A top data scientist indicated that Proof Analytics‘ unique combination of automated analytics and a simplified User Experience (UX) significantly accelerates the operational impact of causal, predictive and prescriptive analytics in complex businesses without any loss of model accuracy.

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Dr. Petyo Bonev, professor of applied and theoretical econometrics and artificial intelligence at the University of St. Gallen in Switzerland, wrote: “Comparing R, Stata, and Proof BusinessGPS, the three tools generated outputs that were exceptionally aligned in their accuracy, using the same statistical procedure and the same data. What really struck me, however, is that while all three tools can be employed to solve the same task, Proof’s use of automation and compelling UX tremendously reduces the complexity and the time to insight for both the data analyst and the business user. No cumbersome data import, no statistical language programming required, no degree in fine arts necessary to produce an elegant and parsimonious graph.

With only several clicks all calculations are performed and the user gets a comprehensive and yet intuitive summary of the results. Importantly, the complex steps of model choice and computation are performed automatically in the background, delivering recomputed results at the speed that new data is provisioned. Thus, the user is “spared” from the tedious technical details of the algorithm, and only the aspects that enhance the business intuition are presented. When using data science teams using R and Stata and other similar tools, there remains a very sizeable amount of human work to be done, slowing the time to insight, limiting the scalability of data science in the business, and dramatically increasing its cost.”

Proof CEO Mark Stouse added, “The automated Proof solution presents extraordinary new leverage in understanding causal relationships, predicting what is probably coming next, and making clear recommendations to business leaders when there’s still time to optimize a response. For example, a routine Marketing Mix Modeling solution offered by Nielsen or Neustar delivers just 2-3 models that are recomputed only every 6-12 months. It’s slow, and as the calculations age-out, they become rapidly less relevant. What’s more, the cost of these programs typically starts at $2.5 million USD per year because the service is delivered conventionally by large numbers of data scientists. It’s a slow, unwieldy process, and the results often arrive too late to be relevant. The power of Proof offers a stark contrast. For less than $65,000 USD per year, Proof BusinessGPS™ delivers ten models that automatically recompute at the speed of the business, and it scales easily from there based on the customer’s needs.”

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Dr. Melissa Kovacs, founder of data science consultancy FirstEval, agrees that Proof shortens the time to a completed model without any relevant difference in accuracy. The result is that Proof users can generate and automate the recalculation of many more models than can be created and managed by non-automated data science approaches. “The scalability, speed and accuracy that automation brings to analytics is very evident in Proof BusinessGPS™,” said Kovacs.

Dr. Bonev continued: “This type of data science is at the core of the Marketing Mix Modeling (MMM) analysis, as well as other business optimization analytics. Here we must say that, to our perception, the average analytics services firm has a hard time performing operationally relevant MMM analysis because of the lengthy analytical process required to generate any update, and because of the large complexity of the statistical procedure itself.

“The advantages of real time analysis are crucial to enable the delivery of a quick and agile marketing strategy amidst rapid change and volatility. For example, using automation to substantially reduce the time-to-value for complex modeling is potentially of enormous value to the user, and the highly automated Proof platform has reduced typical business modeling from many hours and even days to just a few minutes per model. In addition, the automated recompute of models means that if new data is being provisioned daily, the recompute also happens daily, meaning that a marketer would have fully updated causal, predictive and prescriptive analytics regarding any given model in more than enough time to understand changing marketplace conditions and alter course in a time-effective way.”

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