Contextual AI Asset: Dynamic pricing simulator
A global media and entertainment company was looking to digitally transform its presence to keep up with the competition from media streaming services. The client wanted to strengthen their brand presence and work on bettering their customer experience.
The client was looking to increase ROI on rentals. Pricing was a key determinant for optimizing revenue opportunities and improving customer experience. For more than half a decade, the pricing had remained unchanged across product ranges. While most amount of sales was defined by products that were newly released and highly preferred for renting out, a major chunk of the revenue also included ‘late fee’ paid by the customers, which also negatively impacted their engagement with the client.
TheMathCompany worked with the client firm to identify revenue prospects that could be bettered with aptly priced transactions. Analysing relevant parameters such as customer usage of rental services across locations and products, historical trends, etc., pricing levers could be reformed to increase revenue and customer engagement levels.
To reassess the pricing framework, multiple factors were analysed to identify customer trends that had the potential to generate more revenue and help the firm to transition with the changing times, while improving customer experience.
- Past transactions were analysed to identify customer trends with regard to renting from the store and the time of the week when transactions were at the highest frequency
- Existing information was similarly studied/augmented with genre and content-specific details.
- A mapping of probabilities based on past observations was assigned based on the frequency of occurrence
- Design of experiments was set up across different levels and probability combinations
- Ran simulations based on experiment design by leveraging a number of different simulation techniques: Monte-Carlo Simulation, Discrete event simulation, Simulations based on History.
On mapping the trends, the simulations helped to determine the best pricing alternative that added value to consumer and the client, and helped the client to keep up with other medium competitors.
Pricing alternatives: Three major pricing trends were identified – multi-night pricing, inventory-based pricing, cross-dimensional pricing.
- Multi-night pricing would not just improve revenue but also increase customer satisfaction, as opposed to the traditional ‘late fee’ format. [SM1] Rather than charging the customer a late fee for not returning the product rented, customers were given the option of renting for longer durations, at the time of checkout.
- The buzz around new releases could be used for inventory-based pricing that would improve rental frequency and revenue generation.
- Cross-dimensional pricing after analysing pertinent market factors helped to determine the optimal price point for both the consumer and the client.
- An estimated $5 million impact driven through dynamic pricing
- Three pricing trends identified to improve customer relations and increase ROI