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The inclusion of AI in CRM was unavoidable. AI-powered CRM systems offer actionable insights and predictive analytics to help organizations understand their customers.
The major CRM vendors -- and most minor ones -- continue to expand their capabilities with cloud and AI technologies to stay relevant in the market. They all use AI generally and are working to build their own flavor of this technology into their CRM apps. To evaluate each AI-powered CRM platform, marketing and sales leaders should understand each vendor's unique approach to AI and know how it works in its CRM offering.
4 common AI-powered CRM features
CRM platforms rely on four foundational AI features to power many of their apps. These features are the following:
- Machine learning. ML is a state-of-the-art practice in commercial AI. It involves teaching an automated system to behave productively and effectively in changing conditions, based on patterns and inference rather than programming or explicit rules.
- Predictive analytics. Indispensable in enterprise planning and customer interactions, predictive analytics helps organizations focus decisions and resources on the most effective course of action at all levels, including personal interactions with customers.
- Automation. Because automation can perform certain tasks more quickly and accurately than humans, it has become a standard practice in many organizations. AI advancements have enabled the automation of highly complex workflows.
- Sentiment analysis. This functionality helps customer service and sales agents identify customer emotions, such as frustration and satisfaction, across channels like phone, live chat, email and social media. It offers insights into how customers feel about products and brands.
AI functionality across CRM products
Beyond common features like ML and automation, CRM products can vary dramatically as each vendor takes AI in its own direction.
The following AI-powered CRM platforms -- including Salesforce Einstein, IMB Watson and Azure Cognitive Services -- have their own strengths and weaknesses.
Salesforce Einstein is the vendor's AI that powers many features in the Salesforce Customer Success Platform. Its functions include the following:
- ad hoc analysis of big data from various sources;
- an emphasis on sales and customer service;
- Discovery, an app that finds patterns in customer behaviors, store performance, and campaign performance and trends;
- user friendliness to non-specialists;
- AI training resources;
- customer service chatbots, called Einstein bots, that can integrate with external apps;
- natural language processing (NLP) that can identify customer intent and automate responses;
- deep learning features that let organizations detect brands and products in images on social media and other sources to learn how customers feel about them;
- Einstein Prediction Builder to customize business forecasting; and
- Next Best Action to coach agents in real time.
Einstein's weaknesses include modest visualization features and limited or unproven utility beyond the sales and marketing domains.
IBM Watson is an AI system that organizations can apply in various use cases, such as advertising, customer service, financial operations and sales. In 2017, Salesforce and IBM became partners in a deal that integrated Einstein and IBM Watson.
Watson's strengths include the following:
- multilevel accessibility;
- fast performance;
- natural language querying;
- strong visualizations;
- straightforward setup;
- deep learning capabilities;
- strong social media integrations; and
- connectors for integration with other vendors.
The downsides include a lack of real-time streaming analytics and poor integration with Hadoop, an open source distributed processing framework.
Key AI functions to come from the Watson and Salesforce partnership include the following:
- Watson Discovery, which yields customer insights;
- Watson Campaign Automation -- a design canvas that supports personalization, campaign-specific insights and reporting;
- a Lightning-based UI dashboard for AI-based CRM support apps; and
- AI customer service functionality that uses the Watson Assistant to automate tasks.
Microsoft Azure Cognitive Services
Microsoft's cloud AI service -- Azure Cognitive Services -- differs from others in various ways. It offers analytical functionality in tools that help customers build that functionality into their apps, workflows and business systems.
Specifically, Azure Cognitive Services includes the following:
- Databricks, which organizes information for big data analysis, production apps and research;
- NLP that users can embed in apps and processes;
- Power BI, Power Query and Power BI Data Flows for enterprise analytics;
- deep learning functionality, which is somewhat limited to text analysis;
- speech-to-text, text-to-speech, translation, speaker identification and recognition services; and
- sentiment analysis, question recognition, natural language understanding and entity recognition.
However, Azure Cognitive Services offers less ad hoc functionality than Einstein and Watson.
It directly powers several Dynamics 365 modules, which include the following:
- Sales Insights for data-driven customer insights;
- Customer Insights for campaign and sales personalization;
- Market Insights for global and market segment trends and social media analysis;
- Customer Service Insights for proactive customer relationship maintenance inputs; and
- virtual agents to answer customer inquiries.
Oracle Artificial Intelligence
Oracle has been working on AI for over 20 years. The company's efforts began as an initiative to automate back-end Oracle infrastructure maintenance. Two major products emerged from that undertaking, which are the following:
- Autonomous Database, a supervised learning-based expert system for Oracle's SaaS database environment. Based on log analysis, it helps database performance optimization.
- Adaptive Intelligent Applications, which uses ML-driven functionality in Oracle's ERP platform.
Oracle developed its AI resources within a more limited context than its competitors, so it doesn't offer the most generalized AI functionality. However, its AI enables the following features in Oracle CX Cloud:
- predictive analytics;
- customer engagement functionalities;
- real-time coaching for sales and customer service agents;
- customizable settings to help sales agents track their prospects;
- usage analytics to track how employees interact with the CRM;
- Oracle Cloud Infrastructure (OCI) Vision features, which use optical character recognition to identify text in scanned documents, PDFs, video stills and images;
- OCI Anomaly Detection to flag critical incidents; and
- OCI Forecasting capabilities to predict product demand, revenue, services and number of service requests.
SAP AI offers AI functionality for business teams across various departments. Key features of SAP AI include the following:
- intelligent workflow to streamline business processes;
- advanced analytics; and
- AI use cases tailored to specific business scenarios, such as CX, procurement and HR.
SAP's CRM offerings use SAP AI to power the following features:
- Conversational AI, a create-your-own customer and employee experience enhancement that integrates with other SAP products;
- digital assistants that can integrate with workflows across channels and humanize CRM interactions;
- NLP, which facilitates all of the above; and
- SAP intelligent robotic process automation, which works across a broad range of customer-related apps and processes and improves response time.
Adobe's Sensei is somewhat less versatile than the others, yet it performs well in its market. It offers features the others do not, which include the following:
- Anomaly Detection to determine when something unexpected happens;
- Contribution Analysis, a feature that determines why something unexpected happened;
- Intelligent Alerts to notify the right people when something unexpected happens;
- Virtual Analyst, a background cross-channel analysis process that identifies unknown unknowns;
- customer engagement features that can identify users who need notifications and follow-ups;
- intelligent data extraction that automatically labels content from paper and digital assets; and
- customer activation features that help create promotional email campaigns for prospective customers.
More conventionally, Sensei includes the following:
- plain-English querying;
- ad hoc analysis tools;
- software development kits and templates -- TensorFlow and PyTorch -- for its ML framework; and
- direct connection to Microsoft's Azure environment.
In the Adobe Experience Cloud, Sensei's AI features enable the following:
- the Adobe Target personalization engine to fine-tune customer contacts and campaigns;
- real-time CRM decision support;
- passive reporting on how, when and what customers shop for; and
- the ability to track customer online behavior in real time and pick up signals to modify CX.
As computing power has increased, AI use in CRM has seen a significant surge. More organizations want to automate CRM tasks, such as behavioral data analysis and customer segmentation. Organizations can integrate their customer data from social media, invoicing tools, live chat and email, with an AI-powered app to derive useful customer insights.
The diversity of approaches to enterprise AI drives an equally diverse array of AI-powered CRM platforms, each with its own strengths and weaknesses. This diversity may increase as more CRM vendors join the fray.
Editor's note: This article was originally published in January 2020 by Scott Robinson and was updated in November 2022 by Reda Chouffani.