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Fear the fRaNkEnSTaCk! How to automate, orchestrate & personalise your entire customer lifecycle

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How and why your tools, teams, and data can work together to scale, automate and personalise your entire customer journey.

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Fear the fRaNkEnSTaCk! How to automate, orchestrate & personalise your entire customer lifecycle

  1. 1. Fear the fRaNkenSTaCk How to Automate, Orchestrate & Personalise Your Entire Customer Lifecycle Ed Fry @hull
  2. 2. SaaStock attendees have an average of 12 tools That’s excluding tools out of the page. Your CRM. Your database. Server side tech.
  3. 3. MY GOAL This will make you think. Yeah, it’s right after lunch. But I’m intolerant to boring talks too. I work at Hull. We help SaaS startups manage their customer data chaos. It fascinates me. I have 114 slides, tons to tell, and just twenty minutes. Let’s go!
  4. 4. We’ve had customer data for a while. This was the first ever Salesforce website.
  5. 5. We’ve had Salesforce since 1999. We’ve had Salesforce since 1999, Mailchimp since 2001, HubSpot since 2005…
  6. 6. What’s new about customer data? Why is customer data suddenly a “hot topic” for SaaStock? What’s changing?
  7. 7. Three trends b r e a k your data (because of three little lies.) There are three trends which are taking your customer data and breaking it apart. And the force behind these trends are how we are working differently. And how we lie to ourselves. So let’s unpack these three little lies...
  8. 8. THE FIRST LIE “We’re customer centric” Of course, every founder, investor, and team want to be “customer centric”. However...
  9. 9. Who has a view over the whole customer journey? Do you know your customers? Everything about them from their first website visit through years of subscription?
  10. 10. When your startups was founded, you probably knew every customer - like a small, local cafe or bar. Your CRM was your brain. But in real life scaling startups, the go-to-market teams aren’t organised this way. FOUNDERS SALES + MARKETING
  11. 11. MARKETING SALES CUSTOMER SUCCESS When you hire in a startup, you hire specialists. Marketing. Sales. Customer Success. But each of these see only part of the entire customer journey. The full story gets scattered across these separate, specialist teams.
  12. 12. MARKETING SALES CUSTOMER SUCCESS Each of these teams builds their own support function to scale and optimise their processes. But they build silos. MARKETING OPS SALES OPS SUCCESS OPS Your teams are siloed.
  13. 13. MARKETING SALES CUSTOMER SUCCESS Instead of working together, they lasso together the data and tools they need. There’s no bigger picture. There’s no strategy. They’re what some teams call “cowboys”. (I realise that’s sexist. Everyone can try and lasso data.) MARKETING OPS SALES OPS SUCCESS OPS Each team only looks after their own.
  14. 14. “Cowboys in the Wild West!” This is my favourite expression I’ve heard for explaining data governance. You are not customer centric. Your go-to-market operation is team-centric. How do you re-align siloed, specialist teams? You are team centric. (not customer centric)
  15. 15. MARKETING SALES CUSTOMER SUCCESS Revenue operations combines sales and marketing operations. It aligns teams for landing new deals and accounts. But, it still leaves all the rest of the customer journey out of the picture after the first purchase. REVENUE OPERATIONS SUCCESS OPS Some teams run revenue operations
  16. 16. MARKETING SALES CUSTOMER SUCCESS How do you optimise for the accounts that are easiest to grow, have the lowest churn, and maximise lifetime value if customer success is out of the picture? REVENUE OPERATIONS SUCCESS OPS Some teams run revenue operations
  17. 17. MARKETING SALES CUSTOMER SUCCESS Customer operations provides the intelligence across the entire customer lifecycle. It optimises for the best-fit accounts and lifetime value at every interaction, across every team and tool, over the entire customer journey. CUSTOMER OPERATIONS Break every silo with customer operations
  18. 18. Customer Ops provides the software and metrics for the entirety of the customer journey. “ Tomasz Tunguz at Redpoint has talked about customer operations before. Instead of individual teams optimising for individual team metrics, optimising for the whole company’s metrics like customer lifetime value.
  19. 19. Customer Ops provides the software and metrics for the entirety of the customer journey. “ This is the important bit - to understand the whole customer journey. It is the job of customer operations to build that complete picture from all the data for every team and tool. So let’s talk about tools.
