AI Role in CRM

An example:

"the spirit is willing but the flesh is weak."

it became

"the vodka is good but the meat is rotten."

By John McCarthy The Father of AI (1956)

Artificial: A human creation that did not occur naturally

Intelligence: The ability to learn and apply knowledge

AI: Human made systems that are able to perform tasks that normally require human intelligence

It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Branches of AI

Logical AI

Search

Pattern recognition

Representation

Inference

Common sense knowledge and reasoning

Learning from experience

Planning

Epistemology

Ontology

Heuristics

Genetic programming

Applications of AI

1-Agriculture 2-Aviation 3-Computer science 4-Deepfake 5-Education

6-Finance 7-Government 8-Heavy industry 9-Hospitals and medicine

10-Human resources and recruiting 11-Job search 12-Marketing

13-Media and e-commerce 14-Military 15-Music

16-News, publishing and writing 17-Online and telephone customer service

18-Power electronics 19-Sensors 20-Telecommunications maintenance

21-Toys and games 22-Transportation 23-...........

Types of AI

Programming Languages of AI

Goals of Artificial Intelligence

* In early days Artificial intelligence was used to develop reasoning and problem-solving skills.

* With Artificial intelligence knowledge representation has become easy. Knowledge representation is representing information that machine or computer can understand.

* Artificial planning helps agents sequence of actions to perform to achieve goals.

* Artificial intelligence main goal is develop intelligent machines that could learn on their own. No more human intervention for feeding data to machines.

* With artificial intelligence one can develop machines that can read and understand human languages are known as Natural learning processing. Thanks to natural learning processing acquisition of knowledge became easy.

* Artificial Intelligence helps to develop that could act on sensors (take input from sensors) and react accordingly.

* Robotics has transformed thanks to artificial intelligence, that help robots acquire intelligence and perform task smartly.

* Develop systems that can recognize, interpret, process and simulate human effects. All these can be achieved when intelligent systems can predict their motive and emotions. Quality of interpreting human affect could help in better decision making

* Communication, Planning, Prediction, Prescription, Learning.......

Pros of AI in Marketing

1. Incredibly Responsive Customer Experiences

2. Higher Website Conversion Rates Overall

3. More Upsells and Cross-Sells Than Ever Before

4. Better, More Efficient Use of Your Data

5. Massive Cost Savings

6. Better Use of Your Time

7. AI Is Adaptable and Flexible to the Changing Demands of Your Business

8. AI Allows You to Send Personalized Messages, Products and Content

Cons of AI in Marketing

1. Not Everyone Wants to Talk to Robots

2. Algorithms Can Fail

3. AI Still Needs Humans (for Now!)

4. AI Doesn’t Necessarily Replace Human Power

The Fourth Industrial Revolution: Risks and Benefits

1. The Digital-Physical Interface

2. Digital Sphere

3. Human Sphere

4. Complexity of AI software

5. Cyberattacks

6. The Sorcerer’s Apprentice

7. Technology and productivity growth

8. Uneven distribution of impact across sectors, job types, wage levels, skills and education

9. Technology is not destiny - institutions and policies are critical

.....

IoT

Data Science

Big Data

Platforms

VR/AR/MR

Cloud

Blockchain

NeuralLink

3d Prints

....

1. Transcribe and Analyze Sales Calls

2. Analyze Callers’ Emotional States to Optimize Phone Experiences

3. Engage, Nurture, Qualify and Follow-up with Leads through Email

4. Identify Support Ticket Trends and Recommend Best Responses

5. Reduce Ticket Volume with Intelligent Customer Self-Service

6. Dig Through Industry and Social Media Data for Account-Based Marketing and Sales

7. Automate Service Desk Operations with a Digital Service Agent

8. Trawl Through Past Data for Accurate Lead Scoring and Predictive Marketing

9. Predict Caller Intent and Reduce Escalations with Speech Analytics

10. Speed Up Content Production Using Natural Language Generation

11. Analyze Patterns in CRM and Public Data for Daily Predictive Lead Scores

12. Onboard New Sales Reps with Proven Sales Techniques

13. Gather Business Intelligence to Gain Audience Insights and Anticipate Opportunities

14. Allocate Funds Effectively to Increase Account-Based Marketing ROI

• Which customers are eligible for offers, got them, and responded

• Where do customers struggle, pause, or get stuck in their journeys

• What sequence of offers and channels lead to conversion (attribution)

