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Digital analytics lecture1


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A lecture in digital analytics at Aalto University. The lecture is a part of a module in Information Technology Program (ITP).

Summer 2015, Helsinki


Dr. Joni Salminen is a lecturer in digital marketing. Besides online marketing, his interests include startups and web platforms. Contact:

Published in: Data & Analytics
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Digital analytics lecture1

  1. 1. Information Technology Program Aalto University, 2015 Dr. Joni Salminen, tel. +358 44 06 36 468 DIGITAL ANALYTICS 1
  3. 3. Wanamaker’s dilemma (ca. 1901) “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” • The marketer uses several channels for advertising. • He knows advertising increases sales. • But: which channel and how much? • If we cannot measure the results, it’s harder to improve (i.e. kill bad channels and scale up good ones). 3
  4. 4. Voilà! Wanamaker dilemma solved (let’s go home…) 4 Channel Sales
  5. 5. Marketer’s intuition 5 The more experienced a marketer is, the better he thinks he knows things beforehand  However, even an experienced professional can be wrong. With experience, the speed for evaluating different alternatives increases. Simultaneously the ability to think beyond them decreases.  Never forget the fallacy of marketer’s intuition…
  6. 6. Analytics overcomes marketer’s intuition “After analyzing the online buying behavior of over 600,000 consumers across numerous e-commerce sites, I learned that surprisingly 75 percent of shopping cart abandoners would actually return to the site they abandoned within a 28-day period. This defies conventional wisdom: we polled online marketers and 81 percent believed that the majority of abandoners never return.” (SeeWhy, 2013) 6 I’m a marketer. I’m always right!
  7. 7. (The issue with cart abandonment.) • I did a small survey to my students in 2012 • It was discovered that shopping cart abandonment is natural behavior in which users test or window-shop. • If abandonment is natural behavior, does it make sense to do conversion optimization? 7
  8. 8. Problems analytics can help solve • Wanamaker’s dilemma • Marketer’s intuition • Attribution problem • The leaking bucket of customer acquisition • (doesn’t really solve them, but makes us aware of them: awareness → solution) 8
  9. 9. Changes in the marketing landscape a. projects (campaigns) → process (platforms) b. one campaign → hundreds or thousands of campaigns • leads to… – need for continuous optimization (instead of attention- grabbing tricks) – rules/automatization (to help handle the complexity) 9
  10. 10. Finally, don’t forget… There are good opportunities in the job market for people who know analytics! 10
  11. 11. ABOUT THE COURSE 11
  12. 12. Who am I? 12 Joni Salminen PhD, marketing • Bachelor’s thesis 2007: Search-engine marketing on the Internet • Master’s thesis 2009: Online advertising exchange • Dissertation 2014: Strategic problems of early-stage platforms on the Internet Experience: • Teaching digital marketing at the Turku School of Economics (2012 →) • Marketing manager (Elä (2011 →) • Hobbies: floorball & swimming.
  13. 13. Course description The course combines theory and practice to educate students about Web analytics. Students will learn how to choose the right metrics for a given business, how to interpret and report them, and how to apply analytics in business decisions. The key topics include audience, acquisition, behavior and conversion. Students will also learn about attribution models and multichannel tracking. The main platform of the course is Google Analytics. As a part of the course, students will submit a practical report. The report is based on an audit of a case company's use of Web analytics and their actual performance. It will help students understand how analytics is used in companies and how to report performance based on select KPI's and data. 13
  14. 14. Program (1st week) • Monday: Introduction & Basics of analytics • Tuesday: Google Analytics (hands-on stuff) • Wednesday: Metrics time • Thursday: Dashboards, data problems, etc. • It’ll be fun! 14
  15. 15. Program: 2nd & 3rd week • Optimization • A/B testing / multivariate testing • Cohort analysis • Visualization • Universal analytics & multichannel • The real ”Big Data” • Algorithm-based marketing automatization • Data philosophy • …it’ll still be fun :) 15
  16. 16. Course philosophy • Hands-on: bring laptops • Practically useful, theoretically insightful • Always ask! If you don’t, you’re missing out a useful opportunity to learn! 16
