We have built many market models for our clients over the years allowing them to better understand the dynamics of the market and also to measure their performance. Market models explain what happened in the recent past and also allow us to forecast what is likely to happen.
We have years of experience building market models, allowing clients to better understand the dynamics of the market and also to measure their performance. Market models explain events in the recent past and also allow us to forecast what is likely to happen. By including external data we can help you to understand better the competitive landscape in which you operate too.
We have a well-established portfolio of models all built using techniques developed in-house over many years. A common complaint that we hear is that data models can appear to be a black box to users. Data goes in and data comes out. However, often what happens in between is not well described and very hard to understand and improve. At JTA we describe our modeling as Glass Box because we strive to always make the process transparent to our users.
Models need to be carefully designed and developed after a thorough review of all the input sources and dimensions. Should your model drill into just geographies or perhaps, geographies and industry? The answer can depend on how reliable the input data is and whether it can stretches to give the extra industry view. We typically design business models at a facility offsite. This can last for some days where we review input data, population estimates for weighting, business objectives and likely outputs.
Following the facilitated workshop, we provide a detailed design specification, showing each modeling step, prior to coding.
Our automated models will take input data and apply each successive mathematical step in turn until output. The system will also output every intermediate step to a transaction log so that we can review and explain exactly what is happening in the model.
We always connect out modeling system and data to a visualization tool, such as Power BI, to be able to review and understand both the individual steps and the final output.
The models are also connected to a file of technical parameters which control the mathematics at each step. This means that we can alter the way the model runs without having to change any code. In fact, the model can run under multiple sets of parameters, allowing us to compare different versions of output before we publish the results to the business.
Over many years of experience, we have built a large internal library of modeling routines and techniques all developed as functions in R code. Here are some examples of our in-house techniques:
Input data can be a bit rough at times and this can make a model’s output appear to oscillate each month. Users need to gain confidence in a model’s results and these oscillations can distract users interpreting the results. We have techniques to recognize outliers in the data and replace them with interpolated values. We also can deploy a variety of smoothing techniques, such as polynomial splines, to reduce the noise in input data.
Input data can represent only a small sample of the population. The degree to which it represents what is happening in the market needs assessment and control. We can weight data to become representative of the population using a variety of sophisticated methods, such as rim weighting, which run dynamically as we load the data.
Often, the more input sources we have, the better the result. Merging sources which have conflicting opinions can be hard. Our approach is to generate individual scores for each source at each join and to weight the join using the scores. These scores typically consider relative sample size, standard error, level of noise in the data and any known bias. These scores are often influenced by the model’s parameter file.
We can use any mathematical modeling technique including:
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