The power of recommendation engines like Spotify or Netflix
Have you ever seen a show recommended by Netflix and thought, “How do they know me so well?”. Curating a content list that fits your taste isn’t a small feat. It results from well-trained machine-learning algorithms built to predict what content users will enjoy most.
Netflix isn’t the only one leveraging the power of recommendation engines. Amazon, YouTube, and Spotify all rely on machine learning systems to accurately match content with users.
The success of these engines lies in how they leverage user and content data to create custom and personalized experiences, making them leaders in their respective spaces.
But what if we told you that you can now predict which innovation consumers are likely to choose without testing it in that market?
Enter, the Dig Recommendation Engine.
Dig Insights’ proprietary recommendation engine.
Our recommendation engine predicts what you will like based on your past behavior. It predicts how ideas will score in markets where they have not been tested based on how those ideas performed in other markets.
The engine uses the data from previously tested ideas to build AI predictive models of idea scores. This predictive model can then provide the Idea Score of an idea in a country where it wasn’t originally tested. This analysis can be compelling for teams looking to “lift and shift” ideas from one region to another.
Dig’s recommendation engine is a perfect solution for organizations that already have a mass of tested innovation ideas as part of an Upsiide innovation program. The engine identifies innovation opportunities from other markets that have high predicted success in their own markets. This will save time and resources in identifying innovations with potential for success.
Benefits of using Dig’s recommendation engine
Bring successful innovations from market A to markets B, C, D, and E
Have a thriving product in one market, but what to see if it will do as well in a different one? Our recommendation engine allows you to be more efficient with your spending. You can test your ideas in some markets and predict performance in other markets based on modeled similarities and differences.
Take inspiration from elsewhere with products that are not yet available in your market
Whether it’s a unique flavor or a product feature, you can pinpoint products that have a high potential for success in your market, even if they haven’t been tested there yet.
Understand what is similar, and what is not, across different markets and consumers
With the recommendation engine’s data-driven insights, you can make informed decisions about which innovations to pursue, minimizing the risk associated with new product launches in new markets.
How Dig’s custom-built recommendation engine works
Creating a custom idea recommendation engine for a lift and shift analysis is a 3-step process. Here’s what it entails:
1. Country Correlations Matrix
In this phase, Dig’s analytics team will use data from the client’s Idea Score database to form a matrix of Idea performance and countries tested. It’s just like how Netflix collects the data about users and what they like watching.
2. Leveraging AI for Data Predictions
A Deep Neural Network will be leveraged to complete the matrix dataset for incomplete test data with predicted idea scores – essentially creating a full set of predictive scores for all items in all countries.
3. Exploration of Error Margins
Dig’s Analytics team will recommend an acceptable margin of error threshold for the idea recommendation engine in consultation with the client.
What you get from using Dig’s recommendation engine
By leveraging the Idea Recommendation Engine, category leads get several deliverables that will help identify innovation opportunities.
Lift & Shift Simulator
A tool can be built for client stakeholders to leverage that would predict idea scores in markets where ideas have not yet been tested.
Verified Country Proxies
Suggested country clusters/proxies for a specific category based on meta-analysis.
Idea Opportunity Report
A report that details innovation opportunities in countries that react similarly to specific category ideas.
