Introduction​ ​to​ ​Web​ ​Analytics

KEY TERMS & CONCEPTS

A/B​ ​test

Testing​ ​two​ ​different​ ​versions​ ​of​ ​a​ ​web​ ​page​ ​to​ ​see​ ​if​ ​one​ ​has​ ​a​ ​significant impact​ ​on​ ​user​ ​actions.​ ​Also​ ​called​ ​a​ ​split​ ​test.

Click path

The​ ​sequence​ ​of​ ​clicks​ ​made​ ​by​ ​a​ ​user​ ​on​ ​a​ ​website​ ​in​ ​one​ ​session.

Conversion

A​ ​user​ ​completing​ ​a​ ​pre-defined​ ​goal​ ​that​ ​the​ ​website​ ​owner​ ​wants​ ​them​ ​to​ ​take.

Conversion Funnel

A​ ​defined​ ​path​ ​that​ ​users​ ​should​ ​take​ ​to​ ​reach​ ​the​ ​final​ ​conversion.

Count

A​ ​raw​ ​figure​ ​captured​ ​for​ ​analysis.​ ​These​ ​are​ ​the​ ​most​ ​basic​ ​web​ ​analytics metrics.

Event

User​ ​interactions​ ​with​ ​content​ ​that​ ​can​ ​be​ ​tracked​ ​independently​ ​from​ ​a​ ​web page​ ​or​ ​a​ ​screen​ ​load.

Goal

A​ ​defined​ ​action​ ​that​ ​users​ ​should​ ​perform​ ​on​ ​a​ ​website.

Heat map

A​ ​data​ ​visualisation​ ​tool​ ​that​ ​shows​ ​levels​ ​of​ ​activity​ ​on​ ​a​ ​web​ ​page​ ​in​ ​different colours.​ ​Reds​ ​and​ ​yellows​ ​show​ ​the​ ​most​ ​activity​ ​and​ ​blues​ ​and​ ​violets​ ​the​ ​least.

JavaScript

A​ ​popular​ ​web​ ​coding​ ​language,​ ​used​ ​in​ ​web​ ​analytics​ ​for​ ​page​ ​tagging.

Key Performance Indicator (KPI)

A​ ​specific​ ​metric​ ​which​ ​is​ ​used​ ​to​ ​evaluate,​ ​track​ ​and​ ​analyse​ ​progress​ ​against​ ​a business​ ​or​ ​digital​ ​goal.

Log files

Text​ ​files​ ​created​ ​on​ ​the​ ​server​ ​each​ ​time​ ​a​ ​click​ ​takes​ ​place,​ ​capturing​ ​all activity​ ​on​ ​the​ ​website.

Multivariate test

Testing​ ​many​ ​variables​ ​in​ ​different​ ​combinations​ ​to​ ​see​ ​if​ ​one​ ​combination​ ​has​ ​a significant​ ​impact​ ​on​ ​user​ ​actions.​ ​Usually​ ​conducted​ ​as​ ​a​ ​split​ ​test​ ​with​ ​up​ ​to eight​ ​combined​ ​versions.

Page tags

JavaScript​ ​files​ ​embedded​ ​on​ ​a​ ​web​ ​page​ ​and​ ​run​ ​by​ ​the​ ​browser.

PII

Personally​ ​identifiable​ ​information:​ ​any​ ​information​ ​that​ ​could​ ​be​ ​used​ ​to​ ​track​ ​an individual​ ​in​ ​the​ ​real​ ​world,​ ​such​ ​as​ ​home​ ​address,​ ​name,​ ​phone​ ​number​ ​and email​ ​address.

Ratio

An​ ​interpretation​ ​of​ ​data​ ​captured,​ ​usually​ ​expressed​ ​as​ ​a​ ​percentage.

Referrer

The​ ​website​ ​that​ ​a​ ​web​ ​visitor​ ​arrived​ ​from.

