Understanding data visualisation to create insight

Transcription

1 Understanding data visualisation to create insight

2 72hrs of you tube video 571 new websites 100m new s 277,000 tweets.. created every minute

3 Channel growth

4 Data vs Visualisation

5 Where do you start?

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8 Data challenge: quality

9 Data challenge: understanding

10 Data challenge: outliers

11 Thinking about visualisation

12 Question. Question. Question.

13 Beware aggregate data Male gender bias in graduate admissions. 8,442 applicants 44% admited 4,321 applicants 35% admitted At department level: most departments had a small but statistically significant bias in favor of women Situation: Women were applying to competitive departments with low rates of admission Men tended to apply to less-competitive departments with high rates of admission Source: Science, Bickel et al (1975) More info:

14 Beware the y-axis bias March April May June July 0 March April May June July

15 Source: Nate Agrin and Nick Rabinowitz Going beyond the bar chart

16 Going beyond the bar chart

17 Source: Timelines and geo

18 Visualisation Visualisation Overload overload

19 Remember the different channels A step too far

20 What we speak about becomes the house we live in - Hafiz

21 Key takeaways The visualisation should inform not just be pretty Lots of potential insight start small with the most important first Don t forget to understand and clean the data Look at your outliers for potential new opportunities Understand what you are trying to takeaway from the data Let that guide your choice of visualisation Shy away from dashboard overload! Don t forget more often than not in marketing, your data involves people

22 Thank you

23 How Uses Data Visualisation to Enhance Analytics and Insight within Market Research Fergal Smithwick

24 Contents Introduction to MESH Our Challenge New Approach to Data Processing How we use Visualisation Software 2 4

25 Introduction to MESH

26 Who is MESH? Market research agency founded in 2006 Track brand and comms as per traditional market research But uses Real-time experience tracking to get a more accurate read 26

27 How does our Real-Time methodology work? ONLINE REAL-TIME ONLINE 27

28 Why do we gather Real-Time tracking (RET) data? Pick up competitor activity More accurate data RET is a 360 approach Qualitative & Quantitative Captures emotional engagement 28

29 Here is an example of actionable insight we get from RET data 90% Experience Map by Touchpoint Purchase Intent T2B (%) 80% 70% 60% 50% Newspaper Social TV Conversation Me purchasing Online Me using 40% In store Magazine 30% Poster/Billboard Radio 20% 25% 35% 45% 55% 65% 75% 85% 95% Positivity T2B (%) 29

30 Our Challenge

31 Processing this data takes time and costs money Flexibility? Internal time? Cost? External time? 31

32 We needed a solution that met these requirements Faster data More flexible Cheaper Better for Clients 3 2

33 Important components of the delivery process for Market research? Multiple reporting options with a wide variety of visualisation specifications Speed of delivery regarding client deliverables and ad hoc trend investigations Flexibility surrounding client side data accessibility Data accuracy and QA 33

34 New Approach to Data

35 Before and After Old approach New approach 1. Send request to data processing agency 2. Slow delivery of tables in Excel 3. Populate PowerPoint charts for client delivery 1. Log in to Tableau and Export data from existing charts with latest data 2. Populate PowerPoint charts for client delivery 5-7 Days 2-3 Days 35

36 How we use visualisation software

37 Sharing Insights with Clients At MESH, the primary function is for populating client deliverables Consists of charts and commentary that ultimately tell a story around the findings With tableau 8.2, we can now begin to recreate this process within Tableau 3 7

38 The Deliverable Process Tableau Workbook Excel Workbook Powerpoint Deliverable 38

39 For creating bespoke charts for clients 3 9

40 For Ad-Hoc requests 4 0

41 We also integrate different data sources e.g. social media data Capturing and categorising Twitter data MESH DB The Machine learning tool allows us to code the tweets Raw tweets Machine learning tool Coded tweets 41

42 Visualising Social media data 4 2

43 Empowering Stakeholders with Data Transparency 3 Levels of Transparency Basic MESH creating new, insightful dashboards for clients Advanced Workbook access so that the client can play with the data themselves Full Access Full access to the MESH database; clients taught how to set up workbooks, charts, and dashboards themselves 4 3

44 Key Takeaways of implementing a data visualisation tool Real Time Experience Tracking is greatly enhanced using a data visualisation tool for effective reporting Market Research relies heavily on speed of delivery, variety of visualisation options, and data accuracy Our data now works for MESH since we can work more quickly, more flexibly, more cost effectively, and helps build our client relationships MESH is growing; benefits of our visualisation tool allow us to offer a better, and more sophisticated client service 4 4

45 Thank you Fergal Smithwick