Traditional analytics and quantitative methods
Statistical analysis and operational research
to tackle everyday business challenges
brought by seasoned experts
Proven methodologies
Quantitative methods have been successfully applied to business problems in the real world. Statistics is one of the areas where the border between theory and practice has been crossed more frequently for the benefit of the practical user.
algonew staff has the knowledge, background and practical experience to navigate with ease through the various quantitative techniques that can be useful for you. Be it clustering, time series analysis, predictive models or optimisation in its various forms, we are able to match you business problem to the right quantitative technique and deliver a high quality solution that you can implement and profit from immediately
To illustrate the above we mention just two areas of applications following this section where analytics is the tool of choice to tackle the challenge.
Market segmentation
Any company with a large enough customer base feels the need to understand the kind of customers they are serving. One approach is to group the various clients in different segments with relative common characteristics and after that taylor the product or service offered to each segment based on such characteristics.
Typically the data to conduct such segmentation comes from the internal databases of the company, but sometimes external data sources or even ad-hoc surveys are also used.
Working very closely with the company, algonew would process the data, propose segmentation variables, run clustering exercises with proven algorithms, agree with the company on the number of segments and finally provide an intuitive interpretation and profile of those segments so they can be used easily across the organisation.
Customer retention
These days customer loyalty cannot be taken for granted, not even by the most prominent and leading brands. Clients are always open to better deals and the processes to engage in new commercial relationships are more convenient than ever.
Let us accept that there is no better alternative than continuoulsy providing the best service. But let us also admit that making an extra effort to retain a client is expensive. Imagine the company can pinpoint those customers who are both most likely to leave and worthy to retain or reactivate. In this scenario, resources could be prioritised to increase retention chances where they are more profitable.
Predictive modelling based on past customer behavior is the methodological approach to assist in this identification process of likely leavers. algonew works with the client in the whole modeling process, from data extraction and manipulation, to variable analysis and multivariable modelling and implementation and monitoring.
