How one company got 85% improvement in sales after adapting AI in their Marketing? This meetup is focused on real life cases and practical application of AI and Machine Learning in Marketing and Business. Main goal of this event is to give you understanding of what AI can do (and cannot) and to inspire you to start using AI in your company.

1. AI in Marketing
by Andriy Kushnarov, Lean Startup HH

2. ABOUT ME
▰ Experience in IT: 18+ years (I wrote my first line of code when I was 15)
▰ Experience in Product: 6+ years
▰ Everyday job: IT and Product Consulting
▰ Hobby: Bicycle
▰ Goal for today: Inspire you to start using AI
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3. HOW I STARTED WITH AI
▰ Chat bot 🤖
▰ Machine Learning in FinTech 💰
▻ Online credits: credit score based on the ML Models
▻ Credit consulting: ML Models suggest which bank will give a credit
with the best interest
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4. AI IS NOT NEW
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5. WHAT ELSE AI CAN DO FOR YOU
▰ Helps to save environment / ecology
▰ Helps to stay healthy / predict disease
▰ Helps to move
▰ Helps to improve relations in society
▰ Helps in sport
▰ Helps to study???
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Artificial
Intelligence
Machine
Learning

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Source: Machine Learning Marketing

9. Prospect
Visitor
Sale
CLTV
● Detailed targeting / ads personalisation
● Budgeting and bidding
● Content strategy
● Website personalisation
● Recommendations
● Upsale
● Reactivation, remarketing
● Assistance (e.g. voice / text chat)
● CLTV Prediction
● Churn prediction
● Customer feedback
● Errors recognition
● Plan demand
● Plan marketing budget
● Analysis / audiences building
Planing
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AI IN SALES FUNNEL

10. PLAN DEMAND
▰ Alaska Airlines open new routes based on search data as a proxy of
demand
▰ Healthcare company monitors weather and epidemic data to manage
their bids based on geo location
▰ Demand prediction in supply chain planning (SCP) (link)
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11. PLAN MARKETING BUDGETS
▰ Google Ads keyword planner
▰ Google trends
▰ Facebook Ads
▰ Cross-channel planning
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Predictive Customer Lifetime
Value

13. SUCCESS STORY: PREDICTIVE CLTV
What was done:
▰ Predictive CLTV model was applied on 1P data
▰ Retargeting based on the built audience
Result:
▰ 40% increase in revenue
▰ 33% decrease in Cost Per Acquisition (for search)
▰ 37% decrease in Effective Revenue Share (for search)
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Source

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Common sources of 1st party data
Point-of-sale data Email lists CRM data Remarketing lists Analytics data
Opportunities
Use in-app browsing and
behaviour data from your
mobile apps
Mobile
app
Strengthen loyalty programs
that can attract new
consumers while increasing
consumer retention rate and
total transaction value
Loyalty
programmes
Leverage partnerships with
retailers (e.g. discount
stores, warehouse stores)
to acquire valuable,
transactional data
Partnerships
Establish a YouTube brand
channel to identify and target
consumers who directly
engage with your content
YouTube
channel
Create quality content that
pulls new consumers
towards your products
Blog / Newsletter
sign-ups

15. 1P + 3P MARKETING DATA INTEGRATION
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Campaign Cost
Campaign A €223
Campaign B €123
Campaign C €643
Campaign D €302
CLTV
€3600
€100
€480
€3200
3P Marketing data 1P CRM data
CRM
1) User browses
your site
2) After transaction,
track CRM ID

16. DATA ENRICHMENT
▰ 3d party data sources
▰ 1st party data sources
▰ … more data
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Source

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Personalisation

18. CHALLENGE YOUR MARKETING PERSONA
▰ Unsupervised Machine Learning: Clustering
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Source

19. CLUSTER BASED PERSONALISATION
▰ Personalised ads
▰ Personalised website layout
▰ Personalised user experience
▰ Personal offers
▰ Personal up-sale / x-sell options
▰ Personal communication
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20. Personalise for each audience
Personalisation in Ads:
- Connect audience to 3P marketing
tool (e.g Facebook, Google, etc.)
- Apply segmentation in E-Mail
Marketing
- Adjust UX based on the audience
(e.g. Google Optimize, Optimizely)
Manage your customer data
Companies use data warehouse
(data lake) to analyse their data,
e.g.: BigQuery, Snowflake, etc.
To visualise data: Tableau,
Looker, DataStudio, etc.
Web analytics: Google Analytics,
GA360, Adobe Analytics, etc.
Build audiences
Apply right machine learning
models to your data to predict:
- Propensity to convert
- CLTV
- Churn prediction
Tools: R, Tensor Flow,
PyTourch, AutoML, etc.

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Predictive Marketing

22. PREDICT SUCCESS OF YOUR CREATIVES
▰ Computer vision can be used to analyse creatives
▰ Based on the conversion statistics predict success
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Source

23. More creatives w/ attribute
Attribute
performing
better
Keep
it up!
Explore!
Avoid.
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AI based assistance / loyalty

25. HOTELS CHAIN: BETTER UNDERSTANDING OF
THE CUSTOMER
With 4000+ hotels worldwide and a high end position brand, customer
experience and feedback are critical for their business. One of the
largest unused feedback sources was the 1.4 million reviews in
Google maps.
Company better understands drivers of satisfaction/ratings
leveraging machine learning to analyze guest reviews on Google My
Business/Maps.
AI Tools: Natural Language Processing, sentiment analysis.
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26. FACIAL RECOGNITION TECHNOLOGY IN
MARKETING
App Facedeals aimed to target customers with special offers from
businesses they frequent by integrating facial recognition with their
Facebook profiles. Specifically, facial recognition cameras would be
installed at the business entrance which would recognize customers
as they enter. Simultaneously, the customer would receive a
notification of a customized deal to their smartphone based on his or
her Facebook “Like” history.
AI tools: Computer Vision, Facial recognition
Source
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27. GOOGLE DUPLEX: AI DOES RESERVATIONS FOR
YOU
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28. AMAZON SUMERIAN: AI BASED 3D
ASSISTANT/CONCIERGE
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Part #2: AI adoption Strategy
and Mistakes

30. MISTAKE #1: TO START USING AI, I SHOULD BE
AN EXPERT IN AI
As a Business or Marketing person
you should know what AI can do
for you.
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31. MISTAKE #2: AI IS SIMILAR TO IT, JUST PLUG
AND PLAY
AI = Software + Data + Intelligence
IT = Software technology
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32. MISTAKE #3: PLAN AI PROJECTS LIKE IT
PROJECTS
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Plan:
Problem Solution
Reality:
Problem Solution

33. MISTAKE #4: 100% DECISIONS ARE MADE BY
MACHINE
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34. MISTAKE #5: WE CAN DO WITH CUSTOMER DATA
WHAT WE WANT
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35. AI ADOPTION STRATEGY
▰ Make clear goals (short and long term ones)
▰ Set the right expectations (time + data + people)
▰ Choose the project, start small (start from one where you
know your data the best)
▰ Organise Cross-Functional team (data people and
subject-matter experts) 35

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Open discussion:
▰ Your experience
▰ Your learnings
▰ Your mistakes

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THANKS!
Any questions?
CONTACT INFORMATION:
Web: Meetup
Email: andriy@software-it-outsourcing.com