Integrated Marketing ROI (IMR) Framework

Contributors (L-R) Manoj Kumar, Sujit Dora, Thyagarajan. S, (Sitting) Kavya Madhava [divider] Application of forecasting science on transaction data and aiding business decisions has been the norm in supply chain, sales, and finance departments of the company. On the other hand, marketing department applies analytics partly on transactional data and largely on research, macroeconomic, socio-cultural data for demand forecasting and planning investment mix. Even this practice limits forecasting to a single marketing KPI (say sales / consideration) for modelling and for developing an investment mix (with key focus only on advertising and promotions). This process ignores researches undertaken for identifying short term and long term potentials which play an important role while forecasting/analysing the drivers of sales. The fast growing digital and social media space and its effects are rather studied in isolation and not integrated to the investment mix planning. There is also tremendous pressure on marketing research to quantify the role of path to purchase and justify investment made on the decision making process and demonstrate visible Dollar value profits/ savings. At Meritus, we have developed Integrated Marketing Framework (IMR) a proprietary framework to integrate all the marketing data sources, KPIs and provide an integrated view of all direct/indirect and slow/fast moving influencers of demand and help companies to plan their investments better and aid in decision making. This methodology uses time series of multiple data sources to integrate in the system and not cross section or point data to integrate (like in many other traditional approaches). This framework helps the marketer optimize resource utilization for consumer targeted activities. Marketers will be able to communicate better / forecast the demand and hence will be able to plan marketing investments, production and supply accordingly. This process not only quantifies the impact of marketing investments but also drives value through an integrated view of all key KPIs (research, social buzz and all other metrics) in a single system justifying the path to purchase and research investment. Business Challenges Brand / Category Analysis Establish an integrated view of all the external factors, buzz, brand equity research data, consumer targeted activities, and resultant Business KPI (Sales) and forecast expected sales under different scenarios for the brand. Short Term and Long term effect of Marketing investments quantified Short term and long term effect of all marketing drivers (price, distribution, advertising, promotions etc.) are quantified, whereas many other slow moving drivers which are usually relegated as base sales in traditional forecasting models and decision science tools. Meritus’ IMR Framework

• IMR Framework assesses the impact of consumer targeted investments on a multitude of marketing KPIs and establishes the inter-relationships among them
• Helps understand the inter-play on POE (paid, owned and earned context)
• Helps understand the relationship and identify the lead indicators from the perception KPIs that effect performance
• It helps appreciate Market research and Marketing investments better and have better insights from the integration

Methodology

• IMR Framework considers Brand Metric Variables as “path to purchase” KPIs and calculates the effect on the final business KPI (Sales)
• Traditionally followed methods use cross section / point data as an input for system of multiple KPIs, whereas IMR uses time series of multiple KPIs (Secondary KPIs)/ Inputs to evaluate the system. This adds robustness to the recommendations.
• These secondary KPIs are integrated into the model by establishing the effect of consumer targeted four Ps, and these (secondary) KPIs in turn affecting the final KPI.
• In total IMR quantifies direct and indirect impact of marketing activities on final KPI through various paths.
• Advanced statistical techniques have been researched, tested and applied on multiple live projects. This framework involves multiple regression equations being iterated and solved simultaneously.

List of possible data sources analyzed (partial)