This document provides an overview of digital marketing analytics and paths of value. It begins by introducing Marshall Sponder as the lead author of a new textbook on digital analytics for marketing. It then lists 12 topics that will be covered, including internet marketing, SEO/SEM, programmatic advertising, web optimization, first/second/third party data, web analytics, and social media analytics. It provides brief introductions to several of these topics, emphasizing the importance of different data sources for digital marketing and how tools have evolved to analyze user behavior online.

1. Digital Marketing Analytics
Paths of Value
MARSHALL SPONDER
LEAD AUTHOR – DIGITAL ANALYTICS FOR
MARKETING, ROUTLEDGE, 2018

2. Everything I talk about today is
covered in much more detail in my New
Textbook of which I’m the lead author.
The purpose of this book is to provide
the best information about Digital
Marketing field and a way for
businesses and individuals, connect the
dots, to access their development and
process - then determine what they
need to do next.
• www.routledge.com/9781138190672
• http://bit.ly/DAFM_MS (published 10/8/17)
• amazon.com/author/marshallsponder
• http://www.linkedin.com/in/marshallsponder
• @webmetricsguru

3. There is a lot of information in this presentation
and we are just going to fly through it in 90
minutes or so (holding questions to the end of our
talk).
If you want to know more about any of the
subjects, order a copy of the book from Amazon
upon returning home (soft copy or digital).
https://www.amazon.com/Digital-Analytics-
Marketing-Marshall-
Sponder/dp/1138190683/ref=mt_paperback?_enc
oding=UTF8&me=

4. Here today to speak about Digital
Marketing Analytics and its Paths of
Value
Marshall Sponder develops and teaches online and
hybrid courses at Zicklin School of Business and Rutgers
University where he holds a dual appointment.
At Zicklin, he teaches Web Analytics courses while at
Rutgers he teaches an online class called Social Media
for the Arts. Marshall is the author of Social Media
Analytics (McGraw-Hill 2011) and Digital Analytics for
Marketing (Routledge, 2017).
Marshall is a Board Member Emeritus at theWeb
Analytics Association, now called the DAA.

5. The Digital Marketing Major is part of
the success story of Baruch

6. Here are some of the Digital Marketing CORE courses and
ELECTIVES taught at Zicklin School of Business

7. Social Media for the Arts is an Online, Asynchronous
learning course that I author, teach and lead at
Rutgers University – this fall semester there is 1432
students registered and has been growing exponentially
every year – next Spring we expect over 2200 hundred
students.

8. Paths of Value covered in this presentation
1. Internet
Marketing
2.
SEO/SEM/Landing
pages
3. Programmatic
4. Web
Optimization
5. First, Second
andThird Party
Data
6.Web Analytics
7.Third Party Data
Platforms
8. Social Media
Analytics and
Content Marketing
9.Text Analytics
and Algorithmic
curation
10. Mobile
Analytics
11. Geolocation
Analytics
12. Integrating
Digital Marketing
Analytics with
Business Analytics

9. 1. Internet Marketing

10. Digital Media is really about dealing with Unstructured, Structured and Semi-
Structured Data. Unstructured Data take a lot more effort to work with.

11. Unstructured Data makes up more than 80% of all data

12. Digital Analytics developed to serve the needs of
administrators and its users and captures STRUCTURED
DATA ONLY.

13. Unstructured Data now called The “New Oil” or
“Digital Gold”
The term “data is the new oil” reflects the current consensus that the most
asset an organization has is its semi-structured and unstructured data.
Examples of turning data into dollars:
1. Credit card companies detect unusual spending patterns of their
customers using sophisticated algorithms that examine massive datasets,
saving customers billions of dollars a year.
2. Major retailers use unstructured data collected for customer transactions
to refine their online search engines and encourage customers to buy
more products.
3. Food and beverage suppliers merge customers, logistics, and
manufacturing data to improve their plant operations significantly.

