How Marketing Automation is transformed by AI and Data Science

How Marketing
Automation
istransformedbyAI
andDataScience?

Foreword
When Machine Learning meets Marketing Automation
Data collected by SALESmanago
Copernicus AI & Machine Learning in SALESmanago
Expert’s limitations resolved by Machine Learning
Ways to use Machine Learning in Marketing Automation
Smart segmentation and sentiment analysis
Types of AI recommendations in SALESmanago Copernicus
Natural language processing and product recommendations
1.
2.
3.
Expert Approach vs Machine Learning
2.1
2.2
2.3
2.4
3.1
3.2
3.3
3.3.1
3.3.2
3.3.3
3.3.4
Collaborative filtering
Most frequently bought after visit other
Most frequently bought together
Most frequently visited together
4. The channels of recommendation delivery
The new face of Marketing Automation5.
Contents
Stages of implementation2.5
References6.
3.3.5 Mixed statistics with weight
2
HowMarketingAutomationistransformedbyAIandDataScience?

In the blockbuster loved by millions in the world, the main
character travels to the future, to 21st of October 2015. Marty
McFly, as it was his name, marvells at the fact that science
fiction has become reality. The society by then has been relieved
by the machines and enjoyed the creature comforts provided
by the advanced technology. The movie reflected high hopes of
us all - hopes of living comfortably and happily ever after in the
modern world. Nowadays, life may not resemble treacly reality
from the “Back to the future”, but we are surely progressing in
a dynamic pace. We witness extensive modernization in many
fields and observe how simple processes are automated. Also,
many have raised the question whether the machines will
replace human resources? The question is asked with a great
deal of fascination, though it reflects the fear that we, humans,
will become useless. One may wonder what is it exactly that
makes machines capable of performing tasks and the answer is
their ability to learn.
Machine Learning together with the AI seem to be the invention
to the 21st of October. It may be surprising to find out that the
foundations of the two fields were laid in the late 50s. So what
is the difference between Artificial Intelligence and Machine
Learning? The difference may be not so obvious as the terms
are often used interchangeably and the boundary between them
seems to be blurry.
The term Artificial Intelligence was used for the first time in 1956 by
John McCarthy, an American informatician and mathematician. The
term itself refers to machines that demonstrate intelligence which
makes them capable of performing tasks that normally require
human intelligence (e.g. understanding human language, problem
solving, etc.).
Artificial Intelligence
If it comes to Machine Learning, the term itself was coined by Arthur
Samuel in 1959. The definition states that machine learning is the
ability to learn without being explicitly programmed. In practice it
means that machine learning involves “educating” an algorithm.
Educating in the sense of combining learning from experience,
learning from data and following instructions. To facilitate Machine
Learning, delivering an abundance of data is necessary, so the
algorithm can be trained and thus continue self-improvement.
Machine Learning is widely used in the world. An instance of popular
application of machine learning is face recognition.
Machine Learning
Finally, data science, often referred to as data-driven science, is an
interdisciplinary field that focuses on scientific methods, processes,
algorithms and systems which are used as tools to obtain knowledge
and understanding out of the data that takes various forms. Data
science utilizes data for designing processes and finds correlations
between data. Additionally, it offers a range of solutions from which
you can benefit the most. Data science requires a great amount of
data and the team of specialists who will be able to conduct the
analysis.
Data Science
Foreword
3

