AI-Powered CRM Platforms: A Comparison

AI-Powered CRM Platforms: A Comparison

All major CRM platforms have implemented AI as a central feature in their offerings in the last few years. Companies are now much better equipped to understand their customers thanks to AI-powered CRM. With actionable insights and accurate predictive forecasting, the path to better customer understanding is becoming easier.

All the major CRM vendors, as well as some of the minor ones, live within bigger cloud platforms which are quickly increasing their capabilities to stay relevant. AI has been implemented within all platforms generally, and now most of them are working on putting their mark on their own brand of AI in their CRM apps. To evaluate each AI-powered CRM you need to understand each vendor’s approach to artificial intelligence and reviewing how it works within each CRM offering specifically.

Most of the major CRM platforms have similar AI features. This is to be expected due to some aspects of technology having to be universal in order to be economical and also because trends in modern business can’t be ignored. Some of the common AI features you can expect to find in most platforms are:

  • Predictive analytics
  • Indispensable in enterprise planning and customer interaction, this feature focuses resources and decision on what the most effective course of action at all levels is, including personal interactions with customers.
  1. Machine Learning
  2. This state-of-the-art practice involved teaching an automated system to behave effectively and productively in changing conditions. It is not based on programming but on inference and patterns.
  3. A standard practice in the enterprise is now extracting human time and error from processes. AI makes automation of complex workflows practical.

Besides these common features, offerings can vary greatly as each vendor takes its own direction when it comes to AI. We take a look at some of the major AI-powered CRM platforms and discuss their strengths and weaknesses.

Microsoft Cognitive Services

As is to be expected, Microsoft’s AI is different from all others in a lot of ways, some are good, and some not so much. Cognitive Services offers a substantial analytical functionality through tools that enable customers to build functionality into their workflows, business systems and apps.

It includes the following:

-Power BI, Power Query and Power BI Data Flows for enterprise analytics

-Natural language processing, easily embedded in apps and processes

-Databricks data aggregations that organise data for efficient and rapid processing for big data analysis, research and production applications

-Deep learning functionality – limited to text analysis

-Strong, flexible and easily configured visualisation through Power BI

One of the biggest downsides is that Cognitive Services offers very little of the ad hoc functionality you can find in Watson and Einstein.

Cognitive Services directly powers the following Dynamics 365 modules:

-Customer Insights for campaign and sales personalisation

-Sales Insights for data-driven customer insights

-Customer Service Insights for proactive customer relationship maintenance inputs

-Market Insights for global and market segment trends including social media analysis

-Cognitive Services, powering the platform’s adaptable and easily configured virtual agents

Salesforce Einstein

Salesforce’s Einstein is an industry-leading AI presence. It has garnered a lot of media attentions and has acted as a trend-setter in enterprise AI offerings. Its functions include:

-Sales/service emphasis, based on the company’s many years leading the industry

-Ad-hoc analysis of big data, even when it is from a non-Salesforce source

-User friendly for non-specialists

-Effective AI training resources for users

-Discovery, an easy to use pattern-finding app that works without a complex data model

Recently integrated with IBM’s Watson, it is forming a powerhouse alliance that is only starting to reveal its full potential. Some of its weaknesses include modest visualisation features and unproven utility beyond the marketing and sales domains.

So, what does all this mean for Salesforce CRM? The Discovery app itself is one of the key benefits, as its pattern finding utility can be applied to store performance, campaign performance, trends and customer behaviours. Einstein bots are also key. These smartbots are customer/service specific and are easily configured, being directly hardwired into Salesforce customer/product data, also including sophisticated scripting features. Moreover, Einstein Prediction Builder and Next Best Action put complex data-driven AI recommendations in the hands of non-technical front-line employees.

IBM Watson

While Salesforce Einstein is currently leading the pack in the enterprise game, IBM’s Watson is right behind it. Some of its strengths include:

-Robust natural language querying

-Multi-level accessibility

-Fast performance

-Strong visualisations

-Deep learning capability

-Strong social media integration

-Straightforward setup

-Connectors available for integration with other vendors

Some of its downsides include the lack of real-time analytics and a lack of seamless Hadoop integration.

Some of Watson’s best functionality can be found in its customer-based tools:

-Natural language processing

-Watson Discovery (from the Salesforce partnership) yields strong customer insights

-Campaign Automation (Watson Assistant for Marketing) is an intuitive design canvas with Watson in the background that supports campaign-specific insights, reports and personalisation

-Lightning-based UI dashboard for AI-based CRM support apps

-AI customer service functionality that uses the Watson Assistant for support task automation

Oracle Artificial Intelligence

Oracle have been steadily and quietly working on AI for the better part of the last two decades. It started as an initiative to apply intelligent automation to back-end Oracle infrastructure maintenance. Two major products rose from it:

-Adaptive Intelligent Applications, which consists of machine learning-driven functionality in Oracle’s ERP platform

-Autonomous Database, a supervised learning-based expert system for the SaaS database environment. It’s based on log analysis and facilitates database performance optimisation.

One of the main limitations of this offering is that its considerable AI resources that have been developed within a limited context, don’t yet have a generalised functionality as much as its competitors. As for its application in Oracle’s CX Cloud, AI facilitates the following:

-AI-driven customer engagement functionality

-Robust predictive analytics

-Usage analytics on team activity

-AI-powered chatbots

-An AI-driven unified customer experience focused on optimising brand loyalty

SAP Leonardo

This offering has gotten far less media attention than its competitors. However sizeable resources have been brought to bear in support of its well-rounded, generalised AI platform. It is being touted as a “digital innovation system”. This system encompasses the following:

-Advanced analytics

-Versatile, hands-off intelligent workflow

-Blockchain integration

-Open platform with open standards

-Cloud-centric generalisation of business scenarios for rapid exploitation

-Easy integration into other cloud infrastructures, such as Google, Azure and AWS

SAP’s Leonardo is friendly to just about any cloud system. This makes its way into SAP CRM in several ways:

-Easy-to-build digital assistants that exploit the conversational AI feature that’s able to integrate with workflows and tasks across channels, humanising CRM interactions

-Conversational AI that’s a create-your-own employee/customer experience enhancement. This is also fully integrated into other SAP products

-Natural language processing that facilitates all the above

-SAP intelligent robotic process automation that’s applicable across a wide range of customer-centric apps and processes.

Adobe Sensei

One of the least known AI’s is Adobe’s Sensei AI. It is slightly less flexible than all the others, however it is highly effective in its market. Some of the functionalities it provides that others do not include:

-Contribution Analysis: a feature for determining why the unexpected something has happened

-Anomaly Detection: to determine when something unexpected happens

-Intelligent Alerts: to notify the right people when something unexpected happens

-Virtual Analyst: a background cross-channel analysis processes that identified “unknown unknowns”

Conventionally, Sensei includes:

-Ad-hoc analysis tools

-Software development kits and templates (Tensorflow and Pytorch) for the machine learning framework

-Direct connection to Azure

-Plain-English querying

In the Adobe Experience Cloud, these capabilities facilitate the following:

-Real-time CRM decision support for customer actions

-AI-driven customer experience, including Adobe Target personalisation engine

-Passive reporting on how, when and what customers are shopping for

-Target observes customer online behaviour in real-time and picks up signals for instant customer experience adjustment

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