Being able to analyze data and derive relevant insights from it is a requirement in almost every role for both Retailers and Suppliers. There’s an opportunity for many in our industry to learn how to make fact-based, strategic decisions and recommendations by building skills to effectively read and interpret data. A great starting point for learning how to do this is through a category assessment. Creating a category assessment is also the first step in developing a retail category plan, but the flow, measures and logic apply to development of category reviews, business reviews and internal planning documents. You should be able to analyze, benchmark, interpret and identify strengths, weaknesses, opportunities and threats through an effective category assessment.

Data is one of the biggest requirements in category management for moving toward a more strategic, fact-based organization. Many organizations spend millions of dollars on data and sometimes even more money to mine the data through software and front-end reporting systems. But organizations too often forget to train their organization on some of the most important things about data. This is what can lead to misinterpretations and frustrations when using data.

How do you use category management analytics when you have limited data? To complete analytics, you obviously need data. In fact, it’s one of the key requirements in category management and to strategically understand and measure your business. Despite all of the new data sources available in today’s world, many of us still find ourselves in “low data” scenarios – whether it’s in an entire industry, a region or a category. Are you in a limited data environment but want to learn how to be more strategic in your approach? Here are some great resources to help you do category management on limited data: Category Management Continuum: A complimentary download that walks you through the different levels of category management you can practice in a limited data environment Free Resource on “How to Address Data Gaps in a Limited Data Environment"