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Every business is looking for innovation and growth. Experimentation can be a primary driver of both. Watch this webinar to learn about some common misconceptions, mistakes, and why experimentation is worth the hype.

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- Innovation & Growth With Experimentation March 2023 By Haley Carpenter | Founder
- User research & experimentation Consulting, execution, & training mychirpy.com haley@mychirpy.com 512-200-3510 https://www.linkedin.com/in/haley-carpenter-490/ Haley Carpenter Founder Past: CXL/Speero, Optimizely, Hanapin (now Brainlabs)
- Every business is looking for innovation and growth. That’s obvious.
- Innovate “make changes in something established, especially by introducing new methods, ideas, or products” Grow “become larger or greater over a period of time; increase” Google Definitions
- How does your team decide on what changes to make?
- We make educated guesses. Common answer.
- “We think…” “We feel…” “We believe…”
- “We think…” “We feel…” “We believe…”
- How does your team determine increases?
- We’re not measuring anything… we don’t know what to choose. Common answer.
- Our data is broken. Common answer.
- Maybe you’re getting lucky. That’s not strategic winning.
- Experimentation can be a primary driver of both innovation and growth. That’s maybe not so obvious.
- Experimentation… ● Eliminates guesswork ● Validates decisions ● Minimizes bias ● Mitigates risk
- Experimentation will lead you to strategic winning.
- Experimentation you say? What is that?
- “Digital experimentation is similar, if not identical, to the scientific method.” “Businesses attempt to answer a question by establishing a hypothesis, testing the hypothesis through experimentation and analyzing the results.” “…experimentation is ‘the reliable process of delivering winning digital experiences without guesswork or risk…” Contentful Link
- Image Source 50% of traffic 50% of traffic
- How do we start an experiment?
- Precursor: Make sure you have enough data volume.
- Pre-test calculations… one of the most important things you can do.
- WATCH THIS
- Step 1: Get a data point → Create a hypothesis
- What’s a Data Point? Where Do I Get One?
- What’s a Data Point? Where Do I Get One? RESEARCH
- Source
- Research Experiments Source
- Source: Speero Link
- Research will lead you to strategic winning, too .
- Good starting point: analytics (e.g., Google Analytics, Amplitude, Heap, Adobe Analytics, Mixpanel, etc.)
- Example data point: The dropout rate from the cart to the checkout is 92%.
- Ideal: data points from 2+ methodologies
- Example data points: The dropout rate from the cart to the checkout is 92%. Users don’t know about the free return policy or money-back guarantee policy.
- Step 2: Get a data point → Create a hypothesis
- “We know X. If we do A, then B will happen because of C.” Hypothesis Format
- “We know X. If we do A, then B will happen because of C.” Research & data Change something Increase result
- We know the dropout rate from the cart to the checkout is 92% and that users don’t know about the free return policy or the money-back guarantee policy. If we do A, then B will happen because of C. Hypothesis Format
- We know the dropout rate from the cart to the checkout is 92% and that users don’t know about the free return policy or the money-back guarantee policy. If we add content about the free return policy and money-back guarantee policy on the cart page, then B will happen because of C. Hypothesis Format
- We know the dropout rate from the cart to the checkout is 92% and that users don’t know about the free return policy or the money-back guarantee policy. If we add content about the free return policy and money-back guarantee policy on the cart page, then the dropout rate from the cart to the checkout will decrease and transactions will increase because of C. Hypothesis Format
- We know the dropout rate from the cart to the checkout is 92% and that users don’t know about the free return policy or the money-back guarantee policy. If we add content about the free return policy and money-back guarantee policy on the cart page, then the dropout rate from the cart to the checkout will decrease and transactions will increase because friction will decrease due to users feeling there is less risk to place an order. Hypothesis Format
- We know the dropout rate from the cart to the checkout is 92% and that users don’t know about the free return policy or the money-back guarantee policy. If we add content about the free return policy and money-back guarantee policy on the cart page, then the dropout rate from the cart to the checkout will decrease and transactions will increase because friction will decrease due to users feeling there is less risk to place an order. Hypothesis
- “We know X. If we do A, then B will happen because of C.” Guessing with little to no accurate data involved Change something Unclear metrics and broken data are common ???????? ?
- Step 3: Get a data point → Create a hypothesis → Create a variation design
- Design Image Source
- Dev > QA Launch > Monitor Conclude > Analyze Step 4+:
- Result: +61% Action: Implement the variation Insight for what’s next: Should we make the policies more visible elsewhere, too? RUN ANOTHER TEST!
- Source
- “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” “We know X. If we do A, then B will happen because of C.” Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9 Test 10 Test 11 Test 12 Test 13 Test 14 Test 15 Test 16 Test 17 Test 18 Test 19 Test 20 Test 21 Test 22 Test 23 Test 24 Test 25 Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Result Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight Insight
- Wins + Learnings
- Exp Leaders Product Marketing Other teams
- A few final topics
- Prioritization Article Link
- Roadmapping
- Common Misconceptions ● We’ll hack it together and be okay ● You just need a testing tool and an idea ● You don’t need developers ● Every business can test ● It’s too hard ● It’s easy
- Recap ● You need to experiment if you have enough data volume. (Check by running pre-test calculations.) ● Starting an experiment Step 1: Get a data point to inform your test idea. Step 2: Write a hypothesis. (e.g., If…then…because) Step 3+: Roadmap & prioritize your initiatives. ● The more teams involved in experimentation the better. ● If you’re not experimenting and doing user research, your competitors are ahead of you.