Machine Learning and AI are used on a daily basis and it
makes our lives more comfortable. If you have ever used
Google Maps to navigate yourself to the destination or if you
have watched movies or series on Netflix; if you created an
account on Spotify to enjoy your favorite music; if you have
taken advantage of the creature comforts such as taxi service
known as Uber, then your fate intertwined with the blessings
of Machine Learning. Google, Uber, Netflix, Facebook and
many more brands make use of AI and Machine Learning
to develop and improve the services they provide. Machine
Learning is implemented in the applications and programs
that are designed to add up to the creature comforts. But also,
or maybe, most of all Machine Learning and AI are meant for
serious purposes. This advanced technology finds its place
in the marketing branch called Marketing Automation.
Marketing Automation is a dynamically developing subfield
of Marketing that aims at optimizing the work of sales
and marketing departments. An excellent tool for that is
SALESmanago. It helps to design a long business process,
which begins when you acquire the contacts as they visit
your website and fill in the form. The platform identifies users
on the website and monitors their behavior on the basis of
cookie files. SALESmanago collects contact data, behavioral
and transactional data which is saved in the system. Each
user receives points for every kind of demonstrated activity,
it can be a visit on the website, opening an email from you, a
purchase of the product, the activity on social media, etc. It
is an objective rate that reflects the user’s engagement and
the degree of readiness for a purchase.
How does Marketing Automation work?
Alongside the scoring system, the platform operates on the system of tags which are assigned
to contacts manually or after the occurrence of a certain event. The database is segmented in
this way.
All pieces of information are collected on the contact card of each user and at the same time
being a rich source of knowledge and a reference point in choosing a marketing strategy towards
a particular user. On the basis of this information, you can reach customer through
a number of channels.
When Machine Learning meets Marketing Automation
4

Marketing Automation gives you a range of methods of
automating your marketing and sales activities. All these
activities on the platform are coordinated by Marketing
Automation Specialist. The specialist, as a human expert,
has a vast knowledge, rich experience and knows the tool
very well. You can use the tool to segment the database,
define buying personas, and use the particular automation
processes to reach them in a proper moment with the
right content. So in conclusion, a person uses the tool`s
features to programme the platform so it can reach specific
customers with the right content in right time.
In practice, the expert, though well-equipped with
knowledge, experience and a tool, is not always able to
predict the non-standard situation in which the customers
spin out of control from the typical segmentation path. Every
human is an individual entity with their own will, that is why
it is hard to group all people in several groups. In such case,
the expert can in fact lead to conversion of only a certain
part of customers and the rest of them is lost.
This is the place where Machine Learning and AI enter -
technologies that can lift the burden. AI and Machine
Learning allow to collect, process and utilize Big Data. They
consist of the in-built mechanisms and algorithms that let
us predict potential interest of customers. It allows you also
to personalize the content in any communication channel
in real time. Additionally, it prepares reports, analyses data
and self-improves which translates into increased effects.
Utilisation of these highly advanced technologies and big
data can be employed in designing customer journey. The
purpose of it is to guide the potential customer from the first
stage that is getting him or her interested in the offer to the
last stage that is purchase and maintain the contact with this
customer.
In the standard expert approach, the expert designs customer journey, applies automation rules,
workflow and starts the whole process. In the end of the customer journey, the expert is able to
measure effects and test other scenarios by error and trial.
Machine Learning and Data Science reverse this process - it focuses on analyzing the data of
customers who converted and finds correlations between all their interactions leading to this
conversion. It helps to recreate the most effective channels and processes which lead to the
conversion - when these actions are identified, based on the reports the specialist is able to re-
design the processes to make them more effective.
5

Data collected by SALESmanago
Data taken into the analysis:
Website
visits
Products
bought
Products
added to cart
Conversion
paths
Conversion
sources
Buyers’
personal and
demographic
data
Purchased
products`
attributes
Reactions to
direct
marketing
Search
terms used
Chat
conversations
Products
displayed
Cart
value
Offline
behaviour
6

Let’s bring Expert Approach and Machine
Learning face to face. The two approaches differ
significantly. As mentioned before, the expert
approach is connected with the limited amount
of the data and the source of knowledge comes
from the expert’s experience, additionally being
confined to the limits of the human imagination.
Automation rules created by an expert are mostly
designed to solve or react to a specific situation.
The advantage of the Machine Learning over the
Expert Approach lies in the way it works. Machine
Learning functions in a complex environment
and takes into consideration many variables.
The system is provided with the huge amount
of data from which the algorithm needs to learn.
In other words it is trained from an abundance
of examples in a relatively short time, shorter
than a human. While implementing particular
solution, the system takes into consideration
many factors which human mind may not be
able to process or come up with as his or her
knowledge is limited. Additionally, the database is
constantly increasing and the Machine Learning
technology is provided with new sets of data to
be processed.
Expert Approach vs Machine Learning
7

