Category: Machine Learning

In depth information on machine learning applied to CRM & Marketing, including blogs, infographics, and a mini-cast series of instructional videos

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Marketing Results: Machine Learning Episode #4

Getting positive marketing results for your customer engagement efforts is what it’s all about.

In this episode, I explore what to expect in terms of outcomes when you effectively employ machine learning (ML) and artificial intelligence (AI) in your marketing efforts.

I cover performance ranges I’ve seen actual companies achieve including:

  • Churn & Attrition reduction
  • Response Rates for your marketing execution & tactics
  • Sales Lift increases which means revenue to the bottom line
  • ROMI – Return on Marketing Investment
  • NPS – Net Promoter Score improvements
  • AHT – Average Handle Time reduction

You can use these figures to benchmark against your efforts, or even help you build a business case for embarking on using machine learning in your marketing and Customer Experience programs.

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Marketing Timing & Content: Machine Learning – Episode #3

Great timing & content lead to great marketing tactics and performance.

In this episode, I explore how you can use machine learning (ML) and artificial intelligence (AI)  to improve your message timing and the content you employ – further improving experiences for your customers.

I explore “2 Cool Areas” of Machine Learning & Artificial Intelligence applications – to make your marketing smarter:

  • Timing Optimizing for your Marketing Execution & Tactics
  • Automated Content Generation and Predictive Content Recommendations

My tips are aimed at improving your marketing efficiency & effectiveness.

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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.

machine learning

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

AI Automated 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 –

bigdata_value2

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
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Smarter CRM: Machine Learning Episode #2

You want Smarter CRM, don’t you?

In this “Mini-Cast” on machine learning (ML) and artificial intelligence (AI) for marketing & CRM, I outline the numerous applications you can use to improve customer experience.

AI for Smarter CRM
AI for Smarter CRM

In this second episode, I delve into the various areas of AI & ML applied to Marketing, Sales Automation, and Customer Service where ML & AI play huge roles in taking those functions to new levels of insight and intelligence, enhancing productivity, effectiveness, and delivering better customer engagement.

Episode #2 on YouTube:

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Machine Learning Marketing Series – Episode #1

Introduction & History of Machine Learning:

In this inaugural episode, I explore some useful history on the various areas and aspects of machine learning and artificial intelligence, and its evolution leading up to today.

Specifically, I delve into unsupervised machine learning, deep learning, and software robotics as applied to customer relationship management.

Watch my  entire “Mini-Cast” series of short 5 to 10 minute videos on machine learning (ML) and artificial intelligence (AI) used in marketing & CRM to improve customer experience (CX).  The entire series is available from my website linked to my YouTube Channel:

Episode #1: Machine Learning Introduction & History (this one above)

Episode #2: Smarter CRM – Machine Learning Applications for Customer Experience

Episode #3: Marketing Timing & Content – 2 Cool Use Cases for Machine Learning

Episode #4: Marketing Results – Amazing Outcomes using Machine Learning

Episode #5: Marketing Learning Measurement & Monitoring

Episode #6: Major Players in the Machine Marketing Ecosystem

Episode #7: Adtech & Martech Machine Learning Convergence 

Episode #8: Machine Marketing – Hot Trends (coming soon)

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The CSI Guy – Customer Success Investigator

sherlockRecently I read a fascinating article on how crime investigators are using machine learning to find patterns and uncover insights that otherwise wouldn’t be detectable.   Investigations ranged from criminal to cybersecurity, competitive counterintelligence, and corporate litigation.  In one example, a firm showcased how – if this technology had been available during the Enron era – they might have proactively detected the scandal, potentially averting the crisis.  Their demo traversed 500,000 Enron emails, and as it did it learned how to flag suspicious ones.

It’s not news that pattern detection software works for fraud detection.   For years, banks have employed systems that scan structured credit card transaction data, and flag unusual activity.  Yet what occurred to me is this same approach and its specific techniques are extremely well suited to the art and science of developing customer intelligence against unstructured big data.   Customer experience pros could then unlock new mysteries, and take appropriate actions leading to great customer success outcomes.

The first thing criminal investigators do is they gather all the facts they can, from any available source – Emails, phone records, texts, web activity.  The adage being “Leave no stone unturned.”   It’s never clear at the start of any investigation which clues might matter, and may link to others – so all are important regardless of their form.    As the investigation unfolds, machine learning techniques, such as neural networks that use self-organizing cluster maps (known as SOMs) can help find patterns, and eventually help the investigators form a hypothesis.  Available evidence is used to test whether the facts fit the theory.

Shifting the frame of reference, gaining customer intelligence and using it to solve for marketing and customer experience challenges can benefit from this same methodology and technology:

  • Consumers leave clues about their preferences and behavior in many places; sometimes in unstructured forums, like social media, product reviews, and blogs.
  • It’s virtually impossible to sift through this data without the aid of technology and automation.
  • Machine learning can be used to find patterns in customer activity, such as what product they are most interested in buying, or that their sentiment is trending toward total dissatisfaction.
  • Once patterns are detected, predictions can be made and actions triggered in efforts to anticipate needs or alleviate matters.

As a consumer, my natural reaction might be to say, “That’s creepy and spooky”.   Ironically though, most firms simply want to use this to improve your experience with their brands since they know it’s critical to their health.  Repeatedly, surveys show above price and product, people leave because of dissatisfaction with the way they are treated.

But the level of dissatisfaction is qualitative and differs by customer.  One customer who experiences a single network issue may become enraged, while another may be more tolerant.   Knowing this and the value of each customer helps the company treat each situation with a custom tailored response.

That all sounds like common sense and easy, right?  Try doing it on millions of customers, with billions of bytes of unstructured data in their direct conversations and behaviors, and their indirect musings on social media, in blogs, and elsewhere.   Moreover, try to learn when each customer reaches various stages of interest or displeasure, and overtime improve your ability to predict these and take timely action.

Since the dawn of time, we learned that to survive we needed help from machines.   Use this newest breed of machines along with time tested investigation techniques to crack the enigma of your customers, gauge their state of mind, and delight them with personalized experiences.

Note:  These views are my own, and not that of my employer