14. Digital Analytics Platforms evolved in order to analyze and
understand the Data generated by INTERNET users.
• Web Analytics – Platforms used to understand the activities (behavioral
/ clickstream) of users of a specific website.
• Text Analytics -The process of turning text into numbers, similar in
structure to a spreadsheet so that statistics and other types of analysis
can be run on the data.
• Search Analytics –The Analysis of Organic and Paid Search Engine
results. Search Analytics includes the analysis of site search results on
the search engines running on a specific website.

15. Combining Paid and Organic Marketing Strategy
1. Develop content that is based on customer or target audience needs and interests
2. Promote content using SEO to provide far reaching and “evergreen” results
3. Investigate Broad Search Categories andTrends for targeted SEM promotion
4. Narrow Down Keywords
5. DetermineTraffic and Cost
6. SelectTerms and Match Criteria
7. DesignAds
8. Run Campaigns
9. Measure and Refine Campaigns
10. Continue to refine Organic and Paid Search content and results

16. Display Advertising
Display (Banner) ads are advertising that takes place on digital websites. It includes many
different formats and contains items such as text, images, flash, video, and audio. Display
Advertising consists of the following types of ads:
• Video Ads
• Rich Media Ads (Expendables): flash files that may expand when the user interacts on
mouse over (polite), or auto- initiated (non-polite).
• Overlays: ads that appear above content and that are possible to remove by clicking on a
close button.
• Interstitials: Ads displayed on web pages before expected content.
• Sponsorships: Advertising that includes a logo or adding a brand to the design of a
website.
• Ads that appear on a sidebar or top bar – these ads stay on the page unless the viewer
marks it as uninteresting or offensive.

17. Viral Marketing
• Viral Marketing has changed a lot since the days of the dancing
babies and funny cat videos that became popular onYouTube circa
2006.
• Initially thought of as the most sought after form of marketing, it is
inexpensive and very efficient and rapidly disseminated across
social media channels (hence the word "viral").
• Viral marketing is not something to depend on– but is now
becoming much closer to a “science” now and is just another form
of “paid media.”

18. Viral is actually Paid Media, most of the time. Example: 2014 Academy
Awards – most viral video is professionally produced and seeded –
coverage and distribution are often paid for in advance.
In the 2014 AcademyAwards, Ellen’s
Selfie was purported to be worth 1
Billion dollars of “earned media” for
Samsung, an Academy Awards 2014
Sponsor, but was arranged (and paid
for) before the Academy Awards took
place.
There is an element of "leveraging
opportunity" in generating viral content,
but excepting for rare spontaneous
accidents, viral media (as a marketing
tactic) is anything but spontaneous.

19. Social Media Advertising
– Twitter, Instagram, LinkedIn, Snap
• Social Media Advertising is usually associated with the term “Native
Advertising” (NativeAds) which is advertising that looks as if it is a
social media users’ post, but is really an Ad.
• Native Advertising is thought to be more effective and consensual
than other forms of advertising, although social media users have
begun to tire of them.
• Twitter has similar options to Facebook, but is keyword based,
whereas Facebook’s targeting is based on membership and the
behaviors of it’s members.

20. Digital Advertising Spend including SEM projected through 2020

21. Connecting the Dots: DemandMetric
Website Program Maturity Assessment

22. 2. SEO/SEM

23. Two types of Search Results, Organic and Paid:
• Organic results appear mainly because of their relevance to the user’s query.
• Paid search results are tailored to the searcher. For example, if you search up
cars frequently, you will see more ads for cars then your paid search results will
be related to that as well.

24. Introduction to Search Engine Technologies
•Search engines yield two types of search results: organic
and paid.
•Search engines rank organic results based on over 200
ranking factors in an algorithm.
•Search Engine Optimization (SEO), the process of
optimizing a website to receive the most visitors through
a search engine.

25. Search Engine Marketing (Paid Search)
• Search Engine Marketing (SEM) focuses on paid search business
solutions.
• SEM involves the process of buying traffic through paid search
listings-but SEM offers much more control over those listings than
SEO
• SEO and SEM have their advantages and marketers should combine
both, as most organizations have several initiatives that can benefit
from both types of search.

