Press Release: Squark Launches Codeless AI that Transforms How Business Analysts Deliver Accurate Predictions

With AI-powered analytics now as easy as a spreadsheet, Squark Seer equips any organization to make decisions based on probabilities instead of guesses

Burlington, MA – Feb. 28, 2019 – Squark, a software as a service (SaaS) predictive analytics provider, today announced Squark™ Seer, a tool that enables non-programmers to take advantage of the power of artificial intelligence to make timely and actionable predictions. This important advance brings AI out of the development lab and directly into the hands of marketers, managers, and operations analysts.

Accurate predictions allow organizations to shape their futures actively instead of just reacting to what has already happened. For instance, identifying which mobile customers are at risk to switch carriers can enable telecom companies to reach them in time with effective retention programs. That is why AI is in high demand—because it replaces reports with predictions. But adoption of AI has been slowed by shortages of data scientists and expert programmers customarily required to make it work. Squark removes this obstacle by eliminating AI programming completely—delivering results in minutes.

Weary of complexities and delays in their own development projects, industry veterans Dan Hess and Judah Phillips co-founded Squark in 2016 with a mission to build a way for business analysts to create predictions quickly, by themselves. They envisioned using the self-programming power of AI to build models automatically, so that data science knowledge was not required. The system would be built in the cloud with architecture that makes the latest AI algorithms instantly available to users. Their concept was fulfilled with the release of Squark Seer—making predictions with AI is now as straightforward as using a spreadsheet.

“Our goal was to make AI a point-and-click experience. Seer requires only upload of training and production data sets to the SaaS application. AI then does the hard work of creating hundreds or thousands of models using competing algorithms. The best method is then used to make the predictions,” said Hess, Squark’s CEO. “The whole process takes minutes, not months,” he added.

“Leading a robust B2B SaaS demand generation practice, I needed the ability to respond proactively and quickly to changes in demand from prospects at every stage in the sales funnel,” said Brett House, VP of Product Marketing and Demand Generation at Nielsen. “Squark helped my team predict the key drivers of marketing ROI more effectively, which for us means sales-accepted leads and closed deals. This translates to more efficient use of our marketing dollars, better use of my team’s time, and the ability to optimize our programs actively across a variety of dimensions.”

Adam Jenkins, Principal at Digital IQ, said that, “As an agency for high-profile consumer brands, we need to deliver meaningful predictions—ones that increase sales. In our very first project we used Squark to show a beverage client where to aim cross-sell offers. Immediate uplift overnight in the double digits. Remarkable. With no programmers.”

Phillips, CTO at Squark, explained that the company currently concentrates on predictions made with AI techniques that use sets of known outcomes to train predictive algorithms. “We focus on practical business predictions such as marketing, operations, and sales. For example: Which leads are most likely to convert to the next stage in the customer journey? We have binary classifications to do that. Which inventory stocking location is likely to be closest to customers to reduce shipping cost? Squark has multi-class algorithms for that. What is the real forecast for the next period in units and dollars based on today’s actual pipeline? Our regression predictions handle that. And Squark Seer implementation could hardly be simpler: Log in, upload, predict.”

Squark is already at work for customers in a wide range of businesses including marketing services, digital agencies, logistics, healthcare, cloud services, building materials, fundraising, and insurance.

Leaderboard

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Squark Seer produces a Leaderboard that lists the best-performing models that were trained on your specific data from Squark’s set of powerful codeless AI algorithms.  While Squark Seer may have built thousands of models while you waited for results, the Leaderboard only contains the most accurate model for each algorithm we used. For example, if a Deep Learner displays in the Leaderboard, then it is the most accurate Deep Learning model we created, out of perhaps thousands of Deep Learners built for your data.  Since Squark’s Leaderboard only contains the most accurate instances of models and their underlying algorithms, if a model algorithm is absent it is because it was not used.

Squark cross-validates more than 15 algorithms that are automatically applied to your data.  These algorithms include fixed, specific, and dynamic grids and multiple instances of algorithms including:, XGBoost Gradient Boosting Machines, other Gradient Boosting Machines, general linear models (GLMs), multiple “Tree” methods such as Distributed Random Forests, Extreme Trees, & Isolation Trees, multiple Deep Neural Networks, and multiple types of Ensemble Models.

Each model/algorithm is listed in order of accuracy using a default metric.  Squark uses the metric “Area Under the Curve” (AUC) for binary classification, the metric “Mean per Class Error” for multi-class classification and the metric “Residual Deviance” for Regression.

How does Squark rank the Leaderboard?
Squark ranks the best model for your data in the Leaderboard on your results page. The ranking metric is different based on the model class. For binary classification, Squark uses Area Under the Curve (AUC).  For multi-class classification, Squark uses the Average or Mean Error per Class.  For regression, Squark uses Deviance. For all model classes, the best performing algorithm and the resultant model is identified on the top row of the Leaderboard based on the ranking metric.  This best in class model is used to determine the predictions. Squark provides a full listing of Leaderboard metrics, which may be helpful for advanced users and data scientists, including:

