Web analytics: Data Collected via the Internet

Web analytics (incl real-time data)

Collaborative filtering

Facebook advertising

Mobile marketing

Slide set 8

Database Marketing

Fall 2016

Web analytics:

Data Collected via the Internet

• Customers can state preferences on the Internet and

this data can be used to help establish relationships.

• Reputable Internet marketers will protect customer

data and clearly outline privacy policies.

• Individual-level customer data collected on the

Internet can be classified as either registration, behavior, or source data.

• NOTE that it is also possible to manage the behavior

using cookies

Registration Data

n Many e-commerce sites require consumers to register before they can make purchases, get information, or enter the Web site. Examples of Internet registration data typically

collected include

• Name • Title

• Business title (if the site is a b-to-b site) • E-mail address

• Postal address (business or home) • Phone number • Fax number • Age • Income level • Gender • etc Web analytics 3

Behavior Data

n Types of customer behavior data that can be captured includes

• Visits

• Total page views • Specific page views • Time spent at the site • Products purchased

• Customer service requests

• Access to personal account information • Discounts used

Source Data

n It is valuable for the marketer to know what media

convinced a customer to visit the site. Possible sources are e.g.:

n Promotional e-mail,

n Article or site link n Search engine

n Word of mouth

n Online ad

n Some are familiar with the site’s offline store

n Commercials or print ads

n Direct mail

n etc

Web analytics 5

Use of cookies

n Cookies are small text files that are initiated in the web page visitor’s computer when the visitor visits a certain web-page first time n When the visitor makes a re-visit he/she can

be recognized

n Allow to consider web page visitor’s behavior without registration

Challenges in the use of cookies

n Cookies are deleted by users to protect privacy

n People have several devices to access internet (no combining of data)

n Different family members use the same device

n All these mentioned sources of inaccuracy are problems in the use of cookies

Web analytics 7

Targeting Online Customers

n Targeting online customers is very similar to the off-line world and consists of two major steps:

•

Defining the target markets

Executing a contact strategy

message and the manner in which both are extended

Online Analysis of Customers

• For E-commerce applications, clickstream data and traditional direct marketing data can be used in predictive modeling.

• Marketers can also define your online target markets with regression, neural network etc… modeling. Internet data can be used to predict customers likely to “click through” .

• The same methods are used when real-time data is employed but the data

architecture is different there.

Web analytics 9

Real-time data 10

Real-time data and analytics

Real-time data 11

1) Operational monitoring

Datacenters house thousands of discrete computer systems that record data about their state (processor temperature, state of disk drives, processor load, network activity).

This data is collected and aggregated in real time.

Real-time data 12

2) Web analytics

Website activity tracking. There is the need to have a short feedback loop and to collect the data in real time.

Real-time data 13

3) Online advertising 1/2

n When a visitor arrives on a web page bidding agencies (perhaps 30-40 at the same time) place bids on the page view in real time via an advertising exchange. An auction is run, the advertisement(s) of the winner(s) displayed. Elapsed time is less than about 100

milliseconds. All the parties (exchange, bidding agent, advertiser..) collect the related data. The data collected for the advertising exchange are e.g. for billing, data monitoring the fraudulent traffic and data for other risk management activity.

Real-time data 14

3) Online advertising 2/2

n Advertisers publishers and bidding agents are collecting data to optimize the

campaigns: to set the bid (advertiser) / to set the reserve price (publisher).

n A bidding agent represents often many

Real-time data 15

4) Social Media

The data is text data and thus, unlike web or online advertising, unstructured. It must be processed to be understood by automated systems. Social media data is challening for the real.time data sources.

Real-time data 16

5)Mobile data and the internet of things

Smart phones and their number is growing fast. They are communicating with nearby objects using e.g. Bluetooth. Most versatile real time applications (sleep activity activates coffee maker, biometrics, sensor measurements collected by smart phones – Internet of Things).

IFTTT – If This Then That (a web-based service that allows users to create chains of simple

Real-time data 17

The special characteristics of streaming

data

Always on. Processing times need to be short or

the data is lost, shorter than real time.

Loosely structured.

High-cardinality storage. Large set with a high

number of dimensions. Storage space is restricted because very fast main memory storage needs to be used. STREAMING DATA REQUIRES

SPECIAL ARCHITECTURE

Real-time data 18

Sampling from a Streaming Population

Basically analogous to sampling from a fixed population.

Collaborative Filtering

n A technique used in recommendation engines

n We discuss two kinds :

q MEMORY BASED

q MODEL BASED

Pioneer: Amazon

Now the use is spreading outside normal retailing to financial services, travel agencies etc

Collaborative filtering 19

How to predict the missing values

Customer Movie1 Movie2 Movie3 Movie4 Movie5

A 5 2 - 4 1

B 1 - 1 2

-C 5 3 4 - 1

Collaborative filtering 20

The figures are individual preferences of A, B and C for films. We try and find the missing

Memory based methods

n They are neighborhood based meaning they attempt to find a set of users that have

preferences similar to the target user. Once

similar users, called the ”neighborhood” have been identified, the neighborhood

preferences are combined to predict the preference of the target user. Thus we have to define the similarity between users (much in the same way as in clustering).

Collaborative filtering 21

Model based methods

Examples:

1) Methods based on clustering

Customers are simply clustered with the basis of clustering being e.g. how many items of each product the customer has bought. Once the clusters are determined cluster models assign a target user to the segment including most similar customers.

Model based methods (cont.)

2) Item-based collaborative filtering

Consider the items the customer has rated, then calculate how similar they are to the target item (which we recommend or not). Then

those similar items’ rates are be used in the prediction.

Collaborative filtering 23

Combining Content-Based Information with

Collaborative Filtering

n Content based information filtering

recommends items for users by analysing the

content of items that they liked in the past.

Here other users are not employed but instead some kind of similarity measure between two product items.

n Example: if you liked Manga comics we recommend other Manga comics.

Recommendation engines

n Interact with the customer while the customer is ”in” (suggesting related products while filling the customer basket in the e-shop)

Nowadays also text mining used (evaluations of books in Amazon etc.)

Collaborative filtering 25

Facebook advertising 1/3

n Facebook offers you targeting possibilities without

having to have your own database

n They can target e.g. on the basis of location, demographics, interests, behavior, connections.

n The behavior needs knowledge about your browsing

history and can display advertisements on that basis in e.g. in the news feed (cookies use).

n Cookie is data file placed on your computer by thewebsitethat you visit.

n Third party cookie is a datafile placed by another website than which you visit.

Facebook advertising 2/3

n Advertiser may also provide a list of their customers and seek them in Facebook (custom audiences)

n Additionally they can seek Facebook members that are similar to their best customers (lookalike audiences)

Factor Analysis 27

Facebook advertising 3/3

n Facebook tracks your browsing activity if you’re logged in to the network but not actually using it and the modern advertisers show you ads on third-party sites and apps.

n Facebook tracks the third-party websites people visit on mobile devices through the “Like” button, assuming that they are logged into the social network. Based on that advertisers can refine an audience to target ads around “likes” and interests for third-party websites and apps without having to depend on cookies.

Mobile Marketing – marketing on a

mobile device

n Advertisements in apps (Facebook, games) n Geofencing – Location based marketing n QR codes, scanning a code takes you to a

website

n Mobile search ads n Mobile image ads n SMS