Making Sense of Your Bajillion Marketing Data Sources
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Session slides from #data14 including table calcs in appendix.
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Making Sense of Your Bajillion Marketing Data Sources
- 1. Making the Most of Your Bajillion Marketing Data Sources Presented by: Allison Perry Josh Lind Sasha Pasulka
- 2. Sasha Pasulka Allison Perry Manager, Online Marketing Josh Lind Senior Web Developer 2 aperry@tableausoftware.com jlind@tableausoftware.com Senior Manager, Product Marketing spasulka@tableausoftware.co m
- 3. “Marketers should be using all their data to make decisions. That’s what all the smart people are doing.” - Every marketing “thought leader”
- 4. “That’s nice, thanks. Care to tell me how?” - Everyone tasked with actually doing this
- 5. “….?” - Everyone not tasked with actually doing this
- 6. At Tableau, We Are Drowning in Data Too CRM •Salesforce CMS •Drupal WEB ANALYTICS •Google Analytics ADVERTISING •AdWords •BingAds •AdRoll •DoubleClick SOCIAL: •Facebook •Twitter •LinkedIn •G+ •YouTube EMAIL & AUTOMATION: •Eloqua EVENTS: •Cvent
- 7. HOW?
- 8. Making sense of Google Analytics data Making sense of campaign data Making sense of CMS data
- 9. Making Sense of Your Google Analytics Data
- 10. “How are all our Facebook posts impacting traffic to our website?” - Your Boss
- 11. “Which of my landing pages are viewed most often? Why?” - Director of Demand Generation
- 12. “Which of the customer stories I write are the most popular? Why?” - Your Teammate
- 13. Why Tableau? Difficult to drill down or slice and dice in GA alone.
- 14. DEMO
- 15. Making Sense of Your Ad Campaign Data
- 16. “Which campaigns are delivering the most qualified leads at the lowest cost?” - Your Boss
- 17. “Which tactics and publishers are most effective in meeting my lead gen goals?” - You
- 18. “What is our return on media investment per campaign?” - Your Agency
- 19. “Are there opportunities for ad or landing page optimization?” - Your Campaign Managers
- 20. - Why Tableau? While ad platforms offer packaged reports and the ability to create additional reports, they are not meant for in-depth analysis.
- 21. Challenges of ad platform reports & Excel • Regular data updates are tedious • Can’t drill down to ask more questions • Can’t group and filter data that’s related • No closed loop reporting • Visual impact is limited
- 22. How we work with ad campaign data • Export in-platform reports to Excel • Format for Tableau and connect • Mash it up with other data • Analyze KPIs and compare across groups • Publish up to Server/Online • Optimize campaigns based on results
- 23. DEMO
- 24. Making Sense of Your CMS Data
- 25. What? CMS Your primary content Web campaigns Staff, authors, workflow Registrations Traffic
- 26. Why? Visibility without devs Easy filters Annoying to answer questions
- 27. How? Clear questions for devs. Pesky data…
- 28. CMS Data Can Be Pesky Methods: •Export to CSV, etc. •Flattened DB source •Documentation (schema, examples) •Create ‘data source’ on Server – Useful tables, fields, joins – Publish as TDE, or even just a workbook – You just saw how.
- 29. “What blog posts do we have about education?” - Marketing Segment Manager
- 30. “Where do we need more content?” - Content Marketing Team
- 31. DEMO
- 32. Content Coverage
- 33. DEMO
- 34. Oh, we forgot to mention one thing …
- 35. Q&A Bit.ly to this presentation: bit.ly/data14marketing
- 36. Please take the session survey 1.Tap to this session on the Schedule tab of the Data14 app 2.Scroll down to “Feedback” and tap through the 3-question survey 3.Tap Send Feedback
- 37. Appendix of Calculated Fields
- 38. Google Analytics Calculated Fields Finding a substring in a page URL: contains([Page],"/learn/stories/") Categorizing source URLs: if contains([Source],"mail") then "Email” elseif contains([Source],"google") or contains([Source],"bing") or contains([Source],"yahoo") then "Search” elseif contains([Source],"facebook.com") then "Facebook"elseif contains([Source],"t.co") then "Twitter” elseif contains([Source],"linkedin") or contains([Source],"lnkd.in") then "LinkedIn” elseif contains([Source],"(direct)") then "Direct” elseif contains([Source],"Eloqua") then "Eloqua” else "Other” end
- 39. Campaign data calculated fields • RATIOS: • CPC (Cost Per Click) – Sum([Cost])/Sum([Clicks]) • CTR (Click Through Rate) – Sum([Clicks])/Sum([Impressions]) • CPL (Cost Per Lead) – Sum([Cost])/Sum([Conversions]) • CVR (Conversion Rate) – Sum([Conversions])/Sum([Clicks]) CONVERTING DATE/TIME FIELD TO DATE TO BLEND: • Date – Date(Left(str([DateCreated]),10))
Editor's Notes
- Welcome, and thank you for coming to this session on making the most of your bajillion marketing data sources. How many of you here in the room work in marketing? And now raise your hand if you feel like you’re leveraging all your data sources as well as you possibly could? We can’t tackle everything in an hour, but our goal today is to give you some real-world tips and tricks for leveraging different types of marketing data sources, and for all of you to leave the room with at least one new idea about how you can build marketing dashboards or analyze your marketing data.
- So who are we? The three of us presenting here today are all members of Tableau’s marketing team. I’m Sasha, and I work on the product marketing team. Allison manages all our paid campaigns for search and social media. And Josh is our lead marketing web developer. So we’re all marketers, and, as marketers, we hear a lot about marketing. We attend marketing conferences, we read whitepapers, we attend webinars. And when the three of us talked, we realized we were noticing a trend.
