How do we convince the people who come to our website to do all of the things on our website that we want them to do?
It shouldn’t be that hard, right?
Our side is to understand them, specifically through
specific insights in customer journey
Detailed insights into what works
Helping to guide them
3 uses
Two benefits:
Understand customer journey
Detailed analysis of product performance
Introduction What is it that you want people to do on your website?
<cool blue slide>: immediate purchase
<eBay slide>
<Marktplaats slide>: posting, commenting, market saturation, sharing, etc.
<Belvilla slide>: find something that brings them back. Not worry as much about cart abandonment
Transition: Now I’m going to show you a picture of the 2016 London marathon.
The worst thing that can happen is that you just don’t pay attention to what your customers do on the site, until the moment they convert.
This is wrong in so, so many ways.
Like the store owner who sits behind the counter
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The @Leisure Group is one of the largest players in the European online market for accommodation rentals, such as holiday homes, holiday parks and hotels. The organization offers more than 470,000 holiday properties in 36 countries. More than 1.3 million holidaymakers from over 140 countries book their holiday every year with @Leisure.
Last year, the @Leisure board engaged my company to build a completely new analytics division. We’ve been building out a long term strategic roadmap, hiring full time staff, and executing the first analytic initiatives. We’re now at the point where I’m handing over the division to the new full time staff that I’ve helped to hire.
The company has recently been experiencing triple digit growth rates.
Example:
Swimming pool. How many kept it selected the whole time?
Which houses were compared to each other based on back-and-forth?
Which price ranges were selected before a house was booked, and what was the first price range used?
Main Point Understand user intention and preferences
Gain intuition into what’s happening
Ask detailed questions, such as path length, repeated views, use of filters, product substitution, gateway products, etc. etc.
Can generating new KPI’s retrospectively:
e.g. number of return views of an item in a session. Customers also viewed, most likely to buy given certain filters
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Clickstream data:
Example of why this is more important than web data / tagging. path analysis and retrospective questions.
E.g. user intent to purchase or not.
Deep dive question. A/B test: what was the average path length for this version? Are there specific gateway products?
Use:
Generating new KPI’s retrospectively:
e.g. number of return views of an item in a session
Understanding customer intent and preferences: in which order were filters chosen? Which filters were changed and which were not?
What is the appropriate conversion goal?
What is the likelihood of booking at a particular stage in the journey?
Step in with recommendation
Introduce different conversion goals (e.g. Increase advertising
User segmentation. Product recommendations. Filter elasticity. Product scoring (e.g. CTR). Marketing campaign analysis. E.g. bookings are low in Ibiza… who is looking at Ibiza, what else are they looking at, what are they searching for and what are they eventually booking?
A/B testing is about letting your customers do the analysis for you.
“If you’re serious about conversion optimization, you need to get serious about A/B testing, and if you’re serious about A/B testing, you need a big data solution”. (two examples: conversion rate after X searches to decide on advertisements. Deep dive on gateway products.)
Talk thru decisions that were made:
Photo size
Number of filters
Order of results
To place doubleclick advertisement: filled by Criteo, at a cost of 2-4 Euro per mill (fractions of a cent per impression)
Why does eBay think it will make more money from this doubleclick ad then from selling an iPhone?
Many of these choices are made following A/B testing and many require Data Science techniques to implement put into practice.
Why is this about Big Data?
Big Data: allows drill downs / segmentations / balance multiple objectives
Why segment? example of cute kittens or cool motor bikes. Also allows path analysis: Identify serious shoppers versus browsers
Big Data also allows retrospective A/B testing. Real-time implementation will require an additional big data solution.
Are looking into what determines ‘success’ . Looking into what house features most strongly affect success of house and whether we can quantify them.
We tried analysis of image effectiveness, and then realized that our users were already giving it to us
Next three slides show example of the value of having all of this data: `part of it is the fact that I can do retro-spective analysis
200x more likely to get clicked on.
Start with metrics such as revenue and number of bookings and eventually considered also booking lead time, click through rate.
FR-46100-04
White house is 36 times more likely to get booked from VIP (but French house is still 12x more likely to be clicked on)
Are looking into what determines ‘success’ . Looking into what house attributes most strong affect success of house and whether we can quantify them.
Start with metrics such as revenue and number of bookings and eventually considered also booking lead time, click through rate.
DE-54426-14
Can compute value of a SRP and update it based on what we learn about customer segment and intent
Of course this will change with item position, but also with stage of user journey and customer segment
Ideally we want to recommend the items with the highest book rate on VIP.
Big Data let’s us look at this at a more segmented level and tailor to the customer
Provide additional features for DS algorithms, which is especially relevant for recommendations.
Walk through steps in this:
Basic: buy our most popular product
Better: our most popular products that go with something relevant (your search, your current product)
Best: our most relevant product based on who you are and what you’re looking for
This principal can extend to marketing, incentivizing, content generation / UX, prediction (when is the likely purchase), etc.
Advantage of GA: Link to social/demographic data as well as Google advertising in order to study segmented CLV.
This is a conversion funnel for the London marathon
Remember to Say:
Finishing up current project
I’m hiring
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First a brief introduction to who I am.
From the USA, living in NL. Formerly ebay…founded my own company. Helping companies take the next step...
Companies I have “worked for or partnered with in the past”, excluding confidential work.
