Demand Modelling 

Propensity estimation provides a means of converting the characteristics and size of the population at a small area level such as a census output area or postcode sector into estimates of spending power.

Usually, estimating propensity involves combining small area lifestyle or demographic information with client spending patterns or market research data. An increasing amount of purchases are made from the workplace or combined with larger shopping trips, which distort the typically shopping pattern for some products or services.

At The Wendover Group we have experience in developing and deploying techniques for demand reallocation, which reassign a portion of the residential demand to the workplace and the location of major shopping destinations. This allows creating more realistic demand surfaces that can feed in to forecast models.

Residential demand re-allocated to reflect patterns of movement

Forecast Modelling

Demand estimates are one of the key components that feed in to spatial interaction models, which model flows from the small area level to areas of supply. These techniques usually work well when travel times are key to the turnover at any supply point.

For other products and in many other applications different statistical modelling techniques may be more appropriate to produce forecasts.

The Wendover Group has many years of experience in building predictive models. Starting from a list of site locations we can produce a data mart which holds a wide range of site characteristics, levels of demand, competition and other factors. Depending on the size and variability within the estate we can then identify peer groups of sites with similar characteristics, and build an appropriate predictive model for each of these groups. Generalized Additive Models (GAMs) are one of the modern statistical techniques we apply for predictive modelling, which have proven to provide the flexibility to address many of the complexities involved in building forecast models.

Predictive modelling of demand value at household level

Customer Lifetime Value Analysis

Customer Lifetime Value can help you understand which customers bring most value to your business. In combination with survival analysis this technique can be used to efficiently manage customer relationships and optimise the return on investment of your marketing campaigns... read more