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Data-Driven Growth: Lies, Lawyers & Outsized Results

This is not your everyday data talk.

Through working deep inside the fastest growing SaaS startups in our space, we’ve studied the patterns, methods, and models for driving outsized results. The one common thread? How they use their data.

(How else would you grow from one marketer through to a $60M+ Series B just 12 months later?)

How do they make their data accessible, draw the right insights, set effective goals, prioritise and optimise processes, and automate ALL the (right) things.

So brace yourselves: we’re going to be navigating through AI, automation, “moving the needle”, and a minefield of other buzzwords to try to make sense of using your data for growth. But you’ll leave this talk with a simple framework and set of questions you can take and use right away.

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Data-Driven Growth: Lies, Lawyers & Outsized Results

  1. 1. Data-Driven Growth Ed Fry @edfryed
  2. 2. Life in marketing started with crappy adsense websites...
  3. 3. Pitched Distilled on a repeated Summer gigs. Learnt how SEO actually works. Wound up marketing their consulting, training & conferences.
  4. 4. Through Distilled I learned to love conferences. People + ideas + drinks. Drunk-pitched Rand a jobs board for his new “” community
  5. 5. $500/month. $12k “forever” budget. GM @ for Rand & Dharmesh. (both busy with Moz & HubSpot) but it grew beyond a side-project...
  6. 6. VERY orange. Scaling international, freemium sales products & lawyers... HubSpot acquired about 12 months before they went public. Interesting times scaling intl., freemium sales & lawyers...
  7. 7. Grew a remote team from Bucharest to Seattle and built the community to over 150,000 members. Focus for me became engagement & retention.
  8. 8. Focused on personalisation. Use all our tools, teams & data to connect our members with meaningful, useful content. Started sharing what was Got to know more marketers #ConferenceDrinking could call in favours. AMAs/Q&A/campaigns to increase engagement. But favours don’t scale.
  9. 9. But every “hack” that could scale reach and engagement marketers would hate. Chrome notifications, twitter bots, automated email #PageFights Fear!
  10. 10. Frontier of failure SCALEABLE AUTHENTIC&ENGAGING PERSONALISATION AT SCALE #data “HALP ME”. spam-o-matic
  11. 11. Lead me to join Hull to help people manage their customer data.
  12. 12. Data ops deep within scaling SaaS startups Sales Marketing CS Customer Data Operations Customer data is the lifeblood of so many teams. But working out whose job this was?
  13. 13. Painful path to product-market fit Definitely for marketers! W hoa, support tickets... “Technical marketers” seem so much better Of course! Data engineers!
  14. 14. n00b SEO Adsense & affiliate filth Strange story, but there’s a common theme in how data was (& wasn’t) used
  15. 15. There are two types of people in the world. Those who live & breathe data, and those who don’t
  16. 16. There’s those who collect, those who understand & those who act on data.
  17. 17. I think I found the pattern... Beyond better analytics/data vis/reporting/other data talks...
  18. 18. Collecting data Collecting data is not hard There’s literally 1000s of tools to help. Ready to plug-and-play.
  19. 19. GA gives everyone access. I remember funnel/geo reports. Real-time analytics during AMAs. But what happened after.
  20. 20. Website Email newsletter We had these plug-and-play tools...
  21. 21. ...but this was just a tiny fraction of our user journey Setup for you Everything else ? ? ? ? ? ? ? ? ? ?
  22. 22. Access to the right dataWhere is your customer journey? Beyond GA, Email… CRM? Backend?
  23. 23. SELECT location, count(location) FROM users GROUP BY location ORDER BY count(location) DESC; CITY NO. USERS London 12345 New York 9876 Boston 8765 Use SQL to understand your customers
  24. 24. Now we could query all of our members behaviour in our database. Could understand best users by cohort, skillset, country...
