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Financial Analytics

Financial analytics define the cross-border between business perception and business reality. It is the place where human intuition and extensive experience meets data driven performance measures.

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Combination of these two areas helps find answers to vital questions:

  • Which products and services impact my financial performance most?
  • How to improve products and service performance within current market shape ?
  • Are business decisions supported with “as complete as possible” data insights served “on time” ?
  • Can we delegate business decisions to proper levels in organization and simultaneously provide all necessary analytics and insights?
  • Do we measure how this decisions are performed (timewise, profitwise)?
  • Which of these tasks could be automated (ex. rebating, pricing) ?
  • How AI helps improve business performance (using behavioural pricing, on-line product recommendations, stock level predictions)?

Customer Insights

Customers are core part of every business. Customer 360 view is crucial complex combination of internal data (financial, process, behavioural) mixed with external data sources (market share information, social media). This gives useful combination of machine-based customer mass processing with highly customized individual insights.

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Customer insights are useful to answer questions like:

  • Predictive churn analytics – which of my customers are likely to default? How to retain customer before it happens?
  • Customer Lifetime Value – which of my customers should be retained and at what cost? How to improve their CLV?
  • Behavioral offering – what new products this customer is likely to accept?
  • Segmentation – how this customer’s buying patterns are similar to others?
  • Profitability performance – which customers are profitable? What are the main profitability factors? How to improve not-profitable customers?

Optimisation

Process orientation and optimization is crucial part of any modern business management in order to be successful in the national and international competitive environment.

Multi-objective optimization is a mathematical approach to optimization involving more than one objective function to be optimized simultaneously in the presence of trade-offs between two or more conflicting objectives.

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The primary goals in multi-objective optimization:

  • to identify Pareto optimal solutions in the business objective space
  • to evaluate business impact of Pareto optimal solution
  • to provide the decision makers, with a large enough but limited number of Pareto points for selection
  • to automate decision making process whenever it is feasible (pricing, eCommerce)

E-commerce analytics & automation

E-commerce has set up new level for utilizing customers behavior analytics and offering insights. Customer service is not only becoming more and more “smart” and individualized but real timing is a fundamental factor.

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Great front-end offering is combined with smart supporting logic in back-end:

  • Customer Lifetime Value – how customer profitability history impacts current offering ? Should services quality be improved for most valued customers while losing focus on least valued customers? What are the costs of improving CLV and is it compensated by increased value?
  • Targeted offering – what is best combination of products customer is most likely to buy? Could customer profitability be improved by precisely targeted offering?
  • Behavioural pricing – how to build on-line prices based on customer CLV and profitability history? Are market trends effects recognized and utilized here?

Real-Time Decisioning

Business process automation is important part of overall business process excellence initiatives. In the era of e-commerce and instant customer interactions, important decisions need to be made in real-time.

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  • Make traditional (slow) operational decisions Real-Time. Real-time analytics and decisions respond to current conditions and this is vital improvement to traditional (slow) approach with analytics based on yesterday’s or last month’s data.
  • Results tracking and model refining. Real-time decisions are monitored and their results tracked. Constant validation process is ensuring that decision models are working correctly and if needed, rules adjustments are applied.
  • Build decision management capabilities in organization. New real-time decisioning processes should be organized in systematic way and overview by experienced team. This triggers new organizational and competence transformation within organization.

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