Methodology
Scalable Holistic Business Measurement & Optimization using Deep Probabilistic Modeling
Holistic Business Measurement & Optimization
It is time to move beyond just focusing on a single aspect such as marketing, churn or sales. We offer the possibility to consider all of your business activities together and tie multiple business areas into your costs. Gain a view of how everything is connected and see the actual impact on your bottom line.
Data science models relying on predictive analytics face several fundamental challenges that must be addressed for effective implementation. These include ensuring business sanity in model outputs, operating successfully in low-data environments where input variables outnumber available data points, managing nonlinear dynamic relationships, and maintaining model transparency rather than relying on "black box" approaches.
Traditional data science and machine learning tools prove inadequate for addressing these complexities. They typically require large datasets, are susceptible to overfitting, and fail to adequately represent business dynamics.
Our solution employs Bayesian Inference methodology, which allows us to incorporate business expertise as "priors" in the modeling process. This approach significantly reduces the data requirements necessary to train robust models while preventing overfitting issues that plague conventional methods.
Traditional data science and machine learning tools prove inadequate for addressing these complexities. They typically require large datasets, are susceptible to overfitting, and fail to adequately represent business dynamics.
Our solution employs Bayesian Inference methodology, which allows us to incorporate business expertise as "priors" in the modeling process. This approach significantly reduces the data requirements necessary to train robust models while preventing overfitting issues that plague conventional methods.
Model your entire business dynamic
What does it actually mean to "model" all the business dynamics?
Current Practice
Models that only focus on one commercial objective such as optimizing Marketing Mix Models, typically only quantify the effect of media to one KPI, i.e. sales, with some additional variables such as seasonality and macro economical data added.
The issue with this is that your insights only focus on media, ignoring the fact that it also impacts other areas such as brand awareness, customer retention, etc.
We believe that this is not good enough and that we can do better.
What we do
We take it to the next level. We measure the effect on all the commercial objectives of your choice at once.
And we don't stop there.
We include the costs of your activities making it possible to gain insights of the impact on your bottom line as well.
And we don't stop there.
We include the costs of your activities making it possible to gain insights of the impact on your bottom line as well.
Our approach includes all the commercial objectives in the same model. We make it possible to measure the impact of multiple drivers on churn, new customers, brand, etc. and track the respective effects on sales.
Not only that, but we are able to take your insights one step further by including the cost of activities such as pricing, distribution and media to quantify the effect on your bottom line.
How? We combine AI power with Bayesian Inference.
The result is next generation predictive modeling.
Read our blog post and understand why you need to consider all business dynamics.
Bayesian Inference
Building a good predictive model presents a series of challenges; it needs to have business sanity, be operative in a low data environment where the number of data points may be less than the number of input variables, be able to handle nonlinear dynamics, be transparent in the model design (i.e. not a "black box").
Standard tools in data science and machine learning are unsuitable for this as they tend to be data-hungry, prone to overfitting, and unable to express business dynamics. So to overcome this we use deep probabilistic modeling that is based on Bayesian Inference. This allows us to express business knowledge as priors and thus reduce the amount of data needed to train the model without overfitting.
The major beneficial difference with this approach is that it comes with probability distributions of the variables, so that the associated uncertainty can be correctly estimated.
The major beneficial difference with this approach is that it comes with probability distributions of the variables, so that the associated uncertainty can be correctly estimated.
Normal data science on top, what we do on the bottom. (Click to zoom)
Benefits
Bayesian Inference comes with various benefits
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Correct business dynamicsThis is no black box machine learning algorithm. It understands business and business sanity so you have transparency and trust. -
Estimate associated riskEstimate the uncertainty so you have better information when making decisions. -
Robust decison supportProper uncertainty estimation gives you the ability to see how different scenarios have different probabilities of success. -
Faster generalizationOur models require less data given the use of prior data and knowledge of your business. This enables you to gain insights faster. -
High dimensional sparse dataWorks well with high dimensional data and few data points. This allows us to pool information across variables in a hierarchical fashion.
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Avoids overfitting
The model learns the actual dynamics of the business and does not overfit to specific data.
Ready for the next step?