Episode 09 features Dan Moody of dan-moody.com. Dan is a listing optimisation specialist and global private label seller who sells in the UK, Europe, the US and Japan.
This episode breaks down how to optimise your Amazon private label listing, including how to conduct private label product keyword research so customers can actually find your product and then finally an overview on how Paid Per Click (“PPC”) advertising works.
I then ask Dan for the four most common mistakes he sees from Amazon sellers and how you can avoid or fix these problems.
Learn from Dan how you can optimise your private label product listing to increase your sales through conducting effective keyword research.
Dan kindly demonstrates how to perform keyword research using different tools to identify the search terms customers are using to locate yours and competitor’s products on Amazon.
➤ Dan’s Story;
➤ An overview of what Paid Per Click (“PPC”) is, who PPC works for and how exactly it works;
➤ Overview of what are keywords and how can sellers go about researching keywords for both UK & European listings;
➤ What to do next once you have your keywords;
➤ PPC campaign strategies explained;
➤ How to refine your PPC campaign strategy;
➤ Tools to optimise your PPC campaigns listings;
➤ Dan’s four most common PPC and listing optimisation mistakes and how to improve or avoid these mistakes; and
➤ Dan’s services and products he offers.
The following product research and keyword research tools allow you to analyse your competition and see what keywords they are using. This can also help you with discovering keywords to use in your listings and PPC campaigns.
➤ Splitly – A handy tool to enable you to test different variations of copy and images in order to find the ideal images and wording combination that maximises your sales.
➤ Jungle Scout -This product allows you to analyse the sales volume both in units and GBP/USD/EUR value amounts to determine whether a product market share is worth pursuing.
Check out Dan’s exclusive training on how to perform keyword research.