  17. 17. Material & grading • Material: – Lecture slides (will be shared in the Facebook group) – Book: Lean Analytics by Alistair Kroll (find e.g. in Amazon) • Grading: – Scale 0–5 – Team assignment will decide grading. Criteria: quality of analyses, presentation, usefulness of given recommendations. The more effort and the insights discovered from the data, the better. 17
  18. 18. You will learn to… • choose relevant KPIs and metrics for a business • manage data scientists and analytics projects • make and report a website audit • use dashboards to make better sense of data • basic use of the best tools: Google Analytics, Tableau, R • …and, hopefully, how to make better business decisions (and/or recommendations) based on data. 18
  19. 19. Team assignment • Your task is to do an analytics audit for a company of your choosing (or the one given by Joni). – In your report, you will answer select questions. – (Answering some of them requires creating custom reports – don’t worry, we’ll look at those together.) – Then make a final presentation for the class room and voilà! we can all go home. • (Detailed instructions arrive by the end of the week.) 19
  20. 20. Exercises in Tableau (week 2) • Tableau is one of the most used business tools for analyzing and visualizing ”big data”. It requires less learning than R to get started. • We will go through using Tableau in the class, then you will use it to make analyses and visualisations for your GA audit report. 20
  21. 21. Exercises in R (week 3) • R is programming language that enables powerful statistical analyses and visualizations with fairly simple commands. It is free, open source and very expandable (better than SPSS). • In the class, we’ll go through a couple of basic examples; then, you can continue learning R in MOOCs Joni recommends you. 21
  23. 23. What is analytics? “Digital Analytics is the analysis of qualitative and quantitative data from your business and your competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes, both offline and online.” (Kaushik, 2010) 23
  24. 24. What kind of questions can we answer with the help of analytics? • What’s the most profitable source of visitors? • What products are people buying? How much is the average order size? • Where do users come from? How long do they stay on the site? • How do new visitors behave in comparison to old ones? • What content is the most/less viewed? • What keywords people use to find our site? • Where do people exit the site? 24
  25. 25. How does analytics work? (Mullins, 2011) 25 • users • sessions • hits (interactions) Website Javascript code Server Processing • dimensions (qualitative) • metrics (numeric)
  26. 26. What kind of data is stored? • B2C: Information is usually collected anonymously and presented as aggregates (individual users are not identified). • B2B: The exception is so-called people-based tracking, which aims at tracking individuals. This is generally applied in enterprise markets, where the number of buyers is relatively small. 26
  27. 27. Analytics and privacy • Two points to defend analytics & marketers: 1. The data is most often anonymous & aggregate 2. Using data solves the matching problem between ads and consumers (we will have ads anyway, so whatever makes them more relevant for you is a good thing) • (The EU is full of idiots, by the way.) 27
  28. 28. How e-commerce tracking works (Cutroni, 2013) 28
  29. 29. Example of tracking script (Cutroni, 2013) 29
  30. 30. Scripts, scripts, scripts… Setting up the analytics • Here are some of the scripts I’ve installed: – google analytics conversion script – facebook conversion pixel – facebook custom audience pixel – google tag manager – vwo script, etc. 30
  31. 31. Raw data is processed an turned into reports • “Reports package all data collected into a readable format so that the decision makers can study it and draw conclusions.” (Digital Marketing Training Institute, 2014) • Reports can then be customized and reshaped by the analyst (custom reports & advanced segments), and also put into the form of dashboards. 31
  32. 32. Aggregation problem • All data looks the same when looking from far enough! • The solution: • segmentation 32
  33. 33. Segmentation • Segmentation isolates your data into sub-sets for a deeper analysis, and thereby solves the aggregation problem. • You can segment the data by – date and time – user’s device – marketing channels – geographical location – etc. (dozens of options!) 33
  34. 34. Ways to set up an analytics infrastructure a. In-house (tailored system) b. Ready-made tools (e.g. Google Analytics, KissMetrics) • Each one has advantages and disadvantages; for example, in-house systems give the most accurate conversion data, but take time and money to build. 34
