work

Generative CRM, with its AI-driven capabilities, automates routine and mundane tasks. This allows human resources to shift their focus from manual operations to more strategic undertakings. By eliminating repetitive chores, professionals can concentrate on complex account management, fostering deeper client relationships, and creating tailor-made solutions. This results in a richer client experience and enhances the depth and breadth of services offered.

Accelerate automation

Generative CRM pushes the boundaries of traditional automation, turning static workflows into dynamic, self-improving processes. Instead of merely executing predefined actions, this system identifies potential bottlenecks, suggests process enhancements, and even refines strategies in real-time. By doing so, it ensures businesses operate at peak efficiency while staying agile and responsive to changing market conditions.

Get one step closer to digital transformation

Embracing generative CRM is not just a step toward digitalization—it’s a leap into a new era of business operations. Beyond the mere recording of data, generative CRM generates insights, predicts market and client trends, and offers adaptive strategies tailored to each customer’s unique history and preferences. It symbolizes a digital transformation where businesses can dynamically respond to market changes and customer needs with the support of AI-driven insights.

For those interested, here is our article on CRM best practices.

> What are the use cases of generative AI in CRM?

Figure 3. The use cases of Generative CRM

1- Answering simple requests

A lot of queries in CRM are repetitive and basic. Generative AI can be programmed to understand and respond to these queries without human intervention. For instance, “When was a customer’s last purchase?”, “How much did a customer spend last month?” or “Does the customer have an updated address?”. The AI can also be trained to escalate more complex or sensitive questions to a human representative.

2- Shipping process control

Generative AI can optimize and manage shipping processes by:

  • Generating the best shipping routes based on real-time data
  • Predicting potential shipping issues such as empty containers or,
  • Auto-generating communication to customers about their order’s shipping status.

By predicting and managing shipping-related inquiries or concerns, a CRM can offer a smoother customer experience.

3- Payment automation

Generative AI can be used to generate payment-related communications based on the specific details of each transaction or client. For example, if a customer’s invoice is overdue, the system might generate a polite reminder message personalized to the customer’s past interactions and transaction details. Furthermore, for regular customers with recurring payments, generative AI can create and send detailed invoices based on the latest transactions, making the entire invoicing process more efficient.

4- Recommendation system

By analyzing a customer’s purchase history and preferences, generative AI can suggest products that they might be interested in. This recommendation system can be used in email campaigns, online shopping portals, or even in conversations with sales representatives.

5- Data collection

Generative AI can help in auto-populating CRM fields with minimal input. For example, after entering a company name, the AI may provide potential connections, responsibilities, and outreach strategies. Having relevant data with the help of generative CRM can thus assist sales and service teams.

> Real-Life examples of generative AI in CRM systems

As generative AI is a relatively new concept, so are its applications in the CRM. Thus, there are few real-life examples of generative CRM tools as of August 2023. Although there will probably be more companies implementing generative AI into their CRM platform, here are some of the the current real-life applications of generative AI in CRM software:

1- Einstein GPT (Salesforce)

Einstein GPT is a generative AI technology developed by Salesforce and incorporated in their CRM platform.2 It improves its understanding from real-time data and is capable of performing over 1 trillion predictive analyses every week. Additionally, its ability to integrate with platforms like OpenAI enhances its AI capabilities, giving companies a tool to improve their consumer engagement strategy.

2- Generative AI for call-center CRM (IBM Consulting)

Bouygues Telecom, a mobile phone company, could only get partial insights from their CRM platform regarding customers and they partnered with IBM Consulting to enhance its call center operations using generative AI. IBM implemented generative AI models for automatic call summarization and topic extraction, updating the CRM with precise and actionable insights. This advancement led to savings of over $5 million and reduced call operations by 30%.3

For those interested, here is our data-driven list of CRM software.

Further Reading

If you need help in vendor selection process, contact us:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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