Return on marketing investment (ROMI)

Processes: marketing mix¹ and return on marketing investment (ROMI) optimisation

Problems: limited investment for various marketing mix (products, segments, channels etc.) demands, confidence in portfolio company marketing  investments meeting investor target returns and cost of capital and better sales and cash flow forecasting for improved working capital funding costs.

Use case: using AI, predict sales response to different allocations of marketing assets or investments (paid, owned or earned) across hundreds of marketing touch points (store, website, social media, advertising etc.). Identify the mix providing optimal return on marketing investment (ROMI) and use the model to forecast sales at shorter intervals (weekly vs. monthly) with higher confidence and reduced working capital costs.

Case-study: ROMI has been in use by large FMCG companies and accountability driven marketing professionals using statistical tools for some time. Use of AI marketing across narrow parts of marketing processes has  also been growing. We worked with our partners, an agency brand of the Publicis Groupe, the world’s second largest communications group, their client data (116 marketing metrics) and leading edge AI technology from City, University of London including neural network models, time series analysis algorithms, correlation analysis and granger causality analysis and developed AI-enabled ROMI models.

Our AI-enabled ROMI models provide greater accountability and impact from marketing assets and investments (paid, owned or earned) to investors, shareholders and marketing professionals who wish to use the power of AI and data to be ahead of competition.