  25. 25. Questions to ask yourselves on making the right data accessible ➔ Where is your customer journey captured? What data sources make up “God View” ? (Not just plug-and-play GA) ➔ What does you team need to access data? (tools, skills, inputs, logins) ➔ Do they know what data they can access today? And what data they need to provide?
  26. 26. Access does not drive growth Need to draw insights and understand where to focus.
  27. 27. Build models from dataUse data to focus your growth efforts (and avoid dumb decisions)
  28. 28. Economists derive & manipulate equations to understand theory. Can you do the same? Can you model your growth? Model your growth with equations
  29. 29. Growth model at Article Weekly Contributors Weekly Active Users Discussion 0.7x 2.7x 4x For every one additional discussion, we observed 2.7 contributors. For every additional weekly contributor, we observed 4x weekly active (logged in) users.
  30. 30. Plot a “line of best fit” over between any two: 1. People 2. Actions 3. Objects Make an educated guess over what should go on the axis for your model. Find the right things to model Using linear regression to test the relationship between two variables. “Lines of best fit” are easy to make in any spreadsheet tool. Easy to understand graphically.
  31. 31. Our models gave us three hypotheses for growth #focus Discussions > Articles Existing contributors > New contributors ↑ Contributors = ↑ WAU Inbound was never going to succeed on articles alone. Discussions had to be the focus. Conversation is the content. The comment is the conversion (ideally from previous contributors).
  32. 32. Questions to ask yourselves on using data for models ➔ What types of people, actions and objects can you compare? ➔ What is your growth model? ➔ What are the biggest levers in your growth model? #focus
  33. 33. Models do not drive growth You need to get your team onboard. They need a destination.
  34. 34. Goals from dataHow can you use data to align and motivate your team?
  35. 35. “Management by metrics” Pull apart. Clash together. A miracle of alignment.
  36. 36. “The North Star Metric is the single metric that best captures the core value that your product delivers to customers.” ★ NSMs shouldn’t be an outcome (e.g. revenue) or short term.
  37. 37. Assigning objectives behind the North Star Weekly Contributors Weekly Active Users Weekly Discussions North star metric ★ 1500 goal (community team) ↑ Q&A Reactivate contributors This is OKRs (Objectives & Key Results) around your growth model.
  38. 38. Build org structure around your growth model How do objectives sub-divide down: ➔ Channels ➔ Regions ➔ Cross-functional teams Marketing Qualified Leads Inbound Paid Acquisition SponsorshipsSEMSocialSEO
  39. 39. Waterfall charts = The most powerful motivator Plot actual vs. target/past performance cumulatively over time. (Though this example is rate-based)
  40. 40. Management by metrics all together NORTH STAR METRIC Team Objective Individual ObjectiveIndividual Objective Combine all four methods. Derive objectives from your North Star Metric & Growth Equation Use objectives to organise your team structure. Report key results as waterfall charts.
  41. 41. Questions to ask yourselves about using data for goals ➔ What is your company’s North Star Metric? Is it long term & relevant to your entire team? ➔ How does each team and person drive towards this? (Growth model → OKRs → org structure) ➔ How are you reporting progress towards each objective? (Waterfall charts for KPIs)
  42. 42. Goals do not drive growth Need to move beyond theory & understanding your data
  43. 43. Process with dataHow can data decide what you work on day-to-day? </gut-decisions>
  44. 44. There’s no way you can train people who have a bad attitude. It’s just recruitment, redundancy & resignations. Matt Roach, Sanoma @ CXL Live 2018.
  45. 45. Remember this? Weekly Contributors Weekly Active Users North star metric ★ 1500 goal (community team) ↑ Q&A Reactivate contributors We created a repeatable process on the community team to scale Q&A. Weekly Discussions