SALESmanago Copernicus – Machine Learning & AI is an advanced
self-learning environment that analyzes the behavior of individual
customers and predicts future purchases. Then it sends personalized
product recommendations according to what the algorithm deems most
likely to be bought. This cutting-edge Marketing Automation tool provides
insight into customer purchase history, buyer’s journey, and analyzes the
way products correlate in categories, allowing for highly engaging and
eye-catching offers to be delivered to individual customers.
The technology of Copernicus is based on two recommendation models.
Each is optimized to support a specific marketing approach.
For inbound marketing – affinity analysis (or the so-called Inbound
Predictive Marketing) and for outbound marketing – behavioral analysis
(the so-called Predictive Outbound Channel). Used in tandem, the models
enhance both inbound and outbound marketing activities.
The mechanism of affinity analysis relies on sophisticated algorithms used
in association analysis. By thoroughly analyzing transaction data and
correlations between specific products and in categories, they calculate
the optimal combination of items in each offer. After the resulting data
is parsed and modeled, a frame with product recommendations can be
shown to each customer. In addition, the use of metadata makes it possible
to instantly react to changes in customer preferences. The Marketing
Automation system can employ machine learning to compare predictions
from product association analysis for end customers on an ongoing basis.
Then it assigns scoring to each given recommendation in order to indicate
how likely that product is to be bought by individual customers. Moreover,
by updating product exclusion grids, the algorithm ensures that products
are not recommended to customers who already bought them.
Top 10 products category
Copernicus AI & Machine Learning in SALESmanago
8

Expert’s limitations resolved by Machine Learning
Unique
customer
behaviours
Number of
possible
customer
segments
Price
sensitivity
Unique customer
preferences
Advanced
personalization
Identification
of uncommon
actions leading to
conversions
Analysis of
correlations
between multiple
variables in the
same time
Real time process
adjustment
to changes
in customer
behaviour
9

Before you take the full advantage of the AI & Machine Learning
system, you need to go through several stages of implementation.
You can use some algorithms which are ready to use or use the
system in most advanced way - to create your own algorithms and
processes which uses machine learning for very precise reasons.
In that case, you should go through a process of designing such
implementation:
Determine objectives, metrics and constraints
This is a formative stage that will shape the way how the
algorithms will work. You need to think about the objectives. One
of the objectives can be assistance of the Machine Learning with
servicing all B2B marketing campaigns created in Workflow. Then
you need to think how you will measure the results. The selection
of the metrics is of critical importance because choosing right
metrics is the determinant of success. The whole model can fail,
if the selected metrics is wrongly matched.
Assessing data, data collection
it may not be obvious, but the data may not be gathered in
one place. But even such scattered data as well as not pre-
processed, is considered to be of great value and importance
for the Machine Learning approach more than “clean data”. In
the case of insufficiency of data or its lack on this stage, you
need to determine which type of data will come in handy while
solving the task. Once you get to know it, start collecting it. You
need to specify how these data will be transferred to the MA
platform, determine their format and how they will be matched
in the system so it will be easy to process them for Machine
Learning system.
Model training
You need a consultation with a data analyst who will indicate
various factors which may have impact on the model. This step
is also very important as you need to check whether important
elements have not got lost. As previously indicated, you might
need the counsel of experts in the field who will work in tandem
with the data analyst. The data analyst will also be responsible
for training the model and will be equipped with the necessary
tools. The duration of the training may vary as it depends on the
intricacy of the model.
Integration and testing
Once the model is trained It needs to be integrated into the
management system. Then together with the expert, you should
carry out the practical tests. While you are carrying out the tests,
it is obligatory to check the model’s accuracy and the economic
effect it brings.
Model monitoring
In the last stage you will use the model that successfully passed
testing. The model will require constant monitoring as well as
extra training as the new data will be constantly gathered.
Stages of implementation
1
2
3
4
5
10

Ways to Use
Machine Learning
in Marketing Automation
Smart
Segmentation
Sentiment
Analysis
Natural
Language
Processing
Product
Recommendations
11