26. Comparing Organic and Paid Search

27. The Building Blocks of SEO
The factors above impact how content is ranked in Search Engines.

28. Connecting the Dots: DemandMetric
SEO Maturity Assessment

29. 3. Programmatic

30. Programmatic is emerging as the dominant form of
Corporate Paid Digital Advertising

31. Why Programmatic Advertising is Used
• In the last 25 years, we have developed eCommerce – the ease of buying and
selling online (just look at Amazon and eBay).
• The development of precision targeting along with self-service advertising (like
Google AdWords) has turned anyone who wants to be a digital marketer
(whether they realize it or not) but they need to become more sophisticated to
take full advantage of what technologies such as Programmatic offers.
• Digital and Social Media platforms have all included sophisticated and powerful
analytics (and in some cases, customizable attribution models)

32. Madison Avenue is now
Wall Street with the majority
of media planning and
buying being guided and
run by Automated Algorithms
http://venturebeat.com/2014/04/20/why-madison-avenue-is-becoming-more-like-wall-street/

33. Programmatic Advertising
Programmatic Advertising technology automates processes, eliminates costs,
and allows for more time spent on the strategy and the analysis of the data.
Buy Side vs. Sell Side of Programmatic (and it is confusing since Buyers can
also be Sellers and vice versa)
• Buyers / Demand Side - Advertisers are trying to deliver the right message in
front (eyeballs) of the right person at the right time (targeting) for the best
price (return on investment.)
• Sellers / Supply Side – Publishers are trying to maximize the money they can
make selling advertising space (inventory) to advertisers.

34. The Data being used to power Programmatic Advertising is
acquired and provided in a number of ways (Second & Third-
Party), including the in-house customer data businesses have
(First-Party).

35. Data Collection of First, Second, Third Party Data allow marketers to
harvest vast amount of personalized data on individuals (for better or
worse) used largely to hyper-target Programmatic Ads.

36. Programmatic can be used with most Digital Advertising
• Digital - Graphic ads appearing next to content on web pages, IM applications, email.
• Video - Ads appear in the video before, during, or after the video plays. One of the fastest-growing
opportunities online today.
• Mobile - Ads used on mobile devices, such as cell phones or tablets are growing quickly.
• Search – Ads are placed and ranked by search engines on web pages that show associated results
from the user’s search engine queries (can be combined with display networks such as Google’s).
• Social - Produce content that users will share with their social network.
• Native - A form of social media advertising that matches the shape and function of the platform on
which it appears – looks similar to user’s post or newsfeed item but is an ad.

37. Most Programmatic is done using a Third-Party vendor platform,
either through an Agency or In-House – these should be evaluated
for the best marketing technology solution for the business.

38. 4. Web Optimization

39. Web Optimization encompasses tuning and customizing the online content and
its delivery to many online and some offline channels and includes
personalization and hyper-targeting based on First, Second and Third Party
Data.

40. Today, Web-Optimization and Social Media is all about Content
Marketing and the technology and creative that informs it – that’s
why we choose a Content Marketing Soft Assessment.

41. 5. First, Second and Third
Party Data

42. What is First, Second and Third-Party Data?
• First-Party data is collected by issuing the First-Party cookie to the web browser of a visitor
to a website runningWeb Analytics software. Most websites issue First-Party cookies for
applications and login information that takes place on their website.
• Second-Party data is the First-Party data collected from a business partner (via a First-
Party cookie) and shared with another party (usually an affiliate). For example, many online
banking and credit card customers provide First-Party data that is provided to their business
partners (the terms of this arrangement are usually appear in terms of service of the website).
• Note:There are First andThird-Party cookies, but no such thing as a “Second-Party cookie”, just Second-Party data.
• Third-Party data is information collected by using aThird-Party cookie that is issued to a
web browser of a visitor to the website it is issued from. Third-Party cookies are issued by
various services (many of them are advertising based) that follow online users across the
Internet and collect behavioral information about their activities.

