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Articles about how- to Measure, Analyze and Optimize your website : analytics,
optimization and user-oriented!


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 * Web analytics
 * Conversion Rate Optimisation
 * Experience Utilisateur
 * Social media & Mobile


DIGITAL ANALYTICS, BACK TO SCHOOL ! 2 MUST-READ

25/09/201430/08/2021weboptimeez

What’s in this post: 2 Books recommendations – Summer read for Digital Analytics
addict: Fundamentals in Digital Analytics & broader Customer centric view

It’s been a few weeks since summer is over and we are now in Back to School
mood, showing off our nice pic and how tanned we are… And here comes the fateful
question “What did you do this summer?”. Well, now that I am a full grown up and
am living in Asia – I don’t really get this feeling of end of Summer in
September, as it’s still 30 degrees in Hong Kong and it’s not going to stop
before November approximately and I don’t really get the fateful question
anymore as in Asia July/August are month like any other ; the activity is not
slower.
Anyhow, I don’t blog in July/August and treat September as my Back to Blogging
month. And instead of showing off how tanned I am, I’ll show off how studious I
was this summer and share 2 books I read during my trip back to Europe this
summer.

 1. Web Analytics: An Hour a Day (2007) – Author: Avinash Kaushik
 2. It Only Looks Like Magic: The Power of Big Data and Customer-Centric Digital
    Analytics (2013) – Authors: Jennifer Veesenmeyer, Peter Vandre, Ron Park and
    Andy Fisher (MERKLE)



BACK TO THE BASICS WITH AVINASH! “WEB ANALYTICS: AN HOUR A DAY”

I had this book since a while in my bookshelf, over 5 years to be honest –
eyeing me and vice versa – I finally got to read it ! Holidays and long hours
flight are the best. Even if it has been published a while ago, Web analytics,
one hour a day is a great book full of insights, supposedly destined to
beginners but even for advanced analytics expert I believe it’s always good to
take a step back and make sure that you master your basics. As this is still 400
pages to digest, and my memory would never be as organized as my Mac, I took
some notes and highlighted my favorite passages of the book along the way so
that I can make a book report of it later.

Web Analytics: An Hour a Day Author(s) Avinash Kaushik Summary If you are a tiny
bit interested by the digital analytics world, you would have heard of Avinash
Kaushik. His blog is full of ressources and when I started working in the field
I spent hours reading through his posts and still do, his book – which has been
renewed since this 2007 version, is a good deep dive into Digital Analytics.
Avinash share his thoughts about multiple topics: data collection, data-driven
organization and analytics skills and fundamentals. Even though the title
mentions Web analytics, it’s not an under statement to say that beyond website
analytics ; you’ll learn about search analytics, market research, testing,
statiscal concepts inherent to analytics and optimization… Best parts & tips
 1. Reporting and KPI: This section covers the best practices to do great
    reporting, with the best advices of all “start with desired outcomes, not
    reports”. In a nutshell, the author explains that the best way to design
    your report is to first and foremost think ahead of your strategy: what is
    your website goal? what is each of your pages goal is the overall strategy,
    starting from there you’ll design a better tagging implementation and a
    better report. This echoes pretty well to my article: Successful Digital
    Analytics Project Workflow
 2. The power of benchmarks and survey: Benchmarking and surveys are great tools
    to expand your measurement insights. Avinash mentions several examples of
    how to leverage them and why they are so useful. Benchmarking either
    internal or external are crucial to put context around your metrics, it’s a
    better way to tell the story of your figures. Some tools are listed in
    different sections to achieve that such as Hitwise, ComScore, Alexa…
 3. Statistical significance and Calculating Control limits: We are often
    challenged by our clients about the data, there is deep feeling of mistrust
    around the data delivered especially when the results goes against a
    personal opinion. And even if you are not challenged, it’s should be a core
    part of your own work to challenge your results and be absolutely convinced
    by what you are presenting. In diverse part of this book, you’ll find tools
    and examples of how to achieve this and rely on tangible tools to
    demonstrate how much your data and reports can be trusted: statistical
    significance calculator, sample size calculator… are some of them.
 4. I just cited a few of what’s available and was at that time relevant to me
    but there is more… I won’t spoil you the rest of the reading… and more
    especially I would advise to read the 2.0 version “Web Analytics 2.0: The
    Art of Online Accountability and Science of Customer Centricity”

I still had a few hours flight left to burn and decided to continue my reading
journey… Digital Analytics is a moving discipline and there is always to learn.
So I devoted my time to this other fantastic book focusing on

ADVANCED CUSTOMER CENTRIC ANALYTICS “IT ONLY LOOKS LIKE MAGIC: THE POWER OF BIG
DATA AND CUSTOMER-CENTRIC DIGITAL ANALYTICS”

The title though quite self explanatory do not give the full view of what to
expect. In 168 pages, you will go through a pretty comprehensive view of steps
and consideration to have when embarking into a customer centric approach to
pilot your business.