Smart segmentation
Segmentation is the process of partitioning
database into groups. This process is not only
connected with organizing customers into proper
segments according to their preferences or
gender, but it is also about organizing products
into categories or users who have bought
products from a certain category. Segmentation
is used to find certain behavior pattern, similarities
between products and examine correlation
between groups. So later you can target your
customers with better offers.
Some examples of segmentation encompass
organizing database according to the similarity
of the purchases during the year. The analysis
can show us the dependency of the purchases
between age and location. You may be also
interested in the segmentation of the cart value
and see how the age and the frequency of the
shopping correlate with the location. Additionally,
you can segment the products database on the
basis of pictures. The Machine Learning system is
able to convert the picture into a code and group
products which are similar in order to recommend
them later. If you would like to use more basic
solutions, you may use regular segments such
as gender and check the shopping value of men
and women.
Sentiment analysis
Sentiment analysis is the process of computationally identifying and categorizing opinions
expressed in a piece of text, especially in order to determine whether the writer’s attitude
towards a particular topic, product, etc. is positive, negative or neutral. So for instance you
can research the opinion of Twitter users about Italian retail industry in New York.
You may use Machine Learning and AI to familiarise with the customers’ opinions about
a product and thus you can take advantage of it by implementing the dynamic pricing. AI
engine will calculate discounts based on probability of purchase thus maximising income
across all customers. Sentiment analysis may prevent you from the customers’ churn as
well. By analysing behavior and interactions of the users, the system indicates customers
who are likely to be lost.
12

Natural language
Natural-language processing (also known as NLP) is an area of
computer science and artificial intelligence concerned with the
interactions between computers and human (natural) languages,
in particular how to program computers to fruitfully process large
amounts of natural language data.
AI engines are widely used with regard to natural language processing
field. The most known fruit of the fusion is a chatbot that facilitates
conversation between a person and a robot. They are widely used
in the B2C enterprises, but also they are successfully transforming
B2E and B2B sectors in their organizational aspects. According to
the Oracle, 80% of businesses want chatbots by 2020. Moreover,
Juniper Research forecasts that chatbots may cut business costs by
$8 million by 2020. Who wouldn’t want now to make use of chatbots
after such prognosis?
How such chatbots work? On the surface it looks fantastically simple.
When you are not able to service your all customers and answer
their queries, you don’t have to employ people because chatbot will
talk to your customers and help them out with the most frequently
asked questions. If you look closer, the chatbot uses highly advanced
technology and bot learns the most typical scenarios of conversations.
That works miracles with reducing the costs.
Product recommendations
Unlike product recommendation engines available in most Marketing
Automation platforms, AI recommendations are not based on the
product data itself or adjusted to specific user’s in 1-to-1 model. By
the analysis of the data about all users, you do not need to apply 1-to-
1 model. The recommendation prepared by the Machine Learning
system can include new users and the inactive users to which you
are not able to prepare 1-to-1 recommendation model. It is possible
because the system uses information about similar users.
Electronic commerce and cloud computing giant that we know as
Amazon.com generates sky-high revenues and 35% of them are said
to come from product recommendations.
The goal of product recommendation is to increase average order
value and number of transactions. Ordinary product recommendation
includes sending offers with the products a user has recently viewed
or bought or the products which the user has left in the cart. Product
recommendations powered with AI engine facilitate personalization of
product offers for all users, no matter how much do we know about
them. The system learns the behavior of each contact, learns the
conversion paths and analyses other factors which may influence the
purchase decision. Then makes calculations and chooses the optimal
offer for the contact. Sounds like magic, but it’s real.
13

AI Recommendations
in SALESmanago Copernicus
Collaborative
Filtering
(users and
products)
AI recommendations types:
Most
frequently
bought after
visit other
Most
frequently
visited
together
Most
frequently
bought
together
Mixed
statistics
with weight
14

There are five types of AI
recommendations. The first one
is called Collaborative filtering and
it involves two approaches. The
first one called Product-Product
is connected with probability and
frequency of co-occurrence of
different products (not necessarily
similar to each other). The second
approach is called User-Product
approach and it shows which
products may interest a user based
on the interests of other users who
havesimilarprofiletothechosenone.
Generally speaking, the idea behind
this type of AI recommendation
is to offer products based on the
similarity of users and concurrence
of various products.
Collaborative filtering
15

