BIG DATA: IT MAY BE BIG BUT IS IT SMART?

Transcription

1 BIG DATA: IT MAY BE BIG BUT IS IT SMART? Turning Big Data into winning strategies A GfK Point-of-view 1

2 Big Data is complex Typical Big Data characteristics?#! %& Variety (data in many forms) Data in different formats, versions, from different sources, with different dimensionality and structure Velocity (data in motion) Welcome to the fire hose. The real-time data flow never stops. Today's data will be history tomorrow Volume (data in huge quantity) Veracity (data in doubt) Data in massive quantity. "Collect first, think later". Big Data requires sophisticated infrastructures Volume doesn't automatically increase precision. Managing accuracy and reliability requires significant analytical expertise 2

3 and can be misleading Incompleteness Data streams are "happening", not designed for information gathering Biasedness Big Data is often highly selective and unsystematic Analytic limitations Standard approaches to data processing, modeling and analytics not feasible So what is required? Thorough analytics expertise and a reference frame 3

4 Elements of our Big Data GfK Established Pilot projects, R&D Cookie tracking Digital Behavioural Tracking Location Insights Social Media Intelligence (SMI) Analytic Integration of SMI & Sales Data Integration of (Digital) Behavioural Tracking and SMI Social Network Analysis Datafication of Games GfK proprietary Big Data Algorithms Mobile Insights Analytic Integration of SMI & Survey Data 4

5 Smart Data in the real world Use cases 5

6 Use Case #1: Purchase Journey 6

7 Use case 1: Purchase journeys Business question: How do I best allocate on/offline marcoms spend to maximize sales? Context Therefore, marketers have new information needs. Consumers are increasingly using technology along purchase journey and sharing experience in real-time Understand how digital & offline channels interact, what message is best, at each step in the buying process Traditional research can provide some insight but not the complete picture Technology allows us to get more data on the path to purchase 7

8 Use case 1: Purchase Journeys Example: Magnitude of digital behavioral tracking data in the travel industry 1 The haystack 2 Organize the haystack 3 Find the needle One month of data in our GfK Media Efficiency Panel 10,000 Users in the panel 2,100,000 Page Impressions at major search engine 30,000,000 Navigation events overall 1,140,000,000 Server requests 1,500,000,000 MB of textual content E.g. create a taxonomy for the travel industry Categorize 1,322 Websites as Retailers, Aggregators, Accommodation, Airlines, Destinations, etc.. Categorize 16,011 Search Keywords as Travel Organization, Accommodation, Generic, etc.. Analyze purchase journeys for a selected client Identify relevant data on which we can now complete analysis 59 Offline Bookers, 148 online Bookers, 20 specific portal 33,570 relevant Navigation Events (0,1%) 8

9 The purchase journey can be really complex, but still there are certain patterns Brand sites Start 2 1 Search Aggregators 3 Pure online retailers Click & mortar Finish Social networks Forums / blogs / review sites Source: Smartphone Purchase Journey, Russia 9

10 Which touchpoints have a higher impact on brand purchase? Aggregators Click & mortar Search engines Manufacturers Pure players Forums / blogs Telecom operators Social networks 10

11 Manufacturers Forums & blogs Pure online Click & mortar Aggregators Search engine Typical purchase pathway Consumer Segment X Trigger: keep up with trends Visits in online: 25 Offline contacts: 3 Purchased brand: Samsung purchased ONLINE 11

12 Use Case #2: Integrating Social Media Intelligence into Decision Making 12

13 Use case 2: Social media Business question: how is social media affecting product sales? Context Therefore, organizations are no longer in control of their own brand Fast adoption and widespread use of social media = everything more spontaneous, immediate, and dynamic Understanding what is being said about you (and competitors) in earned media, how sentiment is trending, what is influencing trends Traditional research still provides valuable insight but not the complete picture. Technology allows us to take the pulse of the digital world in near real-time. 13

