Building your outreach machine

1. BUILDING YOUR OUTREACH MACHINE MICHAEL KING MANAGING DIRECTOR, IPULLRANK @IPULLRANK

2. DOWNLOAD THESE SLIDES http://bit.ly/outreach-machine

3. IPULLRANK.COM @ IPULLRANKfdsjkalgdsj Link Building Outreach is a Scale Problem

4. IPULLRANK.COM @ IPULLRANK It Takes a Great Deal of Effort Dsfdskl;dfas

5. IPULLRANK.COM @ IPULLRANK People That Are Great At it Evolve Out Quickly fdsafdlskfds

6. IPULLRANK.COM @ IPULLRANK So it’s Mostly Done by Junior Staff

7. Google and Clients Leave You Between a Rock and Hard Place Clients want links NOW, but don’t have anything link-worthy. Google wants great content

8. Naturally, I have some thoughts on this..

9. You Don’t Need New Tactics… But if youwant some here goes…

10. IPULLRANK.COM @ IPULLRANK We Only Use Two Outreach Tools Depending onthe type of outreachwe need to dowe use either Pitchbox or BuzzStream. Pitchbox for the more process driven scaledoutreach and BuzzStream for the more relationship drivenPR outreach.

11. IPULLRANK.COM @ IPULLRANK The Value of Pitchbox Pitchbox value comes in the ability to do prospecting, manage outreach and have analytics on outreach projects all in one place.

12. IPULLRANK.COM @ IPULLRANK Form Letters in Pitchbox Pitchbox also allows youto dobuild form letters that are personalized by members the teamwhose specific user role is just to personalize. These can be reviewedbefore they are sent out.

13. IPULLRANK.COM @ IPULLRANK Separation of Concerns This person runs queries and traverses the web identifying websites for outreach. ONE Prospector This person reviews the sites and inputs features for personalization of form letters. TWO Personalizer This person writes form letters, approves initial outreach personalization and responses to responses THREE Outreach Specialist Pitchbox allows you the effectively turn yourlink building effortsinto a processor an assembly linewith veryfew people that specialize.You can effectivelysend thousandsof firsttouch emails per week.

14. IPULLRANK.COM @ IPULLRANK BuzzStream

15. IPULLRANK.COM @ IPULLRANK The BuzzMarker is Fire Using the BuzzStream’s BuzzMarker plugin is super useful for quickly capturing prospects in freeform as well. http://www.buzzstream.com/blog/buzzmarker-for-chrome.html

16. IPULLRANK.COM @ IPULLRANK Tactic – Reach Out About Most Popular Content Use Buzzsumo to identify the most popular post on a domain and use that in your email because it’s natural that someone would reach out to talk about something people are already sharing. http://www.buzzsumo.com

17. IPULLRANK.COM @ IPULLRANK Persona-based Outreach Persona-driven outreach can help you in prospecting and in developing form letters that will perform. Check out this post by Justin Briggs http://outspokenmedia.com/seo/content-based-outreach-for-link -building/ and my Whiteboard Friday http://moz.com/blog/scalable-link-building- using-social-media-whiteboard-friday

18. IPULLRANK.COM @ IPULLRANK Video Outreach with BombBomb Perform video outreach to which has inherent proof that you are an actual person rather than just a spammer. Use Bomb Bomb https://bombbomb.com/

19. IPULLRANK.COM @ IPULLRANK Credit Requests Reach out to anyone that uses your photos or excerpts of your text and request a link back as a credit. Use Copyscape, Google, Google Reverse Image Search

20. IPULLRANK.COM @ IPULLRANK Vendor Case Studies Identify what vendors your site or client is usingwith BuiltWith andreach out to the those companies to doa case study or give a testimonial to get a link back. http://www.builtwith.com

21. IPULLRANK.COM @ IPULLRANK Brand Fans Find link building quick hits by pulling twitter followers using SimplyMeasured or FollowerWonk and comparing themto the link profile with Majestic, OpenSiteExplorer or Ahrefs.

22. IPULLRANK.COM @ IPULLRANK Brand Fan Issues One issue with using OpenSiteExplorerfor this is thatyou can’t exportALL of your links.An issue with the Twitterexportis the shortened links.

23. IPULLRANK.COM @ IPULLRANK Brand Fans Fixes Step 2 Use my simple ExpandURL tool with the SEOTools DownloadString function. Step 1 Download SEOTools for Excel by Neils Bosma http://nielsbosma.se/projects/seotools/ =DownloadString(“http://www.ipullrank.com/tools/expandurl/?u rl=[INSERT SHORTENED URL HERE]”)

24. IPULLRANK.COM @ IPULLRANK You Don‘t Need More Tactics Jon Cooper (@PointBlankSEO) has put together the most robust resource for link buildingtactics out there. Youdon’t need any more tactics. http://pointblankseo.com/link-building-strategies

25. More so than tactics, you need to establish a baseline of requirements and figure out how to scale.

26. IPULLRANK.COM @ IPULLRANK We Say What We Don’t Do

27. IPULLRANK.COM @ IPULLRANK We Specify Requirements Varied and Integrated Tactics Most clients want to pin you down into one or two tactics that live completely outside of their marketing efforts. We demand the opposite and won’t do outreach without strong content. Branded Email Address Outreach on behalf of a brand is always more effective when coming from the a branded email. Visibility into analytics I’ve found that link building only clients generally don’t like to sharetheir analytics. This is unacceptable as we need to determine business impact of our efforts.

