As a brand, you compete with specific competitors only. For example, if you are Nike you compete with Adidas, New Balance and brands like that. Although you are interested in competitive dynamics, you may not focus on unknown brands unless it is fast emerging and capturing your market share. You can win big in sales if you do competitor analysis in the right way.
Likewise, as a multi-category retailer, you compare yourself to peers like yourself. Yes, you may have a competitor that prices lower than you. But customers may ignore the cheap vendor for low ratings or low trust. Thus, you generally want to compete only with competitors in your cohort. Ie. compare with premium brands if you are premium, and compare with value brands if you are value.
You can maximize revenue and margins by doing competitor analysis with the right cluster of your competitors.
As a retailer or manufacturer, you need precise comparative product and price data at an SKU level. And, you need data on competitors and how customers perceive them. Latter means the competitor ratings, search rankings, revenue, geography of business focus and similar metrics. With this rich multi-dimensional data you can compare your brand against the right cluster or competitors and effectively price. As a multi-category retailer, you may have a set of competitors in one category and a completely different set in other categories and other geographies.
We find that retailers that are doing cluster-based competitive analysis are optimizing profits. Premium retailers are ignoring the bargain sellers and vice versa. We don’t advocate a race to the bottom price war that leads to low profits and brand damage. Global premium brands are also comparing themselves with local competitors that have the same branding as them in the local geography. And they are doing even deeper comparison and creating a cluster of competitors at a category level.
We at GrowByData advocate rich data collection and cluster-based price intelligence to gain a strategic edge.