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MeasureFest _ Angelo Galluzzo _ aip.pptx
- 1. Understanding the impact world events have on users’ search behaviour Angelo Cosma Galluzzo Anything is Possible slideshare.net/AngeloCosmaGalluzzo @angelo_cosma
- 2. How the story starts
- 3. ? ? ? ? ? ? ?
- 4. Gather data
- 5. Gather data
- 11. Academic theory Source: Mavragani et Al. (2018)
- 12. Academic theory 1. Measure the general Web- based interest Source: Mavragani et Al. (2018)
- 13. Academic theory 1. Measure the general Web-based interest Source: Mavragani et Al. (2018)
- 14. Academic theory 1. Measure the general Web-based interest 2. Detect any variations or seasonality of Web-based interest Source: Mavragani et Al. (2018)
- 15. Academic theory Source: Mavragani et Al. (2018) 1. Measure the general Web-based interest 2. Detect any variations or seasonality of Web-based interest
- 16. Academic theory 1. Measure the general Web-based interest 2. Detect any variations or seasonality of Web-based interest 3. Correlate Web-based search queries among them or with official or actual data and events. Source: Mavragani et Al. (2018)
- 17. Academic theory Source: Mavragani et Al. (2018) 1. Measure the general Web-based interest 2. Detect any variations or seasonality of Web-based interest 3. Correlate Web-based search queries among them or with official or actual data and events.
- 18. Academic theory 1. Measure the general Web-based interest 2. Detect any variations or seasonality of Web-based interest 3. Correlate Web-based search queries among them or with official or actual data and events. 4. Predict, nowcast, and forecast health-related events, outbreaks, etc. Source: Mavragani et Al. (2018)
- 19. Academic theory 1. Measure the general Web-based interest 2. Detect any variations or seasonality of Web-based interest 3. Correlate Web-based search queries among them or with official or actual data and events. 4. Predict, nowcast, and forecast health- related events, outbreaks, etc.
- 24. How is data normalised? 100 x n a = Sessions aggregated by week x = Week with highest number of sessions n = Sessions index 0-100 (a)
- 25. Visualise for stakeholders Visualise for stakeholders
- 26. Excluding factors Seasonality Google algorithm updates Bias in choosing keywords
- 27. Analyse the context of event and the type of industry The value of qualitative analysis 1
- 29. Space for errors and testing 3
- 30. The theory Academic Theory Epidemiology, economics and social sciences
- 31. SEO Theory Search behaviour, funnel analysis and Intent
- 32. Results and analysis from a specific client account Case Study
- 33. Case study - BUNAC Results ➔ BUNAC’s organic performance was negatively impacted between 25th Feb to 19th March 2022
- 34. Case study - BUNAC Results ● BUNAC’s organic performance was negatively impacted between 25th Feb to 19th March 2022 Actions ➔ Check performance at the start of the next global event ➔ Nowcast a 20 day impact ➔ Focus paid tactics on impacted queries
- 35. Case study - BUNAC Results ➔ BUNAC’s organic performance was negatively impacted between 25th Feb to 19th March 2022BUNAC’s organic performance was negatively impacted between 25th Feb to 19th March 2022 Actions ➔ Check performance at the start of the next global event ➔ Nowcast a 20 day impact ➔ Focus paid tactics on impacted queries
- 36. Case study - BUNAC Results ➔ BUNAC’s organic performance was impacted negatively by queries related to World Cup fixtures from June 17 to July 2 2018
- 37. Case study - BUNAC Results ➔ BUNAC’s organic performance was impacted negatively by queries related to World Cup fixtures from June 17 to July 2 2018 Actions ➔ Track trends for queries related to the 2022 World Cup Fixtures ➔ Track and optimise for country queries before the 2022 World Cup ➔ Run paid campaign on country keywords that appear affected
- 38. Case study - BUNAC Results ➔ BUNAC’s organic performance was impacted negatively by queries related to World Cup fixtures from June 17 to July 2 2018 Actions ➔ Track trends for queries related to the 2022 World Cup Fixtures ➔ Track and optimise for country queries before the 2022 World Cup ➔ Run paid campaign on country keywords that appear affected
- 39. How to use it?
- 41. Name poll - tell us your fave @aip_media Zeit GraphCast AIPevent
- 42. Thank you!
Editor's Notes
- The idea behind Google Flu Trends was that, by monitoring millions of users’ health tracking behaviors online, the large number of Google search queries gathered can be analyzed to reveal if there is the presence of flu-like illness in a population. Google Flu Trends compared these findings to a historic baseline level of influenza activity for its corresponding region and then reports the activity level as either minimal, low, moderate, high, or intense. These estimates have been generally consistent with conventional surveillance data collected by health agencies, both nationally and regionally. Roni Zeiger helped develop Google Flu Trends.[3] An initial motivation for GFT was that being able to identify disease activity early and respond quickly could reduce the impact of seasonal and pandemic influenza. One report was that Google Flu Trends was able to predict regional outbreaks of flu up to 10 days before they were reported by the CDC (Centers for Disease Control and Prevention).[10]
- The initial Google paper stated that the Google Flu Trends predictions were 97% accurate comparing with CDC data.[4] However subsequent reports asserted that Google Flu Trends' predictions have sometimes been very inaccurate—especially over the interval 2011–2013, when it consistently overestimated relative flu incidence,[6] and over one interval in the 2012-2013 flu season predicted twice as many doctors' visits as the CDC recorded.[6][12] One source of problems is that people making flu-related Google searches may know very little about how to diagnose flu; searches for flu or flu symptoms may well be researching disease symptoms that are similar to flu, but are not actually flu.[13] Furthermore, analysis of search terms reportedly tracked by Google, such as "fever" and "cough", as well as effects of changes in their search algorithm over time, have raised concerns about the meaning of its predictions.[6] In fall 2013, Google began attempting to compensate for increases in searches due to prominence of flu in the news, which was found to have previously skewed results.[14] However, one analysis concluded that "by combining GFT and lagged CDC data, as well as dynamically recalibrating GFT, we can substantially improve on the performance of GFT or the CDC alone."[6] A later study also demonstrates that Google search data can indeed be used to improve estimates, reducing the errors seen in a model using CDC data alone by up to 52.7 per cent.[15] By re-assessing the original GFT model, researchers uncovered that the model was aggregating queries about different health conditions, something that could lead to an over-prediction of ILI rates; in the same work, a series of more advanced linear and nonlinear better-performing approaches to ILI modelling have been proposed.[16] However, followup work was able to substantially improve the accuracy of GFT through the use of a random forrest regression model trained on both the incidence of influenza-like illness and the output of the original GFT model.[17]
- Analyse Show your clients Account for client’s performance Nowcast Take quick actions Adjust your day-to-day strategy Forecast Prepare for the next big event Prepare for recurring events