  35. 35. There are two types of traffic (& hence, analytics…) Analytics of organic traffic Analytics of paid traffic Google Webmaster Tools Google AdWords Facebook Facebook Insights Facebook Ads Manager 35 Google Analytics shows what happens after the click, these show what happens prior to it (data is in the platforms).
  36. 36. Internal and external analytics • Internal analytics = analyzing the data from own website and properties such as social media pages in order to improve the likelihood of desired business results (e.g. Google Analytics) • External analytics = analyzing competitors or the market (cf. business intelligence, competitive intelligence) (SimilarWeb, Google Trends) 36
  37. 37. External analytics: an example • Analyzing social media trends: • ’Growth hacking’ and ’content marketing’ are both popular topics in digital marketing. Which one is more popular? • Let’s see by using two tools: a. Google Trends b. Topsy 37
  38. 38. Competitor analysis: example • Turku School of Economics and Aalto Business School are rivals in getting the best students. • Which one has more traffic? • Let’s run SimilarWeb, and find out! 38
  39. 39. Hi Joni, Thanks for reaching out! I hope you’re having a great Sunday so far :) As for your questions: Our measurements come from a combination of data scraping, powerful web crawlers and click-stream data from our proprietary panel of tens of millions of users worldwide. The SimilarWeb panel is the largest in the industry. It includes data from over 5,000 distinct sources, each representing various demographic groupings and user characteristics. Our panel users have given permission to collect some of their anonymous data such us browsing patterns. We only extract aggregated information – no personal identifiable information is captured by us in accordance with local and international privacy laws. SimilarWeb’s crawler supplements the information collected by the panel and analyzes over 1 billion pages a month, supplying input data for our sophisticated Similarity, Category and Content Analysis engines. Having the largest panel in the industry for web measurement guarantees our data to be exceedingly accurate. However, websites with lower traffic means that our sample size is smaller and for these websites, the level of accuracy may decrease.There is a rule of thumb that a site should have over 200K hits per month to provide a higher level of accuracy. In that case we’ll have up to 15% margin of error, meaning very high level of accuracy. The reason why you may see “not enough data” when analyzing a domain on SimilarWeb is because the engagement to this website is too low to allow us to get enough data to provide an accurate estimation of the traffic to this website. Hope the above helps. Please let me know if I can be of any further assistance. All the best, 39
  40. 40. The application of analytics • analytics can be used for two things (Salenius, 2015): 1) reporting 2) optimizing • …Joni would add: 3) strategic decision making (e.g. budget allocation, attributing marketing performance) • While analytics (data) is the requisite for optimization, it’s also the pathway to automatization (more about this later). 40
  42. 42. Why Google Analytics? • “Google Analytics is a powerful tool which shows you what is working on your website and what is not. It helps you optimize your marketing efforts and maximize the revenue.” (Promodo, 2013) • It’s free! • It’s used by maaaany companies 42
  43. 43. Google Analytics data model (Google, 2014) •User (visitor)—the client that visits the site, such as the browser or mobile phone operated by a person. •Session (visit)—the period of time during which the visitor is active on the site. •Interaction (hit)—the individual activities that send a GIF request (hit) to the Analytics servers. These are typically characterized by a pageview, but can include: •a pageview •an event (e.g. click on a movie button) •a transaction •a social interaction 43 In the basic data model used in Google Analytics, the user (visitor) interacts with your content over a period of time, and the engagement with your site is broken down into a hierarchy. •Each level in this model is defined as follows: Each of these three levels of interaction defines a specific scope of user engagement. This distinction is important in Google Analytics because you may want to do analysis of your data at a particular scope. For example, you might want to measure the number of sessions where users removed an item from their shopping cart. For this particular case, you would be doing a session-level analysis that includes each session during which an item was removed from a cart, even if the sessions are from the same user. On the other hand, you might want to measure the number of unique users who removed items from their shopping cart at any time, regardless of session. For this example, you would be doing a user-level analysis.