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Digital marketing analytics paths of value - 12-4-17

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Digital Marketing Analytics Paths of Value is an adaption of many of the highlights of my book along with the Connecting the Dots methodology used in the book, and that sets it apart.

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Digital marketing analytics paths of value - 12-4-17

  2. 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. 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. 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. 5. The Digital Marketing Major is part of the success story of Baruch
  6. 6. Here are some of the Digital Marketing CORE courses and ELECTIVES taught at Zicklin School of Business
  7. 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. 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. 9. 1. Internet Marketing
  10. 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. 11. Unstructured Data makes up more than 80% of all data
  12. 12. Digital Analytics developed to serve the needs of administrators and its users and captures STRUCTURED DATA ONLY.
  13. 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. 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. 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. 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. 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. 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. 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. 20. Digital Advertising Spend including SEM projected through 2020
  21. 21. Connecting the Dots: DemandMetric Website Program Maturity Assessment
  22. 22. 2. SEO/SEM
  23. 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. 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. 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. 26. Comparing Organic and Paid Search
  27. 27. The Building Blocks of SEO The factors above impact how content is ranked in Search Engines.
  28. 28. Connecting the Dots: DemandMetric SEO Maturity Assessment
  29. 29. 3. Programmatic
  30. 30. Programmatic is emerging as the dominant form of Corporate Paid Digital Advertising
  31. 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. 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. 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. 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. 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. 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. 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. 38. 4. Web Optimization
  39. 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. 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. 41. 5. First, Second and Third Party Data
  42. 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. 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. 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. 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. 46. As there are many Third-Party Market Research Tools/Platforms, first determine your basic needs and findings …..
  47. 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. 48. 6. Web Analytics
  49. 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. 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. 51. Web Analytics Discovery and Instrumentation process
  52. 52. It is necessary to understand how these WA platforms work, otherwise the will not work optimally for us
  53. 53. This process gets repeated, over and over again
  54. 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. 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. 56. KPIs help stakeholders understand and act on the activities generated by the visitor to their websites
  57. 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. 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. 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. 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. 61. Segmentation Examples (using Adobe Analytics)
  62. 62. The Best Use Case for Web Analytics is UX (Web Usability/Design) and WA is only able to measure the last step.
  63. 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. 64. Followed by an Automation Assessment – since the purpose of Websites is to automate and scale marketing activities and customer touchpoints……
  65. 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. 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. 67. 7.Third Party Data Platforms
  68. 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. 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. 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. 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. 72. 8. Social Media Analytics and Content Marketing
  73. 73. Most people in industrialized societies have at least one active social media account
  74. 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. 75. Social Media Analytics vs. Traditional Business Analytics -Insights from Social Media are of a different nature from Business Marketing Analytics
  76. 76. The study of Social Media Analytics began in 2003 but public awareness of it took another 5 years (~2008)
  77. 77. In the book there are 7 layers of Data of Social Media Analytics which can be examined individually, then combined for insights.
  78. 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. 79. Platforms / Layers used for Social Media Analytics
  80. 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. 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. 82. 9.Text Analytics and Algorithmic curation
  83. 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. 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. 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. 86. Text Analytics is used for the various text mining algorithmic operations
  87. 87. Most Text Analytics Operations require ETL (Extract / Transfer / Load) processing
  88. 88. Text Analytics Computational Operations Turn text into numerical data allows analysts and researchers to run mathematical and statistical operations on the data.
  89. 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. 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. 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. 92. 10. Mobile Analytics
  93. 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. 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. 95. Use the Mobile Application Type Assessment to determine what type of Mobile App to create.
  96. 96. Finally, access how adapt the company is by using the Mobile Marketing Maturity (soft) Assessment.
  97. 97. 11. Geolocation Analytics
  98. 98. Marketers now prefer GeoData over other types of data, when they can get it
  99. 99. Geo data is collected directly from mobile devices
  100. 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. 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. 102. 12. Integrating Digital Marketing Analytics with Business Analytics
  103. 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. 104. Aligning analytics with business objectives
  105. 105. Social Media Analytics Alignment Matrix
  106. 106. The Alignment Strategy Worksheet
  107. 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.