It Only Looks Like Magic: The Power of Big Data and Customer-Centric Digital
Analytics Author(s) Jennifer Veesenmeyer, Peter Vandre, Ron Park and Andy Fisher
(MERKLE) Summary In their own words, the purpose of this book is to “enlighten
analytic minds to the power of a fully coordinated effort to use multi-channel
data to drive insights, measurement and decisioning that create optimal
outcomes“. This book covers multiple notions essential to achieve this goal:
best practices to capture data in a multi-channel, multi-devices/screen world,
pitfalls that you’ll encounter but also top notions and basics to master before
kicking off a customer centric project. Which parties should be involved, at
what time and why… How to work with your IT folks, which tools and strategy to
consider (MMM, Attribution, Segmentation, Optimization and Testing) etc… How
different media channels are connected and what can be achieved in this mindset.
Best parts & tips
 1. Data capture: How to create an optimized cross-channel tracking ? In this
    section of the book, the authors point out a very important and well-know
    challenge of defining a unique identifier that can be used across your
    different data sources. This exercise is quite challenging as we know we
    don’t always have all access to the details of one individual in one place.
    How do we connect individual activity outside the website to activities
    onsite when the user is not identifiable – which identifiers are strong
    identifiers (cookies, email, telephone, IP, visitor ID…)
 2. Effective email data integration strategy, this section emphasis the
    importance of email data. Emailing being one of the best tool to capture
    individual information with the conscious agreement of the person ; it’s
    crucial to take advantage of this channel to better connect offline and
    online data. Even though emailing often comes at the end of the journey this
    is the chance to maximize knowledge of our customers.
 3. Mix modelling and Attribution: Top down versus Bottom up approaches. This
    section covers both approaches and how each of them contribute to measuring
    your marketing performance, but also the limitations of each of those
    solutions and how bad interpretation/utilization of those tools may conduct
    to bad media optimization strategy and why market research can supplement to
    a combined approach for a better “truth”.
 4. Predictive analytics, Controlled testing… I won’t spoil you the rest of the
    reading…

I really enjoyed those 2 books for 2 major reasons : (1) The concept and
examples are really close to real business questions we have as digital analyst,
both books rely on real-life scenario and do not bore you with new fuzzy and
trendy concept that won’t help your daily working life. Also those books provide
tools, links, how-to, case studies… you’ll finish your reading with a sense of
having learnt something new and having the tools at hand to apply it (2) No
linear reading. You can cherry pick the topics that interest you and read only 2
chapters if you want, although I will suggest to read it all, some chapters may
not be relevant to you either because you already master it or because you just
don’t need that level of details.
Hope you’ll enjoy the reading !

> If you liked this article, spread the data-love…

Coup de coeur, Web analytics user-oriented, web analytics, measure,
customer-centric


ANALYTICS TRENDS WEEKLY REVIEW: DIGITAL ATTRIBUTION RUBIK’S CUBE !

26/06/201430/08/2021weboptimeez


As marketers thrive to make better informed and data-driven marketing
spend/allocation decision ; Digital Attribution is one of the most hot trending
topics in the Digital World ! According to Adobe/Econsultancy last survey about
Digital Intelligence trend ; 58% of the marketers believe that a perfect model
is impossible:

Good news being that we are not looking for perfection but a model which is as
reliable as possible for your business, which will enable to measure afterwards
your campaigns adjustments and optimization moves lift – and understand which
spends to which channel impact actually your bottom line KPI. Models won’t be
the same for each business ; the level of complexity required is inherently tied
to your business model and your customer path to purchase. To keep it simple,
your goal is to know if you are spending wisely : your attribution model is the
ground for testing your spends strategy.

I have been away for a few weeks holiday and tried to catch up with the latest
articles about it since my latest post on “Fun with algorithms: Attribution and
media mix-modeling” and the 2 latest acquisition (Adometry by Google and
Convertro by AOL). As the list was piling up, it seems to be the perfect time
for a best-of articles.