• When do certain customers show up on the CRM radar; and when do some drop off and why

1. Enter name and dates for campaign

2. Select audience by city, interests (mix of music, pop culture, shopping, sports, etc..) or look-a-like targeting; age (typical bands); gender; language

3. Decide on display ad on desktop or mobile or both

4. Specify budget (e.g., $1000)

5. Upload display ad creative image

6. Add social media promotional ad (if desired)

7. Add URL for click through (analytics tracking automatically setup in Google Analytics)

8. Enter payment method (credit card or PayPal)

1. Data integration and preparation

2. Get your bionic ears & voice on

3. Put AI eyes on customer data, journeys, and marketing content

4. “Hey AI! Create me some emotionally compelling content”

5. Self-driving CRM – Your AI digital agency

6. Building one AI brain

7. AI organizational dynamics – It’ll take neats and scruffs to tango

8. Explainable and transparent analytics and AI

9. Blockchain

....

• Einstein from SFDC

• Watson from IBM

• Sensei from Adobe

• DaVinci from SAP

The amount of data companies have on customers and the number of channels customers are using to interact with businesses have grown significantly in the past decade. Artificial intelligence may hold great promise in optimizing customer and client interactions.

The five largest Customer Relationship Management (CRM) vendors by market share in 2015 were Salesforce, Oracle, SAP, Adobe Systems, and Microsoft. These five companies make up almost half of the entire CRM market. All of them have been investing in their internal development of machine learning and AI, while also buying AI startups.

The most interesting uses of AI in CRM are allowing CRM programs to perform entirely new functions that were not possible before. Among the functions AI has recently added to CRM or that companies expect to add in the near future are:

Chatbots that can heavily augment or replace human agents. The Charly Chatbot experiment by SAP would make a good example for this capability.

Continuously customizing user experiences through predictive recommendations using the customer’s historical data

Natural language processing that can automatically sort and categorize customer service or sales inquiry requests

Voice recognition online and in store

Complete information about consumers across points of contact and social media

Emotional analysis of communications – detecting the sentiment or intent of a customer inquiry

Object recognition to both help consumers find products and companies to quickly analyze product placement

Facial recognition that can change ads in real time

Robots to directly help people in store

SAP

German software giant SAP has the goal – according to Global VP Volker G. Hildebrand – “to build machine learning technology into all our software, across every line of business and industry we serve.” This includes SAP Hybris, their main cloud CRM service.

In July the company relaunched SAP Leonardo, their []integration platform to enable companies to more easily integrate AI and machine learning into their business. SAP Leonardo appears to integrate the various SAP product offerings for machine learning, IoT, big data, analytics, etc.

Similar to Salesforce (and to the claims made by Oracle), SAP is also developing machine vision applications for it’s Hybris CRM product.

Salesforce

Over the past few years, Salesforce has been aggressively developing AI services in-house and acquiring many AI companies. They have also signed major deals with other companies focused on AI.

According to CB Insights, Salesforce acquired four AI startups (Tempo, MinHash, PredictionIO, and MetaMind) during 2015 and 2016 to add their technology to their applications.

Tempo – An AI powered smart calendar app that shows you the information you need before a meeting, which Salesforce has since shut down.

MinHash – An AI that looks for marketing trends, which Salesforce has since shut down.

