AI for Data Monetization

To create circular & transparent value from consensus-based data through AI-driven technologies.

Business goal
1

Maximize profits from ADV Inventory (Publishers)

2

Enable new data-driven revenue streams (Retailers)

3

Generate transparent exchange of value for data generators (users), data collectors (publishers) and data buyers (advertisers)

Case histories and insights about DataLit, MobiLit, VoiceLit, MagPedia and Adapex

We have developed DataLit.AI, the data monetization technology framework based on Artificial Intelligence.

It allows Publishers and Bloggers to:

  • increase and optimise the collection and analysis of first-party data relating to registered users on digital properties (data governance);
  • integrate first-party digital data with CRM and other corporate sources in a specially developed data lake (data integration);
  • apply predictive profiling machine learning models to unregistered users to transform them, like registered users, into advertising audiences (data modelling);
  • maximise revenues from programmatic advertising (data activation).

Retailers, on the other hand, choose DataLit.AI to obtain a new source of revenue, monetising the data of users who do not purchase on the advertising market, without cannibalising their core business.

Advertisers prefer DataLit.AI to: reach unconventional audiences, hyper-profiled thanks to AI and with a greater propensity to buy; to obtain better conversion rates; to have maximum brand safety and transparency.

In preparation for subsequent monetisation, we also help publishers to create and distribute digital publications and content via apps and smart speakers. Where paper archives exist, we can digitise them and identify evergreen content to intelligently enrich the content inventory.

The importance of Alternative Data

Having access to digital Alternative Data (extracted from social networks, blogs, forums, maps, e-commerce platforms) and AI-based monitoring tools offers several benefits, including:

  1. accessing valuable information often overlooked by competitors
  2. getting fresh and daily updated data as opposed to traditional data that is generated in late reports as early as it is published
  3. cross-checking by comparing different and independent data sources

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