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For many years BP has operated with a well-established global segmentation to identify optimal consumer targets and develop strategies, using data collected via an online survey of 1000 customers.
BP NZ wanted to understand if the NZ customer base exhibited the same behaviours illustrated by the global segmentation to ensure they were engaging with Kiwi customers in the most effective and relevant way. Mapping the NZ base to the global segments using transactional information showed that a more in-depth understanding of customer behaviour was needed.
Following this insight, BP NZ commissioned a data-driven behavioural segmentation for targeting, measurement, and strategy. The following information was provided for each customer segment:
The segmentation allowed BP NZ to understand the opportunities and challenges that were relevant to each segment. This informed the way the organisation engaged with AA Smartfuel members (message, channel, tone, frequency) going forward to enhance the relevance of their offers.
The segmentation also created a framework from which BP NZ could deliver campaigns to the AA Smartfuel member base while ensuring resource is focused on the most important activities for each segment and provided a large variable set for each customer, to be used in micro-targeting.
BP strive to make decisions that are backed with solid evidence that will enhance their position in the market place and deliver the best results to customers.
BP wanted to gain a view of their position in the market and see the behavioural differences between customers that use the AA Smart Fuel (AASF) card and customers that do not.
The idea behind this was to gain better insight into how customers use AASF as a rewards program to inform future interactions with these customers.
The solution was to profile the NZ service station market to get a view of where BP sits in the market. This analysis compared BP’s customers against the total market in terms of brand loyalty, average spend, share of wallet, market share and demographic factors.
The AASF base was then analysed to give an idea of the differences between AASF customers and other BP customers. In addition to the above variables the analysis looked at regional distributions, transaction numbers and spend by day of week and time of day, recency and frequency of visits and purchasing behaviour.
Analysis of BP’s market position showed an opportunity to increase BP’s share of wallet. AA Smart Fuel was shown to have an influence on customer behaviour. Product purchasing behaviour also revealed some interesting relationships for BP to evaluate moving forward.