Advertising using big data

ADVERTISING USING
BIG DATA
- RAJATH KOTYAL

What is Big Data?
According to Wikipedia , Big data is a field
that treats ways to analyze, systematically extract
information from, or otherwise deal with data sets
that are too large or complex to be dealt with by
traditional data-processing application software.
In simple terms , Big data refers to the large
amounts of data which is pouring in from
various data sources and has different
formats.

The 3V factors:
➔ High volume—the amount or
quantity of data
➔ High velocity—the rate at
which data is created
➔ High variety—the different
types of data

Sources of Big Data
➔ Analysing the browsing pattern of a user and recommending
products for them dynamically.
➔ Social media sources such as Facebook And Twitter generate tremendous
amounts of comments and tweets. This data can be used to analyse user
patterns.
➔ GeoSpatial Data (GPS) can be used to recommend users about the deals nearby
their location, or restaurant recommendations etc.

Types of Big Data Analytics
➔ Descriptive Analytics : It suggests Backward Looking & gives idea of the
distribution of data, Methods used are reporting/OLAP, dashboards or
scorecards, and data visualization.
➔ Predictive Analytics : It suggests what will occur in the future. The methods
and algorithms used are regression analysis, machine learning, and neural
networks
➔ Golden path Analysis : It involves the analysis of large quantities of behavioral
data to identify patterns of events or activities that foretell customer actions
such as abandoning an electronic shopping cart.
➔ Prescriptive Analytics : It suggests what to do (like a car’s GPS instructions).
Prescriptive analytics can identify optimal solutions, often for the allocation of
scarce resources.

Softwares Approaches for using Big Data :
SQL Database System
The trend with this system is to make analytics part of the database software so
that data does not have to be moved to a different server. With this approach, the
analytic capabilities are part of the database software.
The Advantages to SQL in-database analytics are :
➔ It eliminates the need for a separate server.
➔ It also makes all of the warehouse data available for analysis.
➔ It improves model accuracy.
➔ When the final model is created, it can be used easily with warehouse data.
Columnar Database is one of the SQL DB System.

Columnar Databases :
➔ Sybase IQ (now a SAP company) offered the first columnar database that reversed the
rows and columns, as shown in Figure .
➔ This approach provides greater processing speed for queries and opportunities for data
compression.
➔ A columnar database is an RDBMS, but what differentiates it with a traditional RDBMS
is that the number of columns are lesser due to reversal and that makes analytics run
faster.

Non - SQL Database System :
This type of system can store data of any structure, and do not rely on SQL to retrieve Data
such as XML, text, audio, video, image.
The document files are often stored and retrieved “as is” through key-value pairs that use
keys to provide links to where files are stored on disk.
Non-relational databases examples are : Apache Hadoop, MongoDB, Apache Couchbase etc.
Advantages :
➔ Store data in a scale-out.
➔ Distributed Architecture.
➔ Runs on low-cost.
➔ It is a Commodity server.
The Next Slide explains Apache Hadoop. One of the most commonly used Non SQL DB System.

Apache
➔ Apache Hadoop is a software framework for processing large amounts of data across
potentially massively parallel clusters of servers.
➔ The Hadoop infrastructure typically runs MapReduce programs in parallel.
➔ MapReduce takes large datasets, extracts and transforms useful data, distributes it
to the various servers where processing occurs, and assembles the results into a
smaller, easier-to-analyze file.
➔ The key component of Hadoop is the Hadoop Distributed File System (HDFS),
which manages the data spread across the various servers.
➔ HDFS is file based and does not need a data model to store and process data. It can
store data of any structure(e.g., Web logs, XML files)

➔ When the user logs in first time, system will provide a wizard to help him finish the
first time software set up.
➔ This wizard contains various options and choices which are generalized.
➔ It also provides numerous options to the users..
➔ Based on his personal interests he chooses the options.
➔ This data is further analysed by advertisers and is used to provide each particular
user with the necessary advertisements as per his choices.
Client Design and
Implement

PROBLEM STATEMENT
Types of Advertisements provided vary from person to
person , and personalising ads for each person will take
a significant time. So in order to reduce the time taken
in analysing , A solution needs be implemented based
on a particular region on the map i.e. Regional based
ads needs be shown to all the people residing in a
specific area.

