How to forecast using customer ensemble dynamics

November 18, 2011

Forecasting sales always come up as CFOs continue to push for accountability from Marketing departments.  From a marketing perspective, forecasting provide focus, goals and budgets.  At a high level, marketing departments goal to acquire, grow and retain customers map one-to-one to sales forecast.  Looking at forecasting from customer lens (causal or ensemble) rather than a time-series (non-causal) uncovers causal reasons behind results, hence providing metrics to monitor and correct.

For example, number of customers, order per customer, revenue per order and margin% per order are simple factors that yield the sales, each of which are metrics a marketer knows and manages.  The multiplication of which (over time) yields a revenue and margin forecast.

In a B2B environment, predictions are easier, based on quota of each salesperson and their book of business.  In B2C, the challenge is different, since there are no official account assignment that happens and account management is done at a macro level by marketing.  So how do you forecast based on an ensemble of customers.

One of the answers is ensemble forecasting, based on similar problem of forecasting weather patterns.  Given the weather and underlying dynamics at a point in time, the models would forecast the next period, which is then iterated over time.

Ensemble forecasting tries to predict the future state of a dynamic system.  In this case, the dynamic system is a collection of customers, each with a different buying pattern and relationship with the company.  New customers, high value customers, single-category buyers, all exhibit different behavior.  Forecasting  individual behavior is a common area of modeling usually referred as response modeling.  Here the idea is to predict how an ensemble of individuals would behave.

Ensemle forecasting is a numerical method that is a form of Monte Carlo method that utilizes probability distributions and varying initial conditions and external assumptions that produces accurate results.

In the specific case of forecasting customer dynamics and sales, new customer acquisition and retention rates are probabilistic inputs to the system.