  46. 46. Find & cleanup easily answered questions
  47. 47. Create a segment of ideal contributors >500 members by timezone, skillset, recent activity etc.
  48. 48. Send an email inviting contributions Plain text, gmail-style with a simple text link CTA
  49. 49. Promote the active threads Active threads are valuable to more contributors & readers
  50. 50. Behind the scenes, we unified all our member data. Enabled our community team to take action quickly. Synced updates from SQL database to HubSpot Created segments & properties for key values Used ready-made segments for Q&A outreach
  51. 51. Oz Content built a lead nurturing process 20X increase in MQLs vs. previous “catch-all” nurture. Unify and enrich all leads with Clearbit Create niche webinars for best-fit leads Email webinar invites to best-fit leads Mark attendees as MQLs, and pass to sales.
  52. 52. Appcues created a product-qualified leads process 4 New free trials are enriched with Clearbit Sync best-fit trial users to HubSpot CRM Slack notifications sent with buying signals Reps reach out with personalised message
  53. 53. Questions to ask yourselves on using data for process ➔ What are regular processes that are part of your growth? ➔ How does your data inform what you work on? Where do your insights come from? ➔ How can you turn process into a repeatable playbook?
  54. 54. Process does not drive Growth Remove humans to scale to outsized results. Where inbound failed.
  55. 55. </pirate analogies>. To truly grow using your data, you need a different way of thinking.
  56. 56. Rules from dataDefine your decision making upfront. Build automation & process on top.
  57. 57. Pricing is a rule
  58. 58. Sales compensation is a rule Pay % of subscription $ Pay % retained $ Pay % annual committed $
  59. 59. Content modelling is a rule SEO content ➔ Scalable ➔ “Complete” ➔ Authentic
  60. 60. User reviewsHotel features Location Searcher context “solo travellers” “We speak your language!” Proximity Content modelling is a rule
  61. 61. Content modelling for a blog Title Title Title Body Body Body Author Author Author Title Body Author Content models referencing other content models
  62. 62. Content modelling for a landing page Title A Title B Title C CTA CTA CTA Title D CTA Video Feature B Video Feature D Feature A Quotes A VideoFeature C Quotes A Quotes B Quotes B Alt. CTA Scale landing pages by “lego-blocking” content together. Webflow and Instapage can do this, but what about design?
  63. 63. Design = Rules.
  64. 64. Development = Rules.
  65. 65. Content = Rules. Manage content in a headless CMS ➔ Landing pages ➔ Content blocks ➔ Images ➔ Referencing
  66. 66. Content = Rules.Design = Rules. Dev = Rules. Next, integrate: Mechanical Turk MonkeyLearn Clearbit Reveal G2Crowd Alexa … go crazy :)
  67. 67. Use content modelling to scale SEO & landing pages ➔ Quick sales + product content ➔ Scalable SEO(ish) ➔ 43 unique pages in one week (<3 minutes per page)
  68. 68. Content recommendation Content recommendation rules = Content rules + customer data rules
  69. 69. You’ve collected a ton of customer data
  70. 70. Orchestrate all your customer journeys with three types of rules SEGMENTS Who to talk to TEMPLATES What to say WORKFLOWS When & where to say it
  71. 71. Users invited > 3 times Segments as rules: “Who to talk to” Country United States Demo requested TRUE QUALIFIED LEAD Customer “fit” VERY GOOD
  72. 72. Templates as rules: “What to say” Hi {% if user.first_name != blank %} {{ user.first_name | capitalize }} {% else %} there {% endif %}, {% if user.clearbit_job_role == “marketing” AND user.crystal_disc_scores contains “D” AND user.madkudu_customer_fit_segment contains “high” %}Stop sending generic, send-to-all spam emails {% else %} Website. Email. Chat. Powerful templating like Liquid can transform your messaging. Personalisation (beyond “hi {firstname}!”) & internationalisation.