  20. 20. THE SECOND LIE “We have a working marketing stack” Teams have the feeling that every tool in their arsenal is deliberately chosen, functioning well, and providing value.
  21. 21. You have abundant choice. It’s like a pharmacy. Marketers can choose from a bewildering array different tools to cure all manner of ills.
  22. 22. In the past six years, we’ve seen massive growth in the number of these tools - from 150 to over 5000 since 2011. 150 to 5000 tools in six years
  23. 23. We live in the age of the Martech 5000 This year, Scott Brinker unveiled the Martech 5000 supergraphic. How does this make you feel?
  24. 24. It's no wonder "Actionable and Valuable Data" has become such a high concern for marketers. How can you know what to act on when you are drowning in this mess? “ I shared the Martech 5000 Supergraphic on LinkedIn. I found others were similarly alarmed...
  25. 25. That picture makes my head hurt. “ This response was my personal favourite...
  26. 26. Marketers hoard tools. (And they don’t use them together) Teams can hoard tools faster than ever, without thinking how their system of tools is supposed to work together. Like overlapping live chat, syncing email unsubscribes across 5 email tools, scattered leads management etc...
  27. 27. Data is fuel Invest more in the engine. “ Scott Brinker at Chief Martec says it’s like assembling an engine. But who knows how to do that? It reminds me to the haphazard engines strapped to long-tail boats in Thailand. It “works” but looks like it could break anytime!
  28. 28. Duct-tape does NOT save you. By the time we realise it’s a problem, we’re reaching for duct-tape #cowboys. But, your crazy spreadsheets, hacks, and scripts to clean and stick data together isn’t 100% reliable. You waste a ton of time wrestling with crap data.
  29. 29. Duct-tape not working? Throw dollars instead. As you scale, and wasted time starts to cost you, you’ll start throwing real money at it instead. An assortment of “all-in-one” platforms, real engineering hours on integrations, and a customer data warehouse that’s never in sync.
  30. 30. You have a fRaNkenSTaCk The result is a frankenstein-style abomination. A haphazard combination of tools, siloed data, and frustrated teams. Teams who don’t have the data they need in the tools they use. Teams can’t do their jobs effectively.
  31. 31. Just imagine if your finance team acted like this! It would be unthinkable to have your payroll broken, your investment be the wrong number or your billing system to duplicate and fail. If finance aren’t allowed to get away with this, then why should you?
  32. 32. You need something to organise your tools Instead of a complete mess of “everyone and everything trying to control everyone and everything else” you need a way to organise your tools and data flows. There’s something missing in the middle. Something missing here?
  33. 33. Think of a “hub-and-spoke” integration network Instead of building integrations between everything (n permutations), build one set of integrations between a central hub for customer data. Your CRM, a customer data warehouse, or a real-time customer data platform.
  34. 34. There is no “single” source of truth. There should be no “single” source of truth. Every tool & team should get the “truth”, the whole truth & nothing but the truth, fed from a central hub with data they can trust. You get the truth. You get the truth. You all get the truth!
  35. 35. What makes a system of intelligence valuable is that it typically crosses multiple data sets, multiple systems of record. “ This takes thinking about a system, not just many individual tools. Jerry Chen at Greylock has talked about “systems of intelligence” before.
  36. 36. What makes a system of intelligence valuable is that it typically crosses multiple data sets, multiple systems of record. “ And this is the key point. That a system of intelligence can cross multiple sets of your customer data.
  37. 37. THE THIRD LIE “Our data is usable” We’ve discussed the cross-functional “customer operations” team to break siloes & draw alignment, & centralised “system of intelligence” to sync tools together. But what about the data itself? Your data is ready to use, right?
  38. 38. Your data is messy. You’ve duplicate contacts. Form submissions are incomplete. Your analytics tracking is broken. Someone keeps “dumping” data in your CRM. Someone else set everything up perfectly, then left. Your data is FUBAR.
  39. 39. No one tool can digest all your data. Even if all your data was perfectly clean, it wouldn’t fit in every tool. Analytics. CRMs. Databases. Email tools. Ad networks. All have different ways of storing and using data. You have to compensate for each tools inadequacies.
