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ADM6274Personalized Marketing
Péter Bence ValentovicsEse Djetore
Kartik GoyalNeha Gupta
Muzzamil Saqlain
Personalized Marketing
Personalized marketing is the ultimate form of targeted marketing, creating messages for individual consumers
It is most often an automated process, using computer software to craft the individual messages, and building customer-centric recommendation engines instead of company-centric selling engines
In addition to customized promotions, personalized marketing can also be applied to the products themselves by using a configuration system which allows customers to choose individual specifications for the products they’re interested in
By offering consumers products they already want, businesses are far more likely to convert online visits to sales
Personalized Marketing – Sales Funnel
Attention
ProspectCustom
erRepeat
Personalized Marketing vs. Traditional Marketing
Represent the CompanyFinding Customers
Represent the CustomerBeing Found
Mass AdvertisingDemographics
1:1 TargetingBehavioural
Point in TimeIsolated Channels
ContinuousIntegrated Channels
Third Party DataIntuitive Decisions
Owned Big DataFact Based Decisions
THEN NOW
Personalized Marketing - Recommender Systems Recommender systems or recommendation
systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item.
Examples
eBay.com – Buyer and Seller Feedback
Levis.com – Style finder
Types of Recommender Systems
Content based filtering Collaborative Filtering Hybrid Filtering Knowledge-based marketing
CONTENT BASED RECOMMENDER SYSTEM
Content Based Filtering
In content based filtering , the system processes information from various sources and tries to extract useful elements about its content.
Filtering is based on User Profile i.e. each user act independently and the system require a profile for user’s unique needs and preferences.
Profile includes information about the items of user’s interest such as songs, Apparels , movies, grocery , articles etc and a record of their characteristics (such as TF.IDF in case of document)
Content based filtering techniques try to identify items similar to user’s profile and return it as recommendation.
Information Sources
Purchased items Items added in the shopping cart, email lists Feedback for items explicitly provided by this particular user Recommendations given by this particular user Highest TF.IDF (term frequency. Inverse document frequency) score in
case of articles documents etc
Content based filtering
Show me
more what I liked
User profile
Movie Actor genre
Product Features
Recommendation components
Items
Score
I1 5
I2 3
I3 1
Recommendation list
based on tools such as statistics , Bayesian classifiers, Machine learning techniques, TF.IDF vector
A textual document is scanned and
parsed and word occurrences are
counted
Each document is transformed into a normalized TF.IDF
vector and the distance between any vectors is
computed.
Based on shortest vector length
recommendations such as articles etc are made to a user
Text Based Content Filtering Method
Non-Text Based Content Filtering Method
User’s preferences are recorded based
on content attributes (ex item, video, songs etc)
Item classified based on tools such as statistics ,
Bayesian classifiers, Machine learning techniques like
clustering , decision trees and artificial neural
networks
Items are recommended with similar attributes to
the user’s preferences
ExamplesBased on the product purchased by a user and his preferences such as brand, discount, product view history, the recommendation is made EXPLICIT to him.
“Here comes More Recommendation for you… “
Examples cntd..
Advantages of Content-Based Approach No need for data on other users. Able to recommend to users with unique tastes. Able to recommend new and unpopular items Can provide explanations of recommended items by listing content-
features that caused an item to be recommended
Issues With Content-Based Approach User Profile : User needs to be active and provide the feedbacks time to
time for accurate and usable recommendation It requires the content encoding in meaningful features It does not allow the user to see other user’s judgment for the products Limited to the topics of interest of a user Continuous monitoring required for change in user’s interest
COLLABORATIVE FILTERING
Collaborative Filtering
You purchase or browse for Laptop -> Recommendation will be Laptop Backpack
Kind of “word of mouth” marketing
Information filtering by collecting human judgments (ratings)
User - Any individual who provides ratings to a system
Items - Anything for which a human can provide a rating
Approach - use the "wisdom of the crowd" to recommend items
Basic assumption and ideaUsers give ratings to catalog items (implicitly or explicitly)Customers who had similar tastes in the past, will have similar tastes in the future - Matching people with similar interests
The most prominent approach to generate recommendations- used by large, commercial e-commerce sites- well-understood, various algorithms and variations exist- applicable in many domains (book, movies, DVDs, ..)
Recommender Systems – Collaborative Filtering
Personalised Recommendations
Collaborative: "Tell me what's popular among my
peers"
How does CF Work?
User to User CF Item to Item CF
Movie LensRecommendations
USER TO USER• Run by Group lens – Research
lab – data exploration and recommendation
• Use this information to recommend similar or popular movies bought by others.
• This computation is fast and done online.
Movie Lens Recommendations
Amazon RecommendationsITEM TO ITEM CF
• Item-to-item collaborative filtering• Find similar items rather than similar
customers.• Record pairs of items bought by
the same customer and their similarity.• This computation is done offline for
all items.
ITEM to ITEM
USER to USER
KNOWLEDGE BASED RECOMMENDER
SYSTEM
Knowledge-based marketing Uses knowledge about users and products to generate
recommendations and reasoning about what products meet the user’s requirements.
Emphasis on guiding search interactions, through tweaking or altering the characteristics of an example.
Alternative approach where Content-based and Collaborative filtering cannot be used.
Two approaches of Knowledge-based marketing
Both approaches use similar conversational recommendation process requirements
Constraint based
-Explicitly defined set of recommendation
rules-Fulfill
recommendation rules
Case based
-Based on different types of similarity
measures-Retrieve items that
are similar to specified
requirements
Examples AIRBNB KIJIJI
PROS CONS
No ramp-up required Knowledge engineering is required
Detailed qualitative preference feedback Cost of knowledge acquisition
Sensitive to preferences change Independent assumption can be a challenge
HYBRID RECOMMENDER SYSTEM
Hybrid Recommender System
Mix of 2 or more recommender systems to achieve more accurate results
3 ways to combine recommender systems: Parallel Monolithic Pipelined
Techniques for combining recommender systems1. Weighted2. Switching3. Mixed4. Feature combination5. Feature augmentation6. Cascade7. Meta-level
53 Basic Combinations for HRS
How it works
Example: Amazon
Benefits Creates synergy between recommender systems
Emphasizes the strengths of each recommender system Can be used to solve “cold-start” problem
Problem 1: new items Problem 2: new users
Can also be used to solve plasticity and stability problem Example: change in user profile
Benefits Creates synergy between recommender systems
Emphasizes the strengths of each recommender system Can be used to solve “cold-start” problem
Problem 1: new items Problem 2: new users
Can also be used to solve plasticity and stability problem Example: change in user profile
Personalized Marketing - Challenges
Measuring actual impact of personalized marketing Already underlying trend towards increased online sales How much impact does it really have?
Cold starters and how to market to them? New potential customers No data existing anywhere about the customer
Privacy concerns Customers are constantly under surveillance How far would you go?
Personalized Marketing - Future Trends Moving towards the ultimate segmentation . . . . One customer, one
segment! Personalized marketing and . . . . Personalized products! Increased use by niche product firms
Huge reduction in advertising costs Use of personalized marketing by brick and mortar stores
THANK YOU!
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