28. Outreach and link building are exactly like sales though.

29. IPULLRANK.COM @ IPULLRANK John-Henry Said it Best https://moz.com/blog/link-prospectors-into-lead-generators

30. IPULLRANK.COM @ IPULLRANK We Build a Funnel

31. IPULLRANK.COM @ IPULLRANK We Optimize the Funnel

32. IPULLRANK.COM @ IPULLRANK We Bonus Our Outreach Team Like Salespeople

33. Ultimately though, outreach is a problem of scale.

34. Enter Machine Learning The Core Concepts

35. IPULLRANK.COM @ IPULLRANK They Are Not the Same Thing You often hear these terms conflated by the media

36. Machine learning is all about addressing scale problems.

37. IPULLRANK.COM @ IPULLRANK AI is Comprised of Many Disciplines Deep Learning is a subset of Machine Learning is a subset of Artificial Intelligence. AI many branches of which machine learning is a core branch that we can execute.

38. IPULLRANK.COM @ IPULLRANK Improved Google Translate from Scratch in 2 Months A team rebuilt the broken Google Translate using Machine Learning and within 2 months it was already as good as the version that had taken years to build.

39. Ok. So, What Is Machine Learning? “Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.”

40. It’s Really Just Using Math to Guess and Check

41. IPULLRANK.COM @ IPULLRANK Supervised Learning The machine looks for patterns that match the labeled data that you provide and classifies new data based on that.

42. IPULLRANK.COM @ IPULLRANK Unsupervised Learning The machine identifies patterns in the data and creates clusters based on what it finds.

43. IPULLRANK.COM @ IPULLRANK Reinforcement Learning With reinforcement learning, the model is continually trained based on new data thereby improving the classifier’s ability to perform.

44. IPULLRANK.COM @ IPULLRANK The Machine Learning Process GET & PREPARE YOUR DATA You identifyand clean your dataset in preparationfor solving the machine learning problem CHOOSE YOUR MODEL TRAIN YOUR CLASSIFIER You chose the algorithmor model that you believe will yield the best results then run it in order to trainyour classifier. SCORE AND EVALUATE You score the accuracy and precision of the classifier and test it against other algorithmsto see what performs best. PREDICT OR IDENTIFY OUTCOMES Once you are happy withthe results, you use the classifier moving forwardto make conclusions about new data.

45. IPULLRANK.COM @ IPULLRANK This is an example of how you could predict the demand of cars for a car rental company. It follows the same framework. Car Rental Example

46. IPULLRANK.COM @ IPULLRANK AI Is Gonna Steal Your Job? One of the more common fears of middle America around the idea of artificial intelligence is that robots will replace humans in their jobs.

47. IPULLRANK.COM @ IPULLRANK Obama Had Some Measured Thoughts On His Way Out

48. So what part of this is relevant to outreach?

49. IPULLRANK.COM @ IPULLRANK Supervised or Unsupervised Segmentation

50. IPULLRANK.COM @ IPULLRANK Predictive Modeling

51. IPULLRANK.COM @ IPULLRANK Natural Language Processing

52. IPULLRANK.COM @ IPULLRANK Chatbots

53. IPULLRANK.COM @ IPULLRANK Training Chatbots Training chatbots is similar to training ML classifiers in that you take a knowledge base and run it through NLP then tune it with regard to conversations.

54. Models Typesof Models when you shouldUse Them

55. IPULLRANK.COM @ IPULLRANK There are Tons of Different Models Your best bet is to test and learn.

56. IPULLRANK.COM @ IPULLRANK Seriously Tonnnnnns

57. IPULLRANK.COM @ IPULLRANK The Uses of Each Type Are Difficult to Memorize

58. IPULLRANK.COM @ IPULLRANK Models & Use Cases Random Forest Lead Qualification Logistic Regression Customer Churn Prediction Decision Trees Customer Churn Prediction

59. IPULLRANK.COM @ IPULLRANK Models & Use Cases (Cont’d) Support Vector Machines Text Categorization Apriori Market Basket Analysis (Amazon) Naïve Bayes Sentiment Analysis Recommendation Systems Spam Classification

60. IPULLRANK.COM @ IPULLRANK K-Fold Cross Validation Try out a model and validate it using k-fold cross validation.

61. IPULLRANK.COM @ IPULLRANK K-Fold Cross Validation is a Guess and Check Try out a model and validate it using k-fold cross validation.