BEST-OF ARTICLES ABOUT DIGITAL ATTRIBUTION

I have divided them into 4 categories so that you can pick and choose:

CRITICAL THINKING

 * Re-Thinking Digital Attribution : Why Sophisticated Modeling of Attribution
   is Mostly a Waste of Time
 * Has Google sold billions of dollars in ads that don’t work?
 * Don’t Give Me No Stinking Credit : Re-thinking Digital Attribution

HOW-TO, BEST PRACTICES, ATTRIBUTION MODEL REVIEW

 * It’s not a recent article but always good to read: Multi-Channel Attribution
   Modeling: The Good, Bad and Ugly Models
 * 10 Tips For Creating An Effective Marketing Attribution Program
 * Learning More About That Other Half: The Case for Cohort Analysis and
   Multi-Touch Attribution Analysis
 * Multi-Channel Marketing Manifesto Part Two: Data-Driven Attribution Best
   Practices
 * The Top 5 Online Marketing Attribution Models – and How to Use Them

BEST SHARE OF VOICE / WOM

Article Title Total Share Author Name Sizmek announces new Attribution Suite for
cross-channel analytics 847 n/a 4 things you need to know about mobile ad
attribution | Mobile 641 Adam Foroughi The Case for Cohort Analysis and
Multi-Touch Attribution Analysis 536 n/a Re-Thinking Digital Attribution : Why
Sophisticated Modeling of Attribution is Mostly a Waste of Time 348 Gary Angel
Why does my path to purchase matter? A tale of Soccer Shoes, Marketing, Sports
Sponsorship, the World Cup, and Digital Attribution 334 Nancy Smith

FOOD FOR THOUGHTS TO START YOUR ATTRIBUTION JOURNEY

 * Do You Actually Have an “Attribution” Problem Worth Solving?
 * User Engagement Across Channels by Steve Briley – XCMO 2014
 * Forrester Survey: Data-Driven Attribution Is Hard But Worth It
 * DAA Webinar | Interactive Storytelling in Multichannel Marketing Analytics

In my opinion, Attribution is still at his early stages thus it’s a really
exciting moment to work on this as everything can still be defined, new
definition, new rule, new way of thinking, new best practices… If you consider
moving from the last click attribution to a data-driven / customer journey based
customized model.

HERE ARE MY STEP BY STEP ADVICES:

 1. Start small and improve iteratively your model. We have all those models at
    hand
    and additionally the one everyone talks about “Data-driven attribution”
    model, which is customized attribution model based on statistic and economic
    rules relying on your customer journey statistical analysis.
 2. Make sure that this attribution question is really relevant to your business
    and to what extend before starting The basis of attribution model is to
    understand which channel contribute at which extend to your
    conversions/revenue ; but what if 80% of your conversions happen on a first
    time visit with only 1 channel involved, what if you had only 3 touch points
    in the whole customer journey? What is you have 5 different path to purchase
    ? Depending on your business model, those questions will be relevant or not
    : make sure your ask those anyway.
 3. Be conscious of your data challenges
    
    as this may totally throw off your model and inherent analysis. Be conscious
    of the data you are collecting, are they enough to pull a model together :
    do you already have a unified view of your customer journey or are you using
    5 different tools to measure? In a perfect world, you need as much data as
    possible in the same environment: from TV influence to social media, to paid
    media to retail sales or online sales without forgetting mobile experience
    or emailing… But as said you can start small and improve it !
 4. Understand your customer journey
    try to map it either via digging into your data or looking at benchmark or
    asking your client directly before kicking off – there is no need to start
    from scratch or copy what others do, you have already a wealth of data at
    hand to learn from !
 5. LEARN from existing dataset, TEST and LEARN and OPTIMIZE and TEST again !

> What about you ? What’s your POV? Did read any nice article, whitepaper,
> webinar… about Attribution that should be mentioned here?

