Episode 26: SFMC Bootcamp: Data Views: Get Started with Salesforce Marketing Cloud Data Views

00:43:15
https://www.youtube.com/watch?v=KROUOqzTmUM

Resumo

TLDRDette indhold præsenterer en omfattende introduktion til data views i Marketing Cloud, herunder hvordan man navigerer i disse databaser for at få indsigt i abonnenters adfærd gennem sendninger, åbninger og klik. Det beskriver relationerne mellem forskellige data views og giver praktiske eksempler på, hvordan man effektivt kan bruge SQL til at tilpasse rapportering og analysere engagement. Feedback og anbefalinger til bedste praksis for brug af data views til strategisk markedsføring er også blevet diskuteret. Desuden præsenteres metoder til at generere brugerdefinerede engagementscores og sikre kvalitetsdata uden at skabe forkerte værdier.

Conclusões

  • 📊 Data views giver dybdegående indsigt i abonnenters interaktioner.
  • 💻 SQL anvendes til at trække data fra forskellige views.
  • 🌟 Brug af brugerdefinerede engagement scores kan forbedre målretning.
  • 🔄 Data kan eksporteres til eksterne systemer for videre analyse.
  • ⏳ Data views har en seks måneders tilbageholdelsesperiode.
  • 📈 Effektiv brug af data views kan optimere marketingkampagner.
  • 📋 Dokumentation er afgørende for korrekt brug af data views.
  • ⚙️ Automatisk dataudtræk kan lette rapportering.
  • 👥 Retargeting kan udføres baseret på tidligere engagement.
  • ✅ Det er bedre at have null-værdier end fejlagtige data i systemet.

Linha do tempo

  • 00:00:00 - 00:05:00

    I denne video gennemgås forholdene mellem abonnenter og lister i Marketing Cloud, herunder hvordan man kan identificere, hvilke lister en abonnent er en del af, samt hvordan man kan finde ud af, hvilke abonnenter der har afmeldt sig. Diagrammer bruges til at illustrationere disse relationer.

  • 00:05:00 - 00:10:00

    Der gives en detaljeret forklaring af send-jobs i Marketing Cloud, som indeholder information om, hvilke abonnenter der har modtaget en given e-mail. Det understreges, at for hver send-job er der en række poster for de abonnenter, der fik sendt e-mailen, og hvordan disse relationer kan trackes via job-ID'er.

  • 00:10:00 - 00:15:00

    Videoen diskuterer også åbne og klik data, hvor det fremhæves, at åbning af e-mails kan generere flere poster, da åbningsevents tælles, hver gang e-mailen åbnes. Det påpeges, at unikke åbninger og klik kan anvendes til at få indsigt i abonnenternes engagement.

  • 00:15:00 - 00:20:00

    Narrativet skifter til automatisering og rejseaktiviteter, som introducerer nye datavisninger i Marketing Cloud. Visningerne for automatisering og rejser gør det muligt at spore abonnenters interaktioner med de e-mails, der sendes i forbindelse med automatiseringen.

  • 00:20:00 - 00:25:00

    Der er også en omtale af mobile datavisninger, som dog er mindre udforskede. Disse tilvejebringer information om mobiladresser og SMS-afsendelser, hvilket viser integrationen mellem mobilmarkedsføring og abonnentoplysninger.

  • 00:25:00 - 00:30:00

    Der tages fat på markeringscloudens integration med Salesforce, som muliggør synkronisering af tre nøgleobjekter: kontakter, leads og brugere. Dette sikrer, at oplysninger om disse objekter er tilgængelige i Marketing Cloud.

  • 00:30:00 - 00:35:00

    Videoen beskriver derefter, hvordan man kan uddrage data fra Marketing Cloud ved hjælp af SQL-forespørgsler, hvor man kan sammenflette data fra forskellige datavisninger for at skabe brugbare rapporter.

  • 00:35:00 - 00:43:15

    Til sidst opfordres seerne til aktivt at udforske Marketing Clouds datavisninger for at opdage de rige datakilder, der kan bruges til at forbedre engagement og marketingstrategier. Derudover opfordres deltagerne til at stille spørgsmål og deltage i diskussionen omkring emnet.

Mostrar mais

Mapa mental

Vídeo de perguntas e respostas

  • Hvad er data views i Marketing Cloud?

    Data views er tabeller, der opbevarer data relateret til abonnenter og e-mailsendelser, hvilket gør det muligt at analysere engagementoplysninger som åbninger og klik.

  • Hvordan kan jeg få adgang til data views?

    Data views kan tilgås via SQL-forespørgsler i Automation Studio eller Query Studio.

  • Hvor længe gemmes data i data views?

    Data views har typisk en standard tilbageholdelsesperiode på seks måneder.

  • Kan jeg tilføje brugerdefinerede felter til data views?

    Ja, tilpassede felter kan integreres i send-loggene, forudsat at de matches korrekt.

  • Hvordan kan jeg træne SQL relateret til data views?

    Det anbefales at anvende Query Studio for at få praktisk erfaring med at skrive forespørgsler.

  • Er der dokumentation tilgængelig for data views?

    Ja, der er omfattende dokumentation tilgængelig online, især fra Marketing Cloud's officielle supportsite.

  • Hvad er den bedste måde at udtrække data fra Marketing Cloud på?

    Brug Automation Studio til at køre SQL-forespørgsler og hente resultaterne til dataudvidelser, som derefter kan eksporteres.

  • Hvordan kan jeg analysere abonnenters engagement?

    Ved at oprette SQL-forespørgsler, der analyserer sendninger, åbninger og klik for at vurdere engagementsniveauer.

  • Hvad er fordelene ved at bruge data views i Marketing Cloud?

    Data views giver detaljeret indsigt i abonnentadfærd og kampagneeffektivitet, hvilket muliggør præcise dataanalyser og optimering af markedsføringsstrategier.

