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Business analytics driven development Dmitry Salahutdinov metrics impact 1

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Hey, my name is Dmitry + 2

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AMPLIFR !3

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Part 1: Introduce
 Business analytics !4

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metrics ↗ “Trial to Payment” conversion ↘ “Paid users outflow” !5

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Advanced analytics Payment funnel Trial Retention !6

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Numbers are the only one truth Measure The only One Truth! Everyone in your team has own background and insights Business analytics works both: for developer & product owner !7

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Example: Stick header & footer Average user screen height is ~600px !8

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Example: Publication fails Error rates correlates with users outflow Scheduled Posts User outflow Let developers earn money by increasing code quality: Do refactoring legacy code and give back technical debt ☺ Failed !9

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Part 2: requirements !10

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Tracking events !11

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Show graphical data, compare Data visualisation Scheduled Post Created Draft !12

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Metadata Event metadata stores “as is” User metadata associates within current event Pass extra user & event data to analytics !13

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Metadata usage Scheduled Post
 by user having Billing Plan “A” Scheduled Post
 by user having Billing Plan “B” !14

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Multiple platroms support Mobile application Web(frontend/backend) !15

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Track releases Release:
 feature deployment Release:
 feature improvement !16

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Part 3: Instrumentation ⚒ !17

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Amplitude !18

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10 millions events for free !19

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Amplitude API !20

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Backend integration Ruby sample Node JS Mobile !21

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Frontend integration !22

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Share user properties Render “backend” User properties Pass actual user properties With events !23

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Part 4: empirical tips !24

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verb + noun (e.g. 'clicked signup’) noun + verb (e.g. 'signup clicked') Naming Scheduled Post Draft created Save post saved Fail to post ... ... ✅ ❌ Event naming convention prevent entropy !25

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Separate environments To keep experiments pure and prevent testing events mixing Overall data Testing data Very significant for low traffic experiments! !26

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Organise events & properties And keep it simple !27

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Part 5: Use cases !28

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Existing feature analysis Start to collect metrics - measure feature performance - make a decision: improve or remove Measure business performance metrics before !29

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New feature investigating/testing Start to collect metrics New feature deployment Ensure to have previous and next metrics collected !30

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Incremental improvements Do small experiments with performance analysis !31

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Part 6: workflow - For getting business impact !32

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Success steps •Collect product metrics with analytics that is easy to use and accessible to everyone. •Analyze feature performance and user interaction with the product •Build predictions and hypotheses of features to increase key/financial application metrics •Monitor metrics changes after releases, repeat successful ones, increase expertise !33

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Necessary conditions ✅ Ensure you have enough analytics before & after ✅ Traffic vs time ✅ Prevent hypothesis interception ✅ Overhead for small experiments !34

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Part 5: Amplifr stories !35

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Green errors 6%↗ post popup cancellation !36

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Settings => Subscription ~20% Change Project Name => Activate Subscription Let just force people to change project name and earn !37

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Collaboration feature visibility Unexpected ideas could work ↗ feature visibility with ugly ↗ adding another user ↗ billing plan 4 !38

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For developers? get deeper to a business process essence 5 to increase developer culture (awareness of feature benefits) to reduce communication blockers (distributed teams) to motivate yourself Unique argumentation system to growth experience & expertise Do small experiments with performance analysis !39

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Pet project? !40

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SAINTPRUBY 30% discount coupon ❤ !41

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Thank you ❤ @dsalahutdinov1 @dsalahutdinov https://dev.to/amplifr https://amplifr.com We are looking for passionate Ruby developer ❤ Write us to [email protected] See you! !42