As usual, if you liked this article – please spread the love…

Web analytics attribution, measure


STUBBORN ANALYST LOOKING FOR AVAILABLE KEYWORDS…

08/05/201430/08/2021weboptimeez

I used to work for online dating services, website project management and
website performance analysis and always had a lot of fun reading the ads most of
the time not very interesting but sometimes funny, wacky or truly desperate –
honestly, best lunch break ever ! Don’t get me wrong, it’s really difficult to
describe in a few sentences what you could want in a person and who you truly
are without sounding a tiny bit desperate. Well, no shame on my side :

In reaction to Google encrypting natural and now paid search queries in mass ; I
know that Google do not care a minute of my POV but anyhow as in love I will not
hold back my feelings anymore: angry and desperate ! Or am I just blind maybe, a
lot says that it’s not a big deal that now 70-80% of the keywords are displayed
“Not Available”… I mean, really ? I know it’s not the end of the world: keyword
performance analysis is not dead – Third party PPC management platforms & Google
Adwords are not affected but my point of view here is only analytics platform
focus. I love new challenges and evolution is key. But I kind of like having all
my stuff in one place, sure I don’t mind bringing some external fun from time to
time in my routine but most of the time it’s easier to have everything in 1
place and I rather spend time analyzing than doing data enablement, but that’s
just me. My place being my analytics reports/dashboard Omniture or GA and
external fun being Google Adwords & Google Webmaster Tool, and it’s getting more
difficult to analyze without making too much assumptions.


WHAT HAPPENED AND WHAT DOES IT MEAN?



Let’s focus on the last announcement, being a little bit stubborn, I checked –
double checked – cross checked and ask around ; here are some quotes from my
findings:

> Today, we are extending our efforts to keep search secure by removing the
> query from the referer on ad clicks originating from SSL searches on
> Google.com.

Google Ads Developer Blog

> “What has actually happened is that Google team has announced that they are
> removing the query from the referrer on ad clicks by users who use secure
> (SSL) search on Google.com. So analytics packages et. al. won’t have access to
> this data.”

Avinash Kaushik

> The gist of the development is that when people using secure search click on
> AdWords ads, the user’s search query in the referrer string — the actual words
> that people entered into the search box — won’t be passed to analytics
> packages and third-party software.

Searchengineland.com

> They’re going to be stripping the paid search keyword from the URL, which is
> how adobe/google analytics collects the data. You can access paid keywords in
> adwords or via their API. it will not be available in google analytics or
> adobe analytics.

Adobe Marketing Care

Well, I think it’s clear – Analytics vendors will not anymore have access to
keywords the way they used to ‘via the referrer string’. But let’s be very
clear, it doesn’t mean that natural search or paid search won’t be identified as
what they are – it means that keyword level analysis from an analytics
perspective won’t be easy. When before you looked at your branded keywords, top
keywords, long tail keywords… against your bounce rate, engagement & conversion
rates ; well.. you just have to stop doing that.


WHAT CAN WE DO ABOUT IT?

When last year, the Natural searches were hit – well as everyone, I made myself
a reason and found alternatives:

 1. Looking at landing page report and deducting from there; knowing that most
    of the time your landing pages are optimized for a specific list of keywords
    – we could in a way extrapolate from this using either custom variables or
    classifications to tag each of your page in a set of topics.
 2. Looking at Google Webmaster Tools; knowing that Google Webmaster tools
    reports displays the volume of clicks and impressions by query.
    Unfortunately this is limited as you can’t drilldown this report with your
    site objectives and KPI only again geographic data and you can’t segment
    either.
 3. Looking at paid search keywords reports, as there is a well know synergy
    between paid and organic searches
 4. Looking at site search reports; it can be very useful especially for
    ecommerce websites where the audience is more likely to use your internal
    search function. Also from there you match and extrapolate
 5. Looking at the VED parameter to get more insight about the type of results
    your audience clicked on, you’ll capture information such as type of :image,
    blog, sitelink, placement…

In summary for natural search, no changes here – stick to your routine except
#3. And appreciate that this change brought a change of perspective shifting
towards more user behavioral, user segmentation/personae focus instead of
keywords only. Still, i don’t think it hurts to have both.

As for paid search, what should we do now?

Well, I would suggest that we go on strike ! but being realistic I believe we
just need to adapt as indeed it’s not the end of the world – search vendors as
still working perfectly – you can have your data from Google Adwords or your
third party PPC management platforms, but i had to complain first ! Aside from
the list above which is applicable for paid search as well (#2 use Adwords
instead of Google Webmaster tool in this case) ; Google advise as well that for
generating reports or automating keyword management with query data, use the
AdWords API Search Query Performance report or the AdWords Scripts Report
service and alternatively use ValueTrack parameters.

ValueTrack parameters in a few words ; this feature allows to dynamically
populate the keyword into the destination URL; the keywords not the query the
users typed but if you include also the match type parameter (broad, exact…) you
will have a close idea of the query.

In this example above, the parameter capture is the device type, but any other
parameter can be appended. See below exhaustive list and click ont he image to
be redirected to the methodology.