  73. 73. Templates as rules: “What to say” Intellimize predicts and serves best converting variations of a page using known data about the website visitor and content’s conversion.
  74. 74. #demo_requests Hana Abaza Head of Marketing Toronto Fit: Very Good Signals: Data Warehouse, Salesforce, $SHOP Matching tech: 8 Blog Reader for 2 months Interested in data warehousing Viewed pricing 3 times this week Templates as rules: “What to say” internally 1 Tech stack Website Scoring
  75. 75. Workflows as rules: “When & where to say it”
  76. 76. Control the complexity in your workflows New user? END Profile complete? Drip BDrip A New user? Profile complete?
  77. 77. New user? END Profile complete? Connected Twitter? Connected Twitter? New user? Profile complete? Drip A Drip B Drip C Drip D Connected Twitter? Control the complexity in your workflows
  78. 78. New user? Profile complete? Twitter connected? Location added? Location inferred? Gender? Interests added? Skills Has a blog? Job seeker? Job searched? Job applied? Article submitted? Commented? Bio completed? Has any badges? HubSpot connected? Recently active? Opens email? Follows @Inboundorg Power user? Upvoted? Subscribed daily? Subscribed weekly? Control the complexity in your workflows Epic failKeep it simple, stooopid
  79. 79.
  80. 80. Control your complexity Unify Transform Sync … then let templating do the work
  81. 81. Explore customer journey orchestration bottom up LEAD MANAGEMENT INTENT MANAGEMENT ALIGN
  82. 82. First, align sales and marketing (teams own tools own data) Qualified leads SLA Talking points for reps Data for lead assignment Follow up SLA Share sales activity Feedback & attribution SALES “source of truth” MARKETING “source of truth”
  83. 83. React to leads engagement omnichannel Unified Lead Profile Track reactions → Update segments Meetings Chat SMS Mail Social Email Website Phone Product Actions, tracking, and reactions.
  84. 84. Manage intent data (in B2B) Market to teams who show interest before signup.
  85. 85. 1. Reveal companies visiting your website
  86. 86. 2. Create a segment of best-fit visitors
  87. 87. 3. Create prospecting rules (creates profiles)
  88. 88. 4. Sync prospects to engagement channels (and send hyper-personalised messages)
  89. 89. ROAS > 7.5. 6% lead-to-customer. Another >$10k/day with their SDR team. Both growth teams of ONE. Both driving outsized rules-based growth
  90. 90. Questions to ask yourselves on using data for rules ➔ What are the decision making moments in your growth process? Can you create rules for them? ➔ What is the ideal data flow for each decision? ➔ How can you unify, transform & sync your lead & customer data?
  91. 91. Model Goals Process Rules Access Goals Process Rules Access Model Process Rules Access Model Goals Rules Access Model Goals Process Access Model Goals Process Rules + + + + = Flying blind = Dumb decisions = Misaligned teams = Chaotic workflow = Resource starved = Data-Driven Growth! + + + + + + + + + + + + + + + + + + + +
  92. 92. Access Access Model Access Model Goals Access Model Goals Process Access Model Goals Process Rules Customer data platform with unified data Two funnel diagrams with conversion metrics Marketing: MQL & PQL pipeline to sales + freemium upsell Quarterly objectives optimizing rules in part of the funnel Automated, personalised lead nurturing & handoff to sales. + + + + + + + + + + As an example demand gen growth team...
  93. 93. “Spotted” on Gists of my personalised emails: 17/05/06/the-worlds-most-valuable-re source-is-no-longer-oil-but-data /set-goals-with-okrs/steps/learn-the -abridged-history-of-OKRs/ Creating “trendlines” / lines of best-fit er/6075154?hl=en&co=GENIE.Platform%3 DDesktop ontent-strategy-building-a-predictiv e-growth-model-for-inboundorg is-a-north-star-metric-b31a8512923f of-email-highs-lows-and-lessons-from -100000-outreach-emails mofu-hyper-segmentation-playbook/ Resources at wth-focused-too-much-on-acquisition pensation-2 ot-10x-content =PLDCBEEAFF5571810B&index=43 nt-modelling-basics/ t-components ycle/ ng-ads/ on-without-integrations/
  94. 94. Terviseks! Ed Fry @edfryed