Analytics for Startups - Dublin Web Summit 2015

Andy Young // @andyy // andy@500.co
How do I analytics?
a practical guide for
pragmatic startups
Dublin Web Summit 2015

Hi, I’m Andy!
@andyy

Wait what, why?
Analytics?

What do we measure, and why?
Vanity metrics
Revenue metrics
Conversion rate metrics
Pirate metrics..
We need to know how we’re doing.

If you're not keeping score
there's no point playing the game -
you'll never know if you're
winning or not
- @distrodom

Today’s tools make it
super-easy to track
things
Google Analytics
Mixpanel
KissMetrics
Localytics
Branch Metrics..

Today’s tools make it
super-easy to track
things
BUT they also make it really easy to
- become overwhelmed with data
- focus on the wrong things

Typical analytics challenges/pitfalls
Drowning in too much data
Failure to select + focus on the top metrics that
matter
Not tracking the data you need to answer key
questions

Why analytics?
1. How are we doing?
- are KPIs on the right track?
2. What are the results of our experiments?
- so we can learn
3. What’s happening right now?
- did something great or terrible just happen?

A. How are we doing?

How’re we doing?
1. Identify top-level KPI

Identify top-level KPI
it’s hard.

if you pick the wrong KPIs, you're screwed.
If you pick KPIs and then ignore them, you're
screwed.
If you pick and monitor KPIs diligently, but don't
assess everything you and your whole team does
on the basis of whether your tasks are the most
effective way to grow your KPIs, you're screwed.

How?

Keep it simple!
The good news: there’s probably a
pre-determined answer
for
what drives your business

There’s probably a pre-determined
answer for what drives your business
Spoiler: ultimately it’s $$$

How’re we doing?
There’s probably a pre-determined
answer for what drives your business
Commerce: # sales
Subscription / SaaS: # subscribers
Marketplace: # transactions

How’re we doing?
2. Next, add nuance

How’re we doing?
Nuance behind your top level KPI
E.g. for commerce: # sales
Nuance: average sale $; # customers

How’re we doing?
2. Add nuance
3. Add drivers

How’re we doing?
Drivers behind your top level KPI
E.g. for marketplaces: # transactions
Drivers: # suppliers, # customers

How’re we doing?
2. Add nuance
3. Add drivers
4. Add funnel for these drivers

How’re we doing?
2. Add nuance
3. Add drivers
End up with AARRR

How’re we doing?
2. Add nuance
3. Add drivers
Put in a spreadsheet
(Template Google Sheet: http://bit.ly/kpi-sheet)

How’re we doing?
Put in a spreadsheet
- key KPI at the top, nuance and drivers below,
finally the detailed funnel below for reference
- columns for weekly numbers, w/w growth
- review weekly
- share with whole team

Weekly/Monthly reporting
% week-on-week or month-on-month growth
in your one metric that matters

Tracking events

Tracking events
Events vs. Properties vs. People
Events: something happened
Properties: something about what just happened
People: connect events to particular users
- people can also have properties

Track events from where?
Client/app vs. server
Tracking events

Tracking events
Tip #1: Choose easy-to-read and meaningful
event names
Short!
Pick a convention; stick to it
Omit superfluous words
“user_viewed_homepage”
“Viewed homepage”

Tip #2: Track each user based on a distinct ID
Don’t use email address -
use autogenerated user_id from your own DB
Use aliasing to connect up events tracked
pre/post signup
Tracking events

Tip #3: Annotate your users with source data
referrer; utm tags; install tracking via AppsFlyer
1. Track a signup event
2. Add as user properties
3. Potentially also as properties to key events
Tracking events

Tip #4: (Mixpanel specific) - People vs. Events
Mixpanel won’t let you query for users
who did particular events
So, our options:
- Do this using your own DB
- Annotate your users (People) with properties
for each key event
Tracking events

Tip #5: Ecommerce/revenue tracking
Mixpanel/AppBoy etc have
native support for tracking revenue
Annotate your Purchase events with revenue data
using the relevant properties for each platform
Tracking events

Tip #6: Use a development project for testing
Tracking events

Tracking the funnel
Start with the pirate metrics AARRR
Top of funnel: acquisition; signups/installs
Mid funnel: post-install events; engagement;
retention
Bottom of funnel: purchase / monetisation.

Tracking the funnel
Looking at each stage (AARRR) in aggregate
is a good start
but it will only get you so far
the “truth” is much more nuanced

Tracking the funnel
Users acquired via different channels
will have different behaviours
Different cohorts will have
different experiences of your product
Different users will have been exposed to
different A/B tests

Tracking the funnel
Key: these are all properties of your users
UTM tags: source, medium, campaign, terms
Landing page
Signup time
A/B test buckets
Referrer
Viral source

Tracking the funnel
Annotate your users in your database/analytics
system with these attributes
UTM tags: source, medium, campaign, terms
Landing page
Signup time
A/B test buckets
Referrer
Viral source

Other key metrics
CAC, LTV, churn

Other key metrics
Customer Acquisition Cost (CAC)
how much you spend (on average) to acquire a
customer
Lifetime Value (LTV)
How much revenue $$ an average customer
brings you in all time

If your
LTV
is greater than your
CAC
then you’re in business

If your
LTV
is greater than 3x your
CAC
then you’re in a good business

CAC & LTV: nuances
Payback period: time to recoup CAC
Magnitude of your numbers
e.g. enterprise vs. social

Calculating CAC
Simple approach: total spend / total signups
“50% of the money I spend on advertising
is wasted - the problem is
I don't know which half”
- John Wanamaker
Eventual goal: calculate CAC per channel

Calculating LTV
Problem!
You don’t have a lifetime of data
We don't measure LTV - we estimate it
Extrapolate revenue curve over time

Analysing your data

How not to do Metrics
Outdated information
Just 1 view of your data
Manual calculations
Bad metrics lead you astray

Doing metrics right
Graphs
Automated
Realtime

Cohort analysis?

Analytics = Knowledge

Problems with Cohort Analysis
Time consuming
Delays to get the latest data
Inflexible

Rolling Cohorts

How?

Use your existing database
Users
Learn SQL! It's not hard
Just need a slave database for analytics
- “read replica” - i.e. a live copy

Use your existing data
Users
SELECT COUNT(*) FROM users

Users
WHERE created > ‘2013-07-01’
AND created < ‘2013-08-01’

LEFT JOIN sales USING (user_id)
WHERE users.created > ‘2013-07-01’
AND users.created < ‘2013-08-01’
AND sales.date < DATE_ADD(users.created, 1 MONTH)

1. Automate running queries (every hour!)
2. Store the results in a simple database
3. Create a page to graph the results
(HighCharts..)
Roll your own

Visitor numbers
Usage / engagement
Revenue
Conversion rates
Pirate metrics

Knowledge = power
confidence
sanity

Good luck!

Analytics for Startups - Dublin Web Summit 2015

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