Dr.-Ing. Farshad Firouzi - mVISE AG · 8 Business Statistics Customer Analytics for Digital Transformation 50% 50% increase in loyalty program enrolment 40% 10% 40% improvement in

Dr.-Ing. Farshad FirouziSr. Technical Manager

Mobile: +49 171 4434886

Email: [email protected]

© Dr.-Ing. Farshad Firouzi

Artificial Intelligence Driven Omni-channel Customer Journey: From Awareness, Purchase, Service, to Loyalty

2

Background

Technical Project Manager, Solution Architect, Artificial Intelligence Engineer, and Data Scientist

• Post-Doctoral Degree in Computer Engineering (Full Scholarship)

– (Post-Doctoral: Machine Learning and Alternative Scaling)

– (PhD: Machine Learning on Chip for Predictive Maintenance and Reliability Analysis)

• Publication: 40+ scientific papers in peer-reviewed journals and conferences

• Research (Machine Learning, Reliability, IoT, Big Data, eHealth, Smart City)

– Associate Editor of three IEEE/ACM Journals

– Conference/workshop Program-chair• ICCAD, California, USA

• Complexis, Portugal

• IoTBDS, Portugal

• IoT for eHealth, Washington D.C., USA

– Program Committee Member• ATS, Japan

• Industrial Work Experience (Iran, USA, Belgium, Germany)

– Technical Manager/Leader: Smart Parking

– Technical Manager/Leader: Predictive Maintenance

– Technical Manager/Leader: eHealth Platform

– Technical Manager/Leader: Spike-based ANN System for Pattern Recognition

– Technical Manager/Leader: Statistical Timing and Reliability Analysis Framework


3

Are you ready for AI-driven IoT-based CUSTOMER JOURNEY!?


4

CustomersServices

Physical Store

Advertisement SocialWeb

Chat

MobileEmail

AI, ML, and Big Data give you a 360 degree view over your business and customers

Awareness Consideration Purchase Service Loyalty

Several touch points (email, web, social, etc.) during the journey © Dr.-Ing. Farshad Firouzi

5

UtilitiesUtilities

TransportationTransportation

BankingBanking

ManufacturingManufacturing

HealthcareHealthcare

HospitalityHospitality RetailRetail

InsuranceInsurance

AI, ML, and Big Data for Customer Analytics and Digital Transformation


6

A Real Life Scenario

ML-Driven 360 Customer Analytics: Omni channel says that Nina has propensity to fashion catalogs

ML-Driven Targeted Ad.: we tailor the catalog for her

We notice that Nina clicks on a boot

We find the best channel to approach her by ML

Using big data, we find her address We send an SMS invite her to the

nearby shop for visit

Beacon & Video analytics: We detect when Nina has entered the store

We send relevant information to her mobile app Store associate already know her identity and interest. We sell the boot

We make her loyal to our brand Product recommendation Churn prediction Satisfaction score Customer Lifetime Value (CLV) Appropriate incentive/offer


7

Benefits of Customer Analytics

Increase revenue

Decrease customer acquisition cost

Product enhancementReduce customer churn

Increase customer acquisition


8

Business StatisticsCustomer Analytics for Digital Transformation

50% 50% increase in loyalty program enrolment

40%

10%

40% improvement in call hold time

10% growth in data users

105% increase in offer sales105%

25% 25% drop in customer churn

87% 87% improvement in usage

64% 64% of customer think experience they have is more important than the price they pay!

6% 6% of organizations already started to invest on customer experience using AI/ML/Big data


9

What is Big Data

Structured, unstructured,

semi-structured

Terabytes of data

Batch, real-time, stream processing

Velocity Volume

Variety

three V’s

Structured

CSV, Columnar Storage (Parquet,ORC). Strict data model structure

Unstructured

Audio, video, images. Meaningless without adding

some structure

Semi-Structured

JSON, XML, sensor data, social media, device data, web logs. Flexible data model structure

Relational databases (RDBMS) work with structured data. Non-relational databases (NoSQL) work with semi-structured (streaming) data © Dr.-Ing. Farshad Firouzi

10

AI, ML, and Big Data Deliver Omni-channel Insights

Web Call center

Email

Social network

Mobile

AdvertisingCRM/ERP/Transaction Data

Public data

Big Data, AI, ML, Analytics

85% of generated data by 2020 are unstructured! AI, ML, and Big Data techniques can rapidly

correlate, aggregate, and analyze your data and gain actionable insights

AI, ML, and Big Data techniques can quickly combine and enrich your existing data sets with 3rd

party data


11

Why Do You Need Big Data Solution

Old Technology was based on a Problem Driven Methodology Save some specific data Archive and never visit the rest again SQL Databases (e.g., SQL Server)

Schema on Write (Extract, Transform, Load (ETL)): Structured is applied to the data only when it’s Write!