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Rolagem automática:
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    okay perfect thank you so our list
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    subscribers again have been that view of
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    all of our lists so how does that relate
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    now back onto our subscribers like I
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    said subscribe a key relating back on a
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    one too many relationships we can look
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    back and find which lists our subscriber
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    is part of
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    additionally if you want to find out
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    what subscribers have unsubscribed but
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    we do have our unsubscribe table here
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    which again does relate back on
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    subscriber key to subscriber key but
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    also on our list ID back to our list ID
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    so we know also what lists found
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    subscribed from so you can see here from
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    this diagram it does create this very
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    visual representation of how these data
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    view is all interrelate now Imagine That
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    Sent example I gave earlier down here
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    you'll find so that's send information
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    now an email inside marketing Cloud when
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    you do press send it's known as a send
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    job and that job contains batches so for
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    example our job of sends here we have of
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    course our information about the email
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    that was sent to the emails ID the
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    account number that we sent the from
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    name and email address we sent to that
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    email from as well as the email's name
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    in content Builder and these emails
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    subject line so some really good stuff
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    in here as well for reporting but all
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    this information is just the top level
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    information of that email when we press
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    send women do press send let's say to a
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    data extension of 100 subscribers there
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    will be one row in our job job data View
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    and a hundred rows in our sent data view
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    because there are a hundred subscribers
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    who were sent that job and can see that
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    relationship for ourselves because our
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    scent table here relates back on our job
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    ID back to job ID here we can see that
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    our subscriber key relates and
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    subscriber key back to our full
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    subscribers table here but also the list
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    that we sent you you may have noticed
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    when you press send in marking cloud
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    you can choose what list you send to a
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    publication list or a normal list if you
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    choose all subscribers of course that is
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    actually a list so we'll choose the all
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    subscribers list ID or if you choose a
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    publication list or use that list ID as
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    well
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    now once that email has been sent of
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    course we then want to see who opens it
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    now I good open rates about 20 to 30
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    percent so let's say that 30 of those
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    100 subscribers opened the email well
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    you're going to have at least 30 records
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    here in open now the cool thing is is
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    that the open Pixel that exists inside
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    of the template of your marketing Cloud
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    emails will actually re-render every
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    time someone reopens that email so you
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    will find that even though only 30
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    unique subscribers now opened your email
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    you may have 50 60 or 70 open rows
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    because a subscriber may have reopened
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    that email on their device multiple
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    times and so you would get an open event
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    date every time they reopen that email
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    so if you're trying to find when they
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    first open the email you may have to
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    start with the unique field or find the
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    earliest event date the earliest date to
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    which that email is opened it also means
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    you can find out customers who do go
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    back and reopen emails so a great use
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    case for this is you want to find out
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    what emails people are opening a lot now
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    there are some brands that way send out
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    things like when you purchase an item
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    and they send you some warranty
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    information I'll send you some how-to
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    information I know when I bought a car
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    seat I would have logged an email that
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    shows me how to install the car seat
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    I didn't want to have to read the book
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    I'd rather see a YouTube video so I'd
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    rather someone have sent me an email
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    which said hey here's information on how
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    to install this car seat I would have
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    gone back and reopened that email every
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    time I had to uninstall and reinstall
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    the car seat so you would have seen my
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    data occur every time I reopened that
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    email I would have a whole new entry
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    into the open data View
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    now the same thing happens to click of
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    course when your subscribers do open the
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    email then click on a link every single
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    time a link is clicked a new row is
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    inserted to The Click table and again
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    it's all tracked back that job ID back
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    to jobs the list ID back to list the
