Beta Open for Testing, Gives Publishers and Developers Transparency, Full Ownership and Control of Data

InMobi, a leading provider of content, monetization and marketing technologies that help businesses fuel growth, today launched Meson, a truly independent SaaS mediation platform designed to help publishers white label it as an in-house solution. Publishers will get to fully own and manage their data, control each aspect of their monetization, user experience, and linkages to user acquisition, and transparently review the end-to-end flow of advertising dollars to understand their true take rate.

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“Meson brings a next-generation, independent mediation approach for publishers and provides full transparency and control of the auctions and bidding process as it’s happening”

With ready-to-deploy application programming interface and software development kit integrations, Meson provides publishers a straightforward independent mediation platform with a clean, user-friendly environment to optimize their return on investment while reducing risk of first-party data leakage or having to work with players who have a conflict of interest.

“Meson brings a next-generation, independent mediation approach for publishers and provides full transparency and control of the auctions and bidding process as it’s happening,” said Kunal Nagpal, Senior Vice President and General Manager of Publisher Platform and Exchange at InMobi. “The solution makes it easier for app developers and publishers to understand what data valuation looks like and have a mediation partner that fully supports their vision. In addition, as per our understanding, no other mediation stack offers the breadth of connections for publishers at every auction to maximize the value of their user base.”

“We believe in the importance of having mediation platforms with full transparency for publishers and app developers,” said Michael Ruffin, Director of Analytics at MobilityWare. “With the maturation and recent consolidation in the market, it’s even more important to ensure there are options available which are designed to help publishers fully own and control their data.”

Meson offers a wide-spectrum demand mediation platform different from legacy mediation solutions by solving challenges that the publishers need but traditionally have not been able to address because of conflicting incentives. Meson blends broad functionality and core needs of app publishers by providing a mediation-as-a-service so any publisher looking to in-house their mediation can do so without investing in tech talent and time over many quarters, in addition to then running regular maintenance on it. This is intended to work towards the publishers’ core needs of:

• Transparency: Operational transparency for publishers by facilitating direct access to all aspects of mediation including full visibility on the infrastructure usage, service parameters and associated costs, as well as the opportunity of third-party audit.
• Neutrality: Unlike traditional mediation platforms, Meson is a paid SaaS platform for publishers. There are no incentives to increase the significance of any demand partner by tactics, mechanics or privileged access to data. We simply provide an environment to facilitate monetization.
• Control: Publishers decide which demand partners bid in an auction, incorporate their own models, set the rules and auction dynamics to clear their inventory providing a similar level of control for in-app advertising that a publisher would have for in-app purchases.
• Diversity: An opportunity to connect any demand partner of the publisher’s choice, either via API or SDK; Meson supports the expansion of integration to guarantee freedom of choice for publishers.

In addition, Meson offers publishers a fully visible revenue stream with no hidden fees and will be priced separately based on pre-determined subscription terms. An intuitive user experience potentially allows publishers to better understand and manage the complex auction and waterfall set-up and gives them in-depth analytics capabilities as well as predictive modelling to understand outcomes and allow for near real-time adjustments.

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