Leading parts and service supplier to the trucking industry PACCAR worked with FCB Chicago to find a solution to help fleets cut downtime spent searching for parts while growing market share. Enter the PACCAR CRM Accelerator – the industry’s first personalized B2B truck parts recommendation engine – created in 2019.
The engine uses an AI/machine learning approach to understand PACCAR Parts member redemption behavior and uncover hidden trends. Using historical data of more than 14 million member redemption transactions over the past eight years and generating a machine learning model which can identify hidden traits in the input data, each member can access personalized recommendations and creative teams can identify what to feature at an individual level across digital channels – namely hyper-personalized creative.
Personalization becomes more crucial
The COVID pandemic’s effect on supply chain issues in the trucking industry made personalisation even more crucial. RigDig, a third-party fleet data source, was used to identify industry types for existing members, and by identifying an ‘industry redemption score’, relevant offers were inserted into the recommendation engine so they can be included in industry-personal offer landing pages. Creative teams created new headlines, hero images and messaging to highlight the business industry that PACCAR Parts loyalty members were a part of.
This rapid reactive response saw open rates increase by 12% on average when adding in relevant industry content, while click to open rates upticked by 24%.
Croud US Managing Director Kris Tait was full of praise for the initiative, saying that for a complex subject the initiative was “simple to understand, and the actions and results are great” while Rachel Ooms, Executive Director of Hearts & Science, said it represented an “impressive use of historical data”.
Meanwhile Kyle Jackson EVP, Precision & Performance Marketing for Publicis Media appreciated FCB’s approach to data-driven creative, noting that this was “a great example of how to take large amounts of data and not only distill them into general trends, but to make personalized messages”.