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    subscribing key back to subscribers you
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    see where I'm going here so it all
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    connects in together
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    it's definitely worth exploring the data
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    that exists in each of these views there
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    is a whole of information that you'll
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    see inside these tables that doesn't
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    exactly get you know shown in the usual
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    tracking reports within marketing Cloud
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    that includes Genie Builder the marking
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    Cloud intelligence and also the email
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    Studio reporting some of these fields
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    just don't get shown you do see
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    summaries so for example in the email
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    Studio when you go into the click
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    tracking report you see the email's
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    performance and it will tell you how
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    many Clicks in total and how many unique
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    clicks that's a great summary you can
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    then click of course on that little
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    number and in the report and you can see
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    how many users they were uniquely
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    clicked on the email but then that's a
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    list that you have to download as a CSV
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    and then you'd have to re-import it into
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    marketing cloud and that's a bit
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    cumbersome so instead you could just
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    query The Click table looking for the
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    job ID which you have to find out from
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    the email send that you did the list
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    that you send to returning back those
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    subscriber keys so again some great news
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    actual information as well but my new
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    favorites are the journeys and the
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    automation ones like I said earlier we
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    now have this brand new there's two
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    brand new data views for automations the
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    automation instance and activity
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    instance the instance of course being
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    the automation itself and the activity
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    instance being the activities within the
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    automation
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    now the journey and journey activity are
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    two more data views generally been of
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    course the journey itself and the
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    engineering activity being an activity
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    within the journey now again bear in
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    mind that these links I've created on
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    this diagram do allow you to see how
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    each of these tables relate so a great
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    example which actually covered recently
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    in my videos was how do I find out users
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    who have sent an email engineer Builder
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    and then maybe interacted with it
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    somehow but the best part is if you know
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    the journey because you know the
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    Journey's name you can search for that
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    Julie name and you can link the journey
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    version ID back to the journey activity
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    version ID in this journey activity
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    table you can use the journey activity
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    object ID to relate to a jobs triggered
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    send definition object ID this is the
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    job table oops sorry one more up uh one
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    moreover the job table here so on our
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    job
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    the activity object ID relates to this
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    send the triggered send definition
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    object ID and from here we now have the
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    job ID which allows us to then see who
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    was sent the email who opened and who
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    clicked so from a journey we can find
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    every email in the journey from every
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    email you can find every job from every
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    job you can find every send every send
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    every open and off we go you can make
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    some really interesting data sets
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    because you can now connect these tables
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    together
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    now again it's more than just the email
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    Studio as well if I scroll over to the
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    left hand side of this diagram you'll
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    see a few of the mobile ones now fair
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    warning some of these mobile ones are
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    not officially supported or documented
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    they do exist for the back of mounting
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    cloud and you can find them out for
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    yourself through that experience from a
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    couple years ago I managed to write some
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    of these ones down and expose them so we
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    can see how it all interrelate now I
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    haven't spent too much time as a few
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    unconnected data views here
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    let's reconnects there it is there's a
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    few unconnected ones because I have not
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    spent too much time on the mobile side
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    of the platform to connect these up but
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    you can see there is impacts and
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    relationships between a mobile
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    subscriber that is a mobile address and
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    how they connect up into the SMS send
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    logs and subscription logs and message
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    tracking these been the most common and
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    most useful of your mobile data views
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    mobile address being as close as you can
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    get to a subscribers table for mobile
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    connect the send blog of course being a
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    log and tracking being the tracking for
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    those SMS sends the subscription logs
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    showing you exactly what keywords your
