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 cyber security, 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 signficant 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:
- Task automation
- Detecting & alerting
- Situational understanding
- Predicting a state of mind or need
- Predicting a course of action
Doing these in a faster, more accurate, and more cost effective way versus humans doing it manually. All resulting in a superior customer experience and better business results.
CRM is 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.
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 / outcomes improve and approach optimal.
- Text, Video, Audio and Image Analytics – this code scans unstructured data, and finds common entities, and either classifies them or extracts them. Deep learning is an advanced form of image analytics – using a technique with layers of brain-like neurons (Recurrent Neural Networks); it finds image patterns or classifies images. When it classifies, it can even write captions.
- Complex Event (Monitoring) Processing – an engine fed streaming data from one or more source, which is explicitly programmed to detect certain patterns and then initiate 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.
- Automated text generation – this code takes images (still or moving), and generates text.
- 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
Regarding applications of AI to CRM (e.g., using any code and a combination of the above algorithms), consider these main areas:
- Virtual assistants – a module designed to aid a human with decision making, problem resolution or task completion including:
- Chat bots
- Office Assistants (e.g., automatically schedule meetings)
- Digital recommendation engines
- Next-Best-Recommendations based on predicting customer preferences – ranked or bundled
- Product & Content Recommendations based on brand and product affinity and compatibility – ranked or bundled
- Some combination of the 2 above
Given these areas there are hundreds of potential valuable AI use cases for CRM. Hera 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(s)
- Next best content recommendation(s)
- Next best promotional recommendation(s)
- Next best channel to engage on
- Next Best time to send
Predicting Customer Behavior and Value:
- Models to predict customer churn
- Models to predict customer preferences (including 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:
- Refreshing content – Object recognition
- Cataloging content
- Content generation
- View trending topics
- Forecast Market Size, Marketing Revenue / Costs
- Budget Allocation Planning – Media Mix
- Next-Best lead
- Next-Best sales activity
- Next-Best sale rep
- Sales Contracts
- Sales / Demand Forecasting
- Competitive Intelligence
Customer Service / Support
- Next best service, support, training action – in a conversational mode – to resolve a complaint / problem
- Next best expert / agent to resolve a complaint / problem
- Text & Images uploaded to support resolving cases
- Tag & Classify Support tickets
- Process Automation
- Estimating Service Volumes, Wait Times, and Utilization
- Contact center volume forecasting
- Estimate wait time
- Optimize Scheduling and Support Utilization Optimization
Predicting Customer Service Behavior:
- Models to predict levels of customer satisfaction
- Sentiment Analysis with all forms of data
- Reasons behind high churn likelihood
- Models to predict likelihood of a customer calling and why
- View trending support topics