Many Amazon FBA cook groups and lead providers distribute the same leads indiscriminately to every member.
The problem is that no two sellers source in exactly the same way. Some prioritise higher ROI, others care more about absolute profit or sales per month. Some sellers focus on specific Amazon categories, while others prefer certain suppliers, brands or price ranges.
When everyone receives the same opportunities, a large proportion of those leads can be irrelevant. Worse still, when hundreds of sellers are directed towards the same product at once, competition can increase rapidly and the profitability of that listing can quickly decline.
We want to approach product discovery differently.
We are introducing a new beta feature to SourceSheets called SourceScore.
SourceScore is an algorithmic recommendation system designed to personalise the products you see based on a wide range of signals from your SourceSheets activity and sourcing profile.
These signals can include the suppliers you view most frequently, the types of products and categories you tend to source, your preferred ROI and profit ranges, sales velocity, and much more.
Rather than every SourceSheets member being pushed towards the same handful of products, SourceScore aims to surface opportunities that are particularly relevant to you.
This means two sellers using SourceSheets may receive completely different recommendations, even when they are sourcing at the same time.
The goal is simple: reduce irrelevant leads, improve product discovery and minimise unnecessary saturation.
And SourceScore is designed to improve over time.
The more you use SourceSheets, the more information the system has to understand the way you source. If your strategy changes, your recommendations can change with it.
Instead of requiring you to constantly configure SourceSheets around your sourcing strategy, our long-term goal is for SourceSheets to increasingly adapt itself around you.
SourceScore is currently in beta, and we will continue refining how different signals influence recommendations as we learn more about how our members use it.