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    mobile subscribers are subscribed to
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    which of course will link back into
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    their subscription mobile table here as
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    well so again useful stuff I'm not going
  • 00:07:51
    to mention this because you can go
  • 00:07:52
    research these for yourself the ones
  • 00:07:54
    that are not documented again they're
  • 00:07:55
    not officially supported you will find
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    some documentation across the internet
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    as other users have over the years found
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    these data views and used them but again
  • 00:08:03
    your use case will dictate how you go
  • 00:08:05
    forth and use these tables the key here
  • 00:08:07
    being it's hard to solution for things
  • 00:08:09
    unless you know what exists so hopefully
  • 00:08:11
    through this diagram here you can see
  • 00:08:14
    there are a lot a lot of data views and
  • 00:08:16
    a lot of really cool data that you can
  • 00:08:18
    relate to before I move on I'm going to
  • 00:08:20
    show you one last piece and if I scroll
  • 00:08:22
    up here
  • 00:08:23
    for those of you who are using the
  • 00:08:24
    marking Cloud connector and have access
  • 00:08:26
    to a Salesforce org you will also note
  • 00:08:29
    that there are some very common tables
  • 00:08:31
    that are brought in from your Salesforce
  • 00:08:32
    organs marketing Cloud those being your
  • 00:08:34
    contacts your leads and your users now
  • 00:08:37
    these are the three main objects that
  • 00:08:38
    marking Cloud considers to be humans
  • 00:08:40
    humans mean contacts and therefore
  • 00:08:42
    contactable for marketing purposes other
  • 00:08:45
    tables will have different relationships
  • 00:08:46
    but these ones will always create a
  • 00:08:48
    contact when they are loaded into
  • 00:08:49
    marketing clouds for that reason I have
  • 00:08:51
    included these three data views or these
  • 00:08:54
    three tables there's of course a lot
  • 00:08:56
    more fields to these when you load them
  • 00:08:58
    in through the Salesforce connector but
  • 00:09:00
    I have connected up their contact key
  • 00:09:01
    and ID just for visual purposes so you
  • 00:09:03
    can see how they relate back to a
  • 00:09:05
    contact
  • 00:09:06
    so that in mind if you're doing a more
  • 00:09:08
    purest view of how the data views exist
  • 00:09:10
    I have got in my links here the white
  • 00:09:12
    version of this diagram and this is the
  • 00:09:15
    documentation correct version of data
  • 00:09:18
    views so you'll see it's missing a few
  • 00:09:19
    data views here but it does show how
  • 00:09:21
    it's meant to look as a as a pure form
  • 00:09:23
    with none of my additional fabricated
  • 00:09:26
    tables included so again those two links
  • 00:09:29
    I've got the one link already in the
  • 00:09:30
    chat of today's meeting I'll put the
  • 00:09:32
    second one into the chat there as well
  • 00:09:35
    you can also find some documentation on
  • 00:09:37
    this if you Google it these are the
  • 00:09:39
    marketing cloud data views and you will
  • 00:09:41
    find this link on my blog and my YouTube
  • 00:09:43
    channel
  • 00:09:44
    now moving on I've not put too much
  • 00:09:46
    information around the actual data and
  • 00:09:48
    functions Behind These data views I've
  • 00:09:50
    made these diagrams as a navigatable and
  • 00:09:53
    interactable data view the goal here was
  • 00:09:56
    to make it easy to understand how to get
  • 00:09:57
    to data if I want to find out which
  • 00:09:59
    subscribers clicked on a thing I can now
  • 00:10:01
    see my clicks and see if they're linked
  • 00:10:03
    back through job into my subscribers
  • 00:10:04
    that was the goal of this table but as
  • 00:10:07
    you go forth and start to architect
  • 00:10:08
    Solutions and include your data views
  • 00:10:10
    you may want some more information and I
  • 00:10:12
    can definitely recommend no one better
  • 00:10:14
    than to go with matthias's blog on his
  • 00:10:16
    system data using marketing Cloud I'll
  • 00:10:18
    put his link into the description here
  • 00:10:20
    as well but he's also got fantastic SEO
  • 00:10:23
    so if you do search up your Salesforce
  • 00:10:25
    marketing cloud data views you will find
  • 00:10:27
    his blog obviously at the very very top
  • 00:10:29
    of Google
  • 00:10:30
    he has got some fantastic documentation
  • 00:10:33
    on how these database work and not just
  • 00:10:35
    about the view themselves not just the
  • 00:10:37
    data views at the top level but it also
  • 00:10:39
    includes some great examples use cases
  • 00:10:41
    and he also Dives deeper and talks about
  • 00:10:43
    some of the individual for example pick
  • 00:10:45
    the scaliers here we go within some of
  • 00:10:47
    those statuses so for example this
  • 00:10:48
    status field which you have access to
  • 00:10:50
    inside of our subscriber view has
  • 00:10:52
    multiple options behind it he has gone
  • 00:10:54
    ahead and actually documents what those
  • 00:10:56
    options are so again has gone above and
  • 00:10:58
    beyond when documentation is at stake
  • 00:11:00
    here and you definitely want to be
  • 00:11:02
    referencing his blog when it comes to
  • 00:11:03
    researching how to use these data views
  • 00:11:05
    and how they are structured but also
  • 00:11:07
    some of these known behind the scenes
  • 00:11:09
    examples from the hunter sends data
  • 00:11:11
    points
  • 00:11:12
    so again his link is in the chat
  • 00:11:13
    definitely definitely worth checking
  • 00:11:15
    these ones out
  • 00:11:17
    so that in mind why these interviews
  • 00:11:19
    again important so we do have access to
  • 00:11:22
    a few ways to get data out of Marketing
  • 00:11:23
    cloud and I'm sure a few of you have
  • 00:11:25
    probably already explored these and when
  • 00:11:26
    it comes to getting data out there's
  • 00:11:27
    nothing more you can do really that
  • 00:11:29
    exporting data uh from marketing Cloud
  • 00:11:31
    using the extract function now you can
  • 00:11:34
    use the tracking extract function I've
  • 00:11:36
    done a video on this one previously as
  • 00:11:38
    well where you can extract things like
  • 00:11:40
    your sends your opens your clicks your
  • 00:11:41
    not sent and so many other points of
  • 00:11:44
    data the problem is it's going to use
  • 00:11:46
    that data back in marketing Cloud you do
  • 00:11:49
    have to extract the data unzip the data
  • 00:11:52
    then import the data and then you can
  • 00:11:54
    use it now that may need to be done for
  • 00:11:56
    some use cases but for the most or for
  • 00:11:58
    most use cases you shouldn't need to go
  • 00:12:00
    through such a process you can usually
  • 00:12:02
    just use a data meter solo for you again
  • 00:12:05
    I had done a video on tracking extract
  • 00:12:07
    and re-import process so you can't do
  • 00:12:09
    that for yourself it's on my YouTube
  • 00:12:10
    channel but in the meantime the most
  • 00:12:12
    common use case is going to be just be
  • 00:12:14
    using data view to get that data for you
  • 00:12:16
    now a few examples that I see most
  • 00:12:18
    commonly when it comes to utilization of
  • 00:12:20
    data views is things like retargeting
  • 00:12:22
    and re-making things like measures I
  • 00:12:25
    talked through a quick one earlier
  • 00:12:26
    earlier but I'll go through it again I
  • 00:12:28
    may have had an email in the past that
  • 00:12:30
    I'd sent you now come from the Journey
  • 00:12:31
    For example so I'll scroll and head to
  • 00:12:33
    my Journeys and that Journey email may
  • 00:12:35
    be in a series or a campaign of emails
  • 00:12:37
    but I want to retarget all my customers
  • 00:12:40
    who opened this campaign series I sent
  • 00:12:42
    three emails through message studio and
  • 00:12:45
    I sent two different Genius of the Q a
  • 00:12:47
    snatch and I get all those emails
  • 00:12:48
    together now to do that using a measure
  • 00:12:51
    or a data filter would be almost
  • 00:12:53
    impossible that would take a lot of time
  • 00:12:55
    a lot of that is but by using our data
  • 00:12:57
    views I could make a very simple SQL
  • 00:12:59
    statement in automation Studio where I
  • 00:13:01
    could say get me the journeys named this
  • 00:13:05
    or this I want to get these two Journeys
  • 00:13:06
    I want to get all of the emails across
  • 00:13:09
    all the versions of those two Journeys
  • 00:13:11
    it could be all those activities
  • 00:13:13
    then give me all the jobs of all those
  • 00:13:14
    sends that were conducted
  • 00:13:17
    obviously from the drop give me the
  • 00:13:18
    sense and from the sense get media opens
  • 00:13:21
    as well as the jobs that I send through
  • 00:13:23
    email studio and Union those together
  • 00:13:26
    by making that data set you can one
  • 00:13:28
    simple query query these data views and
  • 00:13:30
    construct yourself your very own
  • 00:13:31
    retargeting data extension the best part
  • 00:13:34
    is because it all can be done with SQL
  • 00:13:35
    you can then apply that to an Automation
  • 00:13:37
    and have that Rerun every day to add new
  • 00:13:39
    subscribers to that list so as a use
  • 00:13:42
    case it now can exceed the power of a
  • 00:13:44
    measure straight away you'd have to
  • 00:13:45
    build multiple measures to do this
  • 00:13:48
    individually but by using a data View
  • 00:13:50
