Our data mining engine will help you develop micro-targeted commercial strategies and provide meaningful recommendations to build a deeper and more effective relationship with your customers.
Unveil the complexities of market responses to different pricing. Evaluate the elasticity of the demand for your product, analyze cross-effect with competitors prices, and optimize your pricing strategy in real-time.
Optimize your pricing and promotions tactics to achieve your strategic goals. Using simulation and optimization techniques, find the optimal combination of commercial efforts to accelerate revenue.
Z Data Lab integrates your company data with external data such as competitors’ prices, economics indicators, weather, social network activity, reputation, among others.
Deep Learning Training
Our proprietary deep learning algorithms will analyze your data and decide with variables are relevant. Our engine will train dozens of algorithms and choose the best predictions.
Z Data Lab injects the results directly into your ERP, CRM or BI tool, so you don’t need to learn to use a new software. Z Data Lab integrates with Tableau, QlikView, Power BI, among others.
Cross-price Elasticity Analysis
Use linguistic analysis from Social Media accounts to determine the personality of individuals
Our client is a leading French media group with more than €5 billion in yearly revenue from they pay-per-view business. Their flagship channel combines sports, movies and TV shows as part of their offering. We used linguistic and psychometric analysis from social media and emails to determine the personality of clients and recommend them specific TV content corresponding to their personality traits.
This analysis was then combined with digital interaction and transactional data to profile their existing client base. This was used to create a recommendation engine providing user-specific price offers, as well as customized content suggestions. This new methodology yielded an increase of 4% in promotional efforts and also increased the relevance of content suggestions by 5.5%.