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Using spatial data analysis to build a location-based pricing model

About the Client

This case study refers to one of our client's that provides residential services all over New Zealand.

The Problem

Our client needed to find a way to accurately price the provision of these services. While existing business pricing rules were still firmly in place, they were increasingly difficult to implement and confusing to understand – even for seasoned professionals.

The result of this was ambiguity and inconsistency around pricing, and a risk that, should this inconsistency be exposed, would be detrimental to the business.

The Goal

With a high level of risk in getting the pricing wrong, it was essential to find a way to price their services so that the business’ reputation and customer relationships were protected by avoiding under or over charging customers.

The goal was to flip around the idea of what urban, rural, and remote are with spatial analysis.

The Challenge

It was irrelevant how far the provider had to travel to get to a potential client, what was important was how big the market was when they got to that area. We deducted that the client’s current address accuracy and processes for pricing delivery were incorrect, therefore we needed to determine a model which would accurately assign pricing information to each product delivery.


We developed a spatial model that reflects opportunity density and can then be used to assign accurate pricing zones to both existing and potential customers. Using road lengths and New Zealand address data we could determine a set of urban, rural, and remote meshblocks that worked soundly within our clients’ existing rules. We also pulled together various data sources, including client specific data, census data and NationalMap, New Zealand’s authoritative and comprehensive road, address, and location data..

And by then validating all this against a number of other variables, we were able to deliver a realistic pricing model that would ensure these rules are always implemented consistently. The client could now have confidence in the model and know that there was a consistent way of defining the cost a customer should be charged.

The Result

Throughout the entire development process, we valued and encouraged input and feedback from the end users. By showing the client the model via an online mapping interface, we engaged in an iterative model development cycle meaning we could be positive that not only did the business have buy-in, but they could present it to the rest of the business in a logical way.

Of course there were a few surprises along the way, for example, it became evident after applying the model that areas we would typically describe as rural actually met the criteria for an urban pricing zone. And it was these surprises that really highlighted the value of going through this exercise. After all, these are the areas that make a real difference to the profitability bottom line.

Putting it all into practice

So what does this mean for the business going forward? It means that they can now better understand the size of the opportunity in the market, ensuring that any risks to their reputation due to under or over charging are greatly reduced. The business can have confidence that their clients are treated equally, consistently and fairly. Also helping their reputation, the model allows pricing decisions to be communicated in an accurate way to customers. 

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