    and some neat SQL you can build yourself
  • 00:13:53
    your very own retargeting set and very
  • 00:13:55
    very quickly
  • 00:13:56
    another example but I'm actually
  • 00:13:58
    particularly fond of is the use of
  • 00:14:00
    creating your very own retargeting
  • 00:14:02
    engagement score now again every
  • 00:14:05
    customer is different every brand is
  • 00:14:06
    different every business need is
  • 00:14:08
    different and everyone's definition of
  • 00:14:10
    engaged is also different I'm a huge fan
  • 00:14:13
    of the OnStar engagement scores and the
  • 00:14:14
    frequency scores it's a great measure
  • 00:14:16
    it's a great way to look at your
  • 00:14:19
    subscribers and how they're engaging
  • 00:14:20
    with your brand the idea that someone
  • 00:14:22
    can be engaged or disengaged they can be
  • 00:14:24
    a Windows Shopper or a Latin customer I
  • 00:14:26
    think is a very great visual
  • 00:14:27
    representation of what is in marketing
  • 00:14:29
    cloud and how they're engaging with you
  • 00:14:31
    but for some Brands the idea of
  • 00:14:34
    fast-moving consumer goods where you're
  • 00:14:36
    buying clothes or buying groceries every
  • 00:14:38
    week is very different to someone who
  • 00:14:40
    buys a car once every three to five
  • 00:14:42
    years
  • 00:14:43
    so the idea of Engagement can change
  • 00:14:45
    drastically between different customers
  • 00:14:47
    again someone who buys milk and bread
  • 00:14:49
    from the grocery store versus a car once
  • 00:14:51
    every three to five years very different
  • 00:14:53
    email campaigns very different kinds of
  • 00:14:54
    Engagement and so sometimes you may need
  • 00:14:57
    your very own form of Engagement your
  • 00:14:59
    very own way of calculating and
  • 00:15:00
    measuring engagement
  • 00:15:02
    so to do this one of the ways I do see
  • 00:15:04
    customers do this is they use things
  • 00:15:05
    like the my the Einstein MC predictive
  • 00:15:08
    scores table again this is a direct
  • 00:15:09
    extension you can see inside of your
  • 00:15:11
    main direct Central folder and they will
  • 00:15:13
    link this up with their existing sends
  • 00:15:16
    opens and clicks now what I mean by this
  • 00:15:18
    is if you know that you're subscribers
  • 00:15:20
    you consider an Engaged customer not
  • 00:15:23
    just to be what Einstein says is there
  • 00:15:26
    likeness or their engagement score but
  • 00:15:28
    perhaps you say
  • 00:15:29
    but you know what I think someone who
  • 00:15:31
    has clicked at least 25 percent of the
  • 00:15:36
    emails that we've sent
  • 00:15:37
    25 of the emails you send in the last
  • 00:15:39
    six months that's an Engaged customer if
  • 00:15:42
    they have not clicked at least 25 of the
  • 00:15:45
    emails I've sent them in the last six
  • 00:15:47
    months I will consider them disengaged
  • 00:15:49
    and you'll probably receive more
  • 00:15:51
    engaging content
  • 00:15:52
    well I can run a very simple query for
  • 00:15:54
    that we could say for every subscriber
  • 00:15:57
    join up against the sense table and
  • 00:16:00
    count how many times they've been sent
  • 00:16:02
    emails for some subscribers that could
  • 00:16:04
    be only five emails in the last six
  • 00:16:06
    months but others who are subscribing to
  • 00:16:09
    all your preferences and want to get all
  • 00:16:10
    your different content types they may
  • 00:16:12
    received 50 emails in the last six
  • 00:16:14
    months so again very different volume
  • 00:16:16
    amounts you can then do the same thing
  • 00:16:18
    can join up against the click table and
  • 00:16:21
    say well okay I've now got for every
  • 00:16:22
    subscriber I know how many emails that
  • 00:16:24
    were sent now I want to know how many
  • 00:16:27
    times they clicked but not just how many
  • 00:16:29
    times they clicked because they could
  • 00:16:30
    re-click the same link multiple times
  • 00:16:32
    and that's that doesn't count so only
  • 00:16:36
    select the clicks where the is unique
  • 00:16:39
    field here is equal to true that is only
  • 00:16:41
    count unique Clicks in the email if they
  • 00:16:44
    clicked multiple links that counts but
  • 00:16:46
    not the same link multiple times
  • 00:16:49
    and all of a sudden I've now got the
  • 00:16:51
    subscriber the count of emails and the
  • 00:16:53
    count of unique clicks and quite simply
  • 00:16:56
    if I divide the number of sends by the
  • 00:16:58
    number of unique clicks
  • 00:17:00
    all the way around then I'll have a
  • 00:17:02
    percentage of their engagement and
  • 00:17:03
    anyone who's got a percentage above 0.25
  • 00:17:05
    or 25 engagement I can consider engaged
  • 00:17:09
    I should send them one-time
  • 00:17:10
    communication
  • 00:17:11
    but if your engagement is below that
  • 00:17:13
    then I'm going to send you something
  • 00:17:14
    different
  • 00:17:15
    so you can very quickly and easily
  • 00:17:17
    fabricate your very own re-engagement
  • 00:17:19
    calculations just using one two three
  • 00:17:22
    data views and a very simple SQL query
  • 00:17:26
    so again some very powerful and very
  • 00:17:28
    simple use cases to go through
  • 00:17:31
    now with that I did want to stop on the
  • 00:17:34
    Showcase of what data views are and how
  • 00:17:36
    they work early because I really wanted
  • 00:17:37
    to hear from the community as to some of
  • 00:17:39
    the examples or some of the questions
  • 00:17:40
    you have about data views the reason
  • 00:17:42
    that there are a big passion point of
  • 00:17:44
    mind and when I agreed to have a chat
  • 00:17:45
    today with you all is because I do truly
  • 00:17:47
    believe that the data within marketing
  • 00:17:49
    cloud is a very very powerful asset that
  • 00:17:51
    so few users truly utilize the user
  • 00:17:54
    interface is great it gives you some
  • 00:17:55
    great information but the data is where
  • 00:17:57
    the party is at there is so much cool
  • 00:17:59
    stuff stored in these tables and once
  • 00:18:01
    you start to poke around and have a look
  • 00:18:03
    and again through this diagram here
  • 00:18:04
    today just see how much data that truly
  • 00:18:07
    is you can begin to think about how you
  • 00:18:09
    can use that data to your advantage
  • 00:18:11
    again I mentioned a few examples today
  • 00:18:12
    the ability to find Journeys and sends
  • 00:18:15
    and people who clicked on those emails
  • 00:18:16
    and those Journeys or to even recreate
  • 00:18:18
    your own engagement score system based
  • 00:18:20
    on your business's idea of Engagement
  • 00:18:22
    not engaged these things are not easy to
  • 00:18:25
    do in the user in the user interface but
  • 00:18:27
    through these data views they're quite
  • 00:18:29
    simple
  • 00:18:31
    so with that I do want to take a second
  • 00:18:33
    now to have a conversation and have a
  • 00:18:35
    bit of a FAQ or a uh any examples that
  • 00:18:38
    you have that can start to answer for
  • 00:18:39
    you in the meantime if you do want to
  • 00:18:41
    hear more about data views I have spent
  • 00:18:42
    some more time on this topic I've got
  • 00:18:44
    two videos to start you off one is the
  • 00:18:46
    introduction to data views inside
  • 00:18:47
    marketing Cloud it's pretty much a
  • 00:18:49
    summary or I'll cover here today in a
  • 00:18:51
    nice little uh some minute chunk so I'll
  • 00:18:53
    put that one into the chat there as well
  • 00:18:55
    one of the big examples I do carry on a
  • 00:18:57
    lot about is also
  • 00:18:59
    the recreation of measures using SQL if
  • 00:19:03
    anyone here who has used measures
  • 00:19:04
    they're a fantastically easy drag and
  • 00:19:06
    drop way to re-engage and retarget based
  • 00:19:09
    on activity that's occurred in your
  • 00:19:10
    instance so for example where they sent
  • 00:19:12
    this email or not send this email did
  • 00:19:14
    they click this email have they clicked
  • 00:19:16
    at least one email in the last 14 days
  • 00:19:18
    these very marketing Centric ways of
  • 00:19:21
    communicating engagement are made really
  • 00:19:23
    really easy using measures the problem
  • 00:19:25
    is that measures use the filter
  • 00:19:27
    methodology and filters can notoriously
  • 00:19:29
    be a little bit slow sometimes
  • 00:19:31
    particularly when you have huge amounts
  • 00:19:33
    of subscribers or huge amounts of Sims
  • 00:19:36
    historically so when measures kind of
  • 00:19:38
    become non-performant for you you may
  • 00:19:40
    want to use SQL and so I do have this
  • 00:19:42
    video here which does carry on for about
  • 00:19:44
    30 minutes sorry I do till quickly in
  • 00:19:47
    the video but there's a lot to cover so
  • 00:19:49
    you can check out this one here which
  • 00:19:50
    does go through a whole series of
  • 00:19:52
    examples I believe I do recreate the
  • 00:19:54
    total unique opens last three days I
  • 00:19:57
    would also create the total marketing
  • 00:19:58
    standard is I've got emails not opened
  • 00:20:01
    must feed data system great use cases of
  • 00:20:04
    where you might want to use these data
  • 00:20:05
    views to recreate things you might
  • 00:20:07
    normally do inside measures
  • 00:20:09
    now with that again we do have some more
  • 00:20:11
    time in this meeting so I did want to
  • 00:20:12
    open up the floor to any conversations
  • 00:20:14
    or questions or use cases you may have
  • 00:20:16
    as you've gone through and hope which
  • 00:20:17
    you've heard today or may have used
  • 00:20:19
    interviews in the past and just had some
  • 00:20:21
    simple questions so I will keep talking
  • 00:20:23
