If you’re a marketer, then you know exactly how powerful a marketing campaign needs to be to captivate a consumer’s attention— the ones that are the most compelling and convince them to hit the “checkout” button are relevant, unique and timely.
Personalization is vital to the success of marketing campaigns, especially when it comes to content recommendations (products, recipes, inspirations, tips articles, etc). Breinify’s Personalization Settings make it simple and quick to activate recommendations for individual consumers with three easy-to-activate features.
Our curation-based recommendations enable you to provide recommendations that are curated for your consumers needs as well as your specific business objectives.
For example, in retail brands often coordinate with merchandising teams to promote specific products. If the merchandising team prioritizes 20 products they want to promote, using curation-based recommendations allows you to choose how you want to maximize personalization. Our AI can help you:
- Save Time: Create a fixed order for those 20 products without needing the help of an engineer.
- Get Smart: Personalize your product recommendations by consumer preference. For instance, Jay might see products 1, 7, 9, and 14, while Levi may see products 2, 6, 11, 8, and 9.
- Predict and Prioritize: From the list of 20, select the top three products for each user and supplement the other 17 slots with similar products within a specific category. That means while Jay is seeing products 1, 2, 3, and 17 wines at similar price points, Levi could see products 1, 2, 3, and 17 wines from a similar region.
With Breinify’s curation-based approach, you can change the content that should be displayed on the web or in an email, no engineering required.
Once you know what type of personalization campaign you want to run, it’s easy to set up. Once the pre-configured campaigns are set, marketers can relax a little—Breinify will take care of the rest.
Brands that want to highlight their recipe content can dive into relevant food trends like keto and vegetarianism to keep users engaged on their site. Traditionally, marketers would have had to go through their CMS themselves, manually updating thousands of content tags in order to only show vegetarian recipes. Now, they can do that automatically.
For example, let’s say a food brand is interested in highlighting vegetarian recipes for users that have viewed their “Signature Mac n Cheese” recipe page. The marketing team doesn’t need to know how to code or update content tags to make this happen – they simply need to specify which category of recipe to recommend after a certain user action, and Breinify’s talented developer team will take it from there.
Once you’ve gotten started with personalization, we’ve added the ability to make your campaigns more specific. Using the post-processing feature, you can create specific filters for your recommendations based on your own criteria or consumer actions.
For example, Levi is a consumer who regularly purchases beer from an alcohol retailer. While he might be interested in beer, that doesn’t mean that every product in that category is a good fit. With that in mind, an alcohol retailer can recommend beer products for Levi and use post-processing filters to remove similar products that don’t match his preferences, like kegs.
A CPG brand, on the other hand, may have data that shows that one of their consumers, Jay, is interested in chicken recipes, but doesn’t view any slow cooker recipes. With that information, the AI can recommend chicken recipes for Jay while filtering out recipes that require a slow cooker.
For many marketers, activating personalized recommendations for each individual consumer requires heavy integration, advanced coding support, and a dedicated team of developers — all in all, it all seems too technical to be achievable.
Without scalable personalization, it’s tough for companies to keep up with competitors who have implemented strategies to prioritize content or product recommendations.
Even after implementing a product recommendation solution, it can be tough to figure out how to configure and scale these tools without a team of engineers there to help. What’s more, it can be difficult for a non-technical person to determine how successful the integration is on their own.
Scalability is an important consideration, but the real benefit comes from how agile the product or content recommendation solution is. Are you able to adapt to changes in recommendations quickly, without having to rely on the help of engineers?
When it comes to ROI and other performance metrics, Breinify utilizes A/B or split testing to measure the impact of our recommendations against previous baselines – your original, manually-curated product recommendations. For example, you can compare recommendations based on factors like product category or weather. Separately, you can compare results from different pricing recommendation strategies.
With split testing, you can clearly see which set of recommendations performs the best, and adjust your product recommendation strategy accordingly.
Get in touch with us today to learn more about our new and improved Personalization Settings or find out how Breinify can help your company reach its personalization goals.