A large US Pharmaceutical and Consumer Goods company is interested in providing a decision support tool to help its marketing division to optimally manage its marketing campaigns. The company’s marketing arm is tasked with meeting its financial goals (as mandated by corporate, annually). The marketing arm uses its budget to schedule promotional campaigns and lift the sales forecast to meet the annual financial target in the face of changing market conditions (like a patent expiry event).
OSI was commissioned to design a forecasting tool to give visibility to the company’s sales forecast versus its annual financial commitment. This was intended to aid the team in making decisions with respect to planning promotional events (campaigns) to close the gap between the target and the forecast.
Marketing divisions in consumer (CPG) and Pharmaceutical industry alike rely on marketing campaigns, promotions, etc. to influence future sales in order to meet financial targets. This is further complicated when the company is faced with competitive pressures that influence its sales – either temporarily (like a competitor doing a promo) or permanently (like an expiring patent). Given a finite marketing budget, it takes an immense amount of planning and scheduling to get to an annual marketing campaign plan that meets the financial goals.
Visualization of the sales forecast versus the financial target is the first challenge. Upon visually realizing the challenge facing the company, the decision-makers need the tools to do ‘what if’ analyses to schedule different marketing campaigns and assess the resulting conditions. The idea is to provide the end-user with a user-friendly tool to
The ultimate solution is to take the guess-work out of the work-flow and give a one-button solution where the end-user will be able to get an optimal set of campaigns (i.e., events) and their respective parameters which minimizes the overall cost of all the events and meet the company’s annual sales targets.
The decision variables for the optimization problem (the one-button solution) are event timing, event type, uptake curve profiles, time of persistence of the events, etc.
The constraints are: total marketing dollars, number of events permitted in a year, etc.
The objective function is: minimize the marketing dollar amount spent (or minimize the difference between forecasted sales and the financial target committed to corporate).
The OSI team implemented the visualization of the sales forecast model first. This model was implemented in Microsoft Excel to give familiar tools, controls, tables, and charts to the end-users. Using this tool, the end-users are able to iteratively schedule the different marketing campaigns and close the gap between the sales forecast and the annual financial target.
The OSI team was also able to formulate the events scheduling problem as an optimization problem and implement a one-click solution. This was done using a high-power mathematical optimization tool. The problem was formulated as a mixed integer programming (MIP) problem. The resulting solution takes the guess work out of the end-users’ workflow.
Our tool provides the following benefits –
A large US Pharmaceutical and Consumer Goods company is interested in providing a decision support tool to help its marketing division to optimally manage its marketing campaigns. The company’s marketing arm is tasked with meeting its financial goals (as mandated by corporate, annually). The marketing arm uses its budget to schedule promotional campaigns and lift the sales forecast to meet the annual financial target in the face of changing market conditions (like a patent expiry event).
OSI was commissioned to design a forecasting tool to give visibility to the company’s sales forecast versus its annual financial commitment. This was intended to aid the team in making decisions with respect to planning promotional events (campaigns) to close the gap between the target and the forecast.
Marketing divisions in consumer (CPG) and Pharmaceutical industry alike rely on marketing campaigns, promotions, etc. to influence future sales in order to meet financial targets. This is further complicated when the company is faced with competitive pressures that influence its sales – either temporarily (like a competitor doing a promo) or permanently (like an expiring patent). Given a finite marketing budget, it takes an immense amount of planning and scheduling to get to an annual marketing campaign plan that meets the financial goals.
Visualization of the sales forecast versus the financial target is the first challenge. Upon visually realizing the challenge facing the company, the decision-makers need the tools to do ‘what if’ analyses to schedule different marketing campaigns and assess the resulting conditions. The idea is to provide the end-user with a user-friendly tool to
The ultimate solution is to take the guess-work out of the work-flow and give a one-button solution where the end-user will be able to get an optimal set of campaigns (i.e., events) and their respective parameters which minimizes the overall cost of all the events and meet the company’s annual sales targets.
The decision variables for the optimization problem (the one-button solution) are event timing, event type, uptake curve profiles, time of persistence of the events, etc.
The constraints are: total marketing dollars, number of events permitted in a year, etc.
The objective function is: minimize the marketing dollar amount spent (or minimize the difference between forecasted sales and the financial target committed to corporate).
The OSI team implemented the visualization of the sales forecast model first. This model was implemented in Microsoft Excel to give familiar tools, controls, tables, and charts to the end-users. Using this tool, the end-users are able to iteratively schedule the different marketing campaigns and close the gap between the sales forecast and the annual financial target.
The OSI team was also able to formulate the events scheduling problem as an optimization problem and implement a one-click solution. This was done using a high-power mathematical optimization tool. The problem was formulated as a mixed integer programming (MIP) problem. The resulting solution takes the guess work out of the end-users’ workflow.
Our tool provides the following benefits –
