The "rise of machine learning in marketing" describes the goal, process, and benefit of AI-driven marketing. In particular, it explores how marketing leverages machine learning models to automate, optimize, and augment the transformational process of data into actions and interactions with the scope of predicting behaviors, anticipating needs, and hyper-personalizing messages. Full research report: https://www.researchgate.net/publication/332865857

1. THE RISE OF MACHINE LEARNING IN MARKETING
Alex Mari / Research Associate / University of Zurich
alexmari@business.uzh.ch
linkedin.com/in/alexmari
@mariketing
May 7th, 2019 / Zürich

2. DECADE 1990s 2000s 2010s 2020s
TECHNOLOGY INTERNET SOCIAL MEDIA & MOBILE BIG DATA & CLOUD COGNITIVE SYSTEM
ACHIEVEMENT
DIRECT-TO-
CONSUMER
DIRECT-TO-
COMMUNITY
HYPER-
PERSONALIZATION
RELATIONSHIPS AT
SCALE
CHANNEL SINGLE CHANNEL MULTI-CHANNEL CROSS-CHANNEL OMNI-CHANNEL
GOAL CONTACT ENGAGEMENT CONVERSION RELEVANCE
INTERACTION PERSONAL INDIVIDUAL CONTEXTUAL PREDICTIVE
SOFTWARE CRM SOCIAL CRM CONTENT AUTOMATION AI-POWERED PLATFORM
ONLINE
MARKETING
DATABASE
MARKETING
DATA-DRIVEN
MARKETING
AI
MARKETING
EVOLUTION
AREA
FROM DATABASE MARKETING TO AI MARKETING Alex Mari (2019)

3. DIFFUSION OF MACHINE LEARNING
1. Inside Marketing Technologies
a) Marketers face an increasing complexity due to (1) explosion of digital and data touchpoints, (2) unprecedented
consumers’ expectations in terms of interaction, content and offer personalization;
b) Such a dynamic drives the adoption of a variety of marketing software that turns historical data into actionable insights;
c) ML diffusion affects marketing departments via both exogenous and endogenous forces;
d) Marketing & AI technologies are deeply affecting one another and, combined, contribute to the rise of “ML in Marketing.”
Figure 1. Marketing and AI
Technologies dependencies
Alex Mari (2019)

4. a) AI is affecting every single functional area of digital marketing;
b) Different levels of technical sophistication and economic results are noticeable between functional areas;
c) Infusing ML in the field of "performance marketing" provide, today, the highest ROI to marketers;
d) Even within the same group of applications, the economic results are extremely variable.
DIFFUSION OF MACHINE LEARNING
2. Across marketing functional areas
Figure 2. AI
sophistication &
ROI across
functional areas
Alex Mari (2019)

5. a) ML is infused at different stages of the consumer journey and require individual, yet coordinated, implementations;
b) Human intervention in each workflow largely vary depending on the defined marketing goals;
c) AI is not “the” solution for all marketing activities. A balance between human and machine-driven activities is required.
DIFFUSION OF MACHINE LEARNING
3. Along consumer journeys
Figure 3. AI-powered consumer journey
of Amazon.com
Adapted from Hackernoon (2018)
Alex Mari (2019)

6. Machine Learning unleashes three contemporary dynamics:
① Replace humans (automation)
② Reduce workload for humans (optimization)
③ Enhance humans’ skills (augmentation)
A successful AI strategy combines elements of automation, optimization, and augmentation.
Managers need to:
- Evaluate how humans and machines augment each other’s strengths to deliver the best possible brand
experience.
- Strike a balance the level of human and machine effort injected in every relevant marketing step and
alongside consumer journeys.
GOAL OF MACHINE LEARNING Alex Mari (2019)

