In our previous post, we covered survivorship bias, a tendency to focus on things that have survived and overlook those that didn’t. It misleads data scientists and distorts their reasoning. Far from being an isolated problem, however, survivorship bias is part of a broader selection bias. The latter encompasses various instances when data sets are not representative of the population intended to be analyzed.
Customers often ask us how much data they need to run successful artificial intelligence (AI) and machine learning (ML) projects. This question is hard to answer in simple terms. A functioning ML model requires clean and large data sets, but their optimal size is affected by a range of factors including the complexity of the model, training method, and tolerance for errors. Fortunately, there are several ways of calculating your data needs and overcoming the lack of data.
Across industries, businesses of all sizes are embracing digital transformation (DX or DT). Using advanced technologies to improve operations and delight customers has become a source of competitive advantage. And companies spare no expense in these efforts. Worldwide spending on DX is forecasted to reach $2.3 trillion in 2023, according to the International Data Corporation (IDC), a global market intelligence provider. The COVID-19 pandemic is set to only further accelerate this trend.
The logical error of only considering the information that is seen.
Kurvv’s AutoForecast product provides customers with a variety of quantitative sales forecasting methods, so they can simply connect their data source and receive a customized, accurate forecast in seconds. Once data is uploaded, AutoForecast tests out several different time-series forecasting methods including decomposition, exponential smoothing, ARIMA and regression (see below for more details about each). Finally, an average of all forecasts is computed and output with the final results. Customers can select the forecast output(s) they feel most comfortable with and customize forecast time horizon and validation time windows.
In Kurvv’s quest to make the power of machine learning accessible to business users, we are happy to say that our Life-Time-Value (LTV) bases Segmentation solution now also works for eCommerce customer additional to hotel (hospitality) customers. (limited to Shopify & Square users but more is on the way) Our LTV based segmentation models predict how valuable new customers and leads will be to your business over their lifetime, and group them into low, medium and high value categories. We do this by analyzing historical data to identify which attributes are most often associated with high-value customers.
Leadership lessons from a scene from ‘Finding Nemo’.
I’m a great Pixar fan. I still remember the awe I felt when I first watched Toy Story and even greater growing awe as I learned about Pixar’s journey as a company. Many leadership lessons can be learned from Pixar’s journey, and there are no shortage of materials to help you do so. Books, case studies, documentaries. (If you don’t know the story I highly recommend reading ‘Creativity, Inc’ by Ed Catmull) .
There has been much said and written about good and bad ideas in the startup world. Most notable and influential to me being Paul Graham’s essay around its counter intuitiveness  and Peter Thiel’s Venn diagram of ‘looks like a bad idea’ and ‘is a good idea'.