New Technology is based on a Data Driven Methodology Store all the data! Extract value from data No-SQL Databases (e.g., Hadoop)

Schema on Read (Extract, Load, Transform (ELT)) : Structured is applied to the data only when it’s Read!


12

Hierarchy of Analytics

Proactive

What will happen?

What Should we do?

What happened?

The data

Do it for me

Perspective

Predictive

Diagnostic

Descriptive

Hie

rarc

hy

of

An

alyt

ics


13

AI, ML, Big Data Across All the Customer Journey Steps

Customer journey begins the moment they become aware of your brand

Get Data

Visualize Results

Retention• High-threat customers• Churn customers?• Timing• Find best future

customer

Service• Product performance• Channels and agent• Satisfaction• Next actionUpsell

• Product/service recommendation

• Timing• ChannelAcquisition

• Potential buyers• Optimal channels• Price optimization


14

The Evolution from Single-channel to Omni-channel

Customers experience a single type of touch-point

Retailers have a single type of touch-point

Customers see multiple touch-points acting independently

Retailers' channel knowledge and operators exist in technical & functional silos

Customers see multiple touch-points as part of the same brand

Retailers have a single view of the customers but operate in functional silos

Customers experience a brand not a channel within a brand

Retailers leverage their single view of the customer in coordinated and strategic ways

Single-channel Multi-channel Cross-channel Omni-channel


15

Customer Acquisition

Will you become my customer?

Reach out???

Calculate Score & Recommend best channel

Prospect Propensity Score

Channel

Farshad 10% Mobile

Stefan 30% Email

Reiner 60% Social Media

Kathrin 90% Pop-up ads

Manfred 45% Phone call

Find customer propensity score for each prospect (potential customer)

Recommend the best channel to contact each prospect

Execute data-driven campaigns


16

Brand Monitoring (I)

Social Network and Sentiment Analysis

Improve customer satisfaction Identify patterns and trends Male smarter (marketing/support)

decision

Assess

Segment

Discover

• Are we invest on right marketing channels

• What is the “share of voice” and “reachability” of our marketing strategy

• What users say about our brand and campaign? Understand the meaning of their comment using Natural Language Processing and convert it to a score ( Positive or negative?)

• Find meaningful insight about prospective customers

• Discover new ideas, trends, etc.• Topic analysis• Sentiment analysis

• What kind of audiences we have?

• Geographics, demographics

• Influence score• Recommenders

Social Media Assessment

Social Media Discovery

Social Media Segmentation


17

Brand Monitoring (II)

Sentiment Analysis: discovere people opinions, emotions, and feeling about a product or service

Good job but I expected a lot more.Totally dissatisfied with the service. Worth customer service ever.

Excellent effort guys. I appreciate your work.

NEGATIVE NEUTRAL POSITIVE


18

Brand Monitoring (III)


60%30%

10%

Female Male Unknown

Share of voice (Author) by gender

60

10

30

10

15

5

30

60

20

35

20

9

B A DE N - WÜRT T E M B E RG

B A V A RIA

B E RL IN

H A M B URG

N O RT H RH IN E - WE S T P H A L IA

H E S S E

Female Male

0

5000

10000

15000

20000

25000

Positive Negative Neutral

Female Male

Distribution of gender across geographics

Sentiment by gender The trend (#mention) of the brand over time in different channels

0

5000

10000

15000

20000

25000

Facebook Instagram Pinterest Telegram Web


19

Brand Monitoring (IV)


can exhaust featuring bigger html bit cnn

Sport cool youtube don’t comment

cnnmoney overview davidcward Farshadtwitterscene July hotel finance AOK million march

Action public answer iot pic days AI droneQualcomm

Automatically thanks round public ces dataOkay

Context of discussion Influence of social media authors

Sara

Alex

Tara

Niki

20.808%

12.203%

11.309%

9.503%

Share of voice

Share of voice

Share of voice

Share of voice


20

Image Processing & Video Analytics (I)

Real-life scenarios

Female: 99% Age (25-30): 95% Happy: 97%

Female: 99% Age (25-30): 95% Happy: 97%

Sarah O'Connor: 98% Last visit: 7.8.2018 Gold Customer Interest: Gucci, Armani Birthday: 21.03.1984 Single Address: Dusseldorf

Sarah O'Connor: 98% Last visit: 7.8.2018 Gold Customer Interest: Gucci, Armani Birthday: 21.03.1984 Single Address: Dusseldorf

Can be combined with other source of data e.g., CRM, Social networks, etc.

Loyalty program Customer satisfaction Upselling Tailored marketing

Customer analytics & product enhancement Pattern analytics for e.g., targeted advertising Demographic (age, gender, etc.) Analysis Location/product analytics: heatmap, #users,

duration of stay, hot products, interaction of users, happy or not?

