mittmedia - amazon s3 · 2019. 1. 28. · past future present providing the editorial staff with an...
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Mittmedia
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Walkthrough
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Mittmedia’s data platform is named Soldr.
The main objective is to solder different products and datasets together to drive
large scale innovation.
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From the perspective of the end user, Soldr is the driving factor behind the majority of content being exposed, regardless of what product and device the user is currently interacting through.
Autonomous publishing decisions are derived from data collected throughout the ecosystem.
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Soldr uses three main data types:
Interactions, content and users
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BlackcapSparrow
From a system perspective the platform enables digital product and business development within the Mittmedia ecosystem. Soldr extracts, transform and load data between different databases and subsystems.
Blackcap is the native mobile app platform and Sparrow is the foundation for Mittmedia’s news sites. In total well over 50 digital products. Tweakit is the distribution platform. Aracua is the content production suite for all editorial personnel. Reacher
is the ad platform used for all ad campaigns driven by target groups defined from user data.
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Mittmedia
Production unitProduction unit
External publicist
Production unit
Other tool
Soldr API
Content robot
Robot content provider
Soldr API
1. Content production
2. Company structure
3. Content intake
4. Content storage Soldr API
5. Platform
The data pipeline is based on a lean approach, optimized for scalability that makes it possible to integrate vendors not directly connected to the production process running within the organization.
This is the driving factor behind Mittmedia´s robot content distribution.
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Interactiondata
Soldr API
Manual Automated Personalized
Apps Sites
Non Mittmedia productsMittmedia products
External platforms
Users/customers
User data
3. Products
2. Product platform
1b. Distribution layer
1a. Content selection
4. Customer/user
5. Data feedback
Distribution of content and tracking of user interactions is a streamlined process, making the development of innovative news products to a large extent system independent. Every product team can chose its own tools, and rely on a standardized way of receiving content and
user data from the platform.
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Using the platform to make a data driven organization innovate.
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Super local mobile push notifications overview, driving new publishing strategies.
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Used by the paid content executives board for quantifying market penetration on a municipality level, exposing potential innovation areas.
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Showing consumption statistics, user acquisition and content scoring on an personal dashboard for more than 400 journalists in the editorial department of Mittmedia.
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Providing the organization with dashboards showing regional user activity, which helps the content strategy team to plan for short term content creation.
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Adeprimo 24
Tableau
QlikviewIO Technologies Data Science
Reacher KPI Reports
NLP / Auto Categorization
Content Network
Activity Map
User Notifications
Mobile apps Sites
Newsletters
Content API Paywall Header Bidding Native Ads
Data Lake
Data Warehouse
Data models
System InterfacesPersonalization
Bootstrapping internal and external system development processes.
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Innovating (and open sourcing) the content metadata structure Soldr, enabling auto tagging and auto classification of content, helping the journalists to free up time by speeding up the publishing process.
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A unified data structure have lead to several university research projects being done on Mittmedia content.
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Building Markov Chains for paid content business development.
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Time of day Day of week Current location Stress level Time since last session
Direct Social media Search engine Email Link referrer
Mobile app Website
Media type Metadata Age
Device
Product
Session
Context
Content Length Paywall
Logged in and paying
Age Home location
User type
Profile Product loyalty Origin Language
Logged in and not paying Paying and not logged in
Gender
Anonymous
Target group Persona
Production cost
Making user data accessible as a complete structure through different types of analytics and data science tools
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Providing data scientists with high quality data, leading to award winning creative research.
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Iterating curated datasets for building machine learning models used for churn prediction.
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Providing dashboards linking user activity to published content.
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Machine learning - content suggestion
assistantUser
interaction data
Editorial short term
planner
Past Future
Present
Providing the editorial staff with an easy-to-use interface to assist in planning what to publish. Soldr learns from the past and suggests future content production.
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Finding correlation between acquisition and content efficiency.
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Analysing user behaviour trends and forecasts using time series.
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Soldr (and Mittmedia developers) always try to find new innovative contexts where the platform can deliver value.
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Innovating the user experience
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By extracting and storing data in the platform the UX-department have a way model their research and to quantify their results. This leads to a overall more consistent user experience for the customers, spanning all digital products.
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Behavioural data is used to create hypothesis about how a product is consumed, which is verified through surveys and user interviews.
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Increased loyalty of the paying customer group because of a higher sense of relevance. Conversion, retention and thus monetisation.
Churn probability within the paying user group.
Project goals
4. Content demand1. Production 2. Storage of content supply 3. Distribution
The editorial staff produces content based on current events and user insights data.
Content is stored with a metadata structure.
The distribution of content is based on learnings about the user’s interests and behavior.
The users are divided in cluster groups based on behavioral data and the metadata connected to the content being consumed.
Description
Use data insights to help journalists create more relevant content for the users..
Shorten the time between publishing and the peak of content consumption.
Making sure that individual users are exposed to the right content at the right time.
Measured by Average content performance. Storage time analysis (Lean six sigma).The amount of exposed content compared to the amount of consumed content, per user.
Content production chain
Soldr is the core foundation for all content personalization throughout the Mittmedia ecosystem. Any new product based on the platform can be personalized out-of-the-box.
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By mapping users to clusters defined both by interests and geography Mittmedia is able to experiment with niche content, such as house sales information.
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By fine tuning the super local push notifications, using data evaluation, the editorial staff is able to iterate the publishing strategy for mobile devices.
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Personalization have lead to a more relevant start page, resulting in a higher click through rate.
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Making target group data from Soldr accessible to the advertisement platform have completely re-invented the sales process, giving the salesforce a great tool but also exposing the end user much more relevant ads.
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Newsletters are sent using the internal content recommendation engine, which have lead to several experimental approaches to content marketing and retention strategies.
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Innovating and iterating product funnels by enabling complex swarm behavior models, through large and easily accessible datasets.
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Making sure that content recommendations are optimized by A/B-testing spanning large groups of users and geographical regions.
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Clustering users for super local content personalization, using neural networks.