    but in the meantime I please do
  • 00:20:25
    encourage you to either throw your hand
  • 00:20:27
    up with that little Emoji I know it's on
  • 00:20:29
    the tray of your Zoom core you can use
  • 00:20:31
    the raise hand emoji I believe it looks
  • 00:20:34
    a little bit like that you can raise
  • 00:20:36
    your hand and you can jump on the mic
  • 00:20:38
    and ask a question otherwise if you
  • 00:20:39
    don't want to jump on the mic feel free
  • 00:20:40
    to use the chat function have a chat and
  • 00:20:43
    let me know what you think
  • 00:20:48
    so in the meantime as you all type in
  • 00:20:51
    any questions that you have uh one of
  • 00:20:53
    the things that I do love about the data
  • 00:20:55
    views here as well is they all stored
  • 00:20:56
    they're all stored in SQL and so all
  • 00:20:58
    these data types here are quite easy to
  • 00:21:00
    use now do bear in mind that you'll see
  • 00:21:02
    some common terms here things like event
  • 00:21:04
    date the event date is related to the
  • 00:21:07
    data view that it's in so as you're
  • 00:21:09
    writing out these SQL queries a quick
  • 00:21:10
    tip make sure you're always remembering
  • 00:21:12
    what table you're operating in when you
  • 00:21:14
    reference the event date for example the
  • 00:21:17
    event date for click will be the event
  • 00:21:19
    time and date to which the click took
  • 00:21:22
    place in the server time which don't
  • 00:21:24
    forget is in the American server time so
  • 00:21:28
    you may have to use asql function to
  • 00:21:30
    then pivot that time around to your
  • 00:21:31
    local time if you're trying to find
  • 00:21:32
    users who clicked on a link before
  • 00:21:34
    midnight or some of the kind of campaign
  • 00:21:36
    requirement for the opens of course the
  • 00:21:38
    event date is when that pixel was
  • 00:21:40
    rendered for the open now be aware of
  • 00:21:42
    that when it comes to things like the
  • 00:21:43
    Apple privacy thing because it will re
  • 00:21:46
    uh pre-case those images including the
  • 00:21:48
    open pixel the users who are using that
  • 00:21:50
    feature so bear in mind that if you do
  • 00:21:52
    see a spike of event date or open Event
  • 00:21:55
    dates that could be users who are using
  • 00:21:57
    the Apple Mail privacy otherwise you can
  • 00:21:59
    also look for users who reopen emails
  • 00:22:01
    consistently and you can also use that
  • 00:22:03
    event time as their reopening time
  • 00:22:06
    I found it's a pretty cool metric to
  • 00:22:07
    find out when you send someone a voucher
  • 00:22:09
    for example for their birthday and that
  • 00:22:11
    email gets reopened a number of times
  • 00:22:13
    you can see the number of times it's
  • 00:22:15
    opened and also the gap between when
  • 00:22:17
    they open that email because you may
  • 00:22:18
    find someone who goes back and reopens
  • 00:22:20
    an email multiple times obviously has an
  • 00:22:21
    intent to use that voucher just hasn't
  • 00:22:24
    got a reason to use it just yet so
  • 00:22:26
    there's a great campaign idea if you
  • 00:22:28
    send that a voucher you could then go
  • 00:22:29
    back and query who has opened this email
  • 00:22:32
    a couple of times add them to a journey
  • 00:22:34
    and say Hey you clearly want to use this
  • 00:22:37
    voucher let's give you a reason here's a
  • 00:22:39
    further 10 off if you buy now
  • 00:22:42
    do you have one question so feel free to
  • 00:22:44
    jump on the mic
  • 00:22:46
    hey hi I'm like um this is a really
  • 00:22:47
    great session thank you for sharing all
  • 00:22:49
    your knowledge with our participants so
  • 00:22:51
    you have one question where apart from
  • 00:22:52
    query studio uh do you recommend any
  • 00:22:55
    other tools that our audience will go
  • 00:22:57
    and then quickly write a query and then
  • 00:22:58
    this get the date of use information
  • 00:23:02
    yeah definitely query studio is an okay
  • 00:23:04
    one
  • 00:23:05
    um I've I'm more of an SQL person myself
  • 00:23:07
    so I've not been using crucial as much
  • 00:23:10
    the methodology is great for all those
  • 00:23:12
    who are learning SQL and trying to
  • 00:23:14
    explore SQL because it does help you by
  • 00:23:16
    creating the table to put that data into
  • 00:23:19
    very quickly for you
  • 00:23:20
    however I do find that especially when
  • 00:23:23
    you're learning how to manipulate the
  • 00:23:25
    data it's actually easier to work with a
  • 00:23:27
    more consistent interface and but that's
  • 00:23:30
    going to be the query part of automation
  • 00:23:31
    Studio
  • 00:23:32
    what I would recommend is because the
  • 00:23:34
    data types are reasonably well
  • 00:23:36
    documented on the official documentation
  • 00:23:37
    so for example
  • 00:23:41
    on our subscriber views here we can see
  • 00:23:43
    that the data extension data type
  • 00:23:46
    for subscriber ID is number for the date
  • 00:23:49
    undeliverable is date I do capture those
  • 00:23:51
    same values here so number and date
  • 00:23:54
    and I carry those forward on the
  • 00:23:56
    subscriber view here number and date I
  • 00:23:59
    do believe that materials is a very very
  • 00:24:00
    similar thing for his use subscriber
  • 00:24:02
    data view Fields subscriber ID number
  • 00:24:05
    and date so you will find the
  • 00:24:06
    documentation it's quite consistent
  • 00:24:08
    across the board about how you can then
  • 00:24:10
    build that dial extension up
  • 00:24:12
    um I I've just realized that Matthias
  • 00:24:14
    does not have the character lengths on
  • 00:24:15
    his one unfortunately but you will find
  • 00:24:17
    the character lengths documented on my
  • 00:24:20
    table and on the original data view I
  • 00:24:22
    think yes so
  • 00:24:25
    um end our chart will have the text
  • 00:24:27
    length for that field so it's going to
  • 00:24:29
    be a text 254 or an email 254 length
  • 00:24:32
    field I would recommend that you build
  • 00:24:34
    the data extension yourself in marketing
  • 00:24:37
    cloud and then populate it it will take
  • 00:24:40
    you a bit longer to get your hands into
  • 00:24:42
    it of course because it won't be done
  • 00:24:43
    for you but it does give you that muscle
  • 00:24:45
    memory of learning what the fields are
  • 00:24:47
    and how they're built the reason I
  • 00:24:48
    recommend this is because the process of
  • 00:24:50
    making a gel extension is one that you
  • 00:24:51
    have to get familiar with
  • 00:24:53
    getting experience quickly building
  • 00:24:55
    extensions will save you time in the
  • 00:24:57
    future in marketing cloud
  • 00:24:59
    it's a very laborious process to make a
  • 00:25:01
    direct extension over and over again and
  • 00:25:03
    so you learn very quickly to think ahead
  • 00:25:05
    of what you are going to need so if I'm
  • 00:25:08
    going to be exploring some data views I
  • 00:25:10
    know that my campaign is going to be a
  • 00:25:12
    campaign to retarget a journey email
  • 00:25:13
    that I built you know three months ago
  • 00:25:15
    so I know I'm going to need to describe
  • 00:25:17
    a key which I can get from subscribers
  • 00:25:19
    or need email address from subscribers I
  • 00:25:22
    need their status from subscribers
  • 00:25:24
    I probably want to know the email
  • 00:25:26
    version they were sent which I'll get
  • 00:25:28
    from the job I didn't know when they
  • 00:25:30
    were centered which I'll get from Cent
  • 00:25:32
    if they clicked it which I get from
  • 00:25:33
    click as a billion field and you can
  • 00:25:35
    start to think ahead you plan ahead of
  • 00:25:37
    the fields you'll want it's a really
  • 00:25:39
    good practice to get into and so for
  • 00:25:42
    that again query studio is great for
  • 00:25:43
    beginners just to learn how to start
  • 00:25:45
    building those queries out but once
  • 00:25:47
    you've passed that learning phase truly
  • 00:25:49
    truly get in there and build your own
  • 00:25:51
    data extensions because it's a trial by
  • 00:25:53
    fire you will learn very quickly to
  • 00:25:55
    think ahead on what Fields you need
  • 00:25:57
    again the process of adding additional
  • 00:26:00
    Fields gets quite difficult after a
  • 00:26:02
    while so you'll you just you'll learn uh
  • 00:26:04
    to think ahead in the fields you want so
  • 00:26:07
    you can start to plan your own
  • 00:26:08
    activities far advanced again nothing
  • 00:26:11
    but practice will get you that mentality
  • 00:26:13
    so I truly recommend go out there and
  • 00:26:14
    practice doing that
  • 00:26:19
    thanks cam yeah yeah definitely I have
  • 00:26:22
    one more question where uh so if let's
  • 00:26:24
    take an example I have my customer who
  • 00:26:26
    has five custom fields
  • 00:26:28
    that he would love to embed into all the
  • 00:26:30
    all the send log so is there any uh
  • 00:26:33
    quick simple method for everyone not to
  • 00:26:36
    go with SQL just to add all these custom
  • 00:26:38
    attributes to our data views and then
  • 00:26:40
    bring some kpis out of which
  • 00:26:42
    yep so uh the job uh sorry the send log
  • 00:26:46
    View
  • 00:26:48
    which is not really a doubt of you it's
  • 00:26:49
    just the send log
  • 00:26:51
    um but I know what you're referring to
  • 00:26:52
    so yes you can add those custom fields
  • 00:26:55
    and I do believe that as also data
  • 00:26:56
    extension or the data source should I
  • 00:26:58
    say that you send from contains the
  • 00:27:01
    exact matching fields in that send log
  • 00:27:03
    then those values will be imported into
  • 00:27:05
    the send log upon send it does not work
  • 00:27:08