7. Every business carries inefficiencies that can be replaced by high performing algorithms.
AI automation is more than a cost-cutting mechanic. Advanced brands automate customer experience.
- Automated bidding is used by bol.com in display and video advertising. Google’s automated
bid system outperformed the manually optimized campaign with a -38% CAC and an estimated
10 hours saved monthly per team member.
- Automated recommendations on websites account for an estimated 80% of the movie
watched on Netflix.
- Dynamic pricing is used by Amazon to drive demand on selected items. The sales forecasting
system detects a growing popularity item. Several actions are triggered automatically. Amazon
updates the inventory forecast and optimizes the supply chain system across warehouses. The
final users see more recommendation of the popular item at an adjusted price. The results
change the sales forecast again.
- Bots will manage 25% of the relationship with an enterprise without interacting with a human by
2020 (Gartner). Duplex, Google’s artificial intelligence assistant can independently handle
service requests.
GOAL OF MACHINE LEARNING
1. Automation
Alex Mari (2019)

8. Enterprises use AI to optimize processes that reduce overhead, decrease turnaround time, improve output.
Marketers can infuse AI into the brand building process to optimize consumer acquisition and retention.
- Customer education is provided by Olay through the Skin Advisor app, a deep learning
powered application that analyzes a woman’s face to determine her “skin age” and recommends
the best product among hundreds of different variations.
- Content suggestion is used by Uber while predicting the rider’s destination with over 50%
accuracy. This system provides context-aware suggestions that facilitate frictionless
experiences.
- Store robots called LoweBot are rolled out by Lowe to help customers answering simple
questions in 70 languages while employees focus on added value services. Because of its ability
to effectively navigate the store, LoweBot can scan the shelves in search of incorrect prices,
misplaced products, and out-of-stocks.
- Convenient shopping is the focus of Amazon Go, a cashierless store supported by computer
vision and behavioral modeling which is planning to open up to 3’000 locations by 2021 to
produce a "lean back" journey for customers.
GOAL OF MACHINE LEARNING
2. Optimization
Alex Mari (2019)

9. Algorithms help traditional teams to get more out of their marketing effort by adding layers of intelligence.
Coexistence between AI and humans is key. Machines can greatly augment human output (85% of managers).
- Next Best Action is a feature offered by Salesforce’s Einstein that leverages rules-based and
predictive models to provide agents with contextual recommendations and offers for customers.
These “next best action” suggested to employees, such as “give free shipping” or “offer zero
percent financing,” lead to higher customer loyalty and upselling opportunities.
- Chatbots created by the French startup Botmind are helping companies to deliver a better
customer experience combining human and artificial intelligence through the same live chat.
Whenever the bot is facing new issues that require conducting extensive unstructured dialogues,
they will immediately transfer the concern to an individual.
- Similarly, Userbot escalates communication to individuals whenever facing new issues that
require conducting extended unstructured dialogues.
GOAL OF MACHINE LEARNING
3. Augmentation
Alex Mari (2019)

10. PROCESS OF MACHINE LEARNING
Self-reinforcing relationships
Figure 4. Designing AI-Driven Experiences
Alex Mari (2019)

11. Marketers create unique and relevant brand experiences in three different ways:
① Predict behaviors (best value proposition at the right consumer journey’s stage)
Otto, the German e-commerce merchant, uses an AI model that predicts what will be sold
within 30 days with 90% accuracy. Otto can automatically purchase more than 2 million items a
year from third-party brands while speeding up deliveries to customers and reduce returns.
② Anticipate needs (best product and services at the right price)
Netflix develops original TV shows analyzing creative elements of successful movies at a
granular level through the lenses of AI. This practice doubled the success rate of original
shows versus traditional ones.
③ Hyper-personalize messages (best messages at the right time and channel)
L’Oréal Paris personalizes videos using Google’s insights on audience’s interest and affinities.
Recently, they created twelve versions of a YouTube video to appeal to each specific segment.
This campaign showed an increase of 109% in brand interest and 30% in purchase intent.
BENEFIT OF MACHINE LEARNING Alex Mari (2019)

12. Figure 5. AI-driven marketing model
AI-DRIVEN MARKETING MODEL
Conceptual framework
Alex Mari (2019)