How to Predict and Respond to Customer Trends with Retail Analytics

Written By: Greg Kaminsky
  • 5/14/2018
PTC Retail FlexPLM

Each season, retailers face the challenge of getting the right merchandise in the right place at the right time. Failing to spot an opportunity can be costly, and the margins for error are thin. Shoppers can be hard to predict, with current events and economic volatility influencing consumer confidence. Competition is fierce and customers are more informed due to online shopping and social media.

To be successful, retailers must not only be able to anticipate trends and forecast demand, they must also be nimble enough to act on those insights quickly. Big data can be a useful weapon to those who know how to use it, but for many companies it’s too siloed to be fully activated. Data from materials suppliers, customer transactions, and consumer behavior are all part of the picture – retailers just need a way to connect and visualize it.

Retail Customer Trends and Predictive Analytics

With a predictive analytics solution, retailers can overcome these silos to view their data in context across multiple sources, enabling them to better predict and respond to customer trends. But how?

Retail Analytics Solutions Capabilities

Pulling historical sales data, demand forecasts, and unfiltered customer feedback into a singular platform lets product developers see the past, present, and future in a visual and easy to use interface. Historical sales data gives users visibility into past seasons’ product performance, highlighting which lines were successful and which lines struggled in different markets. Demand forecasts provide insight into how products should perform in the future, taking into consideration outside influences such as economic volatility and current consumer confidence. Customer feedback provides deeper insight during the product design process, which 75% of retail executives ranked as “a major business advantage of IoT technology platforms”.

Working in Orchestration

In a predictive analytics solution, each of these data sources work together in orchestration to help retailers predict customer trends and respond to them quickly. Retail data that was previously nearly impossible to activate due to vast, siloed datasets can now be used as a competitive advantage thanks to machine learning and analytics. Armed with these valuable insights, retailers can better identify opportunities, react to trends, and plan for changing demand, adjusting as needed for any market uncertainty.

Predictive analytics solutions also take things one step further by offering automated recommendations beyond just presenting and analyzing data. Retailers can get more value out of their connected datasets by applying business rules that help to accelerate decision making. These insights can be used to inform supply chain, merchandising, and pricing strategies, helping retailers ensure they put the right products in the right place at the right time.

Watch our on-demand webinar with Michelle Boucher of Tech-Clarity and Brad Thomas of PTC to learn how to develop products based on consumer behavior and historical sales, rely on machine learning to quickly identify market trends, and evaluate global suppliers in real-time across multiple KPIs.

  • Augmented Reality
  • Retail and Consumer Products
  • PLM

About the Author

Greg Kaminsky Greg is a technology blogger telling real stories about industrial innovation, digital transformation, and futurism in the workplace. As a Content Marketing Manager at PTC, Greg is helping business leaders discover how augmented reality and the internet of things are empowering frontline employees, driving productivity, and differentiating brands.