    retrospectively
  • 00:27:09
    however you could fabricate that if you
  • 00:27:13
    have those values you could of course
  • 00:27:14
    augment your send log then go back in
  • 00:27:17
    time and use an update script to then go
  • 00:27:19
    through and back populate all that send
  • 00:27:22
    log data you could do that I'd probably
  • 00:27:25
    not recommend it though because there is
  • 00:27:26
    a risk through an update function you
  • 00:27:28
    may touch a subscriber record in the
  • 00:27:30
    wrong way and you may update some values
  • 00:27:32
    incorrectly and it's better to have a
  • 00:27:34
    data set with nulls than a data set with
  • 00:27:37
    incorrect values
  • 00:27:39
    I'll say that again to make sure
  • 00:27:40
    everyone got it
  • 00:27:41
    always always it is better to have a
  • 00:27:43
    data set that contains nulls and null
  • 00:27:46
    fields or empty fields or empty values
  • 00:27:48
    then to have a data set that possibly
  • 00:27:51
    has errorist values
  • 00:27:53
    an empty value is easy to get around if
  • 00:27:55
    it's empty you can check for that is
  • 00:27:56
    empty then don't use it but if there is
  • 00:27:59
    a value there and it's errorness it's
  • 00:28:01
    incorrect in some way how are you going
  • 00:28:04
    to know that how is your and scripture
  • 00:28:06
    going to know how's your SQ we're going
  • 00:28:07
    to know that it's not so if you ever
  • 00:28:09
    have the chance empty is better than
  • 00:28:11
    wrong
  • 00:28:12
    so don't go back and back fill unless
  • 00:28:15
    you're very very sure what you're doing
  • 00:28:17
    yeah yeah that's true that's true thanks
  • 00:28:19
    thanks cam so one more question game
  • 00:28:21
    like is there any way that I could get
  • 00:28:23
    my uh data views more than six months of
  • 00:28:27
    data of an engagement
  • 00:28:28
    uh good question and again please uh
  • 00:28:30
    feel free to raise your hand as well
  • 00:28:32
    I'll be going through as the hands arise
  • 00:28:34
    so any questions you got make sure you
  • 00:28:35
    queue them up also feel free to use the
  • 00:28:38
    um uh the chat as well now the six month
  • 00:28:41
    look back is the defaults for your data
  • 00:28:43
    views having said that there are some
  • 00:28:45
    newer data fields that are actually a
  • 00:28:47
    different look back I do believe the
  • 00:28:48
    automation one is a different look back
  • 00:28:51
    period
  • 00:28:52
    if memory serves
  • 00:28:55
    I want to just run through the last 31
  • 00:28:59
    days I think one month look back so
  • 00:29:02
    the answer is no by default these WS are
  • 00:29:05
    set up for this reason they are
  • 00:29:07
    producing a lot of data I mean you think
  • 00:29:09
    about the big companies you're using
  • 00:29:10
    writing cloud and just how many sends
  • 00:29:12
    they are conducting
  • 00:29:15
    it's a lot of data and there's no point
  • 00:29:17
    to keep some of that data just sitting
  • 00:29:19
    around doing nothing if you have a
  • 00:29:21
    practical use case to use this data then
  • 00:29:25
    you'll always be able to find a way to
  • 00:29:26
    save it and reuse it later now again my
  • 00:29:29
    example for a travel customer the gap
  • 00:29:32
    between holidays especially large
  • 00:29:33
    expensive International holidays won't
  • 00:29:35
    be every six months so to retarget our
  • 00:29:38
    customers activities from six months ago
  • 00:29:40
    for their next purchase it's not going
  • 00:29:42
    to happen with data views they're likely
  • 00:29:44
    to buy a large holiday every one two or
  • 00:29:46
    three years so if you really need that
  • 00:29:49
    click data and again allow some more
  • 00:29:51
    physical question as well
  • 00:29:53
    there's a click that you did two and a
  • 00:29:56
    half years ago really impact your next
  • 00:29:59
    possible click in marketing cloud
  • 00:30:02
    probably not that are that old rarely
  • 00:30:05
    has an influence on things that you're
  • 00:30:07
    doing today as a customer
  • 00:30:09
    the old things that I did a month ago in
  • 00:30:11
    my inbox I wouldn't say uh good
  • 00:30:12
    predictions what I might do tomorrow in
  • 00:30:14
    my inbox so a six-month look like is
  • 00:30:16
    actually a huge amount of time there are
  • 00:30:18
    only very very few reasons where you may
  • 00:30:21
    need more than six months and again I
  • 00:30:23
    use those things like those annual uh
  • 00:30:25
    campaigns or promotions if you do want
  • 00:30:27
    those again the idea is there you can go
  • 00:30:29
    back and you can reference those uh
  • 00:30:31
    sending data views the jobs the sensory
  • 00:30:34
    opens the clicks and you can either
  • 00:30:36
    extract those as your click tracking
  • 00:30:38
    extracts or the data extracts sorry you
  • 00:30:41
    can then re-import them into marketing
  • 00:30:42
    cloud and save that data given if you're
  • 00:30:44
    doing that I'd probably recommend using
  • 00:30:46
    a data retention policy on those
  • 00:30:48
    extensions to make sure you do drop out
  • 00:30:50
    the data when it gets to you know 14 or
  • 00:30:52
    15 months old there is no point keeping
  • 00:30:54
    data around just for the sake of it you
  • 00:30:57
    do actually have a data limited
  • 00:30:58
    marketing Cloud so do not keep data
  • 00:31:00
    around just because it's there make sure
  • 00:31:02
    you are dropping data around that you're
  • 00:31:03
    not going to use
  • 00:31:05
    but again if you do have a use case to
  • 00:31:07
    use a longer look back you can invent
  • 00:31:09
    your own Solution by using those queries
  • 00:31:12
    to just append information each time you
  • 00:31:14
    could do this as a weekly activity to
  • 00:31:17
    look back seven or eight days and get a
  • 00:31:19
    last seven or eight days worth of clicks
  • 00:31:20
    and opens and sends and append them into
  • 00:31:23
    your personal creative data View and
  • 00:31:26
    that way you can fabricate that longer
  • 00:31:28
    look back
  • 00:31:29
    yep thanks thanks cam that's really yeah
  • 00:31:32
    that's what we were looking for uh we
  • 00:31:34
    have one more question
  • 00:31:36
    so the question is uh what about uh
  • 00:31:38
    disseminating information
  • 00:31:41
    yeah perfect uh is I'll get you just to
  • 00:31:43
    elaborate on that one for me if that's
  • 00:31:44
    all right
  • 00:31:49
    uh uh UV can you can you elaborate more
  • 00:31:53
    probably you can speak as well
  • 00:31:57
    oh his mic is off okay if you would like
  • 00:31:59
    to elaborate some more details in the
  • 00:32:01
    chat so probably it would help cam for
  • 00:32:03
    answering your question
  • 00:32:09
    okay so let me let me unmute cam so just
  • 00:32:12
    me one second
  • 00:32:17
    yeah I given a permissions you can go
  • 00:32:19
    and run you know
  • 00:32:26
    can you hear me now
  • 00:32:28
    yes I can yeah we can okay uh what I
  • 00:32:31
    mean by that is uh We've you've shown us
  • 00:32:34
    a lot of interesting things but uh at
  • 00:32:36
    times we might want to send that
  • 00:32:38
    information to um stakeholders uh on a
  • 00:32:42
    regular basis so the output uh what
  • 00:32:46
    would you recommend uh using as a
  • 00:32:48
    procedure to be able to send that
  • 00:32:50
    information on periodic basis to our
  • 00:32:53
    stakeholders
  • 00:32:56
    foreign
  • 00:33:29
    views when you do use an SQL activity to
  • 00:33:32
    query a data view it obviously has to go
  • 00:33:34
    somewhere and as I said earlier I'd
  • 00:33:36
    recommend that you actually make a data
  • 00:33:38
    extension uh don't use the query Studio
  • 00:33:40
    to have it made for you build your own
  • 00:33:43
    data extension and that way when you do
  • 00:33:45
    your own query you can have it going
  • 00:33:46
    into that destination now once you have
  • 00:33:48
    that data in a data extension as a
  • 00:33:50
    destination you can of course use the
  • 00:33:52
    automation Studios extract activity to
  • 00:33:54
    export it for yourself so I do have my
  • 00:33:57
    marketing Cloud open right now so let's
  • 00:33:58
    have a quick look for ourselves
  • 00:34:00
    I could make myself a brand new
  • 00:34:01
    Automation and I could say that on my
  • 00:34:04
    brand new automation I'm going to go
  • 00:34:05
    ahead and make a SQL activity to go in
  • 00:34:08
    query some data views once I've query
  • 00:34:10
    that done review I can then do a simple
  • 00:34:12
    data extract and then done with the data
  • 00:34:15
    extract it does go directly into your
  • 00:34:18
    safe house so if you do want to move it
  • 00:34:20
    around you could then use a file
  • 00:34:21
    transfer activity to pick up that
  • 00:34:24
    extract which will be a CSV or a txt
  • 00:34:26
    file and you can use a file transfer to
  • 00:34:29
    throw that file either into another
  • 00:34:30
    folder of your FTP or you could also
  • 00:34:33
    throw it into a separate FTP S3 or
  • 00:34:35
    Google data store as well so if you are
  • 00:34:38
    talking about getting that data out of
  • 00:34:40
    your marketing Cloud instance and into
  • 00:34:41
    something else for example your data
  • 00:34:43
    warehouse should run some reports on you
  • 00:34:45
    can use I can put here in my example a
  • 00:34:47
    query to select the data the extract to
  • 00:34:50
    extract the data from the data extension
  • 00:34:51
    and save it as a CSV and then you can
  • 00:34:53
    use a file transfer to move that CSV
  • 00:34:55
    from your marketing Cloud FTP onto your
  • 00:34:59
    on-prem FTP to then have your bi toolkit
  • 00:35:03
    pick up that data
  • 00:35:04
    that is a very very common workflow for