What about you? How do you intend to deal with “Not Provided” growth?

> Hope you enjoy the reading, please share if you did !

Web analytics, Google Analytics notprovided, keywords, ppc, seo


SLC ADOBE SUMMIT 2014, ANALYTICS REVIEW.

29/03/201430/08/2021weboptimeez

The Adobe Summit in Salt Lake City is just over, although I wasn’t there the
wonderful magic of internet & video allow me to pick up on what I missed and
share with you the analytics pieces ! You can find all the 23 sessions of Adobe
Summit recorded here as well.

I had to make a choice so 2 sessions really picked my curiosity and get to the
point of having me writing those lines for whoever don’t want to listen to 2
hours of video.
The first session


“BEST-IN-CLASS ANALYTICS: HOW TO MOVE YOUR PRACTICE UP THE MATURITY CURVE”

was led by Jeff Allen, Adobe and Mihai Anghel, ThinkGeek. I found this one quite
interesting as it’s an everyday challenge: moving from the left side of this
chart to the right side.


It gets really frustrating to spend that much time of the left side if it, but
it has to be done perfectly so that you are trusted when delivering insights,
prediction and segmentation pattern based on those left side figures. Those
insights are what makes your work valuable and finally help driving the
business.

The main point of this session was to present Adobe Analytics Maturity
assessment tool ; which is a pretty nice online questionnaire that helps you and
your team have a decent idea of where you are at for each of those analytics
dimension above: Descriptive (collecting data, reporting, dashboard…), Diagnosis
(Analysis & Pattern Discovery)…, Advanced Diagnostic (Segmentation…),
Predictive, Prescriptive. This tool aim to map your organization analytics
maturity and leave you a path of improvement to follow and it’s shareable, you
can have a team looking at it and have goals…
My opinion is that this is certainly a nice to have or at least a good starting
point ; it’s always nice to be able to assess where you’re at, spot your gaps
and benchmark yourself against industry. So sure, why not ! You can see for
instance on my left example, that I do have room for improvement – when filling
this I refer to a real life example where I am today reaching to Advanced
Diagnosis and the rest of the report delivered by the tool point out where those
gaps are , not how to fill them but that something you most definitively know
already. For instance, you don’t use segmentation or you don’t import your CRM
data in your analytics tool ? This will be point out and you’ll decide of the
priority of this task and how to best integrate it in your roadmap for Best in
class analytics. And I do love roadmap, it’s organized and you can cross when
your task is over, you can measure your progress…

The second session was about Attribution:


“FUN WITH ALGORITHMS: ATTRIBUTION AND MEDIA MIX-MODELING”

. I understand this seems hardly fun but it’s a really hot topic from which
organization can gain for, I still believe that it’s fancy and maybe too fancy
when some organization are missing the basics but each things has its time and
attribution fall into the high level of maturity in analytics practice.
Especially knowing that the user journey is across devices, platforms, media…
the equation is more and more complex at some point we need clarity to
understand what is influencing what, which media goes first in the path to
purchase, correlation and causality, which media drives awareness,
consideration, engagement or conversion… To go beyond the last click model as
stated before attribution role is to uncover this mix.
The focus of this session was to go through Models available for attribution and
as expected pointing the one that should be THE ONE.
Let’s review first the models available that are well-known:
– Last click… don’t be shy, you most certainly use this model as 90% of us
– First click : not very popular anymore
– Equal : when every touch point get the same
– Custom: when you decide what you consider should be done
– Less to More: when the first touch gets 10 the last one gets 50 for instance.

The rest of the presentation went through the Top Down Attribution model of
Shapley, which is a statistically based on historical data model for
attribution. One of the hint of it was that you’ll have to calculate your
marketing channels elasticities – i.e. the relative effectiveness of a media
channel to drive sales on a given point of time. And especially as this formula
will be relying on YOUR historical data, you better have a statistician with you
!

It’s highly mathematical with words like regression line, variance, formula,
equation… and so on ; so I leave it to you to watch the video if you are a
statistical person. For other people like me, my interest was more into
practical examples, the case study was according to me a little bit
disappointing as the only output you see during the session is : “Thanks to this
model, if you spend 18% in Email, you’ll have 30% sales” ; which is good insight
for media planning don’t get me wrong. My disappointment came from the fact that
I was missing step and by step or how-to guideline, proofs and multiple case
studies instead of one.

At last, I still think that it’s hard to get started ; good thing is some tips
are raising interesting question for me as “Get your data ready for attribution”
; I believe this is the main focus for now.