43. The big problem with Third-Party Data is generally not interoperable across
other First and Third-Party Data Providers making it unreliable when combined
with other sources of data, which marketers often want or need to do.
• Each provider has their own reasons for collecting data – this impacts the context and
format of their product offering, possibly making it incompatible with similar datasets offered
by alternative providers.
• Third-Party Data Providers want to lock users into using their own data products, so they
are not motivated to provide interoperable data sets.
• MostThird-Party Providers are not transparent about how they prepare their datasets,
and too often, we are not able to find out the actual sample size of the panel.
• Just as the bricks of a wall require alignment in the precise dimensions of the building blocks -
layering data requires the same precision, but the precision cannot be verified withThird-
Party datasets.
• UseThird-Party Data sources alongside each other but avoid the temptation combining
the data into a single metric. Notwithstanding, organizations can end up needing to
combine differentThird-Party data feeds because they need select elements in each one that
the others do not have – after all, we do not live in a perfect world.

44. 3 Organizations using First, Second and Third-Party Data
Capital One created a deal optimization engine analyze customer demographics and spending
patterns. With this data they were able to determine when and where to place offers in front of
people; this led to increases in revenue and customer satisfaction.
T-Mobile reduced customer turnover by 50% by examining their usage patterns, geographical
usage trends, customer purchases by location and Customer LifetimeValue. With the massive
amounts of dataT-Mobile collected on its clients, it was then able to identify its most influential
customers and give them extra perks.
Starbucks uses location data, street traffic analysis, demographic info and data culled from
other places to decide where to locate their stores. Armed with the data Starbucks locations
can exist a block away from one another; better serving their audience while remaining
profitable.

45. Example: Using Third-Party Market Research Resources
– ComScore MyMetrix
• ComScore’s suite of measurement products use a “Global Research Panel comprising over 2 million people that
reside in over 170 countries that provide data readouts of interest to digital advertisers and publishers.The online
panel captures the URL, engagement activity, keystrokes and mouse movements and intensity, information parsing,
application usage, data stream captures and many activities on AOL (ISP).
• 85% of users in the panel are identified by the device (single user machine) and 15% (unmarked and multi-user devices) by biometrics, site affinity
and time of day or gender.
• Web site audience defined by projecting the panel data activity on a website/property with census data to the general population at six levels of
classification.
• Web site properties (entities) appear in the reports if the entity generated at least 30 UV (unique visitors) during the period (month) for US reporting
and 15 UV for non-US reporting.
• UDM – Unified Digital Measurement ™ integrates the panel (2 million +) with Global Device Measurement
(maintained with page tagging). Unified UV =Total Census Cookies X Cookies per Person.
• Comscore performs constant updates (enumeration) surveys by landline and cellular on a monthly basis with a
target of 500 completed surveys per month. Panel recruitment by affiliate programs and third parties into two
categories (home and “at work”) that is merged and de-duplicated to create the ComScore Universe.

46. As there are many Third-Party Market Research Tools/Platforms,
first determine your basic needs and findings …..

47. Then determine if external Market Research vendor(s) are needed,
if so evaluate them and choose your vendor/partners carefully as
the choices can vastly impact the final outcome.

48. 6. Web Analytics

49. Why use Web Analytics?
• Optimize websites
• Maximize the marketing placed on websites (combined with
Programmatic)
• Learn how site navigation, content, and aesthetics affect the bottom line,
which should align with business goals
• Learn from past marketing efforts on a website
• Optimize future campaigns to increase conversion on a website
• Recommend website or marketing changes based on an analysis of website
behavior
• Implement site changes or recommends changes to those in authority to
do so having the First-Party data to back up the recommendations.

50. Web Analytics captures the kind of data most marketers marketers want and need
There is a lot more data that can be
used than whatWA collects.
WA can be configured to collect
additional data when it is determined
that is needed, viaAPIs and
additional configurations.
While the platforms are powerful out
of the box, they don’t do much of
business value until they are
configured.

