How Retailers Can Personalize the Customer Experience Using Google BigQuery
Retailers have always had to adapt quickly to changing market conditions. But the pandemic accelerated the need to transform, especially for retailers that needed to ramp up their online presence and ecommerce capabilities and gain a better understanding of their target audience—all while cutting costs to stay competitive. That’s where Google BigQuery and the right partner can help. As retailers are blending their physical and digital environments to create a seamless shopping experience, they also need to ensure that journey is personalized to each customer—in conjunction with keeping up with changing demand, ensuring their shelves are well stocked and scaling inventory as needed. In most cases, transformation has meant migrating to modern cloud-based platforms, which gives retailers the flexibility and scalability required to rapidly respond to evolving market conditions and customer demands. By moving to the cloud, retailers are also creating a foundation to leverage AI, generative AI, machine learning and smart analytics. Transforming retail includes store operations, customer acquisition, merchandising, logistics and fulfillment, omnichannel commerce and product life-cycle management, according to Google Cloud’s Digital Pulse survey. But the most advanced retailers in the survey have one key focus: analytics software.
Why BigQuery for retailers?Whether you’re looking to gain new insights into consumer behavior, deliver a more personalized shopping experience or increase revenue by optimizing merchandise locations, the right tools can help retailers turn data into insights. BigQuery is Google’s fully managed, serverless data warehouse that allows retailers to upgrade to a flexible, scalable infrastructure. Essentially, BigQuery can ingest data from a retailer’s existing sources—third-party or internal—and put that data to work. Retailers don’t have to spend time worrying about whether they have enough capacity to handle fluctuating demands—and disappointing customers when they can’t keep up. Forecasting demand is exactly the kind of insight they can glean through granular data queries in BigQuery. BigQuery can adapt to any data type or format, plus convert formats, without additional charges. It also has robust geospatial capabilities, which can provide spatial visualizations of store locations, customer movements and stock evolution. BigQuery also offers many integrations that add even more value:
- BI tools such as Looker can integrate with BigQuery to help make data-driven decisions.
- Machine learning models can be built in BigQuery using Google ML.
- Vertex AI—which now includes generative AI capabilities—allows users to build and continuously train models over time with BigQuery data.