Can you make better predictions with your data? We will show you how.
Yes, we do R, Python and Machine Learning. But to make better predictions, we start by asking lots of questions. These questions lead to better understanding of humans, machines, processes and behaviors.
Once we have some answers, we create simple hierarchical models to test hypotheses. We discard non-predictive factors and weigh the strongest variables.
For example, many of our clients use Salesforce (SFDC). Forecasts from SFDC are often inaccurate. So how do you get more accurate forecasts?
We start with data already collected from SFDC and look at forecast accuracy. Then, by talking to salespeople with high forecast accuracy (and low accuracy), find the factors make forecasts more reliable. Typically, three to seven factors are the most powerful predictors of an eventual sale to a customer. Those factors are rarely even captured by SFDC.
We add those predictive factors back into SFDC, start collecting data, modeling the results and test with a small group of stakeholders. If forecast accuracy improves, we roll out the changes and dashboards to all the stakeholders. Typically, we find forecast accuracy improves from 30% to over 70%. This provides a huge return on investment.
Our predictive projects include:
- Machine failures
- Project delay predictions
- Medical equipment replacement forecasts
- Vehicle fleet maintenance
- Software project estimates
- Employee engagement measurement and predictions
- Gap analysis
- Sales Funnel predictions
- Retirement community census optimization
and many others.