51. Web Analytics Discovery and Instrumentation process

52. It is necessary to understand
how these WA platforms
work, otherwise the will not
work optimally for us

53. This process gets repeated, over and over again

54. Once the actual goals are
defined they need to be set
up in Web Analytics in that
manner.
(Key Business Requirements
are informed by Key
Performance indicators –
these are usually the
“intermediate metrics”

55. Example of mapping KBRs to KPIs is actually hard, often
compound/custom metrics need to be created because the
defaults are not accurate or useful out of the box.
The Key Business
Requirement is always
much broader than a
simple metric.
In other words, your KBR
should never be to get
more pageviews, visits or
clicks on your website.
The conversion event is
“Where” the
measurement is taken.
Web Metric

56. KPIs help stakeholders understand and act on the activities
generated by the visitor to their websites

57. Configuring Web Analytics Platforms is much harder than it
should be, despite all the PR from Google to the contrary.
• The first digital analytics tools were built to read weblogs, not to measure the
digital world.Though the capabilities of the tools have improved the basic
views they provide, have not changed much.
• While people build the websites for specific purposes, theWeb analytics tools
are not able to determine the purpose of a website.
If they could, analytics reports would be more useful.

58. But the problem with Web Analytics is most of the
basic web metrics are not originally designed to
capture the actual information that marketers
need.
“The challenge with KPIs is most of the standard digital metrics are
almost useless to make marketing decisions because they were
designed to measure the wrong things and do it in the wrong way.”
- Gary Angel (cited in my book)

59. Digital
Fragmentation
impacts the Data
and Metrics we can
derive from Web
Analytics platforms
as the customer /
lead generation
data often is
generated and
collected on other
platforms such as
Facebook, Twitter,
LinkedIn, etc.

60. The most powerful tool Web Analytics has is SEGMENTATION, as
all visit data can be looked at from various lens and filters which
can be refined and customized, as needed.

61. Segmentation Examples (using Adobe Analytics)

62. The Best Use Case for Web Analytics
is UX (Web Usability/Design) and
WA is only able to measure the last step.

63. Ultimately, marketers will want to first soft access how well they
are measuring their current website marketing activities, along
with their other marketing activates besides the website ……

64. Followed by an Automation Assessment – since the purpose of
Websites is to automate and scale marketing activities and
customer touchpoints……

65. Followed by a soft assessment of the alignment between sales
and marketing parts of an organization, as most measurement
problems originate from a schism here.

66. Once all the parts of the organization are aligned, find the right
vendors to execute Web Personalization, as Personalization is
the main reason to heavily invest in Web Analytics enablement.

67. 7.Third Party Data Platforms

68. 3rd Party Data Tools useful for Digital Analytics
• ABI/INFORM Global - Find articles from trade journals and magazines,
scholarly journals, and general interest magazines covering accounting,
advertising, business, company information, industry Information,
management, marketing, real estate, economics, finance, human resources,
and international business.
• Academic OneFile - Articles from magazines and scholarly journals from a
wide range of subjects.
• Business Monitor Online is now called BMI Research - might be useful to find
out about a market in each area.
• eMarketer – eMarketer takes many data from all over the digital marketing
world, re-charts and organizes it for reference and publication.

69. 3rd Party Data Tools useful for Digital Analytics
• Gartner - Analysis of IT markets for hardware, software, IT services, semiconductors
and communications. Reports on IT issues in ten industries including education,
banking, retail, healthcare, and manufacturing.
• Grolier Online - Useful for definitions of topics along with building citations.
• IBISWorld - Already using - reports on over 700 industries plus specialized analyst
reporting.
• Kanopy - might be useful as a source of stock educational videos.
• American Fact Finder - find out details about demographics in a zip code in the US.
• Business Insights: Essentials - Good for SWOT analysis of companies, perhaps of
industries, also allows keyword search. Provides industry reports that might be
useful.

70. 3rd Party Data Tools useful for Digital Analytics
• Mintel Academic - Provides overall sector studies and trend
analysis.
• PrivCo – Information of the filings and financials of private
companies.
• Simmons OneView – Useful for demographic planning.
• Statista - like eMarketer in but has more search capability and
numerical data.
• Warc - Resource with DATA for many marketing data, trends, and
projections

71. Since Third-Party Tools are often used for Market Research as well
as Data Enrichment, businesses need to determine how well they
are prepared to use such tools in-house, before making major
investments in additional platforms and/or data.

