data, insight, & action - university of utah is 6482 data mining jan 2015
TRANSCRIPT
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May 3, 2023
Data, Insight, & Action
Richard Sgro, @SoSgro
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Agenda Introduction
– Me, Localytics The role of big data & the data scientist Stages (early, growth, mature)
– Challenges
– Important segments & funnels
– How data helps
– Stories of note Where the world is headed Closing thoughts
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Introductions
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In 3 Pictures
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Our mission is to help our customers build great relationships with their app users
5,000C O M P A N I E S
25,000A P P S
1.5 billionD E V I C E S
50 billionM O N T H L Y D A T A P O I N T S
May 3, 2023
Localytics
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Customer Growth
2008 2009 2010 2011 2012 2013 20140
500
1000
1500
20002500
30003500
40004500
5000
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The Role of Big DataAnd the Data Scientist
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“The Information Age
Information is the resolution of uncertainty
- Claude ShannonMathematician, engineer, and
cryptographer
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Role of the Data Scientist Navigator & explorer Trusted advisor Disinterested third party Translator
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Early Stage Business
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Early Stage Company size
– 1 - 10 Funding level
– Seed Number of active users
– Up to a thousands Role of the data scientist
– Technical co-founder
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Early Stage Challenges Scarcity of resources & focus
– Got 99 problems
– But can only solve 1 Product-market fit
– Is there something here? Economics
– What (if anything) to charge
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Important Segments & Funnels Segments
– Users, paying users
– Deeply engaged users Funnels
– Sign-up
– Purchase
– Engagement
– Social
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How Data Helps Funding, funding, funding
– MAU, DAU Projections
– More == better Which problems to solve now
– And which to solve later Economics
– How much is there?
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Stories of Note Voxer
– Get the MVP out
– Iterate quickly Hipmunk
– Acquisition ROI
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Growth Stage Business
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Growth Company size
– 10 to 100 Funding level
– Series A/B Number of active users
– Up to hundreds of thousands Role of the data scientist
– Growth hacker, growth specialist, CTO, business analyst
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Growth Stage Challenges Hockey stick growth
– “Right” users Who are our most valuable users?
– Where do they come from?
– How do we get more? Valuation++
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Important Segments & Funnels Segments
– Whales vs. the rest
Funnels– More whales!
– New functionality
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How Data Helps CAC, LTV, and retention
– What *not* to do
– Where to engage
– Keep them coming back Who’s helps us get the most buzz?
– K-factor Optimize all the things
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Stories of Note Facebook
– Less than 10 friends
– More than 10 friends Snapchat, Whisper, Secret
– Get to 10mm users
– Figure out the money later Humin
– 1 new user = ??? VC dollars
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Mature Stage Business
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Mature Company size
– 100+ Funding level
– Series C and beyond Number of active users
– More than hundreds of thousands Role of the data scientist
– Data scientist, modeling expert, analyst
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Mature Stage Challenges BIG data
– Disparate sources
– Different teams How to keep engagement high across the brand
– Diversify solutions
– Increase marketing spend
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Important Segments & Funnels Segments
– Micro segments
– Users likely / unlikely to…
Funnels– Cross app promotion
– Web to mobile to tablet
– Social to purchase
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How Data Helps To fork or not to fork
– Lots of depth
– Point solutions Customer acquisition
– Healthy growth & retention
– Manage the cost
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Stories of Note Box
– 1.00 in revenue
– 1.75 in costs Snapchat & Tinder (breadth)
– Send money, moments Facebook, LinkedIn (depth)
– Messenger, Connect
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The Future.
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Where We’re Headed Tension between tooling & accessibility (Easily) taking action on the data Getting to why Predicting
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Closing Thoughts
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“Conway’s Law
Any organization that designs a system (defined broadly) will produce a design whose structure is
a copy of the organization's communication structure
- Melvin ConwayHow Do Committees Invent?
1968 National Symposium on Modular Programming
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Let’s Go to Work Key skills
– Getting to why (with some degree of certainty)
– Talking to your grandmother Hiring process
– Varied. Expect tech and non-tech questions General advice
– Technology is a means to an end
– The problem remains
– Passion, excitement, well-roundedness
– It’s who you know (and what you know)
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