  40. 40. Personalisation is a data problem. (Not a marketing problem) But we have to digest more data than ever. Personalisation is how you cut through noise. But you need to have data on each person and account to be personal. I’ve a 30 minute deck you can view here.
  41. 41. Data keeps pilingup! You can’t just “sort it later” because data keeps piling up. More tools. More accounts. More complexity. More duct-tape. The mess is getting more unusable.
  42. 42. How is your “engine” doing? Back to Scott Brinker’s analogy earlier, what happens when you feed this mess into your engine?
  43. 43. You have data puke. You don’t have “fuel”. You have data puke. Your system of tools cannot digest all this messy data. And without immediately useful data, none of your tools or teams can piece together the full picture of every customer.
  44. 44. From a customized data puke to getting insights that drive action which will have a business impact. “ I’m not the first to talk about “data puke” at all. Avinash Kaushik notably talks about data puke vs. insights in comparing web reporting vs web analysis. One is a chart (sometimes a pretty chart). The other is useful insight.
  45. 45. From a customized data puke to getting insights that drive action which will have a business impact. “ He looks at it from an analytics perspective. How can we find “insights”. But to scale, automate, and personalise, we need to create rules, processes, and actions with data - we want to make our data actionable. How?
  46. 46. First, combine your customer data in one place. Combine your data into one place. Like with integrations, most SaaS companies we’ve talked to choose to combine their data into one of three places - a CRM, a data warehouse, or a customer data platform.
  47. 47. Next, connect data about each person Once you have all your data in one place, you need to match up the different bits of the customer journey from each tool using email addresses and IDs. You want the “full story” for each person and account - a master profile.
  48. 48. Raw data is not immediately useful But your data isn’t always ready-to-use in all of your tools. It needs processing. Turning into something else first...
  49. 49. Compute new data before syncing. Data cleansing. Data enrichment. Segmentation. Scoring. Transformation. Qualifying. All computations and actions that need to happen before data is useful in all your end tools.
  50. 50. Now sync ready-to-use data to your tools Now you can feed the beast. Immediately useful, actionable, clean, digestible data to all your tools.
  51. 51. How do you use this together? With “customer operations” you have aligned teams. With a “system of intelligence” you have a network of tools. With clean, centralised, ready-to-use customer data… what can we do with it?
  52. 52. Don’t use this as a mental model. The problem with customer data is there’s a lot of moving parts. It’s not just about aligning sales and marketing. It’s not just about integrating Intercom and Salesforce. It’s about the whole system of tools, teams, and data.
  53. 53. Welcome to my brain. How do you make your tools, teams and data work all together? Some call it orchestration. I want to share a simple model for thinking about customer data orchestration that you can take away and use
  54. 54. Orchestration has four core actions.
  55. 55. Send a message Messaging ➔ Email ➔ Ad ➔ Web page ➔ Phone call ➔ Meeting with a sales rep
  56. 56. Track reactions Tracking Messaging ➔ Email open ➔ Ad clicked ➔ Web page viewed ➔ Meeting held
  57. 57. Record to a profile Messaging Profile Tracking ➔ via integrations ➔ CRM ➔ Marketing automation ➔ Analytics ➔ Backend database
  58. 58. Decide actions Messaging TrackingAction Profile➔ via integrations ➔ Trigger drip emails ➔ Sync qualified leads ➔ Enroll in ad audience ➔ Schedule meeting
  59. 59. Send more messages Messaging Tracking Profile Action ➔ Email ➔ Ad ➔ Web page ➔ Phone call ➔ Meeting with a sales rep
  60. 60. This forms the a closed loop. It is actionable! And we can react! Action Messaging Tracking Profile
  61. 61. Not all data is ready-to-use. Messaging TrackingAction Profile➔ Gasp! “Data puke” ➔ Can’t sync data as is ➔ Wrong format ➔ Missing data ➔ Needs processing
  62. 62. So it needs computation. Messaging TrackingAction Compute ➔ Cleanse ➔ Enrich ➔ Segment ➔ Score ➔ Transform ➔ Qualify Profile
  63. 63. Sync it back to the profile. Action Profile Compute ➔ Cleansed ➔ Enriched ➔ Segmented ➔ Scored ➔ Transformed ➔ Qualified Tracking Messaging
  64. 64. Use clean data everywhere. Profile➔ Messaging, email, live chat ➔ CRM ➔ Ads ➔ Website ➔ In-product ➔ Analytics & intelligence Action Messaging Tracking Compute
  65. 65. Use the customer stack to explain orchestration as closed loops. Profile Compute Tracking Messaging Action
  66. 66. Let’s look at three orchestrations... I want to walk you through three examples of orchestrations with the closed loop.