72. 8. Social Media Analytics
and Content Marketing

73. Most people in industrialized societies have at least one active social
media account

74. Social Media Analytics evolved to focus on fragmented online
audiences and their activities, they also developed their own types of
analytics, making integrating this data difficult and error prone.

75. Social Media
Analytics vs.
Traditional
Business
Analytics
-Insights from Social
Media are of a
different nature
from Business
Marketing Analytics

76. The study of Social Media Analytics began in 2003 but
public awareness of it took another 5 years (~2008)

77. In the book there are
7 layers of Data of
Social Media
Analytics which can
be examined
individually, then
combined for
insights.

78. The 7 Social Media layers
1. Text - Social media content, such as comments, tweets, blog posts, and Facebook status
updates
2. Networks - Extract, analyze, and interpret personal and professional social networks, for
example, Facebook, Friendship Network, andTwitter.
3. Actions - Extracting, analyzing, and interpreting the actions performed by social media
users, including likes, shares, mentions, and endorsement.
4. Hyperlinks - Extracting, analyzing, and interpreting social media hyperlinks (e.g., in-links
and out-links).
5. Mobile - Measuring and optimizing user engagement through mobile applications (or apps
for short).
6. Location - Spatial analysis or geospatial analytics, is concerned with mining and mapping
the locations of social media users, contents, and data.
7. Search engines – Extract, analyze and interpret the way that search engines rank content.

79. Platforms / Layers used for Social Media Analytics

80. Social Media Analytics Vendor Assessment -
Various third-party assessment tools such as DemandMetric, provide useful guidance in choosing
the right Social Media Analytics platform for a particular organization or stakeholder need.

81. The Online Presence Assessment (The Analytics Selfie) is a tool I developed
for my students to measure their own Online Presence in Social Media using
third party APIs – and it can also be used for Celebrities and Companies.

82. 9.Text Analytics and
Algorithmic curation

83. What is Text Analytics?
• Text Analytics is turning text into numbers so we can run mathematical and
algorithmic operations, regressions, classifications, neural networks and
Bayesian equations on the transformed data to get insights we might not
otherwise get.
• The world is full of text data (largely generated by the web-based systems
we use to communicate with), having a platform to operate on large amounts
of textual information can be of immense value to organizations, if it yields
vital information (it can).
• The algorithms used inText Analytics are also used in many other domains
of science.
• We believeText Analytics can be used more widely than it is, but for most
organizations,Text Analytics remains a “niche” activity; most organizations
do not understand what it is or how to use it.

84. While Text
Analytics is still
considered a Niche
Business
Intelligence
method, its use is
growing rapidly in
certain sectors
such as fraud
detection and
financial data
mining.

85. Text Analytics Use Cases
Text Analytics is a growing market and is estimated to reach $6.5B by 2020, growing at a rate of
as much as 25% per year from 2013 through 2020.Text Analytics supplies necessary data to
Customer Relationship Management (CRM), predictive analytics and brand reputation
management (see common use cases in the table, below).

86. Text Analytics is
used for the
various text
mining
algorithmic
operations

87. Most Text Analytics Operations require ETL
(Extract / Transfer / Load) processing

88. Text Analytics
Computational
Operations Turn text
into numerical data
allows analysts and
researchers to run
mathematical and
statistical operations
on the data.

89. Hierarchical and K-means Clustering Methods for
Unsupervised Learning
K-Means: One of the most
popular and useful way to
organize information in the
“K-means” algorithm. K-
means algorithm creates a
specific number of groups (k)
from a set of objects. It’s a
popular cluster analysis
technique for exploring a set
of data.
The iterative part of the K-
means algorithm uses the
output of one state of the
computation as the input of
the next stage, continually re-
running the K-means until the
output and input no longer
meaningfully change.