62. IPULLRANK.COM @ IPULLRANK How to Choose a Machine Learning Model https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice

63. IPULLRANK.COM @ IPULLRANK

64. How to Use this for Outreach Scaling the unscalable

65. IPULLRANK.COM @ IPULLRANK Sales Software Has Solved Many of Outreach’s Scale Issues

66. IPULLRANK.COM @ IPULLRANK Lead Qualification / Scoring

67. IPULLRANK.COM @ IPULLRANK Close Prediction

68. IPULLRANK.COM @ IPULLRANK Prospecting

69. IPULLRANK.COM @ IPULLRANK Lead Intelligence

70. If outreach is like sales and many sales tools have solved this, how can we apply the same to outreach?

71. IPULLRANK.COM @ IPULLRANKMost machine learning is done inR or Python, but those are programming languages, but you’re marketers so let’s figure out some things youcan use without too muchcoding.

72. IPULLRANK.COM @ IPULLRANK 3 Things Machine Learning Can Help Scale in Outreach Prospecting Overcoming Objections Initial Outreach Research

73. IPULLRANK.COM @ IPULLRANK Orange Canvas is a Visual Machine Learning Tool https://orange.biolab.si

74. IPULLRANK.COM @ IPULLRANK It Allows You to Drag & Drop and Perform Analyses

75. We’re going to perform supervised machine learning to predict which sites are worthwhile prospects based on our previous research.

76. IPULLRANK.COM @ IPULLRANK Collect, Clean and Setup Whatever Data You Have If it’s a prospect you used, mark it as a , if it was disapproved, mark it as a 0. If there are rows that are missing data remove them.

77. IPULLRANK.COM @ IPULLRANK Import Your Data Specify the column where you have marked a site/page as approved as your target attribute. Skip any text- based attributes.

78. IPULLRANK.COM @ IPULLRANK Take Your Prospect Lists and Data https://www.tools4noobs.com/summarize/

79. IPULLRANK.COM @ IPULLRANK Use the Model that Performed Best to Predict

80. IPULLRANK.COM @ IPULLRANK The Output is a Segmented Prospect List Tada!

81. IPULLRANK.COM @ IPULLRANK Research – CrystalKnows This tool is an example of user segmentation based on social media data http://www.crystalknows.com

82. IPULLRANK.COM @ IPULLRANK Research – PeoplePattern People Pattern is an enterprise tool that accomplishes similar goals, but could allow you to segment outreach targets https://www.peoplepattern.com/

83. IPULLRANK.COM @ IPULLRANK Research – Text Summarization Tool https://www.tools4noobs.com/summarize/

84. IPULLRANK.COM @ IPULLRANK Improve My BuzzSumo Tactic Scale the tactic of reading the prospect’s most popular post using text summarization.

85. IPULLRANK.COM @ IPULLRANK Automate Overcoming Objections with an Email Chatbot http://blog.mila.com/en/2016/08/23/how-chatbots-and-ai-create-email-auto-replies-with-intelligent-answers/

86. IPULLRANK.COM @ IPULLRANK Generate your Chatbot with API.AI http://api.ai

87. IPULLRANK.COM @ IPULLRANK Setup your Responses with Key Variables Based on what the user mightsay in their response to you, setup your own responses and use variables so those responses are contextual and logical. The botwill continue to learn based on the emails itgets.

88. IPULLRANK.COM @ IPULLRANK Connect Your Bot to Email Using Zapier You can avoid the coding element by setting up a trigger in Gmail for newemail, connecting to the API with the POSTaction and sending an email once theresponse is returned

89. These are all relatively simple to do, but if you want some help…

90. IPULLRANK.COM @ IPULLRANK Codementor

91. IPULLRANK.COM @ IPULLRANK Experfy

92. IPULLRANK.COM @ IPULLRANK Kaggle

93. Final Thought Machines are not going to replace humans at outreach, but there are certain components of the busywork that we can train it to do wellto help us scale.

94. Wrapping Up Who amI and where am Ifrom?

95. IPULLRANK.COM @ IPULLRANK I’M #ZORASDAD First and foremost.

96. IPULLRANK.COM @ IPULLRANK MY NAME IS MIKE KING Razorfish, Publicis Modem alum Full Stack Developer Full Stack Marketer Moz Associate

97. IPULLRANK.COM @ IPULLRANK I Run a Better Marketing Agency Called iPullRank

98. IPULLRANK.COM @ IPULLRANK We Do These Things Machine Learning SEOContent Strategy Paid Media Measurement & Optimization Marketing Automation

99. THAT’S ALL I’VE GOT

100. IPULLRANK http://ipullrank.com THANK YOU Michael King Managing Director mike@ipullrank.com