Overview
Electric vehicles are taking a centre stage in shaping a more eco-conscious and technologically advanced world. Data Reply solution is harnessing the power of advanced analytics and open-source data the model accurately forecasts EV adoption trends across different customer segments.
Our predictive machine learning model developed using Amazon Sagemaker identifies regions and businesses most likely to embrace electric vehicles. The model is able to analyse diverse datasets, including valuable purchase history from automotive companies and open data sources such as business registration statistics and historical charging point data. By collating and processing this information, Using intelligent keepership categorization (private, company, and total) and fuel type segmentation (Battery Electric, Plug-in Hybrid Electric (petrol), Plug-in Hybrid Electric (diesel), other fuels),
Armed with these precise insights, dealers can now tailor their offerings to match the preferences of potential customers, leading to improved customer satisfaction and higher conversion rates. This customer-centric approach builds trust, loyalty, and brand advocacy, enhancing the overall image of EV dealers and manufacturers in the market and guiding businesses towards more sustainable choices
Highlights
- Accurate predictions of regions and businesses likely to adopt EVs, leading to optimised marketing strategies and conversion rates and increased sales efficiency
- Improved customer experience with Personalized EV Offerings
- Guiding businesses towards electric mobility, reducing carbon emissions, and contributing to a cleaner environment.
Details
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Vendor support
Data Reply can support you with a POC where the model can be deployed in your AWS environment with elements of customisation to meet your specific requirements and quickly prove value to the business. A POC Funding support might be available depending on the business case.
For further information and to get a private offer please contact: a.main@reply.com or t.silk@reply.comÂ