Artificial Intelligence in Destination Branding - June 2nd, 2017
•
1 like•690 views
"The use of artificial intelligence technology in destination marketing" keynote at "European Cities Marketing (ECM) International Conference and General Assembly" in Dubrovnik, Croatia (02.06.2017) Presented by Pangea & Vienna Modul University.
Recommended
Recommended
More Related Content
Similar to Artificial Intelligence in Destination Branding - June 2nd, 2017
Similar to Artificial Intelligence in Destination Branding - June 2nd, 2017 (20)
Recently uploaded
Recently uploaded (20)
Artificial Intelligence in Destination Branding - June 2nd, 2017
- 2. FACTS & FIGURES KEY FINDINGS ØUse of Facebook by the tourism industry is still lacking ØSuccessful management of social media sites by tourism suppliers and practice of manipulating social media is unknown ØA major success measure of social media marketing is the engagement of the user ØEngagement is measured by the number of actions taken by the user on the site ØThe challenge for marketers is to publish content on social media that would have high engagement rate, hence triggering reach and conversion SOCIAL MEDIA IN TRAVEL ØFacebook Monthly Active Users: 1.71 Billion (July 2016) ØFacebook Small Company Pages: 50 Million (Brandwatch, 2016) Ø52% of social media user says they change their travel plans according their research on social media Ø76% of users post photos to social media while traveling Ø75% of Instagram users take action after being inspired by a post ØInfluencer marketing interest increased more than 90 times since 2013 • Impact of social media in travel industry https://www.tnooz.com/article/impact-of- social-media-on-the-travel-industry-infographic/ • Trekksoft Travel Stats Report 2016, https://www.trekksoft.com/en/blog/travel- tourism-stats-2016
- 5. Destination Challenges in Social Media •Authentic Story Telling •Content Quality •Content Creation/Curation •Reach, Engagement, Conversion •Operational costs Content Marketing •Multiple Content Providers •Content Quality •Brand Awareness Influencer Marketing •Authentic Content •Brand Advocacy •New Targeted Audiences AI can increase engagement, reach & conversion ü AI can predict the quality of visuals that will get a high engagement rank ü AI can predict how many interactions any content might have ü AI supported content strategies can increase the reach to their communities & conversion AI can enrich content data, CRM and databases ü AI can extract objects in any visual and automatically tag any content on databases ü AI can identify contextual results & optimize season, weekday, hour for publishing in various markets AI can inrease ROI and ROE, decrease costs ü AI can predict which content providers will be most effective for any specific Destination or Campaign ü AI can predict which influencers will be most engaging for any specific Destination or Campaign ü AI can auto-compose and auto-post with top engaging hashtags, minimizing operational costs ARTIFICIAL INTELLIGENCE IN DESTINATION MARKETING AI
- 6. METHODOLOGY The AI model analyses the relation of the objects on the image and the chromatic dispersion to predict the engagement results. The history (between April 2014 – February 2017) of some of the most engaging 18 different Facebook accounts of “Travel” and “Lifestyle” brands with global audience, having a minimum of 40K engagements each, has been used to train the AI Model with the features below; • Engagement and Follower Count (comments, likes, shares, emojis etc of the Facebook accounts) • Objects on the images with probabilities (the objects on visual are determined by visual AI) • Chromatic Color Dispersion • Post Type (Link Post- Image Post) • Whether any post was promoted with paid ads or not
- 7. 3.000 6.500 10.000 17.000 17.000 17.000 25.000 32.000 40.000 53.000 53% 55% 56% 58% 61% 63% 70% 71% 77% 82% 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Content Size Object Tags Content Type Paid Organic Color Type/Paid Model Color Model Results in likes Feature Set Improvement Model Improvement AI FACEBOOK OVERALL ALGORITHM PERFORMANCE • AI performance can be increased in 3 ways • Extending training data • Extending feature set • Improving AI model • Together with above mentioned improvements AI accuracy had increased by 30% in Facebook in 3 months • Planned further short run improvements are; • Contextual • Audience detection • Local follower impact
- 8. AUTO-TAGGING 4. Tags all images with a combination of 3 algorithms (Google Vision, Clarifai, IBM Watson) Object Tags : Outdoor, Sea, Wellness & Spa, View, Summer, Boat, Yoga, water, travel, resort, luxury, no person, vacation, island, swimming, swimming pool, ocean, beach, villa, leisure, seashore 5. Enrich visual tags with word vectoring algorithms Enriched Tags : tropical , exotic , relaxation, hotel TRAINING AI WITH FEATURES 6. Analyze all feature set Uses Google Deep Neural Network (Tensorflow) and OpenCV Frameworks 7. Fits the results with 4 models K-means, Random Forest, Support Vector Machine, Deep Learning 8. Predicts engagement ratios for each post according to AI results 9. Predicts like numbers According to fan base CRAWLING AI FACEBOOK CASE STUDY TRAVEL + LEISURE 1. Crawl images • 56.000 images • 18 Travel Pages EVALUATION 10. Comparison between predictions & results Both for engagement rank and like results 11. Evaluation of prediction accuracy For each of Facebook pages analyzed 12. Training AI with results Machine learning from accuracy results 4 MAIN PROCESSES TO ASSESS CONTENT QUALITY & PREDICTION ACCURACY Australia.com Canada Keep Exploring Condé Nast Traveller UK Croatia Full of life Experience Egypt Italia.it Love GREAT Britain Matador Network Rendez-vous en France Spain.info Tourism Malaysia Travel + Leisure Turkey.Home Visit California Visit Dubai Visit Portugal Visit The USA VisitMexico
- 11. AUTO-TAGGING 4. Tags all images with a combination of 3 algorithms (Google Vision, ClarifAI, IBM Watson) Object Tags Boat,Water, Surroundings General, Ship, harbor, travel, boat, no person, vehicle, sail, nautical, sky, ferry, seashore, outdoors, tourism 5. Enrich visual tags with word vectoring algorithms Enriched Tags : transportation system, port TRAINING AI WITH FEATURES 6. Analyze all feature set Uses Google Deep Neural Network (Tensorflow) and OpenCV Frameworks 7. Fits the results with 4 models K-means, Random Forest, Support Vector Machine, Deep Learning 8. Predicts engagement ratios for each post according to AI results 9. Predicts like numbers According to fan base CREATE MEDIA BANK AI INSTAGRAM CASE STUDY ONE ISTANBUL 1. Own / Paid & Ambassador Media • Existing photo/video content • Aggregating #oneistanbul #istanbul • Stock Images • Influencer Networks 2. User Generated Contest • Increased Content Quantity (500.000 Photos collected in 2 years) • Increased Content Quality (Intrinsically social friendly) • AI based objective pre-election 3. Crawl images from travel benchmarks • 12.000 images • 10 Travel Pages • Aimed to train AI Models AUTO-POSTING 10. Identify contextual parameters Day of week, time of day, season, market, audience... 11. Auto-Composer Pick best performing content from Media Bank, Weekly Post- scheduling 12. Semi-auto posting Alert mechanism due to IG lack of mobile posting (soon full automated) 13. Auto-Tracking the results 14. Comparison between predictions & actual results 15. Training AI with results Machine learning from audience 15 STEPS TO MAXIMIZE ENGAGEMENT AND MAXIMIZE ROI