Customer Targeting

Customer targeting through predictive models can boost the performance of your campaigns. Focusing effort and resources on customers with a higher propensity to buy can increase response rates and profits, but finding and reaching those customers is difficult.


Predictive models created with advanced statistical methods can identify customers with a higher propensity to buy (PtB). When applied to x-sell or up-sell campaigns these models can prioritize lead lists focusing company resources on the most valuable customers. By using the Net Lift (incremental response) approach we ensure customer targeting has a real impact and that the marketing budget is spent effectively.


Business Benefits

  • Increases response rates to both x-sell and up-sell campaigns
  • Improves profitability of the of the organization and marketing actions
  • Decreases customer fatigue and improves the customer experience by improving targeting and the offerings presented to the customer
  • Frees channel capacity for more efficient use or for customer experience improvement



For statistical methods we follow the strong and proven CRISP-DM methodology. We focus on business goals, exploit available data sources, use advanced analytics, and bring deep industry and business knowledge to deliver comprehensive solutions suitable for your business.

48% increase in conversion rate Medium sized bank, CEE, 2014


Filip Trojan

Filip Trojan

Senior Manager

Filip is a Senior Manager in the Advanced Analytics deparment. He has over 15 years of experience in analytics, machine learning, mathematical optimisation and data science. He has an extensive variet... More

Veronika Počerová

Veronika Počerová


Veronika is a manager in the Advanced Analytics department. She specialises mainly in analytical end-to-end solutions for clients from the finance, energy and retail industries. She focuses on predict... More

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