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The power of geospatial data views in supporting better business decisions

by Linda Reid, Lightstone Explore

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Contents

Geospatial Visualisation

Business Benefits – Just another tool or game changer?

Case study 1: Catchment area analysis

Case study 2: Service station network performance

Case study 3: Risk modelling for proximity

Along with the increase in availability & granularity of data, geospatial

visualisation is growing in popularity as a means to draw unique insights and

create value from data

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Geospatial Visualisation

Geospatial views are c o o l

But businesses often don’t have money to spend on things that are interesting but don’t change or solve anything

In our opinion, there are specific types of problems that geospatial applications are best placed to solve

There’s nothing wrong with using geospatial visualisation simply to help get another perspective, or to view things differently …

When is geospatial visualisation most helpful?

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… if the money is available

Where do customers come from to buy a car from a particular

dealership?

Where are all the schools of a certain type in an area? What’s the relative

density across areas?

When is geospatial visualisation most helpful?

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We have found that generally, to warrant spending money on a spatial system or spatial consultants, a system must:

Result in increased revenue

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Result in cost savings

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Improve efficiencies (faster solutions) = cost savings

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Allow you to do something that current tools can’t

4 Also – respond to market need faster, satisfy clients quicker

When is geospatial visualisation most helpful?

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There are situations where geospatial views enable solutions in ways that other tools can’t

If you need to interrogate one dataset in conjunction with another, & a) seeing them relative to

each other brings clarity, & b) choosing a relevant area

instead of a list of (eg) suburbs is easier

If you’re trying to find where are the areas that match a particular requirement – and especially if you need to identify areas where two requirements exist

Where the business problem you’re trying to solve relates to where things are relative to each other

Making a spatial area selection is important

Evaluating two metrics in combination

The problem you’re solving for involves proximity

Case Study 1: Where do people live and spend?

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What are the actual primary & secondary catchment areas around each store, & how could adding a new store into the area cannibalise / disrupt current spend patterns?

With the retail market increasingly saturated, retailers face a dilemma; adding another store reduces profitability of existing stores. But it also blocks a competitor from securing a good location and eroding market share.

cannibalise / disrupt current

The Need

1.Understand, for each store & area type, how far people will typically come to purchase from that store

2.Evaluate their spending patterns across stores. On aggregate, this will help to get a sense of potential cannibalisation Ea

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Primary catchment (50%)

Secondary catchment (20%)

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Case Study 1: Where do people live and spend? (…2)

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2

A simple to interpret, quick way of evaluating potential impact of store addition, enabling data-driven decisions t to be made with less potential for surprise outcomes

data driven decisions

In evaluating the selected catchment, the client can see which stores attract the highest proportion of their revenue from people living in the area, & make some assumptions about cannibalisation

Case Study 2: Service station network performance

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Service station companies understand performance in terms of revenue / petrol dispensed. But potential varies so much – how are service stations performing when you take their potential into account?

So many things affect service station performance – access, convenience store performance, volumes of vehicles travelling the route, type of people travelling the route, quality of competition in the area. How do you evaluate area drivers vs other drivers of performance?

potential varies so much

The Need

1.Understand the performance of each service station relative to the potential its area carries

2.Understand behavioural dynamics & the profile of people who use that service station

Case Study 2: Service station network performance (…2)

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We can evaluate the dynamics of people’s behavioural choices within an area to understand performance & potential: •Conversions – drive-ins vs drive-bys

•Loyalty – choice of service station, choice of brand

•Profile – type of person attending each station / brand

•Effect of convenience store

•Frequency – how often people visit

•Changes over time •Etc

Conversions

Loyalty

Profile

convenience

Frequency

over time

Companies can identify & therefore fix the specific issues at each service station & for their brand

Case Study 3: Risk modelling for proximity

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For a distribution client, legislative restrictions were being considered around where they were allowed to operate, based on proximity to other facilities. What would the impact be , & could they propose less impactful distances the impact be less impactful distances?

The distributor is currently allowed to deliver product to all outlets in the landscape. Following proposed changes this would be limited to facilities that are further away than 500m from other types of outlets. They needed to establish and mitigate revenue risk.

The Need

1.Identify which outlets would be at risk

2.Model the impact of proposed legislation

3.Run scenarios with different proximity distances to propose viable alternatives to legislators

Red dots – client outlets

Purple stars – facilities causing 500m proximity conflict

Quantify the impact of legislation; enable ability to negotiate with legislators around more practical proximities

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