Analyze Your Transactional Data: Year of Introduction Merchandising Comparison
This post is part of a larger series around how you can apply transactional data to your overall business strategy. In this post, we will discuss how you can analyze transactional data to help merchandising teams determine what product release years performed well and which ones didn’t to help guide the merchandising program in the right direction. You can use this strategy to:
- Identify Merchandising Issues for New/Older Product Lines
- Forecast Impending Business Success/Issues
- Forecast Merchandising Product Performance
As merchandising teams change and new management sweeps in, looking to make disruptive change in the space, product lines are often modified in an attempt to increase merchandise performance. Considering that older merchandise will coexist with the newly introduced merchandise, retailers need a reliable way to segment the merchandise years based on the year of release to see the true impact on the business. Doing so will allow retailers to quickly pivot if newly introduced merchandise is not performing well to prevent the business from having low-performance years in the future.
One way to look at this is through pulling the transactional data from your point of sale and eCommerce site to see how these products performed at launch when compared to previous years of products being introduced into the market. Pairing this with the year of first introduction, retailers can identify:
- Percentage of Sales from New and Existing Product Introduction Years
- The Historical Volume of Sales for New vs. Older Product Line Launches
- Yearly Total Sales per Item
What Data You Need for a Year of Introduction Analysis for all Items:
- Date Items were First Made Available
- Purchase Date of the Items
- Purchase Price of Item
Interested in pulling your transactional data to make more informed decisions? flexEngage helps retailers to pull this hard-to-pull data from their systems so that they can analyze transactional data for their brand while delivering customers personalized transactional messages to drive repeat purchases, increase brand affinity, and more.