The document discusses personalization in software as a service (SaaS) using machine learning. It defines personalization and outlines a general personalization system architecture. Examples of personalization use cases at Dropbox are also provided, such as personalized search, lifetime value estimates, GUI rendering, Dropbox plan recommendations, churn scores, messaging, content ranking, promotions, and marketing. The key takeaways are that keeping customers happy is critical for SaaS success, personalization can help automate connecting and serving customers for better experiences, and machine learning enables personalization at scale.

1. Boosting Personalization
In SaaS Using Machine
Learning
Reza Rahimi, PhD
Senior Engineering Manager @ Dropbox
Jan 2023.
Virtual Summit on SaaS,
Glorium Technology,
Jan 2023.

2. Content
Review Basics and Fundamentals of SaaS
Look at Customer Problems Using AARRR Funnel
Personalization : Definition, Use Cases and High Level Architecture
Personalization @ Dropbox

3. What is Software as a Service (SaaS)?
Software as a service (SaaS) is a software distribution model in which a
cloud provider hosts applications and makes them available to end users
over the internet through subscription model.
Source Link

4. Some Global SaaS Companies

5. SaaS Bundles : Strategy to Sell SaaS
Products
● SaaS bundling strategy makes sales easier, and increases the purchase value by the customer.
● Bundles are valuable for SMBs, as they often do not have enough resources to deploy, and
manage large amounts of software and apps.

6. Insight about SaaS
1. The SaaS market is valued at $208 Billion in 2023.
2. The global SaaS industry revenue is expected to reach $720.44 Billion by 2028.
3. 11,000 SaaS Company are Running Globally.
1. Saas businesses should maintain an average churn rate between 3 and 8%.
2. In SaaS 64% find the customer experience (CX) more important than price.
3. 66% of consumers had terminated their relationship with a company due to
poor service. Keeping customers happy is key.
4. After a customer has a negative reaction, 58% of them wouldn’t bother going
back to that company.
Ref : SaaS Statistics

7. Understanding Customer Needs is
the Key Success In SaaS
Let’s Look at the Customer Needs
through AARRR Funnel

8. What Are Customer Problems in SaaS?
Why is someone coming to DBX
website?
How do we reveal relevant
features ?
What value are we not provies
to recommend to our
consumers based on the
contents?
ion? Are our consumer engaged
with other people?
Acquisition
Activation
Retention
Referral
Revenue
What are our services worth?
What is our business churn
rate?
A
A
R
R
R
What is the best
What
How do we reveal relevant
features ?
Whrs?
What is the best plan
for our consumers when they
login or log out for the first time?
Why is someone coming to DBX
website?
Why is someone coming to
SaaS Company website?
How do we reveal relevant
features and products?
What workflows do
consumer need once activated?
What are the best
features/Products to
recommend to our consumers
based on their behavior sofar
to increase retention?
How could we have
How could we have
personalized marketing
campaign for retention?
Are our consumer engaged
with other people?
Do they refer us and share with
others?
What are our services worth?
What is our business churn
rate?
What is the good strategy
campaign for prices and
promotions?

9. What is the Solution?
Intent
Understanding
Content/Product
Usage
Understanding
Personalization
Personalization
By understanding Customer
Intent and Customer Content
and product usage try to
Improve CX!

11. Personalization Engine : 100K View
Personalization
Engine
Query = What is the best Plan for the
customer based on his/her usage?
Query = Based on the usage of the
customer, what is the personalized
rendering of the GUI?
Query = Based on the usage of the
customer, what is the personalized
ranking of the user content?
Query = What is the personalized
Lifelong Total value for the customer?
Personalized SaaS
Plan
Personalized Content
Ranking
Personalized GUI
Personalized LTV

