Analytics2015 Presentation
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Data stewardship
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Transcript of Analytics2015 Presentation
Data stewardship
What will we speak about?
o Tomorrow’s marketing
o Data Driven Decisions
o Customer’s footprint
o Personalization vs. Segmentation
My employer
Beauty eCommerce
3 websites • 10000+ references • 500+ brands
in 12 countries
Problematic
o Question: How does our client access our website? • How many times does (s)he see our supports before arriving the
first time / the final time?
• Can we learn from these patterns? Can we predict the “next step”?
• Can we reduce the number of steps before a transaction? Or can we reduce the cost of acquisition?
• Do we use correctly our marketing publishers? How should we configure them for a better ROI?
Part I
Once upon a time
What do we need to solve our problem?
o Think • What do we need to understand how our customer land on the
website?
• How can we get this information? Is it reliable?
• How long would it take?
o Data • Where can we fetch this information?
• What do we want to do with?
• Where will we store it?
Ask our Web Analytics tool
o Google Analytics • Visit / Event centric, it’s not customer centric
à Personalize our GA tracking, to store and retrieve customer information
o We were loosing information • Only last interaction is recorded before a transaction
Build our own analytics engine
o Dump all the raw data from GA • Build all the visits of each of our customers
à We were able to get the last interaction, but also the first one and all the middle ones
o We were still loosing information • Emails were one of the most important “1st interaction”
Enhance our model
o We had to find a way to group our users, even if they were accessing the website from another device • Use our login information
• Build a “cross-email cookie persistence tool”
Part II
Write, write & write
Our problem became an IT problem
o Where do we store this amount of data? • We built our own Data Warehouse
A multi level Data Warehouse
o A warehouse? • We learnt directly from our own “physical” warehouse
• With walkways, with floors
• The main problematic were: when I request (query) something, how can I get the fastest, and the safest response?
o A database • Connected to our backend data
• Connected to our vendors • GA, AdWords, Social media, Remarketing tools, CRM, …
Part III
Tell the story
Get back what we needed
o We wrote our own algorithms to aggregate all our customers’ landings / solicitations
o We split them by • Visit number
• Visit date
• Transaction amount
• Interaction cost
• Interaction type (channel)
Display / visualize the data
o We have built our own interface to display the data • To be able to isolate exactly the information we need to analyze
• For everyone to help, bring ideas, explaining the “wrong” anomalies
• Discovering our first patterns
Part IV
Case studies
