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!