Non-Registered Users Registered Customers


21

Image Processing & Video Analytics (II)

Distributed Architecture

Video Analytics At Edge (Local Store)Video Analytics At Edge (Local Store)

Camera Camera Camera

Analytics on Cloud (Headquarters)

Video Analytics At Edge (Local Store)Video Analytics At Edge (Local Store)

Camera Camera Camera

Send

Watch

-list

Send

Watch

-listReg

iste

r n

ew

use

rs

Reg

iste

r n

ew

use

rs

1

2

3

4

5

6

1. Farshad enters to a local office/store which is equipped with camera

2. A new face is detected by AI-driven video analytics in a local store. Sales associate can register the face with some info (name, address, etc.) in the system

3. Each face is represented by an embedding. The information is then sent to the cloud (headquarters of the business).

4. In the cloud, we store all the data. Please note that we do not same any real-image of our customer. Each customer just represented by an embedding (sequence of numbers). In the cloud we can perform analytics, category management, customer segmentation, etc. Moreover, the cloud can generate a “Watch-list”. A list of faces that should be detected by all branches (local stores) of a same brand.

5. The cloud sends the updated “Watch-list” to all the local stores

6. Now all the stores can detect Farshad. Thus, we can implement several interesting applications such as “loyalty program” and “360 degree customer analytics”

Dr. Firouzi© Dr.-Ing. Farshad Firouzi

22

Oh, hi!

AI-driven Beacon (I)

OH!You’re

nearby!

… Which wake up an application on your mobile

device andLets you to calculate your

location and PROXIMITY To The Beacon

… Which wake up an application on your mobile

device andLets you to calculate your

location and PROXIMITY To The Beacon

Near

Immediate

Far

Bluetooth Beacon transmit small packets of data

23

AI-driven Beacon (II)

Data Ingestion

21

4

3

BeaconBLE transmission

BeaconBLE transmission

Mobile App Sends contextual data (User,

Device, Application & Location) to cloud

Display tailored context-aware profile-based message to users (received from cloud)

Mobile App Sends contextual data (User,

Device, Application & Location) to cloud

Display tailored context-aware profile-based message to users (received from cloud)

Cloud1. Trace Apps/Users2. Combine beacon data with CRM, marketing, and

other sources of data3. Create User Specific Experiences (tailored proximity

and profile-based marketing/info/message)4. Geofence analytics (how many visitors, gender of

visitors, time spent by users, pick time)5. Perform location/traffic pattern analytics6. Perform Demographic Analysis7. Category management & heatmap (which products

get more attention)

Cloud1. Trace Apps/Users2. Combine beacon data with CRM, marketing, and

other sources of data3. Create User Specific Experiences (tailored proximity

and profile-based marketing/info/message)4. Geofence analytics (how many visitors, gender of

visitors, time spent by users, pick time)5. Perform location/traffic pattern analytics6. Perform Demographic Analysis7. Category management & heatmap (which products

get more attention)

Engagement

Social data

CRM

Other data


24

Product/Service Recommendation

What else are you interested in?

Cross-selling &Collaborative Filtering

Content-based Filtering

Social Interest Based

You and your friend like angry bird in Facebook

Kill bill is like… Reservoir dogs is like…

Upselling &Item Hierarchy


25

How Categorize Customers (Customer Segmentation)?

We need to spend our budget in a wise way!

Frequency3x month

Recency5 days ago

Monetary ValueEUR 120

RFM Model

High potential valueHigh Current Value

Keep These Customers

High potential valueLow Current Value

Grow These Customers

Low potential valueLow Current Value

Should you spend money here?

Low potential valueHigh Current Value

Grow These Customers

Potential ValueC

urr

ent

Val

ue

Jim Novo© Dr.-Ing. Farshad Firouzi

26

How Much is Your Future Business Worth?

Focus your marketing focuses on most valuable customers!

Customer Customer Value Class

Farshad Gold

Stefan Silver

Reiner Bronze

Kathrin Platinum

Manfred Silver

Ingestion, Cleaning, &

Fusion

Noise Removal &Feature

Engineering

Data Set

Demographics (e.g., Age, Gender, Income) Transactions

Build a model that predict the customer group of a new customer

Classification (e.g., Random

Forest)


27

Discover Response Patterns

Group customers based on their response patterns

Analyze groups to understand patterns Identify how customers respond to your offer Build data-driven marketing Target each group with a specific campaigns

17 Customers

Clustering

4 groups Channels


28

Customer Retention and Customer Loyalty

Retention Strategy: A plan or process designed to help retain customers after the first sale!

