Pandoras Analytics is focusing on projecting and modeling the future to help companies improve sales, reduce cost, and optimize investments. Since all projections are inherently wrong, our goal is to minimize the forecasting error. We achieve that by using appropriate advanced analytics methods for data mining, forecasting, and business simulation.

Data mining 

 

What are the purchase patterns of your customers and are they changing? What are the trends in your manufacturing costs? Data mining enables to find patterns and relationships in historic data. Algorithms such as decision trees, k-means, or Naive Bayes are applied especially to big data to identify insight behind the numbers. The data is then usually clustered to identify for example high value customers or similar products. Clustering is also needed for forecasting and modeling.

Forecasting 

 

How will the market develop over the next three years? What will my sales be next quarter? Forecasting algorithms can address these questions, but they need to be carefully chosen. With new products or new markets there is little or no historic data available. Customer demand is then projected using Bass diffusion models based on the degree of innovation and imitation. With growing history and more knowledge about customer behavior, causal models are developed based on econometrics & regression analysis to identify dependencies among economy, available substitutes, and complementary products. With long history, time-series analytics such as exponential smoothing and moving average models are applied to project future trends, cycles, and seasonalities. It’s critical here though to keep in mind that past performance is not necessarily indicative of the future.

Simulation

 

What is the variability and risk in my sales forecast? Most forecasting techniques are point estimates, but since all projections are inherently wrong, it only makes sense to apply scenario analyses to get better understanding of what may happen. Monte Carlo simulation is an advanced modeling technique that allows to analyze thousands of scenarios almost instantly to identify a range of potential outcomes instead of focusing on only one. This modeling approach also allows to take into account market and business context instead of blindly trusting the results of algorithmic calculations. 

Packaged solutions & cost examples

Pandoras Analytics offers its consulting services as packaged solutions to allow for better cost predictability. Before proposing a consulting fee, we run an inexpensive 2-hour assessment to analyze the scope and feasibility of the project, including data availability. Projects are quoted based on expected time requirement and data size. 

 

While the project examples provide details about challenges, solutions as well as investments and returns, the following cost examples give additional information about potential consulting engagements. The cost estimates are before travel expenses and value added tax. 

 

  • 3-day data mining project to identify patterns, trends, and outliers in historic sales data for 1000 customers and 100 individual products: 4,000 EUR
  • Time-series and causal forecasting models to project market trends for 100 product categories and 10 geographies: 6,000 EUR
  • Monte Carlo simulation model to analyze R&D investments with 50 model variables: 5,000 EUR

 

 

How can we help you to improve sales, reduce cost, or optimize investments?
jack.lampka@pandorasanalytics.com

 

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