  • 00:35:07
    customers who actually want to extract
  • 00:35:08
    the engagement information from
  • 00:35:10
    marketing cloud and plot it in their own
  • 00:35:12
    bi tools a good example that again will
  • 00:35:15
    be things like your sends your opens
  • 00:35:17
    your clicks so if a customer who has a
  • 00:35:19
    very Advanced business intelligence and
  • 00:35:22
    data analytics team again for those
  • 00:35:24
    customers who've not come from an area
  • 00:35:26
    where they've had their own bi
  • 00:35:27
    dashboards they have a much more Legacy
  • 00:35:29
    approach to having data analytics and
  • 00:35:31
    data science conducted on-prem they may
  • 00:35:33
    want to have all that data all those
  • 00:35:35
    sends those opens those clicks coming
  • 00:35:37
    out of Marketing Cloud into their data
  • 00:35:39
    warehouse so they can run their own data
  • 00:35:41
    models to create their own engagement
  • 00:35:43
    lists their own retargeting lists a very
  • 00:35:46
    very common use case and again that's
  • 00:35:47
    the exact thought I'd do it query the
  • 00:35:50
    data views and whatever else you need
  • 00:35:51
    and dump into a data extension extract
  • 00:35:53
    the data extension and then fire that
  • 00:35:55
    data extension from your FTP onto your
  • 00:35:57
    server alternatively of course if your
  • 00:36:00
    data warehouse team can get the file for
  • 00:36:02
    marketing Cloud you can of course drop
  • 00:36:04
    that last step and just help the extract
  • 00:36:06
    sitting there every day ready to go and
  • 00:36:09
    they can schedule their service to go
  • 00:36:12
    and get the file for the marketplace FTP
  • 00:36:14
    with the correct credentials of course
  • 00:36:16
    it does depend if you do want to make
  • 00:36:17
    sure they are picking up that file every
  • 00:36:19
    day on time it may be easy to throw the
  • 00:36:21
    file to them rather than have them get
  • 00:36:24
    the file from you because if a failure
  • 00:36:26
    occurs in your data they may be getting
  • 00:36:29
    the same file uh the next day because
  • 00:36:31
    it's not been updated so I'd recommend
  • 00:36:33
    if possible using a file transfer to
  • 00:36:35
    throw the file to them rather than
  • 00:36:37
    having them get the file from you
  • 00:36:39
    how that answers your question feel free
  • 00:36:41
    to use the chat big thumbs up even
  • 00:36:43
    better thank you so much
  • 00:36:45
    so I think we do have some microphone
  • 00:36:47
    permission pieces so again please do use
  • 00:36:49
    chat and jump in if you have any
  • 00:36:51
    questions you'd like answered on this
  • 00:36:53
    topic
  • 00:36:55
    yes thank you Cam I think I'll ask if
  • 00:36:58
    anyone has any questions anyone has any
  • 00:37:00
    questions team
  • 00:37:03
    again a great toolkit the data views not
  • 00:37:08
    as many platforms are out there as I'd
  • 00:37:10
    like would actually have data views I
  • 00:37:12
    know a lot of other large email sending
  • 00:37:14
    platforms and marketing Cloud platforms
  • 00:37:16
    out there they do give their users the
  • 00:37:19
    data but not in this level of detail not
  • 00:37:21
    in this immense immense level of detail
  • 00:37:25
    can you get dirt out of other platforms
  • 00:37:27
    so we are very lucky to have such access
  • 00:37:29
    in a platform like marketing Cloud to
  • 00:37:31
    get the raw data out
  • 00:37:33
    if you think of platforms that are much
  • 00:37:35
    easier to use more I'm going to say
  • 00:37:37
    marketing friendly they never give you
  • 00:37:39
    this kind of power the powder Jump Right
  • 00:37:41
    In and extract every single point of
  • 00:37:43
    data so it's incredibly valuable it's a
  • 00:37:46
    big point of difference that you have
  • 00:37:47
    this totally unblocked access to all
  • 00:37:49
    this engagement data so truly spend some
  • 00:37:52
    time go through that documentation again
  • 00:37:54
    just to see for yourself read the
  • 00:37:56
    introduction as to what each of these
  • 00:37:58
    data views do the click data view what
  • 00:38:00
    is it how does it work what are the
  • 00:38:02
    values
  • 00:38:03
    it's a great way to understand what
  • 00:38:06
    toolkits you have access to in marketing
  • 00:38:08
    Cloud so go through and read it for
  • 00:38:09
    yourself and we've done that make sure
  • 00:38:11
    you visit matthias's blog and make sure
  • 00:38:13
    you see for yourself because again he
  • 00:38:14
    goes through in a lot more detail as to
  • 00:38:16
    how these views work and of course he's
  • 00:38:18
    even written some of this stuff for you
  • 00:38:19
    have his own queries pre-revision for
  • 00:38:21
    you ready to go just make sure that when
  • 00:38:23
    you build your own data extensions you
  • 00:38:25
    do name them appropriately with the
  • 00:38:27
    correct names and that way they will go
  • 00:38:29
    into those correct pieces for you
  • 00:38:31
    once you've got that as well you jump
  • 00:38:32
    over my diagram you can see if yourself
  • 00:38:34
    how they're all linked so you do go
  • 00:38:35
    ahead and write your join statements
  • 00:38:37
    you'll have all the information here
  • 00:38:39
    that you want
  • 00:38:45
    and so yes there is some comments there
  • 00:38:48
    around data Cloud as well so if you do
  • 00:38:51
    want to link this information back into
  • 00:38:53
    your data Cloud I'm going to assume
  • 00:38:55
    you're referring to the fully known CDP
  • 00:38:57
    exactly right so you can use the name of
  • 00:38:59
    connectors and migrations to pass that
  • 00:39:01
    data back into your own platform thank
  • 00:39:02
    you exactly right so you can use that
  • 00:39:05
    connector for yourself to pump that data
  • 00:39:07
    back into your CDP and use that as an
  • 00:39:09
    attribute to then use for segmentation
  • 00:39:11
    you absolutely can do that and in fact
  • 00:39:14
    it's a use case that again is a good one
  • 00:39:16
    to use for those who don't have data
  • 00:39:18
    Cloud though and you perhaps have your
  • 00:39:19
    own on-prem or something separate that's
  • 00:39:21
    why I say using an extract function
  • 00:39:23
    that's similar to this is who you may
  • 00:39:25
    then get that data out for your on-prem
  • 00:39:27
    teams teams that want to conduct their
  • 00:39:29
    own segmentation or their own data
  • 00:39:31
    analytics or science with their existing
  • 00:39:33
    toolkits I have heard of some customers
  • 00:39:35
    who have done a science teams who use R
  • 00:39:37
    and python in other languages to create
  • 00:39:39
    some really Advanced segments for their
  • 00:39:40
    customers again that's obviously an
  • 00:39:42
    opportunity for them to use an advanced
  • 00:39:44
    platform like data cloud but in the
  • 00:39:46
    meantime they do have on-prem teams use
  • 00:39:48
    their on-prem tools and this is how we
  • 00:39:50
    get that data out
  • 00:39:51
    so again I love the fact that there is
  • 00:39:53
    no blockers in marketing Cloud you
  • 00:39:54
    really can get the data as you need it
  • 00:39:56
    and move it around as you need it
  • 00:39:59
    but great question
  • 00:40:00
    yep thanks thanks cam uh I think we have
  • 00:40:03
    I think that I think we don't have much
  • 00:40:05
    questions on this thank you for your
  • 00:40:07
    valuable time and then spending with us
  • 00:40:09
    on the short time and uh really
  • 00:40:11
    apologies for being late for this event
  • 00:40:13
    which is occupied from other internal
  • 00:40:16
    meetings again thank you for joining
  • 00:40:18
    with us today and then uh really
  • 00:40:19
    appreciate the if you did what you put
  • 00:40:21
    here thank you
  • 00:40:22
    not my pleasure like I said Team uh do
  • 00:40:24
    reach out you have questions again I've
  • 00:40:26
    got a an absolute sweet spot for uh I
  • 00:40:28
    love you so reach out uh we have plenty
  • 00:40:30
    of slack communities and Linkedin groups
  • 00:40:33
    and of course stack exchange areas as
  • 00:40:35
    well so make sure you do reach out I
  • 00:40:37
    think there's one more question for you
  • 00:40:39
    before we break things off
  • 00:40:43
    yes I think he has a question to me
  • 00:40:48
    yes yes
  • 00:40:58
    when can we get the recordings of the
  • 00:41:00
    last two days uh I think I already
  • 00:41:01
    updated the recordings of the last two
  • 00:41:04
    sessions in the
  • 00:41:06
    in our Channel you we probably can go
  • 00:41:08
    and then verify it it's up to date
  • 00:41:12
    I could not see them
  • 00:41:16
    okay uh just give me one second Prashant
  • 00:41:18
    can you hear me
  • 00:41:32
    uh I think they we updated everything we
  • 00:41:35
    can you uh can you share your screen if
  • 00:41:38
    you don't mind so that I can check
  • 00:41:41
    I'll give it host access
  • 00:42:00
    thank you thank you Anita
  • 00:42:04
    oh thanks thanks cam thank you so much
  • 00:42:07
    uh for being with us today really great
  • 00:42:11
    session I really love it uh the way that
  • 00:42:13
    we explained that abuse and everything
  • 00:42:15
    looking forward for more sessions to
  • 00:42:17
    talk on this event thank you
  • 00:42:18
    my pleasure thanks again
  • 00:42:21
    thank you take care bye
  • 00:42:27
    thanks everyone for joining with us so
  • 00:42:29
    let's join uh solo today Saturday we
  • 00:42:31
    have a great session on server side
  • 00:42:33
    JavaScript and web studio and all the
  • 00:42:35
    stuff we have started this and they're
  • 00:42:37
    great development sessions are going on
  • 00:42:38
    thank you
Etiquetas
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