14 Use case 2: Social media Example: Magnitude of digital information available to monitor a product launch in the tech industry 1 The haystack 2 Organize the haystack 3 Find the needle One month of data for an Social Media Intelligence Monitoring in six countries for a product launch 3,162,185 pieces of content found 11,579 single domains crawled 44,942 API requests Create a basis for analysis After cleaning 1,786,353 pieces of content for further analysis we Categorized content into 8 channels: Blogs, Forums, Video, Web, SocialNets, MicroBlogs, News and ecommerce Separated user and non-user content Clustered content into pre-defined topics Put the social media results into context for the client and connect it with other data sources e.g. retail sales data Identified the best days for launch announcement and first client feedback Identified top 15 domains for ongoing monitoring Identified top 5 key issues that are evaluated negatively by customers Identified impact of social media communication on retail sales 14

15 Integrating Social Media Analysis into Brand Trackers Case study: Sam Adams vs. Budweiser Trend Action Signals Budweiser Sam Adams 70% 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% Positive: 37% 30% Buy Positive: 61% 66% Negative: 4% 2% Use Net: +33% +28% No Action Not Use Negative: 3% 1% Net: +39% +65% Drop Wave 2 Wave 1 Buy (Wanting, buying or planning on buying a product, shop for, search for, looking for a product) Use (Have, use or consume a product) Not Use (Not have, not use or not consume a product) Drop (Switching, getting rid of, dropping rejecting a product, stop using) 15

16 Integrating Social Media and real observed purchase behavior* to reliably calculate Social ROI Unique User (in Tsd.) selected URLs Facebook-Sites YouTube-Channels Blogs & Boards % % % Change in % Customer Value SoM Customer Value Buyers Total *GfK Media Efficiency Panel, Germany 16

17 Integrating Social Media Listening and GfK Retail Sales Data (example of GfK dashboards) 17

18 Use Case #3: Mobile Insights 18

19 Mobile Audience Measurement Define mobile internet audience by demographics, device usage and behavior. What is the mobile web behavior of a certain target audience? How and where are brand s targets spending mobile web time? How does mobile relate to their total media usage? How is the competition doing compared to a specific brand in reaching its own targets? 19

20 Mobile websites Ikea.com vs. Johnlewis.com Oct 13 John Lewis saw over 50k more mobile site visitors than IKEA 50k 100k 150k 200k 250k Device Both ikea.com & johnlewis.com see a higher level of penetration on ios devices. Therefore, the opportunity remains for IKEA to cement themselves on Android. Household income Ikea.com s penetration is similar across the income brackets, but peaks in the low end (under 20k). 3.5% Demographics 3.9% Ikea.com penetration amongst females. Location 20k In contrast, Johnlewis.com peaks within the highest income bracket (over 50k). Therefore, this represent a key target area for IKEA. 3.7% Ikea.com penetration amongst under 25 year olds. Johnlewis.com is stronger amongst slightly older females (25-54 years old), who represent a potentially more affluent sector. Ikea.com sees its highest level of penetration in London (4.5%). However, opportunities remain in Northern Ireland & Yorkshire, where penetration is only 2%. % = penetration of retail sites. Source: GfK Mobile Insights, UK (based on mobile operator data analysis) 20

21 Fashion retail mobile websites 2.1 million unique users in November 2013 Unique users Fashion retail penetration Brand X Sessions per user Duration per user (mins) 155k 7.4% Unique users Fashion retail penetration Brand Y Sessions per user Duration per user (mins) 132k 6.3% % 82% 13% 87% 36% users % users % social class C1 31% social class C1 20% 15% 10% 5% brand-y.com brand-x.com When do users visit the sites? 0% Source: GfK Mobile Insights, UK (based on mobile operator data analysis) 21

22 Key take-aways Don t necessarily rely on Big Data on its own it might not tell you the whole story Without understanding the consumer context, the value of Big Data for marketers is limited Combine consumer data with reference data for better insights 22

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