SOCIAL INFLUENCE ANALYSIS: A NEW INSIGHT TERRITORY
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1 SOCIAL INFLUENCE ANALYSIS: A NEW INSIGHT TERRITORY Luis Fernandes - Expedia EMEA Research and Consumer Insights Director Anthony Fradet - Linkfluence UK Chief Operating
2 The mission of the researcher
3 The traditional toolkit QUANT QUAL
4 The new toolkit QUANT SOCIAL QUAL
5 Qualitative data on a quantitative scale In vivo (vs in vitro)
6 Social data: qualitative data on a quantitative scale Vast collection of individual opinions Identification of major trends and weak signal Wide possibilities from social KPI measurement to in-depth content analysis
7 Social data: in vivo (vs in vitro) Untamed, spontaneous material No interrogation bias Opinions expressed reach an audience, triggering tangible influence mechanisms
8
9 What makes research methodologies fit for insight generation?
10 Traditional research Business Question Quotas Samples Representativeness Discussion guides Structured / bias-controlled data Framed Analysis Valid Insight
11 Social media research (for him) Business Question Quotas Samples Representativeness Discussion guides Lack of control on key parameters Unstructured / biased data Framed Analysis Valid Insight
12 Social media research Business Question Unstructured data Structured data Framed Analysis Valid Insight
13 Exploiting unstructured data is one of the greatest technological leap ever
14 Technology is adapting to humans Social data is an opportunity for research to adapt to consumers
15
16 1) Definition of the data universe (semantic profiles) 2) Data segmentation 3) Data contextualisation through relevant social analysis frames
17 1) Definition of the data universe (semantic profiles) 2) Data segmentation 3) Data contextualisation through relevant social analysis frames
18 Social influence is an element of context unique to social data
19 Social influence? An individual s ability to affect other people's thinking in an online community
20 Numbers?
21 We are in the age of the tyranny of numbers
22 Numbers are good liars
23 Social influence goes beyond numbers, it is 100% contextual and based on relationships and influence dynamics
24 Understand the context
25
26 TECHNOLOGY (RADARLY) CLIENT SERVICES MARKETING INTELLIGENCE
27 01 UK POLITICAL WEB OBSERVATORY HOW NETWORK ECOSYSTEM ANALYSIS PREDICTED THE EU REFERENDUM RESULT
28 Published early June
29 Colours refer to communities Nodes are websites Node size indicates the level of influence of a website Links are hypertext links between two websites (inbound/outbound)
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31
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33 02 DANONE CITIES HOW SOCIAL AUDIENCE MAPPING HELPS UNDERSTAND CONSUMER MINDSET
34 2 objectives Identify the key communities of interest structuring the social landscape in 4 major cities Recommend smart actions to engage these communities in a meaningful way
35 Can we trust individuals to tell the truth about what they are influential on?
36 Good proportion of what I post Good proportion of what I post Post rarely Almost never post Don t trust what people say they are! Interest is not influence
37 To make sense of audiences, content analysis at scale is necessary
38 The key Organising individuals in communities based on the topics they really talk about (not what they say they are)
39 Topic Drops
40 Key outcomes for Danone Sharp understanding of cultural background Identification of relevant content area Food for innovation in brand initiative
41 03 EXPEDIA - DIGITAL FOOTPRINT HOW HIGHLY CONTEXTUALISED INFLUENCE ANALYSIS INFORMED EXPEDIA S INFLUENCER STRATEGY
42
43 Modes & context
44 121
45 Business objective Increase site visits and number of transactions
46 How to achieve this? Increase brand presence throughout the decision making process prior to booking Identify the right influencers for the right mode at the right time
47 How is the travel ecosystem structured in different markets?
48 The UK Travel Ecosystem Colours refer to communities Nodes are websites Node size indicates of the level of influence of a website Links are hypertext links between two websites (inbound/outbound)
49 Travel Online Ecosystem UK vs Germany 1,685 websites 25 sub-communities 2,051 websites
50 Travel Online Ecosystem UK vs Germany Travel News 1% Sports 2% Family 4% Innovation & Tech 4% Business & Finance 5% Travel 31% Online Media 22% Beauty & Fashion 15% Travel 12% Lifestyle 11% Food & Drinks 10% Culture & Entertainment 3% Business & Finance 4% Beauty & Fashion 4% Family 5% Food & Drinks 7% Lifestyle 9% Online Media 15%
51 Travel Online Ecosystem UK vs Germany Travel News 1% Sports 2% Family 4% Innovation & Tech 4% Business & Finance 5% Travel 31% Online Media 22% Beauty & Fashion 15% Travel 12% Lifestyle 11% Food & Drinks 10% Culture & Entertainment 3% Business & Finance 4% Beauty & Fashion 4% Family 5% Food & Drinks 7% Lifestyle 9% Online Media 15%
52 Travel Online Ecosystem UK vs Germany Travel News 1% Sports 2% Family 4% Innovation & Tech 4% Business & Finance 5% Travel 31% Online Media 22% Beauty & Fashion 15% Travel 12% Lifestyle 11% Food & Drinks 10% Culture & Entertainment 3% Business & Finance 4% Beauty & Fashion 4% Family 5% Food & Drinks 7% Lifestyle 9% Online Media 15%
53 How to integrate context into affluence and make it operational?
54 Contextualised influencer identification Social audience Blogs with a high influence score * Sites with strong social following Influence score* Monthly visits and views Potential for working with brands Identification of matching Expedia Travel Mode Relevance for Expedia *Influence score: Score (from 0 to 100) based on the inbound links received by the websites within the ecosystem.
55 Travel modes in the UK travel ecosystem Share of influencers by travel mode 10% 7% 31% TRAVEL MODE A, B & D are the three main travel modes in the Travel Ecosystem, with 83% of the top influencers within the UK Travel ecosystem. 10% These three travel modes provided most traffic. Travel mode B gathers the stronger follower base, with an average 880k followers. 26% 26% The most influential opinion leaders: Key influencers Cumulated social audience Dominant Travel Mode 25.5m TRAVEL MODE A 4.2m TRAVEL MODE B 1.8m TRAVEL MODE C 1.5m TRAVEL MODE B 1.4m TRAVEL MODE D AVERAGE MONTHLY VISITS AVERAGE SOCIAL FOLLOWERS TRAVEL MODE A 368K 197K TRAVEL MODE B 35,5k 880K TRAVEL MODE C 71,4k 84,9K TRAVEL MODE D 103K 295K TRAVEL MODE E 13,8K 18,1K TRAVEL MODE F 95,3K 46,2K
56 Travel mode C 103k MONTHLY SITE VISITS ON AVERAGE 295k SOCIAL FOLLOWERS PER INFLUENCER (AGGREGATED AVERAGE) Place of travel within their life Travels to relax mentally and physically Booking Behaviour Destination is less important than an ideal resort. Likely to book all inclusive packages, sometimes offline Want hassle-free booking What kind of content do they produce? Visual posts, documenting breaks that are largely relaxation, culture or luxury focused (e.g. warm destinations, spa days, boutique hotels) Other interests Food Fashion Style Weekend breaks Health
57 Outcome for Expedia: Involvement in contextual conversations - Opportunities to develop more relevant content for specific context or simply to share it in the right place - Key influencers to work with beyond number of followers
58 Two things to remember
59 The new toolkit QUANT SOCIAL QUAL
60 Context is everything One thing to remember
61 Luis Fernandes - Expedia EMEA Research and Consumer Insights Director Anthony Fradet - Linkfluence UK Chief Operating
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