Advanced Analytics displayOur use with Advanced Analytics

For us, Advanced Analytics are about making that leap once you have your solid reporting structure in place. For many companies, their first hurdle is managing their data and getting a solid framework of descriptive analytics in place. They begin getting insights, answering questions and making data-driven decisions. Next they want to make predictions based on their historical data. They want to look at how they might be able to guide future behavior in the form of prescriptive analytics.

After we have gleaned as much information as possible from your data through descriptive analytics, we can begin to forecast future trends. Whether we’re talking quantitative indicators such as sales volume, number of products per client, or more abstract components of your business such as probability to buy again based on gender, everything can be predicted if sufficient historical data is available. On this journey, we employ advanced statistical methods, data mining and machine learning to model, cluster, forecast and test the models. Once models are fully tested and confirmed future-proof, we develop various dashboards to enable technical and business people alike to use the information in an easy, clear and intuitive way.

For data modelling we use a vast array of tools, depending on the client’s IT environment, volume, speed and density of the data, as well as the scope of the project. By entering the field of predictive analytics, our clients don’t just understand their customers, segments and market, but are better prepared in terms of workforce, production or marketing needs for the future. They gain a competitive advantage through understanding their demand, resource needs and the geographical distribution of their clients. In addition to forecasting, we assist some of our clients in making data-driven decisions using advanced prescriptive statistical techniques.

In building prescriptive models we take into account those variables that your company can control and recommend how to best make use of them to achieve a desired scope. For example, a school can model the number of hours of teaching required to teach a particular subject in order to achieve a pass rate of 85%. Similarly, a retail insurance company can use client characteristics to forecast their probability to renew a plan the next year. Moreover, by modelling the marketing variables in the process, such as emails, phone calls, price sales promotions, we can establish a minimum threshold of contacts needed to increase the odds of keeping the clients at risk. We are upfront in our approach. No model is perfect or is relevant forever. Here at Perception we are perfectionists. So, while we set you up with the best model we can find, we remain committed to re-visit the data, remodel and improve every time new variables or significant changes in the data present themselves and change the course of the business.