AI in CRM: top use cases, best platforms, and guidelines
June 29, 2023
Artificial intelligence in customer relationship management (CRM) software helps businesses automate routine processes like inputting and cleaning customer data, reduce human errors, and improve decision-making via data analytics and forecasting capabilities.
Itransition offers comprehensive CRM consulting and development services to help you select, design, and adopt an AI-powered solution matching your corporate goals and requirements.
expected CRM market value by 2029, with AI as a major demand driver
Fortune Business Insights
of companies consider improving customer experience the top AI use case
Statista
faster response to customers and partners by companies adopting AI
Oracle
12 use cases of AI in CRM by business area
AI-based technologies сan augment your CRM's functionality in various use cases and workflows.
Sales
Enhance your CRM solution with Itransition’s guidance
AI-enabled CRM implementation examples
Look at real-life CRM implementation scenarios to assess the potential impact of artificial intelligence on your customer-centric operations.
A major commercial property management company in the UK partnered with Itransition to implement a Salesforce-based CRM solution. Its AI features include automated reporting and predictive analytics to forecast customer behavior.
+ 15%
conversion rate
The system built by Itransition helped us improve decision-making and get a 360-degree view of our customers.
Top 5 CRM platforms with AI-based features
Most major CRM platforms currently include an extensive range of AI-powered features to enhance customer-related operations. Check out the following options on the market:
Customer 360 relies on Einstein, a powerful AI that integrates easily with Salesforce’s services and provides valuable business insights.
AI-based features
- Prediction Builder to forecast customer churn, lifetime value, and other business outcomes
- Customizable chatbots automating customer support tasks across multiple digital channels
- Identification of potential leads and opportunities based on past deals and sentiment analysis
- New Einstein GPT functionality to deliver AI-generated content, such as personalized emails
Pricing
- Four bundles (sales + cloud services)
- from $25 to $325 per user per month, billed annually
How to implement AI in CRM
While the roadmap to build and adopt an AI-powered CRM solution can vary depending on the model selected, these are some common stages you should be ready to go through.
1
Discovery
Start by framing your current process and CRM flaws, business goals, and user requirements to choose the main AI use cases your future solution should target. Then, set up an implementation plan defining an MVP, budgeting, and change management. You should also select suitable internal and external data sources to integrate your CRM with.
2
Design
Prepare a functional specification encompassing various scenarios for different user roles and describing the CRM architecture, along with a visual mock-up and wireframes for the user interface. You should also detail the tools, technologies, and AI-based platforms you’ll rely on, along with the deployment model and the software integrations you plan to configure.
3
Development
Proceed with the coding phase, covering front-end, back-end, and integrations. As for the AI and ML features developed from scratch for a custom CRM, their operation will rely on ML models built through complex data preparation and algorithm training. Alternatively, you can opt for a platform-based CRM and customize built-in AI functionalities.
4
Rollout and support
After performing accurate functional, performance, and usability tests, you can deploy your AI-powered CRM solution on local servers or in a cloud-based environment. The adoption should be facilitated through ongoing user training and support, followed by regular audits and software maintenance to solve issues or address user feedback.
Benefits of AI in CRM systems
Supported by AI technologies like machine learning, a CRM platform will strengthen your customer relationships and improve business outcomes.
Benefits of AI in CRM systems
Improved decision-making
based on data-driven insights and trend forecasts
Better sales performance
via segmentation, targeted marketing, and lead scoring
Enhanced customer engagement
due to personalization and 24/7 user support
Faster and more accurate operations
through robotic process automation
Improved data reliability
via automated mapping, cleansing, and integration
Easier development
with AI-based platforms enabling low-code CRM customization
Safer data management
through ML-based anomaly detection and fraud prevention
AI-based CRM implementation challenges and solutions
When implementing AI functionalities in your CRM software, you can face a number of business and technical challenges. Here are some recommendations from our experts.
Challenge
Solution
The performance of the algorithms powering AI-based CRM systems largely depends on the data they process. Such information can be outdated, inaccurate, or spread across multiple data silos.
Challenge
Data quality and availability
Challenge
Solution
Collecting high-volume, reliable data requires a solid data management strategy. This includes data mapping of suitable sources, data cleansing and transformation, and integration among multiple systems via APIs or ESBs. The top CRM providers generally offer data preparation features (such as Salesforce Data Prep) and cloud data integration tools with built-in APIs (like SAP Data Services).
Whether you design a custom CRM or select an AI-powered CRM platform, you should define a suitable feature set addressing essential use cases in your business scenario.
Challenge
Feature and use case selection
Challenge
Solution
When implementing AI-based features, prioritize use cases based on feasibility and impact. McKinsey suggests campaign retargeting and brand management. Some platforms have tools to identify suitable AI use cases and CRM functionalities you can fully leverage (if your data availability matches their requirements), including Einstein’s guide to AI use cases and readiness assessor.
The ML algorithms used to process information and build the data models that fuel most AI-based features should be trained on vast datasets. Also, AI systems require massive computing power.
Challenge
Data processing
Challenge
Solution
If you build your solution on top of an AI-based CRM platform, model training and data processing won't be a major issue, as you'll rely on the selected platform’s native capabilities. As for a custom CRM, you can still integrate your software with cloud-based ML tools, such as Amazon SageMaker, to benefit from their built-in algorithms, pre-trained models, and scalable computing resources, or purchase your own infrastructure with powerful GPUs, depending on your business and technical requirements.
Bridging AI and customer data
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As customer interactions, driven by the rise of ecommerce and other digitalization trends, progressively shift towards virtual spaces, the volume of generated data constantly expands. The latest advancements in AI technologies provide the right tools to turn these data assets into valuable insights. AI-enabled CRM represents the perfect embodiment of this data-driven approach to customer management. Equipped with AI functionalities, CRM platforms enhance decision-making, streamline business processes, and improve customer satisfaction and retention. To fully seize the benefits of an AI-driven CRM, rely on an experienced partner like Itransition.
