Machine Learning & AI Use Cases for CRM

Updated: May 6, 2020

Below you’ll find a good organization of classes of AI use cases for CRM (Customer Relationship Management) & CX (Customer Experience).  Presently, there are countless examples of applying Artificial Intelligence (AI) and Machine Learning (ML) to solve a broad class of problems beyond CX, from cancer diagnosis, robots in manufacturing operations, streamlining product development, detecting anti-money laundering, spam filtering, improving cybersecurity, to self-driving cars.

Great marketers and CX experts have always been change agents.   As new-age change agents, they focus on AI use cases that enhance CRM, and have either already been proven to lead to successful outcomes, or that show significant commercial promise.

But what is AI and how can it be used to take customer engagement to new levels?  To make it simple, think of AI as the application of software technologies using standard hardware for:

CRM AI purposes –

• Task automation:
  • Doing something in less time with fewer steps
  • Accomplishing something with less total effort
  • Doing something with less or even zero human intervention
• Detecting, classification, and alerting:
  • Sensing and understanding a current problem (customer is dissatisfied or struggling)
  • Sensing current customer intent (wants help; wants more information; trying to buy)
  • Placing similar things together (customers, products, content)
  • Informing someone or some other system once a certain confidence threshold is reached
• Predicting something is likely to happen:
  • Customer likely to buy more (or less; or never buy again)
  • Customer is likely to call for service
  • Contract is likely to close
• Suggesting (in reaction or pro-action) a course of action leading to more optimal outcomes:
  • Doing X will resolve the problem
  • Recommending Z will satisfy the requirement
  • Offering Y will increase customer lifetime value and/or maximize profit

Machines are capable of doing these in a faster, more accurate, and more cost-effective way versus humans doing them manually.  When that happens, the result is a superior customer experience and better business results.

CRM is a technology that improves customer experience (either unassisted or assisted by an employee) as it relates to customer’s interactions with a brand’s marketing, sales, and service, and the fulfillment of commitments by those three functions.

Artificial Intelligence (AI) technologies –

Regarding different sub-areas of Artificial Intelligence and its class of technologies, consider this organization:

(Note: these categories are not mutually exclusive in that a given application may benefit from one or more of these technologies)

• Machine learning – Algorithms using statistical methods designed to predict or forecast something. The learning is either supervised (i.e., assisted) because we either know the outcome trying to be achieved (or we have a teacher to inform us) and can determine if the model is right or wrong – or unsupervised (i.e., unassisted) because we have no idea of the patterns or outcomes that may be best).  The system “learns” if over time its predictions and performance improve.

• Text, Video, Audio, and Image Analytics – this code scans unstructured data, and finds entities/objects, and classifies them (and/or extracts them). Deep learning is an advanced form of image analytics – using a technique with layers of brain-like neurons (Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN)).

• Complex Event (Monitoring) Processing (CEP) – an engine fed streaming data from one or more source, which detects certain patterns and then initiates follow on actions or processes.

• Deep Q&A – using an AI system that has access to previously compiled knowledge sources; it scans, filters, and presents the best likely answers to questions.

• Natural Language Processing – this code translates spoken voice to text, or to some other form useful as input to another system. It can also do the reverse, translating some other system’s output to spoken voice.  It also can translate from one language into another, or just detect the language.

• Natural Language Generation – this code takes input (images (still or moving), text, etc..), and generates text descriptions of what its seen or found.

• Numeric Analytics – this code uses commonplace mathematics (simple formulas) to surface insights, focusing the receipting’s attention to aid their future decisions.

• Robotic Process Automation – this code repeats tasks that it’s instructed to repeat.

• Deterministic rules – if/then/else statements that don’t change unless a programmer changes the rule.

Click here for an AI diagram of how I’ve organized these into building blocks so you better understand where each belongs in terms of its contribution to automation or true machine intelligence

CRM AI Vehicles –

Regarding implementing AI use cases in CRM for improving customer engagement (e.g., using any code and a combination of the above algorithms), consider these main vehicles:

• Virtual Assistants – a module designed to aid and assist a human with decision making, problem resolution or task completion including:
  • Chatbots (such as Facebook Messenger)
  • Conversational interfaces (such as Alexa, Google Home)
  • Office assistants built into desktop software or mobile apps (e.g., automatically schedule meetings)

• Digital Recommendation Engines – ranks and bundles content recommendations, product recommendations, offer recommendations, and service recommendations and uses containers/spots on web, mobile, agent desktops, email, kiosk, and other digital devices to serve these to:
  • An employee or agent to sell a product or solve a service case
  • Engage directly with a consumer through any digital device

CRM AI Use Case Categories –

Given these areas, there are hundreds of potential valuable AI use cases for CRM.  Here are some examples:

Marketing

Profiling and Tactic Execution:

• Predicting missing or outdated customer data values

~Forecast data value using time series, or simple inflation adjustments
~Predict data value from an image

• Segmentation, clustering, targeting
• Next best product / service recommendation
• Next best content recommendation
• Next best promotional recommendation
• Next best channel to engage on
• Next best time to send
• Next likely transaction (and timing of it)
• Next best search keyword

Predicting Customer Behavior and Value:

• Models to predict customer churn
• Models to predict customer preferences (including a preference for a particular product/service)
• Models to predict customer lifetime value
• Models to predict current customer value
• Models to predict customer wallet share
• Models to predict customer re-activation

Dynamic Price Optimization

• Optimize retail price of products/services
• Optimize incentive levels (discounts) of products/services
• Optimize price paid for ad buys

Marketing Operations / MRM / Marketing Planning:

• Tagging/filtering content – Recognize an object in image or video
• Tagging/filtering content – Match an audio track to known copyright material
• Tagging/filtering content – Classify image content type
• Content generation
• View trending topics
• Forecast market size, marketing revenue/costs
• Budget allocation planning/media mix optimization

Influencer Marketing:

• Content identification

Sales Automation

• Next best lead
• Next best sales activity
• Next best sale rep
• Sales contracts
• Sales and demand forecasting
• Competitive intelligence

Customer Service

Customer Service / Support

• Next best service, support, training action to resolve a complaint/problem
• Intelligent routing: Find the next best expert/agent to resolve a complaint/problem
• Text and images uploaded to support resolving cases
• Tag and classify support tickets
• Support process automation
• Estimating service volumes, wait times
• Optimize scheduling and support resource utilization
• Contact center volume forecasting
• Estimate wait time
• Optimize scheduling and support utilization optimization

Predicting Customer Service Behavior:

• Models to predict levels of customer frustration or satisfaction
• Sentiment analysis with all forms of data.
• Reasons behind high churn likelihood
• Models to predict intent (likelihood of a customer calling and why)
• View trending support topics