  67. 67. RECIPE #1 Personalised onboarding This is familiar to most of you. Onboarding is key to activation, and personalisation helps us deliver the most relevant message.
  68. 68. Send better onboarding messages by job title and user actions Action Compute Messaging Tracking Profile
  69. 69. Get job role Action Tracking Messaging Profile Compute ➔ Signup form ➔ Data enrichment
  70. 70. Get user actions Action Compute Messaging Tracking Profile➔ Analytics tools ➔ Product database
  71. 71. ➔ Job title ➔ Actions taken Messaging Action Tracking Compute Profile Compute segments
  72. 72. Sync segments to messaging tools Messaging Profile Action Tracking Compute ➔ Email ➔ Live chat ➔ Website audiences ➔ In-app messages
  73. 73. Trigger and send messages to each segment Messaging Profile Action Tracking Compute
  74. 74. React to user actions and update our personalised campaigns. Profile Action Tracking Compute Messaging
  75. 75. RECIPE #2 Product qualified leads Many startups, including many at SaaStock, have a free trial based sales model. Whilst this is great for getting signups, it’s hard for sales to turn a firehose of (often junk) leads into actionable sales leads. Enter PQLs...
  76. 76. PQL outreach grew account value by 30% Using product usage data to direct sales to the right accounts and cue them with what to say, DigitalOcean this drove an average 30% increase in account value across a cohort of their 500,000 customer accounts
  77. 77. Product qualified leads eliminated 30% of leads from Appcues’ sales funnel You don’t need to be DigitalOcean’s size and scale. Working with Appcues, we cut 30% of their trial leads from sales funnel, whilst also giving sales the insights and reasoning how the account was getting value for outreach.
  78. 78. Messaging Tracking Compute Profile Action Can sales say something better than “let’s have a call”.
  79. 79. Use product usage data to inform sales who is getting value. Compute Profile Action Messaging Tracking ➔ Analytics tools ➔ Product database
  80. 80. But “streams” of product usage data are not easy to sync straight to CRMs. Compute Profile Action Messaging Tracking
  81. 81. ➔ Every action ➔ For every person ➔ In every account Compute Profile Action And it’s overwhelming to sales reps. Messaging Tracking
  82. 82. Transform “streams” of user actions into a CRM-friendly, rep-friendly insights. Action ➔ projects_created = 24 ➔ last_active = 2 days ago ➔ trial_limits_modal_viewed = TRUE Profile Tracking Messaging Compute
  83. 83. Enrich profiles with company data too. Messaging Action Compute ➔ employee_count = 500 ➔ industry = software Profile Tracking
  84. 84. Create a real-time segment of product qualified leads Messaging Action Compute Profile➔ Latest actions ➔ Updated segments Tracking
  85. 85. Sync product qualified leads to your sales CRM. Action Profile Tracking Compute Messaging ➔ New PQLs added ➔ Fading PQLs removed
  86. 86. Have reps engage each user based on the actions they’ve just taken Action Profile Tracking Compute Messaging
  87. 87. Action Profile Tracking Compute Messaging Continue to engage qualified accounts ➔ Automated insights for reps ➔ Proactive messages ➔ Personalised to user actions
  88. 88. RECIPE #3 The Reveal Loop The Reveal Loop is a powerful automation for building sales pipeline in B2B.
  89. 89. RECIPE #3 aka. “10x account-based marketing” It works by identifying and engaging best-fit accounts at scale. Some called it “10X account-based marketing”
  90. 90. Add $10,000 per day net new revenue We’ve worked with a number of customers implementing this. One of our best results was with our partner Segment who made this their third highest source of sales opportunities within three weeks with just one channel.
