V. Kupriyanov - The Power of Data-Driven Storytelling

Some principles behind data-driven storytelling in digital marketing, a few campaign examples, and learnings from the field.

Transcript

1. The Power of Data-Driven Storytelling @v_kupriyanov
2. Hello! Volodymyr [vo-lo-di-mir] Kupriyanov Data analyst / journalist / consultant Ukraine -- UK -- Denmark
3. My work I help digital marketing teams and agencies use data in their marketing campaigns
4. How? By gathering and analysing data Digging out insights and stories Helping with visualisations
5. Why? Data = source of stories Viz is a powerful way to communicate them Interesting stories make for effective campaigns
6. What’s new? Marketing & PRs have been trading in stories for ages
7. Getting (more) data-driven
8. Three directions
9. 1) Data-informed ideation Customer surveys Industry reports General news and cultural landscape Web analytics ++
11. Core questions What does our audience care about? What topics are of interest to them? What content do they like to consume?
12. 2) Grounding campaigns in data Government data and national statistics Industry reports Internal client data Surveys
13. Core questions What data source can we use? How recent and comprehensive is the data? What biases does it carry?
14. 3) Data-driven visualisations Dashboards Static graphs Big interactives Motion graphics
15. Core questions How do we visualise this? What makes the most sense for • audience • data • budget
16. From theory to practice
17. Example 1
18. Client Business financing platform focused on tech and startups in the UK
19. Audience Founders and entrepreneurs People working in tech
20. What are they interested in? Tech, innovation, the startup scene Famous tech companies Famous tech founders and entrepreneurs
22. Existing narrative
23. Existing narrative
24. How can we enrich that knowledge? What data can we use? How can we visualise it in a way that would show something new?
25. Relevant data source
26. Four things we were after For each “mafioso” we wanted to find out the companies they went on to… 1. Start 2. Invest in 3. Lead 4. Advise
27. Some light-hearted scraping
28. Some light-hearted scraping
29. Following the hunch We knew … that these guys founded some big-name companies … that they often worked and invested together But what we didn’t realise was just how connected they are
30. What is an effective way to show connections?
31. Prototyping
33. Core story points The sheer degree of collaboration between members of “the mafia” Well-known names, like LinkedIn and YouTube Staggering number of ventures Non-tech ventures, such as “Thank You For Smoking” film and the lobbying group FWD.US
34. Scrollytelling
35. Interactivity
36. Mobile
37. Results Features on VentureBeat, Gizmodo +70 sites Thousands of social shares and engagements Building authority as a tech-savvy brand
38. Personal highlights
39. Why did it work? Tapped into what the audience care about Used relevant data to add insight Visualise it in a way that reveals something new Make it work for the audience and platform
40. Example 2
41. Client GPS fleet management solutions provider (≈ telematics)
42. Audience insight US Drivers (basically, the whole country) Enjoy driving, road trips, travel Don’t like getting stuck in traffic
43. Federal Highway Administration (FHWA)
44. Highway Performance Monitoring System A database of all the roads in the US detailing, among other things • Location • Length • Volume of traffic
45. What can this data tell us? The busiest roads in America but also… The quietest roads in America
46. Turns out it’s a thing
47. Something people care about
48. Fairly simple analysis Extract data layer from a shapefile Split state-by-state Estimate avg flow of vehicles Low # of vehicles = quietest roads
49. Given our audience, something simple Scannable at a glance Minimal interactivity ++ Imagery Visualisation
50. PR: People like maps. Me: But this has no regional patterns to warrant a map visualisation. PR: People *really* like maps. Me: But maps aren’t mobile-friendly! PR: The client likes maps! Me: Fine... Actual conversation at the time
51. Visualisation
52. Visualisation
53. Visualisation
54. The Atlantic, Thrillist, Lonely Planet + 175 other news articles Thousands of social shares Brand awareness ++ Results
55. Why did it work? Brought data to the conversation Revealed something previously unknown Chose the audience-appropriate visual format
56. Learning along the way
57. Do the research Research your audience Figure out what’s important to them Ask yourself, how can you add insight/value to that? What data can you use?
58. Add insight with data Bringing *good* data to conversations that aren’t traditionally data-rich Adding new data to existing data-rich narratives
59. Add insight with visualisation Using visualisation to make data more accessible Dataviz doesn’t have to be complex to be effective
60. As long as we’re: Not lying with data Communicate something meaningful and compelling to our audiences
61. Thank you!
62. Sources and references: Paypal Mafia • http://fortune.com/2007/11/13/paypal-mafia/ • https://www.crunchbase.com/ • https://www.nytimes.com/interactive/2014/06/20/sports/w orldcup/how-world-cup-players-are-connected.html • https://gephi.org/ Quietest Roads • https://www.fhwa.dot.gov/policyinformation/statistics.cfm • https://www.thrillist.com/travel/nation/most-fun-stretche s-of-road-in-america • https://www.outsideonline.com/1926186/americas-best-car- touring-roads • https://www.r-project.org/ Sources and image credits Image attribution (where required): • https://xkcd.com/1732/ • marcinignac on Visualhunt.com / CC BY-NC-ND • https://commons.wikimedia.org/wiki/File:Elon_Musk_Roy al_Society.jpg • https://jamesaltucher.com/wp-content/uploads/2015/09/p aypal-mafia.jpg • https://www.instagram.com/p/BUkUZh7DxOl/ • https://www.informationisbeautifulawards.com/awards/20 16 • http://datadrivenjournalism.net/ • https://www.linsonbusinessconsulting.com/wp-content/u ploads/2017/09/Non-disclosure-Agreement-1024x675.jpg