Recommendation Strategy Algorithm
General Steps :
➔ Define an advertisement profile by an advertiser.
➔ The profile includes the following nine variables: Age, Education, Gender, Income,
Weather, Ethnicity, Race, Population, and House. In addition, the advertisers can input
the weight point for each variable.
➔ Fetch demographic baseline information for each area in the database. It will use the
same nine variable directions as stated above.
➔ Choose one of the 3 strategies of recommendation algorithm : SIS , SlOWS, and
SIWWS.
➔ Apply the chosen strategy against the specific advertisement preference profile and
demographic baseline metadata and make a recommendation.

Synthesis Index Strategy (SIS) Algorithm:
➔ This method uses the spider diagram area as a whole to decide whether a recommendation
is suitable.
➔ First, the Advertisement Preference Meter Area (APMA) for each product at the
evaluating time is calculated by using the following formula.
➔ Second, the Demographic Baseline Meter Area (DBMA) for each location in the census
year is calculated by using the following formula.
➔ Third, the difference between APMA(P,t) with DBMA(L,y) in each area will be calculated.
➔ The location with the smallest difference between APMA(P,t) with DBMA(L,y) will be
recommended.

Spider Web showing
graph of APMA &
DBMA
Web Utilised in
concentrating the area
using google maps

Problem Statement
The Basic Goal of any advertisement is to reach the
maximum number of users . But this approach is not as
effective as it should be and is demanding an insane
amount of revenue in order to reach the users .
A solution is required where the revenue per ad is
reduced along with max efficiency of the ad .

Method Overview - Looping Approach
Feedback Loop :
A Feedback loop, which constitutes a network effect, is linked to
the number of users as depicted in Figure and it runs as follows :
➔ The basis of a feedback loop is collecting data from the users in
huge volumes. So more users means more data.
➔ Which in turn, means better quality of the service in a general way
- on the basis of general indicators such as language, location etc.
➔ It also provides a personalised way too - on the basis of the profile
that has been built of a specific user.
➔ which in turn, attracts even more users to the service.

Monetisation Feedback Loop
A second feedback loop i.e. the monetisation possibilities as
depicted in Figure 2.0 runs as follows:
(1) more users means more data.
(2) which in turn, means better targeting possibilities for
advertisement when additional data are necessary to improve the
targeting algorithms.
(3) which in turn, raises the likelihood that users click on the ads
that are displayed to them, hence increase monetisation under the
commonly used pay-per-click model.
Fig 1.0
(Pay per click model)

(4) This attracts more advertisers because of the higher
probability that a user buys the advertised product.
(5) which in turn raises again the revenues of the
provider.
(6) those increased possibilities of monetisation
increase in turn the possibility of investment to
improve the service and attract more users.
(7) which also contributes to the increase of advertisers.
Fig 2.0

Problem Statement
The usage of Ad-blockers in web browsers &
apps has put a huge downfall impact on
revenue and marketing for numerous
companies and advertisers. A strategy is
required to curb this issue.

Method Overview
A Strategy was researched very recently in 2020 at Wharton University.
Keeping customer as a first priority in mind, This research was made and the
best practice available now to tackle the Ad blockers is discussed. The current
strategy that could be implemented is the Advertising 2020 project.
The companies must realign their ad posting philosophy around R.A.V.E.S
i.e. The ad content should be:
● Relevant and Respectful.
● Actionable.
● Valuable and value generating.
● Exceptional Experience to the Customer watching the ad.
● Share-worthy story from the end user.