  91. 91. Most people (95%?) never give their email address after visiting your website. Tracking Compute Profile Action Messaging
  92. 92. So how can we re-engage them? Tracking Compute Profile Action Messaging
  93. 93. Messaging Action Compute Profile Tracking But we do know they visited our website
  94. 94. Messaging Compute Profile Tracking You’ve already got retargeting ads... Action
  95. 95. Messaging Compute Profile Tracking But can we send something more engaging than an ad? Action
  96. 96. Use Reverse IP lookup to identify anonymous website traffic. Action Profile Compute Tracking Messaging ➔ Send IP address ➔ Return company domain name
  97. 97. Don’t engage every account that visited. Profile Messaging Tracking Compute ➔ Customers ➔ Existing sales opportunities ➔ Partners ➔ Competitors Action
  98. 98. First, match against your CRM records or user database. Profile Messaging Tracking Compute ➔ Customers ➔ Existing sales opportunities ➔ Partners ➔ Competitors Action
  99. 99. Qualify new accounts that are eligible. Action Profile Messaging Compute Tracking
  100. 100. Then, prospect for contacts amongst qualified accounts Action Profile➔ Job role ➔ Seniority ➔ Contact details available Messaging Compute Tracking
  101. 101. Create a real-time segment of “revealed prospects” Action Profile Messaging Compute Tracking ➔ Every stakeholder associated with each “revealed” account ➔ Account are eligible for outreach ➔ Account is qualified ➔ Target job role to talk to
  102. 102. Sync the real-time segment of “revealed prospects” to all your tools Messaging Profile Action Tracking Compute ➔ Sales CRM ➔ Sales email cadences ➔ Website audiences ➔ Ad audiences ➔ Live chat
  103. 103. Now you can re-engage best fit accounts across multiple channels Messaging Profile Action Tracking Compute ➔ Retargeting ads = 1 person x 1 channel ➔ Reveal Loop = many people x many channels
  104. 104. And you can react to the entire account’s engagement. Profile Action Tracking Compute ➔ Ads clicked ➔ Website visits ➔ Email opens and clicks ➔ Live chat conversations ➔ Meetings booked and held Messaging
  105. 105. RECIPE X Total Orchestration To wrap up, I want to get you thinking of your own recipes.
  106. 106. Orchestration is like using lego blocks. Think how different pieces can plug together.
  107. 107. Think how you can (re)use all of these pieces ➔ Reverse IP lookup ➔ Live chat ➔ Data enrichment ➔ Sales CRM ➔ Product qualified leads ➔ Sales engagement notifications ➔ Prospecting for related contacts ➔ Automated drip emails ➔ Ad audiences ➔ Transform events streams into properties ➔ 10X retargeting ➔ The Customer Stack ➔ Analytics tools ➔ Backend database ➔ Marketing automation ➔ De-duplicate accounts ➔ Ad platforms ➔ Calendar views ➔ Meetings held ➔ 10X account-based marketing
  108. 108. You can do this! (You just need to be a bit creative) It takes a mindset to make it work, and to solve for your tools, teams, and data - not just individual challenges.
  109. 109. What works for startups like yours? Let’s meet 1:1 at SaaStock! hull.io/get/saastock Whilst I’m here in Dublin, I’d love to help you guys work through different orchestrations that might work for your startup. I’ve a Calendly link above to book a time. But in case we don’t get time, here’s the high level principles...
  110. 110. Where is your data? CRMs. Backend databases. Analytics tools. Marketing automation. Data enrichment. Ad audiences...
  111. 111. How do you need to transform your data? Cleansing. Enriching. Segment. Scoring. Transforming. Qualify. Make data useful for your end tools
  112. 112. Where can you engage leads and customers? Email. Ads. Live chat. Sales meetings. Website. Snail mail...
  113. 113. Get the slides, Our customer data academy, (and book a meeting here at SaaStock) hull.io/get/saastock You can get the slides, our customer data academy, and grab a slot to talk about your three orchestrations through the link above - hull.io/get/saastock.
  114. 114. Get the slides, Our customer data academy, and three orchestrations hull.io/get/saastock Questions? I’m @edfryed ed@hull.io
  115. 115. See how Hull works Or share this slideshare

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