Silvia Cariolo. 9 Novembre ALL RIGHTS RESERVED - Neodata Group, 2015

Transcription

1 Evoluzione dei modelli di digital publishing e advertising L esperienza di Neodata nell utilizzo dei big data per l erogazione di contenuti specializzati Silvia Cariolo 9 Novembre 2015

2 Highlights Founded in 2004 with an emphasis on data-mining and ad-serving Today: 35 full-time employees in Milan, Catania 2010: Awarded 1 st place FIT innovation prize from Italian government ( 450k) 2011: Closed 2.5M VC round to support worldwide expansion 2011: Selected by UCLA s School of Management Global Access Program 2012: Developer of the Business Intelligence components of SIGMA, a 5M publicly financed project 2012: First Italian company to develop a DMP for publishers 2013: Selected by PWC to be part of Big Data - Analytics and decision making study as one of the most EU innovative companies : Second ad-server in Italy (according to IAB) at 18% penetration 2014: Delivers video advertising on web and TV for the top 3 Italian broadcasters: Mediaset, RAI, Sky 2014: First Italian company to develop a DMP dedicated to brands 2014: Technological partner of Turbo, the first Italian independent trading desk

3 Certifications Official partner Yahoo! Network (Standard, Rich Media or Tracking) Google Display Network via AdWords Google Display Network via AdX YouTube Reservation Microsoft Advertising Atlas Partner Comscore Third Party automated integration Facebook Adv Providers on FB Platform Tracking (in progress) IAB Framework on Online Behavioral Advertising MediaMind mobile

4 Customers

5 Alcuni numerisul mercato Digital in Italia 47% 2,5X 80% +191% Delle ricerche su Google sono effettuate da mobile Crescita e-commerce prevista entro il 2016 in Italia Utenti online utilizzano regolarmente i social network Crescita delle vendite di wearable technology in Italia nel 2014 ( pezzi venduti)

6 Perché si parla di big data

7 Tempo necessario per raggiungere 50 milioni di utenti

8 Il digital in Italia

9 Tempo medio speso dagli utenti su internet e internet mobile

10 Cosa si compra su internet

11 Il processo d acquisto di oggi

12 Le fasi del processo di acquisto

13 Mobile Advertising Le spese per Mobile Advertising sono aumentate del 48% nel 2014 portandosi a circa 300 milioni di euro secondo i dati del Politecnico di Milano. Le app più scaricate sono quelle del settore bancario/finanziario, seguite dal settore viaggi/trasporti e telecomunicazioni

14 Le sfide per le aziende

15 Our mission WE MASTER TECHNOLOGY TO HELP BRANDS, PUBLISHERS AND BROADCASTERS TO ENGAGE THEIR AUDIENCE IN A PROFITABLE RELATIONSHIP Ad serving & personalized content recommendation Big Data Analysis Editorial tools for newsrooms ALL RIGHTS RESERVED NEODATA GROUP SRL 2015

16 DATA COLLECTION AUDIENCE SEGMENTATION DATA ACTIVATION Data are elaborated with a view to search for behavioral patterns Tailor messagesto users FIRST PARTY DATA SECOND PARTY DATA OTHER DATA STREAMS Audience Analysis INCLUDE / EXCLUDE Choose which segments / profiles to target LOOK ALIKE Search for users with similar characteristics CROSS&UPSELLING Test new marketing messages to drive sales Ingest and normalize a wide array of data streams. Turn data into both insights and targetable audiences. Real-time data flows to marketing channels for targeted delivery.

17 A practical use of consumer data GETS IN TOUCH WITH YOUR BRAND WE OBSERVEBEHAVIOR WE PROFILEAND CLUSTER Owned Media Age / gender / location INDIVIDUAL PROFILES SEGMENTS E-commerce Advertorials (news publishers) Social Media Direct Marketing Interests Frequency and recency of contact Response to solicitations/ conversion: profile those who perform your desired actions and look for similar profiles Dedicated offers (direct marketing) More efficient paid media campaigns On-site targeted offers Targeted PR actions Advertising PRESS DIGITAL Internal Data (CRM info) Personalised content Individual adv targeting Shopping recommendation We track individualsand integrate feedback intoprofiles

18 Platform Architecture Main functions 18 TRACKING PROFILING TARGETING TRACKING - Tracks user actions on a webdomain, such as page visualization, timeon page, comment writing, like, share, advertisement visualization, click on advertisement, and more. Each user is identified through a unique key; all actions performed during subsequent visits by the same user are reconciled, normalized andstored together in Neodata s Big Data Hub using a distributed, scalable and high-performing architecture in the Cloud (AWS Amazon Web Services). Online data are integrated with any internal or external data sources (including offline if available). PROFILING - Contents are classified and users profiled based on the available data, using a number of proprietary algorithms that look for correlations between characteristics and behavior. Demographic information,as well as any other type of information made available for registered users, can be inferred also for anonymous users: automatic classifiers are trained by the online behavior of registered users. Collected data are structured in predefined hypercubes and stored in a fast, fully managed, petabyte-scale cloud data warehouse (Amazon Redshift). An html5 customizable and user-friendly UI is provided for visualizing and creating reports and charts. TARGETING - Profiles can be used for personalized content delivery in real-time. Specific APIs are provided to interact with external systems (adservers, DMP, RTB, survey services, DEM andother marketingplatforms) andexchange user profiles for content targeting. Our ad server ad.agio is integrated with the Audience Analysis platform and can be used to deliver any digital content (advertising or editorial) to identifiedsegments on the customer sites.

