Transforming your syndicated data into business insight
Let’s start with stating the obvious: Shoppers cannot buy your products if they are not available where they shop. Even though your brand may have 12 different items in its product line, not every retailer carries every item. You can identify which retailers sell which items using a relatively straightforward data pull from IRI or […]
It’s a pleasure to feature this guest post from our colleague and fellow consultant Dan Wasser. Dan’s data expertise and savvy insights derive from long experience with syndicated data and category management in a variety of roles. You can learn more about Dan (and how to contact him) at the end of this article. Robin […]
This is the second in a series of posts on how to quantify the impact of improving the various elements of consumers’ purchase behavior. These purchase behavior analyses are based on household panel/shopper data (not POS/store data). Take a look at these posts if you need a refresher on this type of data: Overview of […]
This is the first in a series of posts on how to quantify the impact of improving the various elements of purchase behavior among consumers. Some posts will focus on increasing sales for a manufacturer (probably more useful if you are in Marketing) and others will focus on increasing sales for a retailer (more useful […]
We’re delighted to have guest contributor Sandy Skrovan, content marketing manager for Profitero, sharing her expertise in ecommerce intelligence. Please direct comments and questions directly to Sandy. Her contact details can be found at the end of this article. A flurry of activity this past year is transforming the eCommerce data, measurement and analytics landscape. As companies […]
This is a follow up to this previous post that dealt with why you need to be very careful when comparing manufacturer shipments to retail sales (also called consumption or takeaway or POS data). Once you are sure that 1. periods are aligned properly, 2. geography and product definitions are as close as possible and 3. […]
