Optimizing digital marketing expenditure

Problem statement

Technology companies spend their marketing budget over a variety of marketing channels. It is essential to know the performance of these marketing channels to create an effective marketing expenditure plan. Our client wanted us to model the performance of the marketing channels to derive insights about the returns from marketing investments. Once the efficient marketing channel is identified, more budget can be allocated to these channels. For the marketing channels that are not performing well, opportunities to improve them can be investigated. To study the impact of each marketing channel we measured metrics like social media engagement, click through rate on digital advertisements, quality of queries raised on product forums etc

Our solution

We started the data acquisition phase of the project by collecting the marketing expense records from their digital records. We also performed web scraping on the social media handle of their products to measure user engagement, product reviews and general sentiment. We then converted this multi feature dataset into appropriate feature vectors to make it amenable to ML training. We modelled the Machine Learning activity as a Ranking problem where the algorithm will assign ranks for the marketing channels based on the multi feature data points pertaining to this channel. We also built a neural network architecture based on Multi Layer Perceptron followed by a Decision Tree. It was observed that the MLP method performed the best in terms of both accuracy and execution time. We packaged the MLP based ranking model as a self contained analytic tool that our clients can use on a periodic basis to identify performance of the marketing channel. The analytic tool ingested the marketing expense digital records and produced reports that monitor the performance of the marketing channel.

Key metrics

We are able to decrease the marketing expenses by 20% and yet retain the same level of lead conversions as before. This project was developed in a time frame of 16 weeks. The MLP based ranking model was more than 95% accurate in its predictions.

Technology stack

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