Analytic Edge, a provider of marketing and sales analytics solutions, today announced the latest version of their flagship solution Demand DriversTM. Deployed by some of the biggest brands in the world, Demand DriversTM is a Marketing Mix Modelling solution that enables brands to continuously measure the effectiveness of their marketing spends and programs to maximize Return on Marketing Investment and drive growth. The latest version of the solution delivers two powerful capabilities for brands – real-time marketing insights, and do-it-yourself interfaces that allow clients to run analytics on demand without external dependencies.
Currently, marketers face two big challenges with most typical Marketing Mix Modeling solutions. The first is the long turnaround time for recommendations and insights, making them ineffective for data-based decision making on future marketing programs. The second is the overdependence on large consulting firms or analytics providers to manage, run and interpret marketing analytics. Demand DriversTM now leverages Machine Learning, automation and advanced analytics to provide near real-time insights based on the latest data so brands can tweak their media and promotional campaigns on-the-go for maximum impact. Second, it removes dependence on specialist firms by providing an intuitive, easy-to-use, do-it-yourself interface for marketers to log in and run various analytics on demand.
“For marketing insights to be truly actionable, they need to be as near real-time as possible. This means they should be based on data that is at best a few weeks or a month old” said Rahul Budhraja, Co-Founder and Managing Partner, Analytic Edge. “The latest release of Demand DriversTM uses an always-on approach to Marketing Mix Modeling. This enables real-time and actionable insights that our clients can use to regularly adjust their marketing campaigns to achieve their top-line and bottom-line targets.”
Demand DriversTM revolutionary “always-on” approach is enabled by deeply leveraging the latest technologies across different parts of the solution and across the Marketing Mix Modeling process. Some significant examples include:
- Collecting the latest data continuously and automating data ingestion
- Leveraging Machine Learning (ML) for quality control to identify incorrect data and capture outliers
- Updating models quickly and regularly based on the latest data
- And providing businesses with in-house, transparent, “do-it-yourself” access to Marketing Mix Modeling through intuitive interfaces and easy-to-use tools
Published on: 05th Nov 2019 www.martechadvisor.com