  • Area Under the Curve or AUC (in Binary Classification Only) is used to evaluate how well a binary classification model is able to distinguish between true positives and false positives. An AUC of 1 indicates a perfect classifier, while an AUC of .5 indicates a poor classifier, whose performance is no better than random guessing.
  • Mean Per Class Error (in Multi=class Classification only) is the average of the errors of each class in your multi-class dataset. This metric speaks toward mis-classification of the data across the classes. The lower this metric, the better.
  • Residual Deviance (in Regression Only) is short for Mean Residual Deviance and measures the goodness of the model’s fit. In a perfect world, this metric would be zero. Deviance is equal to MSE in Gaussian distributions. If Deviance doesn’t equal MSE, then it gives a more useful estimate of error, which is why Squark uses it as the default metric to rank for regression models.
  • Logloss (or Logarithmic Loss) measures classification performance; specifically, uncertainty. This metric evaluates how closely a model’s predicted values are to the actual target value. For example, does a model tend to assign a high predicted value like .90 for the positive class, or does it show a poor ability to identify the positive class and assign a lower predicted value like .40? Logloss ranges between 0 and 1, with 0 meaning that the model correctly assigns a probability of 0% or 100%. Logloss is sensitive to low probabilities being erroneous.
  • MAE or the Mean Absolute Error is an average of the absolute errors. The smaller the MAE, the better the model’s performance. The MAE units are the same units as your data’s dependent variable/target (so if that’s dollars, this is in dollars), which is useful for understanding whether the size of the error is meaningful or not. MAE is not sensitive to outliers. If your data has a lot of outliers, then examine the Root Mean Square Error (RMSE), which is sensitive to outliers.
  • MSE is the Mean Square Error and is a model quality metric. Closer to zero is better.  The MSE metric measures the average of the squares of the errors or deviations. MSE takes the distances from the points to the regression line (these distances are the “errors”) and then squares them to remove any negative signs. MSE incorporates both the variance and the bias of the predictor. MSE gives more weight to larger differences in errors than MAE.
  • RMSE is the Root Mean Square eError. The RMSE will always be larger then or equal to the MAE. The RMSE metric evaluates how well a model can predict a continuous value. The RMSE units are the same units as your data’s dependent variable/target (so if that’s dollars, this is in dollars), which is useful for understanding whether the size of the error is meaningful or not. The smaller the RMSE, the better the model’s performance. RSME is sensitive to outliers. If your data does not have outliers, then examine the Mean Average Error (MAE), which is not as sensitive to outliers.
  • RMSLE is the Root Mean Square Logarithmic Error. It is the ratio (the log) between the actual values in your data and predicted values in the model. Use RMSLE instead of RMSE if an under-prediction is worse than an over-prediction – where underestimating is more of a problem overestimating. For example, is it worse off to forecast too much sales revenue or too little?  Use RMSLE when your data has large numbers to predict and you don’t want to penalize large differences between the actual and predicted values (because both of the values are large numbers).
  • Confusion Matrix, if calculated, is a table depicting performance of the model used for predictions in the context of the false positives, false negatives, true positives, and true negatives, generated via cross-validation.

Confusion Matrix

A Confusion Matrix, if calculated, is a table depicting performance of prediction models on false positives, false negatives, true positives, and true negatives. It is so named because it shows how often the model confuses the two labels. The matrix is generated by cross-validation – comparing predictions against a benchmark hold-out of data.

Root Mean Square Logarithmic Error or RMSLE

RMSLE, or the Root Mean Square Logarithmic Error, is the ratio (the log) between the actual values in your data and predicted values in the model. Use RMSLE instead of RMSE if an under-prediction is worse than an over-prediction – where underestimating is more problematic than overestimating. For example, is it worse to forecast too much sales revenue or too little?  Use RMSLE when your data has large numbers to predict and you don’t want to penalize large differences between the actual and predicted values (because both of the values are large numbers).

Root Mean Square Error or RMSE

RMSE is the Root Mean Square Error. The RMSE will always be larger or equal to the MAE. The RMSE metric evaluates how well a model can predict a continuous value. The RMSE units are the same units as your data’s dependent variable/target (so if that’s dollars, this is in dollars), which is useful for understanding whether the size of the error is meaningful or not. The smaller the RMSE, the better the model’s performance.  RSME is sensitive to outliers. If your data does not have outliers, then examine the Mean Average Error (MAE), which is not as sensitive to outliers.

Mean Square Error or MSE

MSE is the Mean Square Error and is a model quality metric.  Closer to zero is better.  The MSE metric measures the average of the squares of the errors or deviations. MSE takes the distances from the points to the regression line (these distances are the “errors”) and then squares them to remove any negative signs. MSE incorporates both the variance and the bias of the predictor. MSE gives more weight to larger differences in errors than MAE.

Mean Absolute Error or MAE

MAE or the Mean Absolute Error is an average of the absolute errors. The smaller the MAE the better the model’s performance. The MAE units are the same units as your data’s dependent variable/target (so if that’s dollars, this is in dollars), which is useful for understanding whether the size of the error is meaningful or not. MAE is not sensitive to outliers. If your data has a lot of outliers, then examine the Root Mean Square Error (RMSE), which is sensitive to outliers.

Logloss

Logloss (or Logarithmic Loss) measures classification performance; specifically, uncertainty. This metric evaluates how closely a model’s predicted values are to the actual target value. For example, does a model tend to assign a high predicted value like .90 for the positive class, or does it show a poor ability to identify the positive class and assign a lower predicted value like .40? Logloss ranges between 0 and 1, with 0 meaning that the model correctly assigns a probability of 0% or 100%. Logloss is sensitive to low probabilities that are erroneous.

Residual Deviance

Residual Deviance (in Regression Only) is short for Mean Residual Deviance and measures the goodness of the models’ fit. In a perfect world this metric would be zero. Deviance is equal to MSE in Gaussian distributions. If Deviance doesn’t equal MSE, then it gives a more useful estimate of error, which is why Squark uses it as the default metric to rank for regression models.

Mean Per Class Error

Mean Per Class Error (in Multi-class Classification only) is the average of the errors of each class in your multi-class data set. This metric speaks toward misclassification of the data across the classes. The lower this metric, the better.