The second type of the AI
recommendation is most frequently
bought after visit other. Based
on what product the customer is
currently displaying on the website,
the system analyzes purchases of
other customers who also displayed
this product and recommends the
products purchased by the others
to the user.
Most frequently bought after visit other
16

The third type of the AI
bought together. The name of
the recommendation type speaks
volumes. The system analyzes
the products the customer has
purchased. And also the system
analyzes the products which have
been purchased by other users
along with the same products.
The user can encounter this type
of recommendation while buying a
product, he or she may be offered
“similar products which other users
bought”.
Most frequently bought together
17

The fourth type of AI
visited together. As the name
suggests these are the products
that are often viewed together by all
users. The system offers products
which were browsed by other
users along with these products.
Such type of recommendation may
contribute positively to the customer
experience as you can provide the
users with products which were
browsed together and thus save
their time and energy on searching
for the one and only product.
Most frequently visited together
18

The fifth and at the same time the
last type of AI recommendation.
The mechanism behind this
recommendation type employs
all previously enlisted types of
recommendations and additionally
assigns weight for each action.
The value of the weight can be
determined by you. How does it
translate into practice? The system
creates connections and analyses
products bought by the contact,
recommending in the first place
several products which are probable
to be bought, then products which
the user wants to see and so on and
so forth prioritizing the rest of the
products with regard to the actions.
Mixed statistics with weight
19

Now you may wonder what are the channels in which you can make use of AI and Machine Learning. You may have a quite
nice scope to work as every channel available in SALESmanago supports delivering recommendations
Website
Display dynamic content on your website, may it be a pop-
up, iframe, product frame or something else in which you
can present a product, then the AI-based recommendations
may boost the click rate of your forms on the website.
Web Push notifications
This is the quickest method of communicating both with the
anonymous and monitored contacts. All stagers of the online
marketing are familiar with the concept of a small notification
showing in the corner of the screen. Empower the dynamic
content by AI recommendations.
Email marketing
You may use AI and Machine Learning technologies to
adjust the email scenario to the customers’ preferences.
Social media
With AI and Machine Learning you do not have to worry
any longer if you have enough people to answer the
customers’ queries
Ad networks
Once you integrate with ad networks, you can display
product recommendations outside your website.
The channels of recommendation delivery
20

Without doubts we are entering the new era of Marketing Automation. The SALESmanago platform has
become a place for omnichannel data collection in which data-driven processes are adjusted in real time.
Because of that, the omnichannel communication is going to be even more precise and concise, as each
customer will receive consistent communicates on every channel. What is more, the system is going to
be self-improving in Machine Learning thus increasing the effectiveness of product recommendations.
Additionally, it has an impact on the skill set and role of a marketer as the stress is put more on the
data than marketing itself. As the data science is starting to play the first fiddle now, some of experts’
responsibilities will be taken over by the Machine Learning. Marketing is liable to be overridden by the
Machine Learning technology, so the expert will need to know more about servicing this technology.
The analytical and data-processing skills are going to become an asset. Marketing will stop oscillating
between the content and creation and focus more on delivering AI recommendations. However, the
experience the expert have gained throughout the years will not be lost as they can be used while creating
the data models for the purposes of Machine Learning work. Last of all, here is a handful of statistics to
ponder about:
The new face of Marketing Automation
of executives believe that
the most significant growth
benefit of AI and Machine
Learning will be improving
customer experience and
support
believe that AI and Machine
Learning will provide ability to
improve on existing products
and services
of enterprises are tackling
the most challenging
marketing problems with AI
and machine learning first,
prioritizing the customer
care and new product
development
57% 44% 58%
21

https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991
https://chatbotsmagazine.com/how-ai-powered-enterprise-chatbot-platforms-are-transforming-the-future-of-work-dceed4cdfe50
https://dzone.com/articles/how-to-implement-product-recommendation-engine-and
http://www.eenewseurope.com/news/five-stages-machine-learning-implementation/page/0/2
Artificial Intelligence: What’s possible for Enterprise in 2017, Forrester by Mike Gualtieri
Turning AI into concrete value: the successful implementers’s toolkit, Capgemini Consulting, 2017
References
22

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How Marketing
Automation
istransformedbyAI
andDataScience?

How Marketing Automation is transformed by AI and Data Science

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