Segmentation

Breaking​ ​down​ ​a​ ​whole​ ​into​ ​its​ ​parts​ ​based​ ​on​ ​distinct,​ ​shared​ ​characteristics that​ ​enable​ ​analysis​ ​by​ ​segment.

User

An​ ​individual​ ​(that​ ​is​ ​not​ ​a​ ​search​ ​engine​ ​spider​ ​or​ ​a​ ​script)​ ​visiting​ ​a​ ​website.

Web​ ​analytics​ ​software​ ​collects​ ​many​ ​small​ ​bits​ ​of​ ​browsing​ ​data​ ​to​ ​tell​ ​the​ ​story​ ​of:

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  • How​ ​users​ ​got​ ​to​ ​the​ ​site

● When​ ​they​ ​arrived,​ ​and​ ​which​ ​part​ ​of​ ​the​ ​world​ ​they​ ​were​ ​from

● Whether​ ​they'd​ ​been​ ​to​ ​the​ ​site​ ​before

● What​ ​devices​ ​they​ ​were​ ​using

● What​ ​they​ ​did​ ​on​ ​the​ ​site,​ ​and​ ​for​ ​how​ ​long

● What​ ​they​ ​bought

● When​ ​they​ ​left.

Off-site​ ​and​ ​on-site​ ​analytics

On-site​ ​web​ ​analytics​ ​refers​ ​to​ ​measuring​ ​a​ ​user's​ ​activity​ ​as​ ​they​ ​enter​ ​and​ ​interact​ ​with​ ​your​ ​own website.​ ​This​ ​includes​ ​the​ ​sites​ ​they​ ​came​ ​from,​ ​visitor​ ​information,​ ​the​ ​content​ ​a​ ​user​ ​views​ ​on​ ​your website,​ ​and​ ​conversions.​ ​This​ ​type​ ​of​ ​data​ ​helps​ ​you​ ​to​ ​identify​ ​your​ ​website's​ ​key​ ​performance indicators​ ​(KPIs),​ ​which​ ​measure​ ​site​ ​performance​ ​against​ ​your​ ​objectives.

Off-site​ ​web​ ​analytics​ ​is​ ​the​ ​measurement​ ​and​ ​analysis​ ​of​ ​a​ ​user's​ ​actions​ ​and​ ​interactions​ ​on​ ​the wider​ ​web,​ ​for​ ​the​ ​most​ ​part​ ​across​ ​other​ ​sites​ ​and​ ​tools​ ​(such​ ​as​ ​emails,​ ​social​ ​networks​ ​and mobile​ ​app​ ​stores).​ ​This​ ​information​ ​tracks​ ​a​ ​user's​ ​internet​ ​habits,​ ​interests,​ ​browsing​ ​activities​ ​and navigation​ ​patterns,​ ​which​ ​in​ ​turn​ ​identify​ ​and​ ​measure​ ​potential​ ​market​ ​opportunity.

Quantitative​ ​and​ ​qualitative​ ​data

Quantitative​ ​data​ ​is​ ​objective,​ ​factual​ ​information,​ ​while​ ​qualitative​ ​data​ ​is​ ​subjective,​ ​opinion-based information

Quantitative data: Statistically​ ​reliable​ ​results​ ​that determine​ ​if​ ​one​ ​option​ ​is​ ​better than​ ​the​ ​alternatives. E.g: Clicks,​ ​pageviews,​ ​time​ ​on​ ​site, abandonment​ ​rates

Qualitative data: Looks​ ​at​ ​the​ ​context​ ​of​ ​issues​ ​and aims​ ​to​ ​understand​ ​users' perspectives. E.g: Sentiment,​ ​feeling​ ​towards​ ​brand,​ ​user experience

Integrating​ ​quantitative​ ​and​ ​qualitative​ ​data

Many​ ​people​ ​assume​ ​that​ ​clickstream​ ​data​ ​is​ ​the​ ​source​ ​of​ ​all​ ​online​ ​decision​ ​making.​ ​Clickstream data​ ​is​ ​objective​ ​web​ ​data​ ​which​ ​tracks​ ​the​ ​sequence​ ​or​ ​stream​ ​of​ ​clicks​ ​that​ ​users​ ​make,​ ​however this​ ​represents​ ​only​ ​a​ ​small​ ​part​ ​of​ ​web​ ​data

