AWS for Industries

Adaptive Analytics in Retail: Influencing Shopper Behaviors and Engagement

No retailer would ever question the importance of data in daily operations. The evolution of operational data in retail decision making—from batch reporting to decision support to business intelligence systems—has been quite profound. Today, adaptive analytic platforms are driving industry innovation so retailers can:

  • Detect critical business signals.
  • Derive the current meaning and predictive insights of those signals.
  • Respond with data-driven customer, product, and marketing strategies.

However, there are numerous strategic challenges facing retail leaders as they construct an adaptive analytics platform, such as rapidly changing shopper influence and behavior triggers, demographic shifts, generational market-level impacts like COVID-19 and major economic downturns, and the increasingly wide array of retail staff roles that require predictive insights to do their customer-facing or operational jobs effectively.

Key Elements to an Adaptive Analytics Strategy

Across the AWS retail industry landscape, we view the concept of “improve your insights” as a strategic pillar to help retailers achieve success across a wide array of measures, including customer journeys, marketing campaigns, personalized offers, product assortments, in addition to location- and channel-specific sales forecasts.

As we work with our global retail customers and AWS Retail Competency Partners to deliver innovative intelligence platforms, we’ve found these key priorities should underpin an investment in a retail analytics platform.

Start with a Cloud-based Data Lake

The foundation of an adaptive retail analytics platform starts with a singular, scalable, cloud-based data store. It should be separate from storage and compute resources, and it should support standards-based data formats, ETL methods, allow any type of analytic processing (e.g., operational, real-time, predictive, big data, etc.), and manage data security and platform governance. It certainly helps if the data lake is cost effective, too.

AWS offers two key tools to develop and deploy a cloud-based data lake. These are quite literally “must-haves” for a well-architected data lake.

  • AWS Lake Formation—To quickly and efficiently define data sources and security policies.
  • AWS Glue—For serverless data integration, just like the name implies.

Canadian retailer Parkland deployed an AWS Cloud data lake to support its digital innovation program. The company plans to use the data to optimize its loyalty program with personalized customer offers and fine-tune pricing real time with machine learning (ML). As the company accelerates its efforts to define the future of convenience retailing and customer engagement, the AWS data lake is a key element of its long-term strategy.

Broaden and Deepen Data Sources

With a cloud data lake foundation in place, you can leverage additional operational data sources, like real-time computer visioning, customer location data, IoT sensor data streams, behavioral analytics, weather forecasts, and sentiment analysis from social media posts. By combining additional intelligence with existing data sources, you can gain significant, granular insights very quickly.

AWS offers several products to help retailers augment intel with additional data, such as:

This expansion of available operational data, where new levels of insight and intelligence can be analyzed, will help further optimize both customer-facing and associate-oriented areas of the retail enterprise. This includes, but is not limited to, product mix, personalized engagement, food safety, digital commerce, and energy management.

Embed AI/ML-driven Automation and Intelligence into Every Data Metric

After enhancing operational data with additional, rich data sources, you should leverage artificial intelligence (AI) and ML models to automate the analytics lifecycle so you can quickly uncover granular insights. Without granular-level intelligence baked into an analytics platform and its insights, you can severely limit the business impact and ROI of your adaptive analytics efforts.

AWS offers several AI/ML-based data services, such as:

  • Amazon Forecast—To build forecasts with time series data.
  • Amazon Personalize—To enhance applications with a wide range of personalization experiences, like product recommendations, product re-ranking, and customized direct marketing.
  • Amazon Fraud Detector—To automate fraud detection processes.
  • Amazon SageMaker—To design and deploy customized detection/prediction models.

After Indian retailer More Retail made AI/ML a key component of their inventory optimization strategy, the company’s forecasting accuracy increased from 27% to 76%.

Deliver Persona-driven User Experiences

The faster you can put analytics intelligence to work in your operations, the quicker you’ll see a return on your analytics effort, especially if key insights are driven by ML recommendations. This means delivering actionable information tailored to specific retail personas, like store management, supply chain, finance, and executive management—in the most proactive and frictionless way possible. Ideally, the data delivery functionality should be flexible, so key stakeholders can personalize the data and how they receive it.

AWS offers these powerful business intelligence tools:

I’ve really just scratched the surface here. There are many other factors, considerations, and challenges to create and leverage an adaptive retail analytics platform. With that said, I’ve outlined the fundamental strategies to build a next-generation retail intelligence and insights engine to help you differentiate your brand, influence customer actions, and increase revenue.

To find out how AWS can help with your retail analytics strategy, please get in touch with an AWS representative.

Scott Langdoc

Scott Langdoc

Scott Langdoc leads worldwide strategy and thought leadership for the grocery chain, drug, and convenience/fuel retailing segments at AWS. In this role, he helps fast-moving retailers use technology to navigate changing customer expectations and market dynamics. Before Scott joined AWS, he spent more than 30 years in technology, market research, consulting, and leadership positions in the retail industry.