how to win at conversion optimization
TRANSCRIPT
HOW TO WIN AT CONVERSION OPTIMIZATION
- SNEHAL SAMANT
WHO’S SNEHAL SAMANT? Leads Product Marketing @ VWO – World’s easiest A/B testing tool
Head of Digital @ TastyKhana – acquired by FoodPanda Co-founder @ Black Bean Engagement
Creative Director @ Grey
OPINIONS ARE LIKE ASSHOLES, EVERYONE HAS ONE
(BUT EACH THINKS THE OTHER ’S STINKS)
NO 1 REASON WHY COMPANIES FAIL AT GROWTH?
Not enough ideas. We aren't tracking the right data. Don't have a testing framework.
They don't run enough experiments
ONLY 1 OUT OF 7 TESTS WIN 6 OUT OF 7 LOSE
Then why do people keep experimenting?
WITH EACH EXPERIMENT LEARN SOMETHING MORE ABOUT YOUR CUSTOMER
NOW YOU ARE CONTINOUSLY WINNING
MORE CUSTOMER KNOWLEDGE = GROWTH
Your customer knowledge
Revenu
e
1m ARR 10m ARR
100m ARR
BETTER FEATURES BETTER PRODUCTS BETTER SERVICE
WHERE WINS LIVE?
MEASURE SHIT
GREAT INSIGHTS
PERSUASION
This deck isn't a compilation of hacks. LET'S LEARN TO FISH.
Chamath Palihapitiya - how we put Facebook on the path to 1 bill ion users
IDENTIFY YOUR MOST IMPORTANT METRICS • AARRR framework • Start tracking • Establish baseline to
predict impact
Growth Hacking Like A Pirate, A Beginner’s Guide To Pirate Metrics
Point-click to define events and push to GA, MixPanel etc
COME UP WITH A STRONG HYPOTHESIS Changing
[element tested - button, headl ine etc] from ‘________________’
to ‘_____________’ will increase / decrease
[a convers ion metric - cl icks, downloads, s ignups etc] . by ‘______%’
BECAUSE ‘________’
A STRONG HYPOTHESIS TARGETS PAIN Changing
THE CANCEL ACCOUNT SCREEN from ‘Letting people cancel ’
to ‘Letting people pause their account by downgrading to $9 plan’ will decrease
Account Churn Rate by ‘20%’
BECAUSE ‘some customers do not want to lose their data which we delete on cancel lation .’
HOW TO FIND PAIN #1 - DATA ANALYSIS Find aberration
High traffic + high bounce, Paid traffic + high bounce Heatmaps, clickmaps, scrollmaps, visitor recordings
Look at internal search Look at search landing pages from organic search
Become a GA ninja
HOW TO FIND PAIN #2 - HEURISTIC ANALYSIS
Structured approach to critically evaluating UI / UX. LIFT is one of several frameworks.
An intro to the LIFT model
HOW TO FIND PAIN #3
TALK TO PEOPLE! Ask users – Website / Email Surveys Cal l them!
HOW TO PERSUADE PEOPLE TO DO WHAT YOU WANT (ZE BLACK ARTS)
HOW #1 - USE USER VOICE People skip marketing-speak
Use real-speak from surveys, conversation responses Keep it simple
How #2
VISUAL HIERARCHY It works
Don’t fuck it up
HOW #3 - NUEROMARKETING 101
THE REPLTILIAN BRAIN TRIGGERS
Is There a Buy Button Inside the Brain?
HOW #4 - NUDGES - EXPLOIT BIASES A nudge alters people’ s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives. Putting fru it at eye level counts as a nudge. Banning junk food does not.
COMMON BIASES Anchoring effect
40,000 6,000
COMMON BIASES Anchoring effect
900 200
Sarojini Market
Perception of cost is relative.
COMMON BIASES Current Moment Bias People choose the pleasurable option available right now. Constant feedback helps drive behavior.
COMMON BIASES Projection bias
Most people think like me, act like me and like what I like.
Most innovative, data-focused marketers use VWO for driving growth
at products like Slack.
COMMON BIASES Observational Selection bias
Suddenly noticing things we didn’t notice that much before and wrongly assume that the frequency has increased. A perfect example is what happens after we buy a red car and we inexplicably start to see red cars virtually everywhere.
COMMON BIASES Confirmation bias
We love to agree with people who agree with us.
We tend to be put off by people and news sources that make us feel uncomfortable or insecure about our views –
cognitive dissonance.
We focus on data which confirms our pre-conceived view.
5 Principles of Persuasive Web Design 20 cognitive biases that skew decisions
RUNNING EXPERIMENTS Simple sequential testing – establish baseline, make a change,
expose 100% of your audience Control + Variation – expose 50% random of your audience to
each at the same time Use a tool l ike VWO – visual editor + repeat visitors see same
control or variation + Bayesian stats engine (most use Frequentist which takes too long and uses p value)
WHY EXPERIMENTS CAN SKEW Calculate sample size in advance for
statistically significant results (not required with VWO) Don’t make changes mid-way
Test in weeks for equal number of Mondays, weekends etc Be wary of external forces
INTERPRETING YOUR RESULTS Aim for validated learnings, not just wins
THINK BEYOND HACKS Improve processes Pilot new, different mediums
GROWTH IS OPTIONAL The easiest choice is to choose to do nothing.
Get these slides on SNEHALSAMANT.COM