Generating​ ​insights​ ​is​ ​the​ ​process​ ​of​ ​looking​ ​at​ ​all​ ​the​ ​available​ ​data​ ​-​ ​qualitative​ ​and​ ​quantitative​ ​- and​ ​then​ ​applying​ ​your​ ​own​ ​knowledge,​ ​common​ ​sense​ ​and​ ​lateral​ ​thinking​ ​to​ ​it,​ ​in​ ​order​ ​to​ ​try​ ​to understand​ ​what​ ​has​ ​happened,​ ​and​ ​why.​ ​It's​ ​also​ ​important​ ​to​ ​consider​ ​the​ ​context​ ​based​ ​on​ ​other business​ ​activities​ ​(in​ ​other​ ​words,​ ​what​ ​the​ ​business​ ​or​ ​its​ ​departments​ ​have​ ​done​ ​that​ ​might​ ​help to​ ​explain​ ​performance).​ ​This​ ​context​ ​is​ ​often​ ​needed​ ​for​ ​insightful​ ​analysis.​ ​Even​ ​the​ ​most expensive​ ​analytics​ ​software​ ​won't​ ​yield​ ​actual​ ​insights​ ​on​ ​clickstream​ ​data:​ ​it​ ​will​ ​just​ ​produce​ ​lots​ ​of data​ ​and​ ​reports.

Principles of Data Analysis

Focus​ ​on​ ​performance​ ​metrics,​ ​not​ ​vanit​y.

Vanity​ ​metrics​ ​are​ ​figures​ ​or​ ​measures that​ ​look​ ​interesting​ ​and​ ​useful​ ​on​ ​the​ ​surface,​ ​but​ ​don't​ ​really​ ​contain​ ​any​ ​substance when​ ​examined​ ​more​ ​deeply.​ ​They​ ​are​ ​the​ ​metrics​ ​that​ ​people​ ​like​ ​to​ ​brag​ ​about,​ ​like follower​ ​numbers​ ​on​ ​social​ ​media​ ​and​ ​visitor​ ​counts​ ​on​ ​websites.​ ​While​ ​it's​ ​great​ ​to​ ​see that​ ​a​ ​certain​ ​number​ ​of​ ​people​ ​are​ ​visiting​ ​the​ ​brand,​ ​this​ ​doesn't​ ​say​ ​anything​ ​about whether​ ​they​ ​are​ ​participating​ ​meaningfully.​ ​For​ ​example,​ ​many​ ​Facebook​ ​pages​ ​have​ ​a large​ ​percentage​ ​of​ ​"legacy​ ​likes”​ ​-​ ​people​ ​who​ ​liked​ ​the​ ​page​ ​in​ ​the​ ​past​ ​but​ ​never returned.

Performance​ ​metrics,​ ​on​ ​the​ ​other​ ​hand,​ ​better​ ​indicate​ ​whether​ ​the​ ​brand's​ ​objectives​ ​are being​ ​met.​ ​Some​ ​examples​ ​here​ ​include​ ​conversions​ ​(like​ ​making​ ​a​ ​purchase)​ ​and​ ​engagement metrics​ ​(like​ ​time​ ​spent​ ​viewing​ ​content​ ​or​ ​comments​ ​on​ ​a​ ​blog​ ​post).​ ​

Look​ ​at​ ​trends,​ ​not​ ​absolutes

Web​ ​analytics​ ​is​ ​the​ ​process​ ​of​ ​looking​ ​at​ ​changes​ ​over time,​ ​rather​ ​than​ ​isolating​ ​one​ ​specific​ ​moment.​ ​Trends​ ​give​ ​real​ ​insights,​ ​since​ ​they show​ ​how​ ​things​ ​are​ ​improving​ ​(or​ ​not)​ ​over​ ​time.

