(0:12) Sarah’s role at New Balance Athletics, Inc.
- Sarah leads Consumer analytics team for North America
- She and her team are responsible for understanding the consumer journey
- Her team transforms data into actionable insights to build relevant consumer experiences
(01:02) Harnessing data through a consumer data hub
- Sarah started out by recognizing that all consumer data the company has is in disparate and siloed systems
- Then work on breaking down the silos to create comprehensive insights about every consumer to deliver experiences that resonate with them
(03:00) Driving relevant digital experiences for consumers
- Sarah and her team use the rich data to support traditional digital experiences such as product recommendations
- More importantly, they use data to help mirror great in-store experiences in the digital environment
(04:48) The impact of social proof and events in influencing demand
- Having the community weigh in about a product of interest through their likes, views and reviews gives the singular online shopping experience a more community feel
- Such compelling experiences instill confidence in the product for the consumer, which in term helps to not only drive conversion but also helps in customer retention
(07:49) Using location and weather for personalization
- Leveraging rich data is important to make consumers’ digital experience relevant and their shopping more convenient
- Using weather and location intelligence is critical for lifestyle products because what is useful for a consumer living in one region is not of the same value to another residing elsewhere with different weather conditions
- For Sarah and for New Balance Athletics, it’s all about leveraging consumer data to provide seamless experiences that make shopping convenient for them
- They do this by ensuring they understand the consumers’ constantly changing needs and lead them to the products that they want or might be interested in
(12:38) The role of machine learning (ML) to better understand consumers
- She has used ML for demand forecasting and planning
- Additionally, ML models work well with intelligent product recommendations, applying weather and location intelligence
- Furthermore, using ML to predict consumers’ time to next visit and understanding what kind of experience to deliver to motivate them to come back sooner
Session AI’s patent-pending ML models run on AWS’s highly resilient architecture using EC2, S3, WAF, CloudFront, Config, and CloudTrail to deliver a significant increase in conversion rates for eCommerce sites.
Last Updated: June 1, 2022