Other than those 2 sessions, some others tweets I went through or video sessions
I scan briefly raised other nice stuff to follow, such as using R to access
Adobe Analytics APIs or knowing that now Genesis is Free or Ben Gaines SiteCat
Tips & Tricks session. I’ll may dig into that later and let you know !

> Stay tuned and if you like this article, please don’t be shy… Share !

Mes projets, Web analytics measure, analyst, AdobeSummit 1 Comment


TERRIBLE METRICS, HOW NOT TO BANG YOUR HEAD AGAINST WALLS !

27/03/201430/08/2021weboptimeez

Sometimes your are confronted to situations where you look at your pretty
dashboard of the week and everything stops, it’s not so pretty anymore, red
doesn’t suit you… WHY so much decrease and red ! What’s wrong with my website,
why is this “metric-that-I-will-not-mention” so damned high…


Literally, you want to go back to sleep (at least I do) or slap someone with
your dashboard – true story, I am quoting someone who actually told me that !

So, before you hit the PANIC button and scare the hell out of you HIPPO (Highest
Paid Person in the Office or Boss or Client) ; take those 3 advices into
consideration.


TAKE 2 OR 3 STEPS BACK : BE REMINDED OF YOUR OBJECTIVES

Business Objective is your compass – either macro business objectives or micro
page-level objectives! Like when you are cooking, one ingredient is not
sufficient on his own, well when evaluating your performance 1 metric on its own
is not sufficient. This metric has to be put against your objectives and some
context. If for instance you look at Time Spent on Page ; the gap of performance
between 2 pages can be puzzling at first ; though against the page main
objective you’ll not evaluate the results in the same way. If Page A is an
article you wrote with a video for instance while your Page B is a form
subscription ; you’ll know that Page A type of page objectives in engagement
hence the longer the time spent the better and you may look at content velocity
to cross check that it is effectively good while Page B type of page is
conversion hence a long time spent on page will not be a metric you care about
except when it’s a sign that your users are having issue to convert and in that
case you’ll be looking at form completion rate to cross check the performance. I
believe that each metric that you measure must have a real connection to your
business objectives and tactics to achieve them, and has to be mapped out to a
measurement logic where business objectives come first and are turned into KPI
which are measured by some metrics for which you have set targets against. The
granularity doesn’t have to be the page level each time but it’s by defining
methodically your objectives, how to measure them etc… that you will know what
each metric you have in your dashboard is for. With this logic applied to real
business objectives, you’ll know why you need to dig further, panic or wait and
see.




REDUCE THE NOISE: SEGMENTATION IS QUEEN!

Segmentation is your magnifying glass: Precision matters to be able to remove
all possible of data misinterpretation, data analysis can’t really be done in
mass – you need to segment your results. Performance can be looked at through
many angles:

 * by channel driver (e.g. new OLA campaign)
 * by user type (e.g. new versus recurring)
 * by contextual information (e.g. new layout, seasonality…)
 * by devices, technology…

If we take the example of the channel drivers performance, one channel cannot be
compared to the other without considering the users journey & your sector. Each
channel contribute to a different moment of your user journey hence each channel
may not perform the same for your general key metrics but each channel will have
specific goals and metrics attached to it.
This example below illustrates in France for the Retail industry the user path
to purchase and where which channel driver assist :

Another example related to your user journey would be to segment by user type.
Your company may use Personae to define user type, those personae can be
considered as segment in your analytics tool. If we take the example of a travel
website ; the same objectives, metrics and targets will not apply to Paulo,
Frequent Traveler than Linda, First Time Buyer or Louis, a New visitor.




OPEN YOUR MIND: BENCHMARK YOUR RESULTS

“Benchmarking is the process of comparing one’s business processes and
performance metrics to industry bests or best practices from other industries“.
From my usage in a digital analytics context, there are 3 types of benchmark :
historical data (looking at your trend), Target data (looking at your
objectives) or Competitive data (when industry benchmark are available).
Benchmark data are also here to give you some perspective !




If all of this is not enough to understand this ugly dashboard you have… that’s
when the fun begin ! Being an Data Scientist Wear your McGyver, Holmes + Lara
Croft outfit (be precise, methodical & aim for something)

> If you like this post, please share the data love…

Mes projets, Web analytics, Conversion Rate Optimisation metrics, analytics, BI
dashboard


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Hi, My name is Alexandra Selly, Analytics, Webmarketing & Ecommerce addict since
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