Identify​ ​patterns​.​

Once​ ​you​ ​have​ ​gathered​ ​web​ ​data​ ​over​ ​a​ ​long​ ​enough​ ​time,​ ​you​ ​will start​ ​to​ ​see​ ​emerging​ ​patterns​ ​-​ ​these​ ​could​ ​be​ ​daily​ ​traffic​ ​spikes,​ ​regular​ ​weekly​ ​user patterns​ ​or​ ​annual,​ ​seasonal​ ​changes.​ ​Understanding​ ​these​ ​tells​ ​you​ ​the​ ​optimal​ ​time​ ​for posting​ ​new​ ​content,​ ​when​ ​to​ ​expect​ ​annual​ ​dips,​ ​and​ ​much​ ​more​ ​-​ ​excellent​ ​for business​ ​planning​ ​even​ ​beyond​ ​web​ ​analytics.

Investigate​ ​anomalies​

The​ ​beauty​ ​of​ ​understanding​ ​patterns​ ​means​ ​you​ ​can​ ​quickly identify​ ​if​ ​anything​ ​out​ ​of​ ​the​ ​ordinary​ ​is​ ​happening​ ​on​ ​your​ ​website.​ ​Any​ ​unexpected increase​ ​or​ ​decrease​ ​in​ ​typical​ ​traffic​ ​patterns​ ​should​ ​be​ ​investigated.​ ​This​ ​could​ ​reveal new​ ​opportunities​ ​or​ ​problems​ ​that​ ​need​ ​to​ ​be​ ​resolved​ ​quickly.

Don't​ ​forget​ ​the​ ​real​ ​world​.

Context​ ​is​ ​an​ ​essential​ ​consideration​ ​when​ ​working​ ​with web​ ​data;​ ​after​ ​all,​ ​things​ ​on​ ​the​ ​internet​ ​don't​ ​happen​ ​in​ ​isolation.​ ​They​ ​are​ ​directly influenced​ ​by​ ​news​ ​events,​ ​personal​ ​characteristics,​ ​trends,​ ​national​ ​and​ ​religious holidays,​ ​and​ ​much​ ​more.​ ​Keep​ ​a​ ​close​ ​eye​ ​on​ ​your​ ​brand's​ ​real-world​ ​context​ ​to​ ​ensure you​ ​have​ ​a​ ​full​ ​picture​ ​of​ ​what​ ​your​ ​data​ ​is​ ​trying​ ​to​ ​tell​ ​you.

Objectives, Goals, KPIs & Targets

Objectives

Your​ ​online​ ​objectives​ ​need​ ​to​ ​reflect​ ​and​ ​support​ ​your​ ​business​ ​objectives.​ ​Ask​ ​yourself:​ ​why​ ​does your​ ​website​ ​exist?​ ​What​ ​role​ ​does​ ​it​ ​play​ ​in​ ​your​ ​business​ ​model?​ ​How​ ​does​ ​it​ ​help​ ​to​ ​achieve​ ​the goal​ ​of​ ​making​ ​money?​ ​It​ ​is​ ​very​ ​important​ ​to​ ​align​ ​your​ ​website​ ​(and​ ​web​ ​analytics)​ ​objectives​ ​with business​ ​objectives​ ​so​ ​that​ ​your​ ​online​ ​investments​ ​bring​ ​the​ ​desired​ ​results​ ​to​ ​the​ ​business.​ ​This​ ​is not​ ​only​ ​true​ ​for​ ​ecommerce​ ​businesses​ ​-​ ​clear​ ​objectives​ ​are​ ​important​ ​even​ ​if​ ​your​ ​website​ ​does not​ ​make​ ​money​ ​directly.

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5 ​types​ ​of​ ​digital​ ​strategies​ ​that​ ​all​ ​have​ ​slightly different​ ​objectives

Ecommerce - Selling​ ​products​ ​and​ ​services

Lead generation - Collecting potential leads

Content publication - encouraging engagement & repeat visits