Are you already familiar with Marketing Cloud Personalization and its use of machine learning in marketing campaigns? Then you may have heard about Einstein Recipes. But what are Einstein Recipes precisely, and how can they be used? Find out in this new blog by James Hubbard, BrightGen’s Marketing Cloud Consultant.
What are Einstein Recipes?
Einstein Recipes are configurable recommendation algorithms in Salesforce Marketing Cloud (MC) Personalization. They allow non-technical business users to select the “ingredients” used by the algorithm to determine the best content to display to a particular customer (known or anonymous). The user can implement these recommendations on websites, emails or mobile apps to showcase tailored products or content in real-time.
The ingredients can be catalog-based (product/article-based) or customer behaviour-based.
- The catalog based ingredients give you options to generate recommendations “similar to”, or “most likely to be bought-with” a customer’s current item
- The behaviour based ingredients use the data built-up of the customer’s actions to profile them and make recommendations based on “people who look like them”
You can combine and weight these ingredients, allowing for highly sophisticated recommendation strategies across digital channels. You can also set guardrails for exclusions and variations, giving you even more control over the recipe’s configuration.
Einstein Recipes provide businesses with a powerful tool for utilising machine learning in their digital marketing. This allows them to create highly personalised recommendation strategies for their customers.
How can Einstein Recipes help you build your marketing strategy?
Optimise e-commerce website recommendations
Use Einstein Recipes for optimising cross-sell and up-sell opportunities on e-commerce websites. For example, on a product detail page, you could create a content zone to feature complementary products that are likely to be of interest to the customer. Then, building your Einstein Recipe you could:
- Set the ingredients to make Marketing Cloud’s algorithm look for recommendations for products most likely to be bought with the item currently being viewed by the customer
- Exclude any items that the customer has recently purchased from recommendations
- Prioritise product categories that the customer has shown a strong affinity for
- You can even set the level of product variety returned by the algorithm
This results in highly tailored, 1:1 recommendations of complementary products for each customer visiting your website.
Nurture conversions
Use Einstein Recipes to keep customers on your site longer. As they view more pages and consume more content, it increases the chances of a successful conversion.
For example, a financial services company may use a home page content zone that personalises article recommendations using an Einstein Recipe:
- They set the ingredients of the algorithm to use the profile of the customer’s behaviour to recommend articles that similar profiles have viewed before
- When a customer then spends a lot of time viewing pages related to credit cards, the next time they visit the home page, they will see tailored recommendations for credit card articles most relevant to them
This creates a more personalised and engaging user experience, ultimately leading to higher customer satisfaction and increased conversions.
Tailor mass email campaigns 1:1 in real-time
Integrate Einstein Recipes into email campaigns, regardless of the email service provider or marketing automation platform being used.
For instance, in mass promotional emails, you can include code for a MC Personalization content zone in your email allowing you to feature the most popular products that are currently in stock at the time of email open. To do this your Einstein Recipe could include:
- Product popularity as the main ingredient
- Boosting the product categories that the customer has strong affinity to
- Excluding any products that are currently out of stock
This means that each time a customer receives your email, they will see relevant and available products that they can click through to buy.
Trigger abandon browse campaigns with personalised recommendations
If you are using MC Engagement as your email service provider, you can trigger campaigns that will be sent through Journey Builder when your customer falls into one of your MC Personalization segments. You can use this to build advanced abandoned browse campaigns that will, using Einstein Recipes, recommend products customers were looking at previously or similar items.
This can increase the chances of converting customers who you would have otherwise missed out on.
Using Einstein Recipes and Marketing Cloud Personalization can help you keep your customers on your site longer, to view more products and ultimately generate more revenue. You can A/B test recipe strategies and further fine-tune them as you learn more about your customers.
By continuously improving your Einstein Recipe strategies and personalising your marketing efforts, you can:
- Increase customer loyalty
- Drive more conversions
- Grow your revenue!
To find out how you can utilise Marketing Cloud’s Einstein Recipes to develop your own recommendation strategies, reach out to our experts.
Guide to CDP and RTIM
Read on to learn more about customer profiling in our interactive guide to customer data platforms and real-time interaction management. This guide will help inform your decision on what kind of martech you need to achieve customer-centricity.
Read next:
- Three New Features in Marketing Cloud Spring ’23 Release by James Hubbard, Peter Scott and Balwinder Tank
- Unlocking the Power of Personalisation by Peter Scott
- Why Your Customers Should Be at the Heart of Your Digital Transformation by Saniul Ali