Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (20)
Similaire à LVIMA DPD 2015 - Netmining
Similaire à LVIMA DPD 2015 - Netmining (20)
Plus de Chris Evans
Dernier
Dernier (20)
LVIMA DPD 2015 - Netmining
- 1. LVIMA – Digital Planning Day NETMINING 1
- 2. 2 EXPERIENCE
- 3. 3 DATA LOOP DESKTOP MOBILE DYNAMIC 1:1 MESSAGING DMP ON-SITE LOOK-A-LIKE CONTEXTUAL SIGNALS INTENT MODELS WEATHER CRM TRAVEL INTENDER MOBILE / LOCATION DATA SOURCES ACTIVATION SEARCH DATA
- 4. 4 DATA ACTIVATION DYNAMIC MESSAGES DESKTOP MOBILE DIRECT TO PUBLISHER PRIVATE MARKETPLACE OPEN EXCHANGE HIGH IMPACT RICH MEDIA VIDEO DYNAMIC MESSAGES DISPLAY ACTIVATION CHANNELS PROGRAMMATIC INVENTORY CREATIVE FORMATS FACEBOOK STANDARD
- 5. 5 SMART REMARKETINGSM zz On-Site Data 1st Party Demo Data Highest Interest Product Historical Location Data Consumer/Marketer’s Geo Locations Demo Data Dynamic Localized Creative CONSUMER CONVERTSRESULT CONSUMER VISITS SITE/HOTEL
- 6. 6 FINDING AND KEEPING YOUR BEST CUSTOMERS v Utilize historical visitation data of your visitors to drive them back to your property. Upsell consumers on location for rooms, shows, dining, and nightlife. Conquest your competition’s competitors and deliver them to your property. v
- 7. COLLECT AND MATCH AVAILABLE DATA POINTS TARGET YOUR OFFLINE CONSUMERS ONLINE BRING OFFLINE DATA ONLINE OFFLINE DATA ATTRIBUTES Gender Frequency Spend LoyaltySale Products Email Coupons Address Customer Database (CRM) Address Frequency Sale 7
- 8. 8 PMP DEALS We pride ourselves in building strong and valuable partnerships. Here is a snapshot of our private marketplace deals.
- 9. SECOND LINE Build models based on signals via browsing behavior, contextual content, sites visited by engaged consumers Identifies which types of browsing behaviors are most likely to lead to a conversion Understands what types of interest segments/categories over index and then serves ads to those audiences We can build out different segments for each travel/hotel line of business INTENT SEGMENTS 9 NETMINING DATA SCIENCE TEAM BUILDING THE UBER TRAVEL SEGMENT LAS VEGAS INTENDER NEWLY ENGAGED URBAN SOCIALITE HARDCORE GAMBLER
- 12. 12 CROSS-DEVICE ANALYTICS FLEXIBLE PRICING SCALE / SITE QUALITY PROPRIETARY DATA WHY WE ARE DIFFERENT
- 13. 13 QUESTIONS?
- 14. Data Types: • 1st-party • Explicit engagement/interactions on market web site • In-store purchase data • 2nd-party • Predictive modeling • Historical visitation patterns by physical location (mobile) • Real-time location (mobile) • 3rd-party • Implicit/behavioral • Data Partnerships 3rd party 1st party On-site Behavior Geo/ Lat-long Browsing Behavior Habits Shopping History AUDIENCE INTELLIGENCE (our version of AI) 14 UNDERSTANDING DATA SIGNALS
- 15. 15 DATA-DRIVEN PROSPECTING Our data science team builds out proprietary data segments that are modeled from your 1st party data and enhanced by our 3rd party data relationships Engage app users and website visitors using both desktop and device data. Utilize geo targeting strategies and historical location data LOOK-ALIKE MODELING INTENT SEGMENTS CROSS-DEVICE TARGETING We identify your most engaged users and uncover new prospects showing similar attributes
- 16. 16 COMPETITIVE CONQUESTING 3rd Party Data On-site Behavior/ Exposure Path Data Intent Segments Contextual Signals Geo Location: Consumers Geo location: Marketer’s Locations Geo Location: Competitor’s Locations Demo data COMPREHENSIVE UNDERSTANDING OF COMPETITIVE LANDSCAPERESULT CONQUESTING TACTICS
- 17. 17 HIGH IMPACT
- 18. 18 PREMIUM VIDEO Netmining offers a wide range of video targeting capabilities. All campaigns incorporate our proprietary audience scoring and optimization technology. Video units see 3x more engagement than the standard ad *Rhythm Media, 2014 ADVERTISERCTR AUTO 1 0.04% 0.124% 3X CTR 2X CTR MARKETER RUNNING DISPLAY CAMPAIGN AUTO 2 TIRE 1 0.04% 0.079% TIRE 2 W/O VIDEO W/ VIDEO
- 20. Netmining’s Dynamic Insights Reporting Dashboard takes all of your display data and summarizes it into clear insights that are actionable at a glance. Interface Examples: 20 DYNAMIC INSIGHTS
Notes de l'éditeur
- Consumer visits marketer’s website/store Desktop · On-site behavior data is considered · Marketer’s 1st party data is utilized (loyalty programs, consumer's past purchase history) · Demo data · Ad is served to consumer with the object they showed highest interest product Mobile · Historical location data is considered · Consumer and Marketer's geo location is utilized · Demo data · Ad is served to consumer with details of local and online / promotion Result: Consumer clicks to site and makes purchase
- We have a strong client/data set for the industry, so we have a lot of data to pull from when modeling
- Hyperlinked to: http://creativelibrary.netmining.com/cheapcaribbean/cc/Cheapcarribean.html http://creativelibrary.netmining.com/HTML5/client/esa/v6/esa_nm.html http://creativelibrary.netmining.com/Expedia.co.uk/Expedia.html
- Display SMART Remarketing Audience Extension Intent Data Science Segmenting Mobile Hyper Local Geo-Fencing Targeting Polk Auto Data with Geo-Targeting Remarketing from Dealers.com Site to Mobile Multi-Cultural Targeting US Hispanics DMP True Data Integration Rich Data Profiles Segmentation and Advanced Personalization Analytics and Insights
- Is there even a linear purchase path anymore? Does the idea of a purchase funnel make sense any more? Show of hands: how many of you believe that the process a consumer goes thru, how they’re influenced, etc. has fundamentally changed in the last 10, 20 or 100 years? Let me ask that another way: have the fundamental influences on us as consumers changed, or are we simply better equipped to weigh those influences and make a more informed decision because of our universal access to information?
- Everyone talks about “big data”, but which data is important, and does it matter how we weight the data signals in terms of their importance on moving the hearts and minds of your consumers? What about the order in which people take actions: ROBO or research online, buy offline; or go to store, touch and feel the product, and then go buy online (Amazon showrooming phenomenon) Then of course there are the multitude of influences on us, from childhood to current friends: what detergent did you grow up with? What car did your parents drive? What credit card(s) did they use? Those aren’t really targetable, but they’re important. Goes to the degree to which they’re susceptible/receptive to being swayed.
- Consumer visits competitor's website/store new slide afterward Desktop · 3rd party data · On-site behavior · Intent segments · Contextual signals Mobile · Geo locations of consumer · Geo locations of marketer's store · Geo locations of competitor's store · Demo data of consumers and defined area · Historical location data Result: Comprehensive understanding of competitive landscape
- Hyperlinked to: http://creativelibrary.netmining.com/CPE/ESA.html http://creativelibrary.netmining.com/bbjs/CPE_4s.html
- Hyperlinked: http://dynamicinsights.netmining.com/ Login: demotravel Password: Abroad!