Advanced Analytics

Empowered data results

During the past couple of years, organisations have invested a lot in data integration and reporting. In most of the cases this improved the company governance significantly. However, data gathering and reporting is not sufficient to be one step ahead from the competitors. The question should be raised whether your current reports give you enough insight about what will happen; do they give you a view on the expectations and risks?

• Do you quickly and accurately react when you receive new incoming data?
  
• Do you make optimal decisions and follow up actions based on data-grounded facts and figures?
• Do you optimally align your production with your expected future sales?
• Do you efficiently allocate your marketing resources?
• Do you differentiate the individual clients and optimally take their needs and risks into account?
• Do you sell the right product to the right client?
• Do you have the right product/market combinations?
• Do you have transparency on your procurements?
• Are your employees and your organisation’s culture well developed and set up in order to create value while using analytics?

These are the questions in scope for analytical value creation: create value by managing the available data fast and cleverly and take appropriate actions. It is critical for companies to develop their analytical capacity. Client behaviour and market development are better understood if the available data are analysed more precisely and wisely.

Analytics

Analytics focuses on effective use of data to anticipate the results of business actions. Efficient data mining means looking up the links in the data, experimenting trials, and making forecasting analyses. When going faster, more rationally, with accuracy and fact based operations, companies can obtain huge advantages. Decision support tools based on super-crunching techniques such as statistics and data mining support the managers in improving their organisation’s performance.

Some key topics are: customer and marketing analytics, web analytics, defect analytics, spend analytics, fraud analytics and risk analytics.

Domains

We have applied Advanced Analytics in different domains for different clients. Below an overview of the different domains we have identified in our Advanced Analytics practice:

• Customer analytics
• Supply-chain analytics
• Financial analytics
• Workforce analytics
• Risk analytics
• Sentiment analysis

Client cases

Data Mining and Customer Targeting

Use of transaction data and external databases to prioritize

prospects for business expansion of selected B2B services.

Pricing Analytics

Use of segmentation and predictive modeling to assess the

importance of price variation, and establish multivariate effects

of socio-economic indicators.

Data Mining and Campaign Optimization

Evaluation of various tools, and development of models to

improve targeting of investment products.

Customer Lifetime Value

Use of Auto-Regressive Vector models to assess the lifetime

value of the client base.

Segmentation and predictive modeling

Segmentation and predictive modeling to build rational selection

criteria for underdeveloped or overdeveloped commercial areas.

Demand Driven Forecasting

In collaboration with SAS, we developed a model to

predict sales down to SKU family level.

Government Applications

Statistical and economical impact analysis for various

entities of the Belgian government.

Stochastic Modeling

From data collection to modeling of customer flow to optimize

organization of security screening, and evaluate pricing levels.