By Neeraj Kulkarni
The challenge in attributing value to your marketing mix has never been greater. Today, a variety of analytic solutions related to last touch attribution, statistical time series analysis, and market media planning tools is being used to optimize the marketing mix. Most of these solutions, however, fall short due either to reliance on imperfect information related to direct attribution or failing to take into account management assessments, brand studies, product recalls and other business uncertainties.
We have developed a novel analytic modeling approach that integrates Bayesian network modeling and traditional mixed model regression techniques to solve this problem. It incorporates available marketing data, management decisions, stakeholder user experiences, business trends and market research studies to provide a robust and predictive multichannel marketing optimization process that enables us to identify and forecast of the contribution of various offline and online media channels for client decision support and business optimization.
Neeraj presented a client case study for a leading consumer truck rental company to show the flexibility of the approach. His presentation covered the following topics in detail:
- Incorporation of data and expertise as key inputs to the network model.
- Understanding the contributions and return on Investment for marketing channels in a multi-channel environment.
- Running decision support scenarios to predict business outcomes, optimize budgets and meet business goals.