Retention Effort

Meaningful Memorable Personal

Customer Retention: Strategic actions and

efforts that promote loyalty Customer loyalty: An emotional bond

between customers and a business Loyalty is merely a symptom or a result, and

retention efforts are the cause Retention is all about proactive efforts.


29

Customer Lifetime Value (CLV)

Invest your marketing budget on most important customers!

Customer CLV

Farshad $2,132

Stefan S1,200

Reiner $3,750

Kathrin $10,000

Manfred $950


Fusion


Engineering

Data Set

Demographics (e.g., Age, Gender, Income) Transactions

Regression

80% | 80% of your business comes from 20% of your customers 80% | It costs 10x less time to sell to an existing customer than finding a new

customer


30

Are you Happy with me? (I)

Customer Service Goals Minimum questions Early resolution Keep track of context

Customer Service Goals Self-service and chat bots Need for intelligence Predictive analysis can help

WWW

2

1

3

4

5


31

Are you Happy with me? (II)

Solve the problem in the first interaction!


Fusion


Engineering

Data Set

Sales Transaction Product Amount Order Date Status Shipped On Delivered Returned

Previous Contacts Date Reason Agent Duration Root Cause Resolved Ticket

Classification(e.g., Random

Forest)

Customer Predicted issue

Agent

Farshad Product issue

Agent 121

Stefan Billing issue

Agent 52

Reiner Shipping issue

Agent 21

Kathrin New product

Agent 15

Manfred Returns Agent 7

1. Predict the intent of contact and assign an appropriate agent with relevant skill/knowledge to solve the issue in the first interaction!

2. Processional customer services are expensive, so we also need to optimize their time by assigning critical issues to more professional one

3. Be proactive and start by asking the customer a very simple question. Are you experiencing any problems with our products or services?


32

Are you Happy with me? (III)

Find unsatisfied users and predict customer churn!

Only 10% of customers answer to surveys

We need to find unsatisfied users, or they go to our

competitors Preparation & Machine Learning (e.g., Regression)

Data Set

Customer SAT. Score

Farshad 1.1

Stefan 2

Reiner 4.3

Kathrin 5

Manfred 3.5

Demographic History Transaction Social medias Surveys


33

Are you Happy with me? (IV)


Fusion


Engineering

Data Set

Association Rules Mining

First item Second item

% times

Lifetime > 3 years

Age < 27 90%

Single German 75%

Find Customer Attrition Patterns by Data Mining


34

Are you Happy with me? (V)

ML-driven incentives recommendation and loyalty program engine

Extended Warranty

Free Software

Coupon

1. Based on attrition and satisfaction score, we can detect which customer is willing to leave us!

2. Marketing and support team to reach customer with an offer that makes them stick with us!

3. We need to find an appropriate offer for each person, since different customers react differently to different offers (longer warranty, coupon)

Machine Learning (Recommendation Engine) will find incentives for each user

Farshad

Stefan

Kathrin


35

Path Analytics

34%

23%

11%

2%

Majority of interactions happen is based on multi-event, multi-channel journeys

To better understand users to be able to improve the product/service and provide high-quality uniform user experience at each user touchpoint, it is important to understand user interactions with the brand

Big data and machine learning can help us to visualize and analyze the paths customers take, over time and across channels

Big data and machine learning also enable us to contact the customers on a personal level and create a long-term relationship to build loyalty.

Focus your effort (e.g., Marketing), time, and budget on important paths & channels


36

Dr. Firouzi

Home SAT. Score Churn Services

0

2

4

6

CLV

(EU

R)

Time Longer warranty

Coupon

Discount

Free Software

Analytics Firouzi

Customer Satisfaction

63%

Probability to leave

83%

Dr. Farshad Firouzi

Male 1983

Bettina-von-arnim weg 7Karlsruhe

+49 1714434886

Probability to Call

95%

Customer Lifetime Value (CLV) Appropriate Incentives

Customer Journey

July 1, 2017

Dec 1, 2017

Dec 5, 2017

OverallOverall

PaymentPayment

Customer ServiceCustomer Service© Dr.-Ing. Farshad Firouzi

37

Conclusion

► Customer journey: Interaction between an organization and a customer in different channels over the duration of their relationship

► Digital transformation allows you to automate the whole process from customer acquisition to service, retention, and loyalty

► Identify and priorities your business

► Build faster and higher customer conversion rates► Percentage of users who take a desired action

► You can validate your customer's journey. It also enables you to find your customer's pain points

► Increase customer satisfaction, loyalty and word-of-mouth recommendations

► You can be proactive. You can predict the next move of your customer

► Increase customer satisfaction, loyalty and word-of-mouth recommendations


38

Thank you.


Documents

Dr.-Ing. Farshad Firouzi - mVISE AG · 8 Business Statistics Customer Analytics for Digital Transformation 50% 50% increase in loyalty program enrolment 40% 10% 40% improvement in