Monthly Archives: March 2018

A Solution for Cross-Media Sales Challenges

The most serious question facing media organizations in the cross-platform world is how to value inventory, optimize the use of inventory, and negotiate advertising campaigns utilizing that inventory.

The solution? Use intelligent agents and data integration to surround existing infrastructure — aggregated operational systems — and elevate the decision process above that infrastructure.

Rather than rely on supplemental manual workflows to achieve sufficient integration to execute business processes and analysis, add an intelligent layer around operational systems. This approach enables an elegant and immediate solution for unifying people and processes, for overcoming fractured workflows, and for maximizing revenue opportunities.

BIAnalytix delivers all of these capabilities and more. The Decentrix media analytics platform allows the sales organization to organize itself to sell cross-platform or to specialize in a delivery vertical. Instead of trying to build pricing bundles across incompatible delivery vehicles, the organization’s pricing and planning team can develop ad products through research and policy-making. Clients can negotiate cross-platform campaigns more quickly with higher execution fidelity. Inventory is leveraged more fully and at a higher value.

How does BIAnalytix do it? The platform accurately tracks all available inventory for sale across all audience targets and distribution channels, and it prices that inventory based upon demand. Using data from the many operational systems above which it sits, the platform forecasts capacities and sellouts, and then reconciles that information with delivery across all delivery vehicles and inventory pools. As a result, the media organization can easily construct cross-platform campaigns with optimum inventory usage and audience fidelity. Intuitive visual interfaces simplify evaluation of proposed campaigns for maximum revenue gain with inventory usage efficiency.

To ensure accurate billing and revenue projection, the platform not only forecasts alignment of audience measurement but also tracks the campaigns in flight and makes adjustments so that contractual obligations are met automatically. All of this occurs in real time.

By removing sales friction, preventing revenue leakage, enhancing the value of ad inventory, and maximizing the yield of campaigns without manual stewardship overhead, BIAnalytix allows media companies to unlock the hidden potential of their media inventories.

Using AI to Maximize Ad Sales Revenues

Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technologies — these are the new drivers of the modern media business. As the industry adapts to new and emerging content delivery technologies, data and analytics are the tools essential to extracting actionable intelligence and presenting it to the right people at the right time. AI and machine learning are taking analytics to a new level, improving and enriching the quality of data and, in turn, offering even more specific forecasts and more accurate insights into potential revenue opportunities.

National and local broadcast groups traditionally have considered monitoring of key performance indicators (KPIs) to be sufficient for performance analysis. They have relied heavily on one such KPI, revenue performance, which compares the networks or stations actual and projected revenue trend against their budgets. While executives can use this KPI to understand if they are on track to meet their financial commitments, they need more and better information if they are to understand how to improve revenues and address the factors contributing to budget shortfalls.

Only AI can effectively examine the tens of millions of data points that reflect sales performance, market trends, and business cycles across many different categories, such as lines of business (local, national, digital, etc.), products, sales offices, and even individual advertisers. In addition to monitoring sales operations and surfacing new alerts, opportunities, and risks to relevant stakeholders, AI learns as market conditions change and as the business grows and evolves. Using machine learning (ML), AI-driven analytics systems use algorithms to parse and learn from data, and then use that understanding to make forecasts or predictions.Product Data Point Analysis

AI learns what “normal” business cycles look like, then continuously monitors for new risks and opportunities.

While many industries are applying AI and ML to the massive collections of data associated with their operational systems, the media industry is just beginning to leverage these powerful tools to support business strategy and success. In fact, Decentrix is the first media technology company to leverage sophisticated media-centric AI and ML to enhance the revenue opportunities of media, entertainment, telecommunications, and advertising companies in the cross-media marketplace. Our BIAnalytix platform uses AI and ML to expose critical data within operational systems and to deliver insights that yield maximized inventory pricing, enhanced audience values, and optimized campaigns across all properties and platforms, across linear and digital business models, and across OTT and ATSC 3.0.

Both national and local sales enterprises may be able to produce some of these insights without the benefit of AI or ML, but the sheer size and scope of data prevents them from doing so quickly or comprehensively. Implementation of media-centric, AI-driven analytics allows a media group to automate and accelerate analysis of all data and to extract the insights essential to capitalizing on opportunity in a complex and competitive marketplace.