Do you get the maximum out of your data?
The power of data is in its interpretation. We work with organizations across virtually every industry, in critical parts of their business, to empower data through analytics. Analytics is about applying machine learning, predictive modelling, statistics, and advanced visualisation to (big) data sets with techniques such as predictive modelling, machine learning, and text mining in order to gain actionable insights. Analytics supports decision making in the business domains of customer, supply chain, finance, workforce, and risk.
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 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.
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
Data Mining and Customer Targeting
Use of transaction data and external databases to prioritize
prospects for business expansion of selected B2B services.
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.
Statistical and economical impact analysis for various
entities of the Belgian government.
From data collection to modeling of customer flow to optimize
organization of security screening, and evaluate pricing levels.