Why purchase intent matters more than keywords alone
A traditional keyword strategy often starts with a list of search terms, and don’t get us wrong, this still has value, but it does not go far enough for modern ecommerce advertising.
AI-driven search means shoppers can express intent in thousands of different ways. Many of those searches will be long-tail, specific, and difficult to predict manually, and they often have a search volume of 0. The opportunity is to use actual Shopping campaign data to understand which searches show purchase intent for each SKU, and at Bidnamic, this is a key part of how our technology works.
Our system uses the data generated by Shopping campaigns to understand how individual products perform against real search terms. Instead of treating a product category as one large group, we can identify the search terms that matter at the SKU level.
That means purchase intent can be transformed into active search term insight, especially now with the rise of AI search.
Rather than asking, “What keywords should we bid on?” the better question is, “Which search terms show buying intent for this specific product?”
Turning purchase intent into active search terms for individual SKUs
Most ecommerce retailers have thousands of products, and manually identifying the right search terms for every SKU is almost impossible. Even if a team could update all SKUs once, or even monthly, search behavior still changes constantly. New products, new trends, new competitors, and new AI-driven searches all change the landscape.
This is where automation becomes essential, and Bidnamic’s technology is built to automatically transform purchase intent into active search terms for individual SKUs, which is essential for long-tail search.
Long-tail queries may have lower individual search volume, but they often carry stronger purchase intent. A shopper searching for “men’s waterproof walking boots size 10 wide fit” is usually much further along in the buying journey than someone searching for “boots.”
AI-driven search is likely to create more of these specific, descriptive queries, and the retailers that can capture them efficiently will have an advantage.