➔ The company can focus on delivering the advertisement via a much more exclusive
environment. The major advantage here is being able to know the user completely.
➔ It must focus on relevancy. If the customers are reading travel news, the company can
sort out or recommend advertisement related to the topic to create value.
➔ The company should be able to understand and improve user interface and
experience.
➔ They should include a concrete action on redesigning the customer journey
throughout the ad and make it interactive.
➔ They should understand why the users are frustrated with advertisement placement,
recommendation etc. If these pain points are addressed, users will not find the
incentive of having to install Ad blocker software.
Improving Company - User Relation :

➔ The main focus must be a threshold ad size/page size ratio. This method
weeds out ads that slow down page uploads.
➔ Minimum correlation must be between the search term and ad tag. The
company will also set a budget for ad time per unit of browse time.
➔ There must be a minimum time and click interval between ads i.e. not to
annoy the user.
➔ The threshold also depends on the exposure history i.e not repeating the
same ad over and over again.
➔ A simple demonstration of the strategy divided into three major
components is given in the next slide.
Improvising technicalities of an Ad :

Consumer benefits :
➔ With targeted advertising, consumers are exposed to more relevant ads that
better match their interests, which facilitates access to products and services
that correspond to their tastes.
➔ Consumers are more and better informed.
➔ More effective targeted advertising reduces advertising costs for
advertisers/sellers.
➔ Reduction in cost stimulates the demand for online advertising. Online
sellers/publishers revenues are then expected to increase.
➔ These higher advertising revenues can be partially passed through to
consumers in terms of lower subscription fees or higher quality of service.

Potential consumer harm :
➔ There is some evidence that consumers can react negatively to
targeted advertising.
➔ One possible explanation is that consumers may foresee that
firms could also use their personal information to set personalised
high prices.
➔ It seems that consumers also have some direct disutility or
distrust from intrusive or targeted ads.
➔ The advertising literature talks about consumer "reactance" where
Some policy report that these privacy concerns may more
generally also undermine consumers trust in online markets.

Summary➔ This Presentation provides a decision based approach to handle various cases
associated with pushing relevant ads towards the end-users.
➔ The objective of the project is to undergo the whole process of complete testing and
benchmarking which would enable us to put forward a scalable big data Ad processing
platform in the current market.
➔ The project also provides a pilot data analytics approach for the merchants to view
their end-users.
➔ The scope of the Big Data is very large. A lot of features can be added to the current
Data system and made more feature rich and more scalable to deal with real-time
data.

“Without big data analytics,
companies are blind and deaf,
wandering out onto the web like
deer on a freeway.”
-Geoffrey Moore

REFERENCES
[1] Charles G. Jobs, y David M. Gilfoil, Steven M. Aukers , DeSales University (2016) :
How marketing organizations can benefit from big data advertising analytics.
[2] Dr. Ananthi Sheshasaayee , H. Jayamangala :
A Study on The New Approaches for Social Network Based Recommendations in Digital Marketing
[3] Badrish Chandramouli† , Jonathan Goldstein# , Songyun Duan‡ †Microsoft Research, Redmond
#Microsoft Corp., Redmond ‡ IBM T. J. Watson Research, Hawthorne : Temporal Analytics on Big
Data for Web Advertising
[4] Hugh J. Watson University of Georgia, hwatson@uga.edu : Tutorial: Big Data Analytics: Concepts,
Technologies, and Applications
[5] Lei Deng School of Computer Northwestern Poly technical University Xi' an, China : An Advertising
Analytics Framework Using Social Network Big Data ,

[6] The First IEEE international conference on Big Data Computing Service, and
Applications, At San Francisco Bay, California, USA :Building a Big Data Analytics Service
Framework for Mobile Advertising and Marketing.Laxminarayana
[7] Yashaswy Akella,, Associate Consultant, Capgemini India., yashaswyaln@gmail.com (2020) :
Ad-blockers — Rising Threat to Digital Content Business Analytics Study

Advertising using big data

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