PredictionIO – An open source machine learning company, which Salesforce acquired for their technology and donated the open source addition to the Apache Software Foundation

MetaMind – A deep learning company that specialized in technology, which Salesforce has since shut down.

Oracle

Oracle announced the launch of their Intelligent Cloud Applications the exact same day Salesforce announced Einstein. At the time, Oracle’s project leader told ZDNet they were “trying to avoid the hype and build apps that people can buy, use and make money with.” Instead of creating a single, all-encompassing new “AI brand,” Oracle has been focused on specific AI and machine learning applications in their cloud services — ready-to-use apps that can be quickly and easily tailored to a specific uses.

Earlier this year, Oracle introduced several new AI powered functions to their customer experience cloud (CX Cloud Suite). According to the company, these include:

AI-powered personalized marketing/experience – personalizing the content each customer receives.

Predictive recommendations – using a customer’s data to recommend products they would be most interested it.

Optimizing the selling process for representatives – opportunity analysis of clients to create guidance to help close deals.

Chatbots

1. More efficient data management

2. An optimized sales strategy

3. Better application development

4. Consolidation of customer confidence

5. Increased customer satisfaction

6. Virtual assistants and bots

7. Segmenting customers becomes easier

8. Increase customer engagement

9. Close more deals, learn from your mistakes

10. Be able to predict future customer behavior

....

1. One to one marketing(USP-VBM)

2. Better STP

3. Better fulfilling the demands

4. Having Insights/Machine prescriptions and predictions/Feedback from data will help us a lot

5. Better strategy and decision making

6. Better understanding of CJM and having higher CLV due to proper offerings

7. Better functioning in Funnel

8. LOCATE efficiency & accuracy

...

Twenty Applications of Predictive Analytics

TARGETING DIRECT MARKETING

1. What’s predicted: Which customers will respond to marketing contact.

2. What’s done about it: Contact customers more likely to respond.

PREDICTIVE ADVERTISEMENT TARGETING

1. What’s predicted: Which ad each customer is most likely to click.

2. What’s done about it: Display the best ad (based on the likelihood of a

click as well as the bounty paid by its sponsor).

BLACK-BOX TRADING

1. What’s predicted: Whether a stock will go up or down.

2. What’s done about it: Buy stocks that will go up, and sell those that will

go down.

PREGNANCY PREDICTION

1. What’s predicted: Which female customers will have a baby in coming

months.

2. What’s done about it: Market relevant offers for soon-to-be parents of

newborns.

EMPLOYEE RETENTION

1. What’s predicted: Which employees will quit.

2. What’s done about it: Managers take the predictions for those they

supervise into consideration, at their discretion. This is an example of

decision support rather than feeding predictions into an automatic decision

process.

CRIME PREDICTION (AKA PREDICTIVE POLICING)

1. What’s predicted: The location of a future crime.

2. What’s done about it: Police patrol the area.

FRAUD DETECTION

1. What’s predicted: Which transactions or applications for credit, bene­fits, reimbursements, refunds, and so on are fraudulent.

2. What’s done about it: Human auditors screen the transactions and

applications predicted most likely to be fraudulent.

NETWORK INTRUSION DETECTION

1. What’s predicted: Which low-level Internet communications originate

from imposters.

2. What’s done about it: Block such interactions.

SPAM FILTERING

1. What’s predicted: Which e-mail is spam.

2. What’s done about it: Divert suspected e-mails to your spam e-mail

folder.

PLAYING A BOARD GAME

1. What’s predicted: Which game board state will lead to a win.

2. What’s done about it: Make a game move that will lead to a state

predicted to lead to a win.

RECIDIVISM PREDICTION FOR LAW ENFORCEMENT

1. What’s predicted: Whether a prosecuted criminal will offend again.

2. What’s done about it: Judges and parole boards consult model

predictions when making decisions about an individual’s incarceration.