- How many of you have heard something like this recently?
- And as the people tasked with actually doing this, our response tends to be something like this.
- And we feel like we haven’t been able to get a really good answer.
- If you feel the same way, we want you to know that YOU ARE NOT ALONE, we are drowning in data too. This is the landscape of the data sources we work with every day here at Tableau. And even if you don’t have these exact data sources, you probably have something similar.
- We really wanted to make this presentation about the how. We going to present specific use cases for how we’re leveraging marketing data, and we’re going to show you how we built the dashboards we’re using. This is an intermediate session, so we will get into some slightly more technical material. But our goal is for marketers at any level to be able to take something actionable from this talk.
- I’m going to talk about optimizing web content using Google Analytics data, Allison is going to talk about optimizing paid campaigns, and Josh is going to talk about a data source you might not even know you can use: your content management system.
- We pull all of this data into Tableau and mash it up with other data sources to help us start to tell a story and answer frequent questions being asked such as: Which campaigns are delivering the most qualified leads at the lowest cost?
- Which tactics and publishers are most effective in meeting my lead gen goals?
- We pull all of this data into Tableau and mash it up with other data sources to help us start to tell a story and answer frequent questions being asked such as: Which campaigns are delivering the most qualified leads at the lowest cost?
- Now we’re going share some tips about how you can start to make sense of your marketing campaign data sources. At Tableau, we have plenty of these data sources floating around including: AdWords, Bing, Yahoo & Baidu for search advertising AdRoll for retarget advertising Twitter, Facebook, & LinkedIn for native social advertising DoubleClick for direct publisher advertising
- We pull all of this data into Tableau and mash it up with other data sources to help us start to tell a story and answer frequent questions being asked such as: Which campaigns are delivering the most qualified leads at the lowest cost?
- Which tactics and publishers are most effective in meeting my lead gen goals?
- What is my return on media investment?
- Are there opportunities for ad or landing page optimization?
- Why might you want to use tableau to answer these questions if you’re not already? While ad platforms have many packaged reports plus the ability to create additional reports and export to Excel, they are not meant for in-depth or ad-hoc analysis.
- Challenges of ad platform reports & using Excel Say you needed to update your reports for your boss each week, well if you’re using Excel, chances are you’d have to re-create your pivot tables each week… You’d guess in advance which questions people would want to ask of the data because they aren’t interactive… Plus you have to create a ton of different reports to compare metrics between different attributes instead of just one report with filters for each… Not to mention, if you’re like most organizations, conversions are based on “pixel conversions” which is a inflated compared to how many leads actually make it through to your CRM system… Ontop of all that, the limited chart types and flexibility make it hard to tell an impactful data story
- How exactly do we leverage this campaign data at tableau? Export in-platform reports to Excel on a weekly basis (since there aren’t native connections yet) Format for Tableau and connect Mash it up with our CRM data (SFDC/Eloqua) for a 360 degree view of our campaigns Analyze KPIs and drill down by region, channel, campaign topic, adgroup, etc. Continually optimize campaigns based on results of our data analysis Decrease bids for low performing keywords, ads, campaigns Increase bids for high performing keywords, ads, campaigns ----- Meeting Notes (9/4/14 09:40) ----- Decrease efforts - instead of decrease bids
- We mash up data from all the various data sources I mentioned before, but I’m just going to cover one instance in a quick demo which is how we blend our AdWords & Bing data with our backend CRM data. Before you get started, since this is a data blend, be sure your two data sources have at least one common field. I use the adgroup field because we have the same naming convention across search engines and we push adgroup data all the way through to a field on the lead record in our CRM system. For this case we’re going to pull the ‘ad group performance’ report and ad a date segment so you can build reports that show metrics overtime. I’ll show you what that looks like in adwords, the workflow in bing is very similar. *show report generator in adwords* Select: Adgroup Performance Report Add date segment: Daily Select appropriate date range All appropriate accounts/campaigns Export both search engine reports to Excel. *open blank workbook on desktop & show data source connections* Set up a couple of calculated fields: Ratio’s CTR (click-through rate) Sum([Clicks])/Sum([Impressions]) CPC(cost per click) Sum([Cost])/Sum([Clicks]) CVR (conversion rate) Sum([Conversions])/Sum([Clicks]) CPL (cost per lead ) Sum([Cost])/Sum([Conversions]) The reason you’d want to do this is because if you're aggregating, ratios must be calculated with aggregations built in to get accurate data. For conversion rate, you must use SUM(Conversions)/SUM(Clicks), NOT (Conversions)/(Clicks) Why? If we sum the row-level ratios, when these rows are aggregated up, the scale of each order is lost. Let’s build a quick dual axis chart, which I find useful and tracking/sharing trends overtime We’ll blend at the number of leads (from our CRM) with our cost data (from our google/bing) to see CPL overtime. *From ad data - drag out date field (MY discrete), go to alpo data – drag out # leads, go back to ad report – drag out CPL, then format to look nice* *Show finished dashboard, explain each metric and each filter*
- “Content Management System” ----- Meeting Notes (8/20/14 11:16) ----- Undiscovered data source if you have CMS
- Undiscovered data source.
- Data source: real time, secure, single source of truth. Yet another reason to clarify questions. Elaborate data plan, with help.
- Which tactics and publishers are most effective in meeting my lead gen goals?
- 127.0.0.1 – root – root node taxonomy_vocabulary_4 taxonomy_term_data Classification may not match ours RENAME both tables and fields Find content themselves Ask devs
- Visibility. Coverage. Filtering. Joins. “If someone wants to know what blog posts we have about education, it’s easy to tell them”
- 127.0.0.1 – root – root node taxonomy_vocabulary_4 taxonomy_term_data