אנליטיקס מותאם אישית - כשגוגל אנליטיקס ומיקספאנל כבר לא מספיקים איך לבנות מערכת אנליטיקס מותאמת אישית? גוגל אנליטיקס, מיקספאנל, KISSmetrics... יש היום בשוק הרבה מערכות אנליטיקה אבל לא תמיד הם מתאימות ב-100% לצרכים שלנו בארגון. בהרצאה זו דנ-יה תפרוס בפניכם את האסטרטגיה שלה בכל מה שקשור לבניית מערכת אנליטיקס שמתאימה לצרכים שלכם. בהרצאה תלמדו מהם היתרונות, החסרונות, המגבלות ואפילו האמונות הטפלות שיש לאנשים לגבי מערכת אנליטיקס מותאמת אישית, ולבסוף תדעו מה עליכם לעשות כדי לבנות כזו מערכת עבור הארגון שלכם.

1. Custom Analytics Stack
What is it and who is it for?

2. Who are you?
Show of hands

4. Company data evolution - identify yourself
MVP - Asking the first question
Tactic driven - Asking several questions, by several different teams
Integrated analytics - Asking advanced questions. End to End and cross
platform
Data applications - Using data as building blocks

5. MVP / Bootstrapping
Asking the first question
Data sources: 0-1
Data points: few dozens at most
Dedicated HR: 0 analysts, working with browser :)
You know you are there if: you have one screen you refresh obsessively. One
metric. Usually - users / leads / pipeline.
Key challenges: nevermind the data. I need a seed.

6. Tactic driven
Asking several questions, by several different teams
Data sources: 3 and up (GA, Mixpanel, monitoring, advertising…)
Data points: >50 data points distributed
Dedicated HR: 1-5 analysts, working with msExcel, google sheets, your browser.
You know you are there if: you have to login to multiple platforms to get the
insights you need.
Key challenges: Aggregated data. Transparency. Scale. Value

7. Integrated analytics - single source of truth
Asking advanced questions. End to End and cross platform
Data sources: 5 and up (Marketing, Product, Sales, Financial...)
Data points: hundreds of data points over different data types. databases, APIs,
Services...
Dedicated HR: Data scientist and analysts working SQL and Python. Using a BI
service.
You know you are there if: you see positive ROI on from data

8. Data application
Using data as building blocks
Data sources: over 20
Data points: in Ks
Dedicated HR: data engineer, data scientist, BI people, DevOps.
You know you are there if: you are using your data in real time to optimize the
offering, service, product…
Key challenges: the sky's the limit. focus, budget.

9. Custom analytics - who is it for?
MVP - mean lean singlytics machine
Tactic driven - a world of silos
Integrated analytics - single source of truth
Data applications - when data becomes part of the product

10. Top 5 reasons to go custom
Raw data == Meaningful insights and end-to-end analysis
Out of the silo and into the light
Agility and flexibility
Ownership of the data
Cost reduction

11. Why shouldn’t you go custom?
SQL, do you speak it?
Bigger / Other fish to fry
Everything is awesome. Nothing to
optimize
Your ideas

12. Who am I?
Obsessed with user level data
Can’t stand data lakes :)
Gotta have positive ROI
Not a big fan of abstraction layers
I <3 SQL and pretty fond of Python

13. My custom analytics stack
DATA PIPELINE Data Warehouses BI, Analytics, Other
outputs
Data Sources

14. Build vs. Buy

15. Use cases
Things you can do with custom analytics

17. ROI positive user acquisition
DATA PIPELINE Data Warehouses BI, Analytics, Other
outputs
Data Sources

19. Harnessing real-time data to improve customer
experience and sales

21. #AMA
@danjas
dan-ya@alooma.com
Dan-ya Shwartz Bar-El