AUTOMATIC SUSPECT DISCOVERY

1. What’s predicted: Whether an individual is a “person of interest.”

2. What’s done about it: Individuals with a sufficiently high predictive

score are considered or investigated.

CUSTOMER RETENTION WITH CHURN MODELING

1. What’s predicted: Which customers will leave.

2. What’s done about it: Retention efforts target at-risk customers.

MORTGAGE VALUE ESTIMATION

1. What’s predicted: Which mortgage holders will prepay within the next

90 days.

2. What’s done about it: Mortgages are valued accordingly in order to

decide whether to sell them to other banks.

MOVIE RECOMMENDATIONS

1. What’s predicted: What rating a customer would give to a movie.

2. What’s done about it: Customers are recommended movies that they

are predicted to rate highly.

OPEN QUESTION ANSWERING

1. What’s predicted: Given a question and one candidate answer, whether

the answer is correct.

2. What’s done about it: The candidate answer with the highest predic­tive score is provided by the system as its final answer.

EDUCATION—GUIDED STUDYING FOR TARGETED LEARNING

1. What’s predicted: Which questions a student will get right or wrong.

2. What’s done about it: Spend more study time on the questions the

student will get wrong.

TARGETED MARKETING WITH RESPONSE UPLIFT MODELING

1. What’s predicted: Which customers will be persuaded to buy.

2. What’s done about it: Target persuadable customers.

CUSTOMER RETENTION WITH CHURN UPLIFT MODELING

1. What’s predicted: Which customers can be persuaded to stay.

2. What’s done about it: Retention efforts target persuadable customers.

POLITICAL CAMPAIGNING WITH VOTER PERSUASION MODELING

1. What’s predicted: Which voter will be positively persuaded by political

campaign contact such as a call, door knock, flier, or TV ad.

2. What’s done about it: Persuadable voters are contacted, and voters

predicted to be adversely influenced by contact are avoided.

The marketing concept does not mean giving the customer (only) what they want, because:

1) the customer’s wants can be widely divergent;

2) the customer’s wants may contradict the firm’s minimum needs; and

3) the customer might not know what they want. It is marketing’s job to learn and understand and incentivize customer behaviour to a win–win position.

So far we have focussed on customer behaviour. Marketing, to be marketing, is about understanding and incentivizing customer behaviour in a way that consumers get what they want and firms get what they want. Customers want a product that they need when they need it at a price that gives them value through a channel they prefer. Firms want loyalty, customer satisfaction and growth. Since a market is a place where buyers and sellers meet, marketing is the function that moves the buyers and sellers toward each other. The customer is the agent that makes this take place so of course great emphasis should be on the customer.

The overarching point is that marketing science (and marketing research and marketing strategy) should all be focussed on customer (and overall consumer) behaviour. Good marketing is customer-centric.

Marketing science is important and offers incredible value. Those analysts that do marketing science need a right view, a theory of causality for providing insights. An insight is not an observation(72 per cent of our customers enter our store wearing jeans.) An insight is something new, that explains customer behaviour and provides a competitive advantage. There must be financial implications following an insight.

Segmentation

What Tomorrow May Bring

I can promise you this: Anything with electricity will be connected

to the Internet and anything that can be connected to the Internet will

be connected to AI systems.

◾ Your watch

◾ Your flashlight

◾ Your clock

◾ Your toaster

◾ Your shoes

◾ Your shirt

◾ Your glasses

◾ Your rug

...

* THE PATH TO THE FUTURE

* MACHINE, TRAIN THYSELF

* INTELLECTUAL CAPACITY AS A SERVICE

* Conscience Support System

* What If We’re All Just as Smart?

* DATA AS A COMPETITIVE ADVANTAGE

* Data as a Business

* Data as a Sideline

* Insight Automation

* Like a Boss

* Pretending to Be a Human

* Beyond Human

* YOUR BOT IS YOUR BRAND

* MY AI WILL CALL YOUR AI

* Your Personal Shopper

...

References

Any Questions?