19 Platform Logical Architectural Scheme user related data sources content related data sources

20 Platform Segments Activation TRACKING AND PROFILING BIG DATA PROCESSING MANAGER Users and Content info are collected, normalisedand organized into our DB AUDIENCE ANALYSIS User / Content analysis and segmentation for analysis purposes (aggregated data) CLUSTERING INTERFACE Clusters creation for targeting purposes (individual cookies) You can reproduce segments i dentified through audience analysis DELIVERY Automated via ad.agio (Neodata s ad server) Requires integration via external platforms AD.AGIO / YOUR AD SERVER / YOUR CMS To deliver digital content (edit orial or advertising) on your properties (websites, mobile) Requires integration DEM PLATFORM / CRM To deliver DEM campaigns or other campaigns managed via CRM (registered users only - must reconcile cookies with registered users) Automated Via API or File export PROGRAMMATIC / RTB To sell or buy segments on advertising trading platforms ALL RIGHTS RESERVED NEODATA GROUP SRL 2014

21 Our Products Suite

22 Glossario digital advertising Banner Formati pubblicitari Campagna clicks conversions Viewability Time on page Publisher Advertiser Media Agency

24 Barilla Data Management Platform 24 Segmenting Barilla s audience to identify buying personas for marketing purposes Integrating data from a number of first and third party sources. Objective : Use Customer Insights to drive consistent and efficient digital marketing

25 Editoriale Domus Data Management Platform 25 Designing Editoriale Domus DMP for Programmatic Adv Trading. Using navigation data collected across all websites in the network Objective : know Editoriale Domus audience to drive analysis and increase adv inventory value.

26 Gruppo Espresso 26 Providing a DMP to Gruppo Espresso since the DMP is currenlty linked to programmatic trading platforms as well as to the sales systems in use by Gruppo Espresso s advertising sales agency Manzoni. Objective : know Gruppo Espresso users and sell profiled segments in programmatic advertising platforms

27 Turbo Data Management Platform 27 Turbo is the first independent Italian trading desk offering the best performing RTB and Programmatic services to agencies and advertisers Objective : equip their indipendent trading desk with a proprietary DMP designed to optimize media campaigns performance for brands

31 Mix & match ads and contents Typically used to optimized unsold inventory Dynamic widgets Registration campaigns Recommended articles ALL RIGHTS RESERVED NEODATA GROUP SRL 2015 Advertisements Videos Recommended videos

32 Dynamic repubblica.it Gender based tab selection Placed on HP Used to steer traffic to other sections Automaticgender generalization procedure

33 «More on the Quattroruote 33 Based on Natural Language Processing concept extraction algorithms Optimized for CTR Totally customizable layout through Ad.Agio Old articles re-surfacing Main goal: Deep dive into subject, increase horizontal navigation Avg performance in 2014 >5% CTR

34 repubblica.it Automatically generate an additional last-slide in a photo gallery Acquire photo galleries trough rss feed Galleries are categorized Optimized for maximum CTR probability Hide previously seen galleries by same user Main goal: Increase site traffic Avg performance in 2014 >20% CTR About 2,5M click/month About 50M PV/month ALL RIGHTS RESERVED NEODATA GROUP SRL 2015 ALL RIGHTS RESERVED 34 - Neodata Group, 2015

35 Slide-in espresso Used to promote products based on specific marketing rules If no recommendation available it may default to standard article recommedation MAIN GOAL: Provide a marketing tool to advertise ad- hoc commercial/editorial products to specific clusters of users in a highly dynamic fashion ALL RIGHTS RESERVED NEODATA GROUP SRL 2015

36 Local local newspapers 36 Automatically fed through web-services (ad-hoc dev) Managed dynamically by ad.agio interface Real estate / Cars / Job offers Timed tab rotations MAIN GOAL: Automate local classifieds based on user geo- location ALL RIGHTS RESERVED NEODATA GROUP SRL 2015

37 Foreclosure local newspapers 37 Automatically fed through web-services (ad-hoc dev) Managed dynamically by ad.agio interface Auto-rotation based upon most viewed MAIN GOAL: Automate local foreclosure auctions based on user geo- location ALL RIGHTS RESERVED NEODATA GROUP SRL 2015

38 Rumbo - Ancillaries Based upon flight search, propose ancillary thridparty products Hotels Parking Car rental Transfers Local events City Discover MAIN GOAL: Sell ancillary (third- party) products at check- out time. No manual configuration required! ALL RIGHTS RESERVED NEODATA GROUP SRL 2015 ALL RIGHTS RESERVED 38 - Neodata Group, 2015

39 e-commerce product recommendation Clusters are defined by marketing Regular expressions rules to define clusters Fed directly by the warehouse Recommendations can be shown within banner space (maximizes unsold space value) MAIN GOAL: Sell products from editors e- store. Increase registered user base. ALL RIGHTS RESERVED NEODATA GROUP SRL

40 CRM Stage ESSENTIAL DUTIES AND RESPONSIBILITIES: Your role is to support Neodata s clients in the following areas: Develop and implement digital campaigns through our proprietary ad server Test and implement 3rd party tracking, VAST/VPAID, Javascript Identify and troubleshoot discrepancies Traffic campaigns by setting the correct targeting and goals Oversee campaigns performance Generate campaign reporting and analytics You will be also in charge of: Analyzing business requirements submitted by the client and elaborating functional specifications Analyzing new client requirements and configuring the adserver platform in the client startup phase Supporting account managers and sales initiatives

41 Development Stage FRONTEND Web technologies, JavaScript, HTML and HTML5 BACKEND API, Java, Database

42 You focus on your business, we will do the maths