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August 17, 2010

Deep Dive Into Analytics: When Bounce Rate No Longer Floats Your Boat — SES San Francisco

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My humbling first attempt at live blogging, including Word freezing and losing half of Tami’s excellent presentation. [Editor's note: That just means you've got a battle scar, Jonah! Welcome to the club. :) Liveblogging is rarely pretty.]

Moderator:

Bryan Eisenberg, SES Advisory Board and NYTimes Bestselling Author, bryaneisenberg.com

ses san francisco logo

Speakers:

First up is the always entertaining Marty Weintraub.  Cha-Ching: Marty Weintraub, President, aimClear.

Conversion reports, Yes, you can. Cha-Ching.

How you organize the data and how you look at it is what really matters.

Look at behavior that surrounds conversion events:

Helpful Tip #1:  Conversion trends are lost in the noise of visitors. Use Advanced Segments to isolate visitors who convert. [Great tip, however, it should come with a warning, segments are based on sample data, not all data]

  1. Isolate traffic sources to sourced/keywords that generate conversions/leads/sales.
  2. Separate conversion segment by paid and non-paid.

Tip #2:  Conversions by Time of Day:  Time of day data feels useless until you dive into time of day and suddenly this data is actionable.

Tip #3:  Segment converting users by any means possible:

  1. Are mobile users converting?
  2. Look at by platform (iPhone, Android, Symbian)
  3. Different categories do better for different platform, lead to new advertising channel, budget for iPhone app development.
  4. YouTube conversions
  5. New vs Returning

DON’T LET CONVERSIONS GET BURIED

Matthew Bailey, SES Advisory Board & President, Site Logic Marketing

3 Obstacle to Deep Dive into Analytics

  1. Dashboard: Managers and bosses love big numbers, deep dives are little groups.  Big numbers sound impressive but don’t mean anything.  Dashboards get you into a cycle of velleity.
  2. Velleity: “The desire to change but not enough desire to take any action”.
  3. Hamster wheel analytics: After the fact explanation of what happened instead of actually doing the deep dive and deciding where to go:

Cure: Really understand customer.

Conversion rates should be separate for each product/specific group of people because the intent and reaction/behavior will be different.

  1. Intent:  Find intent by segmenting the keywords that people use to find your website.
  2. Expectancy: Subtle differences in keywords such as singular and plural can have dramatic differences in outcome.  Discover the terms with the right intent and try to optimize for them. Subtle differences in keywords such as singular and plural can have dramatic differences in outcome.  Discover the terms with the right intent and try to optimize for them.
  3. Reaction:  Where did they come from, what did they do on the site?  How did they engage? Users who come through direct referrals and/or mainstream media articles do the most, engage the longest and have the highest conversion. [View conversions by traffic source:  Social traffic does not convert the same as search traffic. MSM traffic converts the best.]
  4. Behavior
    • Compare behavior by browser size
    • Compare behavior by strategy (look for words within key phrases)
    • Measure the reaction by entry points (Make sure the landing page matches the intent of the user).  [Matt’s example shows entries on the second page had higher conversion and lower bounce than entries on the first page of results.  The numbers didn’t add up for traffic, but the point is excellent.]

Tami Dalley, Director, User Experience Optimization, ROI Labs

Case studies of Analytics Deep Dive: “If you can’t measure it, you can’t manage it.”

Fantastic lessons that relate to analytics: Visiting Thailand, one of her friends started talking to these fantastic looking “ladies” and decided to take her to the hotel.  Somewhere between the beginning and the end, one small piece of data changed the outcome.

Generally 1 or 2 metrics don’t give the full context:

Process of auditing analytics: Focus on where you want to take it

  1. Create a hypothesis
  2. Define your scope
  3. Pull data
  4. Follow the crumb trail
  5. Translate into action

Process for geo-targeting analysis:

  1. Cluster Analysis looks at keywords by CPC, conversion and average order sold to come up with average value per visit.
  2. Separate keyword groups/clusters to see if the conversion, cost per click or average order varies by country.

At this point, I had lots more interesting stuff to tell you, but Word froze up and I lost all the rest of my notes.

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