Early Bird Ends in:
Jose Maria Torres Bruna
Telefonica S.A., Spain
José María is Market Research manager at Telefonica in the CDO unit. With a Physics PhD from University of Zaragoza, he has spent more than 19 years in different positions inside the company, mainly related with market research and customer perceived quality, trying to understand, model and forecast customer behaviour and needs. He is also associate professor of applied economics at Universidad Autónoma de Madrid.
A Game Theory Approach for Data Driven Business Decisions: Use Case in Portfolio Optimization
Companies have a lot of useful information obtained from data that can be transformed into analytical models ready to use in business decision processes.
In this session, we will explore how this information, data and models, can be adapted and enriched in order to include in these company’s decision processes the existence of other players in the market, especially the competitors.
Each player in the market have their own strategies. Are we sure that when we are taking a decision, we are also taking into account all possible strategies of the competitors including retaliate actions? For example, if a company reduces prices, the first expected consequence can be the increase of market share, and then, the increase of the incomes. However, if competitors also reduce prices as a retaliate action the price reduction can be turned into an income reduction (same market share but lower prices). The consequences can be even worse if the initial price reduction starts a price war.
Is It possible to anticipate this outcome in order to avoid it? We propose to study the interaction between different competitors (players) in the market under the game theory scheme. Game theory can be used to answer very relevant business questions like “How can I optimize my portfolio?” or “Can my action start a price war?”. We have built a 3-step process:
First: We build a market simulator based on current customer behaviour, that allow us to simulate any possible portfolio obtaining gains and losses for each player for all possible scenarios.
Second: each player try to optimize the portfolio, modifying it. The portfolio for each player evolves following Monte Carlo simulations under some business conditions.
Third: the portfolio evolution finish when a given convergence criterion is reached, applying game theory equilibrium concepts.
In summary, this model provides Marketing Units with valuable information about possible evolutions of the company portfolio, or even the competitor’s one. Helps Marketing Units understanding the competitive dynamics of the market providing useful insights for the decision processes. So, business units can take initiative when needed, of planning retaliate actions if other players are going to take actions first.