get deeper insights from your optimizely results

Get Deeper Insights From Your Optimizely Results

Hudson ArnoldDarwish Gani

#OptimizelyResults

Housekeeping notes

Just a reminder:● Chat box is available for questions● There will be time for Q&A at the end● We will be recording the webinar for future viewing● All attendees will receive a copy of the slides and

recording of today’s webinar

Get Deeper Insights From Your Optimizely Results

Hudson ArnoldDarwish Gani

#OptimizelyResults

Table of Contents

● Results page feature overview

● How do I know if I have a winner?

● What do I do next?

How do I know if I have a winner?

Statistics in Optimizely Deeper Analysis

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Terminology

Hypothesis TestStatistical Inference methodology used to determine if an experiment result was likely due to chance alone

Assume two variations to be the same. Determine ‘confidence’ that we can disprove assumption

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Terminology

Significance Level Deals with risk of encountering a false positive.

How likely am I to declare a test inconclusive (no difference) when my variations have no difference in conversion rate

Example: 95% Significance = 5% of A/A tests will report a significant difference, when none exist

By setting a Significance Level of 95%, we are accepting a 5% False Positive Rate

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Terminology

Power Deals with risk of encountering a false negative

Example: 80% Power = 20% of A/B tests will accurately detect a difference

By setting a Power of 80%, we are accepting a 20% False Negative Rate

Tradeoff between power of a test and the size of a difference your test can accurately detect

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By random chance, some of your tests will be incorrectly declared as winners or losers

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By using Chance to Beat Baseline you can control these errors

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Chance to Beat Baseline Likelihood that the observed conversion rate improvement is not due to chance

Compare to a set significance level (95%)

Technical Note:P value: Likelihood that we saw a result this extreme, were the original and variation actually the same.

Chance to Beat Baseline = 1 - P value

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The procedure outlined today will help you maximize the accuracy of your testing program

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1. Set sample size a. How many visitors? Use Sample Calculatorb. Which Visitors? Ensure you use Representative Sample

2. Make a decision on the test a. Chance To Beat Baseline > 95% or < 5%b. Improvement > MDEc. Don’t make make decisions before this.d. Don’t decide to run the test longer

How to run a test

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“Representative sample”Weekend vs Weekday Traffic? Promotions? etc..

Sample size that provide adequate powerUse Sample Size Calculator (link)

Setting the appropriate Minimal Detectable Effect (MDE)Opportunity cost of waiting to detect effects and running tests

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At Optimizely, we only target conversion rate improvements of >12% (MDE) on our home page

Opportunity cost of running more tests vs detecting smaller differences

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Using Chance to Beat Baseline

>95%

<5%

Winner: Variation is Better than Original

Loser: Original is Better than Variation

5-95% Inconclusive: We don’t know if the variation is better or worse.

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Why do I need to set a sample size?● Classical Hypothesis (made in early 19th century) tests assume that there is only 1

decision point in the test● You should make calls on significance when your test is adequately powered

Why can’t I look at results before I reach my sample size?● By making decisions on a test at multiple points, you are biasing your results, giving

yourself a higher chance of finding a variation that looks like a winner or loser

Some questions you may have...

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Change is on the way!

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Other best practices when analyzing results

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4 Steps for Analyzing Every Test’s Results1. Ensure your test has an appropriate sample size2. Use chart functionality3. Use segments4. Be strategic with your goals

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Chart Functionality

Toggling charts to different views can help deliver much needed context to results analysis.

● See the volume of conversions rather than the conversion rate.

● Visualize volume of visitors, % improvement, CBB, etc.● Zoom on a given time period and view annotations.

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SegmentsYour total conversion rate can be thought of as the average performance of many different segments.

Segments can show consistent results (a strong corroboration of total results), or can vary (a strong case for a personalization strategy).

What do I do next?

Iteration Strategy & Telling a Story

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Difficult results

Every result leads to iteration!

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Iterating on losers

“Failure is not fatal, but failure to change might be.”

- John Wooden

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Iterating on losers: play again?

Try again!

You’ve identified an influential element, and succeeded in generating significant results - why was the winner better? How else could we execute this test concept?

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Iterating on losers: move on!

Experimentation is just that; every new thing you try won’t always be better than the way it is now.

Especially if you’ve tried a given test multiple times using different executions, take a step back, and shift focus to other test ideas.

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Iterating on inconclusive resultsEven more so than losers, inconclusive results can be confusing.

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Inconclusive results: Making small tweaks sound alluring; low effort, much-publicized by the CRO community (Optimizely included), and dangling high ROI in front of you.

The reality is that these kind of tests do generate wins, but irregularly, and with diminishing returns as you execute more and more of them.

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Iterating on inconclusive results: go bigger!Test more than one element at a time, and make bolder changes to each variation to tackle the problem with a greater chance of avoiding inconclusive results.

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Bringing ‘Why’ to the ‘What’: Telling a Story

● Be consistent with your documented test hypotheses when analyzing and communicating results.

● Reference the data and criteria that led you to run the test in the first place, note what was changed, how it was changed, and why.

● Note any external events that occurred that you had and hadn’t planned for, such as traffic spikes from media campaigns and coincidental release cycles.

● Connect this individual result to the history and goals of the larger testing/optimization program, and the larger business goals on the line.

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Takeaways:

● Estimating sample size in advance is essential for ensuring that your test is statistically powered.

● Take advantage of advanced features to deliver valuable context.

● Every test should feed iteration. Winning, inconclusive, and losing tests all have different paths forward.