How technology can drive a new age of private brand category management
When it comes to category management, an important part of understanding which store brands are popular with consumers at any given time is the ability to gather and analyze data — not always an easy feat given all the barriers to effective data aggregation and analysis.
The good news is, advanced analytics, automation and AI are providing a new way to collect, analyze and leverage data, proving that there’s so much more retailers can do with modern technology.
In addition, deploying technology that’s more collaborative, internally and externally, can improve the effectiveness of a category management approach and the efficacy of the output. The key is knowing what technology to use, and how to apply it.
Why an integrated approach?
Even today, data silos are common within retail and CPG organizations because category management plans were developed within functional categories and not linked to other areas of the business. But today, advanced analysis tools can drive integration across disparate data sets and maximize the value of data, offering insights that are holistic and actionable.
The new approach to category management will rely on automation, AI, and machine learning to uncover otherwise unseen relationships. This can fuel dynamic and robust insights, enabling smarter and faster decision making.
For example, we can now use AI to drive deeper into customer purchase data across both in-store and online channels. By combining this data with insights from retailers, CPG companies and market research, category management teams can develop more compelling consumer-centric plans.
Also, the potential to integrate structured and unstructured data provides insights into relationships never identified before, such as gaps in strategy or trends that otherwise would have gone unnoticed.
A collaborative environment fosters growth
With ever-changing customer expectations, the dependency between retailers and CPG suppliers, especially for store brand products, is more crucial than ever. Operational speed, agility and personalization are now shared expectations. And both need ready access to shared insights in order to optimize the customer journey, improve loyalty and increase conversions.
Insights must be gleaned throughout the entire journey, from predicting customer behavior, all the way through examining the consumer experience post-purchase. And, true collaboration (which can be a competitive advantage) can only be achieved when the whole picture comes together.
To support this, retailers and CPGs are adopting automated business analysis, or ABA, systems which use a combination of automation, AI and machine learning. An ABA solution offers daily guidance on unexpected changes in data and behaviors. It pulls from hundreds of data sources and provides recommendations on how to optimize category management programs, marketing, inventory, staffing, pricing, support and more based on unexpected data or behavior changes.
ABA also enables root cause analysis so teams can address the underlying cause of change, not the symptoms. For example, a large drop in conversations of a product category may not be an indicator of changing customer preference. It may simply be tied to the end of a marketing promotion, or it could indicate an out of stock. By understanding details, teams can determine true performance metrics.
When developing complex category management plans, retailers and CPG suppliers should remember to place the consumer at the center of the program. But now, with the ability to leverage richer, more integrated data and insights, uncovered through the application of technologies such as ABA, they can ensure a 360-degree view of behavior and create an environment where leaders are making smarter, more informed decisions for category management.
Jerry Stephens heads the global consumer and retail team at Outlier, an AI-driven automated business analysis solution provider that helps retailers understand changes in their business data.