12. 4 Levels of Personalization
Level 1: One to ALL - Fit All
Level 2: One to Many - Rule Based
Level 3: One to Some - User/Item/Feature Similarities
Level 4: One to One - Fully Personalized
● Same solution for all Customer
● (+) Simple, Fit for all, (-) High-Churn,
● Using some Rules/Heuristics based on, survey, demography, geographic, psychographic
information to recommend a solution (Static Info)/ business domain knowledge.
● By understanding user/item/product similarities ML models are used to
create and assign related item/feature/bundle/score to the users.
● ML model is fully personalized and tuned per user activity,
● $$$
Customer
Experience Level
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13. Personalization Use Cases In SaaS
● By understanding how customers interact with products, features and contents, personalization engine tries to
recommend the best plan or bundle to improve customer experience.
● Use-Cases : Dropbox plans, etc.
● Based on the content that users created, shared and viewed, personalization engine tries to provide search results
that are closer to customer needs and intent.
● Use-Cases : document search, video search
● Based on the content that users created, shared and viewed, personalization engine tries to recommend results that
are closer to customer needs without doing any search.
● Use-Cases : document recommendation, movie recommendation, feature/product recommendation.
Personalized SaaS Plan
Personalized Search
Personalized Content Ranking

14. Personalization Use Cases In SaaS - Cont.
● By understanding how customers interact with products, features and contents, personalization engine tries to target
and find new customer or engage more current customers to company and product growth.
● Use-Cases : marketing emails and campaigns for the products, features and contents, etc.
● Customer lifetime value (LTV) is a measure of the total income a business can expect to bring in from a typical
customer for as long as that person or account remains a client.
● Use-Cases : This is the strategic metric for SaaS company to measure its growth and do the strategic planning on
resources and customer acquisition.
● By understanding how customers interact with products, features and contents, personalization engine tries to render
the GUI to improve CX.
● Use-Cases : Reordering of the product functionalities, widgets, etc.
Personalized Marketing
Personalized Lifetime Value
Personalized GUI Rendering

15. Personalization Use Cases In SaaS - Cont.
● By understanding how customers interact with products, features and contents, personalization engine tries to render
the correct message/text related to the customer needs.
● Use-Cases : Marketing emails or prompts are crafted based on each specific user needs not just a generic
message.
● By understanding how customers interact with products, features and contents, personalization engine tries to finds
eligible customers for promotions to increase customer engagement or revenue.
● Use-Cases : Marketing emails target price-sensitive users for new promotions.
● By understanding how customers interact with products, features and contents, personalization engine tries to
generate churn score for each customers.
● Use-Cases : marketing people could proactively approach those customers for solution and possibly reduce the
customer churn.
Personalized Messaging
Personalized Churn Scores
Personalized Promotions

16. Personalization System Architecture
● Two-phase general architecture will be discussed here, but some of the parts could be easily
removed for the simpler use cases for more optimization.
● It could be implemented in real-time or batch based on the use-cases and needs.
● For more resources you can watch Personalization at Scale: Challenges and Practical
Techniques
Query :
ex.
What is the interest
score of User_i to
product/Items?
Feature
Store
Item/Product
Store
Item/Product_k
Personalization Engine
Query :
ex.
What is the interest
score of User_i to
product/Items?
Query :
ex.
What is the interest
score of User_i to
product/Items?
Query_1 :
ex.
What is the interest
score of User_i to
product/Items?
Item/Product_k
Item/Product_k
Item_k/Product_k
Contain Statistical information
and ML-generated features
related to the users, products
and items.

17. Personalization System Architecture - Cont.
Items/Products
Items/Products
Items/Products
Candidate
Generating
Feature
Store
Item/Product
Store
Business
Logic
Ranking/
Scoring
Impression
Logs
Feature
Computation
Queries
Queries
Queries
Candidate Generation
could be removed if
there are not so many
candidates for some ML
Applications.
Filtering the results
based on some business
Rules like limit of some
items, etc.

18. Dropbox
● Our mission is to design a more enlightened way of working.
● Today, it means designing products that reduce busywork so you can focus on the
work that matters.
● 700M+ registered users globally,
● 800B+ pieces of content,
● 17.09M paying users and 80% of subscribers use us for work.
Dropbox
Capture

19. Personalization @ Dropbox
Personalized
Search
Personalized
Lifetime Value
Personalized
GUI Rendering
Personalized
DBX Plan
Personalized
Churn Scores Personalized
Messaging Personalized
Content
Ranking
Personalized
Promotions
Personalized
Marketing
Personalized
…

20. Key Takeaways
Keeping customer happy is the success key in SaaS
Need to automate the process of connecting and serving customers for better CX
Personalization is the key pillar to improve CX
Machine Learning is an enabler to achieve Personalization@Scale in SaaS