90. Machine Learning - Supervised Learning
Supervised Learning:A
model is created based on
previous observations as a
training set using a set of
documents are tagged by
humans to be part of a
category.

91. Text Analytics is best used in situations
where there is a clearly defined business
need, a large amount of text/image/sound
data, and clear patterns of information that
are being data mined.

92. 10. Mobile Analytics

93. • Mobile data is constantly generated by our mobile devices, and there are
two main methods to view and analyze this data which we will cover in this
chapter.
• MOBILE APPLICATIONS are becoming an integral part of our lives (i.e.: the
apps we download from the iOS or Android App Stores.)
• Applications (or apps) are special-purpose software developed to perform
certain tasks on the go (on our mobile devices) – these devices produce a
wealth of data that can be of interest to both device owners and marketers.
Mobile Devices is where most ecommerce is happening
now.

94. Reasons to ensure that a website is
“Mobile Friendly”
• Better Search Engine Ranking: Since 2015 mobile compatibility has been a
Google search engine ranking factor and, overall, most of the referral traffic
arriving on websites originates from mobile devices.
• Many websites have been designed to work on desktops or laptops rather
than mobile devices; creating frustration in the user experience while trying to
be used on mobile devices.
• There are strong evidence that searches performed on a mobile device are
highly correlated to customers who are willing to visit a local business, and
make a purchase the same day (mobile devices broadcast their location to ISPs
and Search Engines), as well as many mobile apps.

95. Use the Mobile Application Type Assessment to determine what type of
Mobile App to create.

96. Finally, access how adapt the company is by using the Mobile
Marketing Maturity (soft) Assessment.

97. 11. Geolocation Analytics

98. Marketers now prefer GeoData over other types of data, when
they can get it

99. Geo data is collected directly from mobile devices

100. iBeacon and Bluetooth
• Locational data can be gathered with precise location up to a few feet with
Bluetooth based transmitters and receivers (it sounds like a form of radio,
and in a way, it is, because most of the data is broadcasted from Bluetooth
transmitters). One feature about iBeacon is that it is relatively inexpensive to
set up and deploy, and there are integrated middleware platforms that provide
the iBeacons together with campaign management and analytics.
• One issue that iBeacon solves is privacy. iBeacons are an opt-in technology
and users must first run an application on their mobile device which then
broadcasts their location and communicates with the nearest iBeacon
transmitter, allowing for a customized message to be sent to the
user/customer as they walk by a particular area. iBeacons, though still in an
early stage, can provide many uses, it can be expected to be widely deployed
soon. iBeacons are an Apple protocol but it’s widely deployed since launching
in 2013; any mobile device running application can communicate with iBeacons
placed in a physical location.

101. Near Field Communications (NFC)
• NFC is radio-frequency identification technology originally developed in the
1980's (known as RFID) allowing compatible hardware to communicate with
passive electronic devices using radio waves; RFID is used for product
identification, authentication, and inventory tracking.
• Near Field Communications creates a cell area with a 150-foot radius (Geo-
Fencing) with mobile devices and is like a “movingWeb Analytics” data collection
pixel. Mobile devices that are in the radius of a "named" Geo-Fenced area receive
communications about businesses in the location.
• The issue with NFC technology was that it was not a widely adopted standard
available in most mobile devices until recently.
• Android Pay and Apple Pay utilize NFC technology so that users with an
Android/Apple phone can simply tap their devices to a pay screen at a store to
pay for their goods.The Android/Apple Pay accounts are connected to the user's
bank account.

102. 12. Integrating Digital
Marketing Analytics with
Business Analytics

103. The alignment of
social media analytics
with business
objectives can be seen
as analogous to the
famous ChineseYin
andYang philosophy,
where two seemingly
opposing forces
complement and
reinforce each other.
Understanding The Social Media Business Alignment

104. Aligning analytics with business objectives

105. Social Media Analytics Alignment Matrix

106. The Alignment Strategy Worksheet

107. Summary:
• We covered many of the highlights of the Digital Analytics for
Marketing book.
• Pick up a copy of the book if you want to go into these and many
more subjects in greater depth.