VistaPrint, a Cimpress business, is the design and marketing partner to millions of small businesses around the world. For more than two decades, VistaPrint has empowered small businesses to quickly and effectively create the marketing products – from promotional materials and signage to print advertising and more – to get the job done, regardless of whether they operate in-store or online.
To support small businesses on their brand-building journey, VistaPrint provides customers with personalized product recommendations, both in real time on vistaprint.com and through marketing emails. These product recommendations improve their customers’ experience by making it more efficient to find the products they need, while increasing VistaPrint’s conversion rates. Since implementing Amazon Personalize, VistaPrint increased their conversion rate by 10 percent and reduced their total cost of ownership by 30 percent.
In this post, we show you how VistaPrint uses a combination of Amazon Personalize, Twilio Segment, and auxiliary AWS services and partner solutions to better understand their customers’ needs and provide personalized product recommendations.
Prior solution and challenges
Prior to their current solution, VistaPrint had an internally developed product recommendation system hosted on-premises. The first challenge with their prior solution was that the solution couldn’t scale automatically when demand increased. The second challenge was that changes to the in-house developed system were time-consuming, because a high degree of machine learning and ecommerce domain specialization was required to make modifications.
These challenges led to the decision to create a new cloud-native system that can scale with increased demand and consists of serverless and software as a service (SaaS) components that externalize much of the domain-specific functionality to allow for easier operations and faster time-to-market for changes.
The new VistaPrint personalized product recommendation system
Figure 1
As seen in Figure 1, the steps in how VistaPrint provides personalized product recommendations with their new cloud-native architecture are:
- Aggregate historical data in a data warehouse. Data from upstream systems including customer data platforms (CDPs) like Twilio Segment, order management, product catalog, and user management systems are collected in a data warehouse, which in VistaPrint’s case is Snowflake.
- Transform the data to create Amazon Personalize training data. Amazon Personalize uses data about users, items, and interactions, and this data is ingested from Amazon Simple Storage Service (Amazon S3) in CSV format. In VistaPrint’s case, they use Databricks to perform the required data transformations before landing the data in Amazon S3.
- Import bulk historical data to train Amazon Personalize models. After bulk historical data is ingested into an Amazon Personalize dataset, one or more solutions are trained using this data. In VistaPrint’s case, they use the User-Personalization and Similar-Items model recipes.
- With User-Personalization, Amazon Personalize predicts the items that a user will interact with based on previous interactions across all users.
- With Similar-Items, Amazon Personalize generates recommendations for items that are similar to an item you specify.
To maintain the relevance of the personalization models, steps 2 and 3 are repeated on a regular basis to keep the training data up to date.
- Stream ecommerce website events to a CDP. A CDP is used to capture events from an ecommerce website, for example when a user views a product or adds a product to their shopping cart. A CDP can also perform identity resolution, which helps to identify the user regardless of whether they’re accessing a platform from a mobile or a web client. VistaPrint uses Twilio Segment as their CDP.
- Generate real-time product recommendations as a customer navigates the ecommerce website. As a customer navigates an ecommerce website and these events are captured by a CDP, they are also forwarded to Amazon Personalize. Amazon Personalize in turn generates recommendations for additional products that a customer may be interested in. These recommendations are placed back into the ecommerce website experience in real-time.
- AWS Lambda is used to send data from Segment to Amazon Personalize using Segment’s Amazon Lambda Destination. VistaPrint uses the Segment Amazon Lambda Destination to perform additional data transformations and to get flexibility to integrate with additional observability tooling not shown, but other AWS customers can consider Segment’s Amazon Personalize Destination which is suitable for simpler integrations.
- VistaPrint created a personalization service that sits in front of Amazon Personalize. This service provides additional functionality on top of Amazon Personalize APIs, including the ability to cache recent recommendations in Amazon DynamoDB, and integration with VistaPrint’s authentication and authorization systems.
- VistaPrint created a placement and offer engine (POE), which allows data scientists and marketers to collaborate. Placement templates are used to create customized placements by allowing a marketer to select an Amazon Personalize model, the visual style of the placement, and extra features like whether to display a customer’s logo as it would appear on the final manufactured product. Figure 2 shows an example of one of these placements, called More with your design, as seen on vistaprint.com.
- Generate product recommendations as part of email marketing campaigns. In addition to providing real-time product recommendations on their website, VistaPrint uses personalized product recommendations in email marketing campaigns. The same POE system is used to design and place product recommendations into email templates.
Figure 2
Business Impact
Since implementing its new personalized product recommendation system, VistaPrint has realized a 10 percent increase in conversions originating from personalized recommendations. Amazon Personalize also reduced VistaPrint’s total cost of ownership by 30 percent compared to the previous on-premises solution.
Conclusion
VistaPrint’s cloud-native personalized product recommendation system helps the company deliver a more efficient and helpful experience to their customers, while increasing the company’s conversion rates.
Amazon Personalize is at the center of VistaPrint’s personalized product recommendation system, providing a fully managed, machine learning powered solution.
A customer data platform like Twilio Segment allows companies like VistaPrint to build a connected, 360 degree view of their customers by aggregating data from all of their customer touchpoints across multiple business domains. This cohesive view of the customer leads to more accurate and personalized product recommendations when paired with Amazon Personalize.
Next Steps
The VistaPrint personalized product recommendation system is one product within a larger data mesh of products. Read more about Vista’s data mesh strategy in this previous post How Vista built a data mesh enabled by solutions available in AWS Marketplace
Also read more on the other topics in this post:
About the Authors
Ethan Fahy is an Enterprise Senior Solutions Architect at AWS based in Boston, MA. Ethan has a background in geophysics and enjoys building large-scale, cloud-native architectures to support scientific workloads.
Mouloud Lounaci leads the Engineering team for Marketing Optimization at Vista. He is a Machine Learning enthusiast with around 10 years of experience in building AI-powered software products to solve complex customer problems. Whenever he gets a chance, Mouloud jumps on a plane to discover cultures, food, and landscapes from around the world.
Emeline Escolivet is the Engineering Manager for the Recommendations team at Vista. With 10+ years of experience as a Software Engineer, she enjoys turning complex business issues into reliable software solutions. In her free time, she likes to describe herself as a hiker, dancer and food lover.
Vibhusheet Tripathi is a Senior Data Engineer in the Recommendations Team at Vista. When not experimenting with machine learning systems, Vibhu likes to read, play sports and listen to music.