As a personalization platform, Monetate enables businesses to deliver personalized experiences to each one of their customers in real-time across all of their brand touchpoints (think website, email, mobile app, in-store).
As the Product Design Lead, I manage a 3 person design team and lead the holistic design process across all projects from kickoff to market release. I work cross-functionally with other designers, product managers, engineers, data scientists, and members of other departments within the organization to help ship product built from an understanding of user needs that drives the bottom line.
Over the last 4 and half years I've helped design and deliver many new areas of our platform and updates to existing functionality, often stepping into a role within projects to help define both strategy and scope.
I was the first full-time UX Designer on a design team of 3 when I started at Monetate. I helped to establish the fundamentals of our user-centered design process:
Started Monetate’s user research and testing programs, putting in place a toolbox of methodologies and outlining processes for our team to you use while building or redesigning features of our platform: a combination of lean feedback techniques including interviews, surveys, journey mapping, card sorting, rapid prototyping, think alouds, and usability testing.
Helped build our personas and created journey maps for several major workflows.
Worked closely with our services team to foster client relationships that allowed us to research and validate our ideas throughout the design process.
Built close relationships with our services team who uses our platform daily and understands the shared needs of our clients. While nothing can replace actually interacting with our customers, our services team enabled us to test ideas and interaction design concepts quickly.
Helped build our UI kit and pattern library, bringing consistency to the platform and speeding up the design process.
Built a data driven design culture, regularly using a combination of Heap Analytics and Looker to measure product usage which inform product and design decisions.
More recently I have worked with our product team to help us focus on and measure the value of every feature Monetate releases. Not only is this critical in allowing our small team to execute on an aggressive product roadmap quickly by keeping scope small (all the more important when dealing with legacy portions of the codebase), it makes it easier to design by providing a clear goal at the outset of any project.
Over the last 4 years I’ve helped design and deliver many new areas of Monetate’s platform and updates to existing functionality. These projects ranged in length from 2 weeks to nearly 10 months and were usually run concurrently. Some examples of these projects include:
Many businesses see 1:1 personalization, where each customer is delivered a uniquely relevant experience, as a key to their brand’s success. Brands typically optimize their experiences for customers in a one size fits all manner (e.g. addressing a site-wide drop off in completed checkouts) while some invest in peronalizing for different segments (e.g. tailoring the home page to different personas). But each time a new segment is created, new content must be created, tested, and analyzed, which is resource intensive and makes it impossible to personalize in a 1:1 manner. Using machine-learning allows marketers to personalize at a scale that can’t be achieved through manual A/B testing and segmentation. We’ve created two types of machine-learning powered campaigns: one that finds the best experience for the majority of an audience and one that finds the best experience for each individual.
I led research and design on these projects. I worked closely with our data scientists and product team members to find the sweet spot of how to explain how these black boxes worked. We delivered both campaign types iteratively.
Organizations are deeply rooted in testing habits. A key part of this project was tailoring the explanation of how the machine-learning algorithms work (they’re inherently black boxes) in a way that matched people’s expectations and supported current workflows: we needed to help people change their way of thinking while bridging the gap to their familiar A/B testing world.
A large portion of the client base has adopted the 1:1 personalization platform. We’ve seen results that effectively increase brand engagement by up to 40%. Customers have found the insights generated by these algorithms as validating and fuel for further iterations of personalized experiences.
For many businesses, a major part of personalizing a customer’s experience involves curating which products to show and when to do so. Have you been looking at shirts online for the last hour? You probably need a hand picking the right one. Or are you a new customer coming to the site for the first time? You probably want an overview of the brand and would benefit from seeing their best selling items. The goal is to guide and enable marketers to personalize their product recommendations at scale across all channels. This project is an update to Monetate’s existing recommendations offering that had not been updated since 2014.
I am leading the design process from research, to design and prototyping, to testing, all while helping our Product Manager home in on the key differentiators of our new offering
A major consideration throughout this project has been determining what elements of our existing offering, both in terms of technical architecture and design paradigms, to hold onto. The recommendation space is a mature market with several well established point solutions. Our team is ruthlessly focused on delivering differentiated value in a lean, iterative fashion.
Initial beta client feedback and early sales feedback has been overwhelmingly positive with clients appreciating the increased flexibility, reduction in manual work, and new, sophisticated use cases that they are able to run.
Clients have a wealth of data about their customers stored in CRMs or third-party systems. Getting this data into Monetate allows clients to further personalize their experiences. This data onboarding feature involved creating a flexible means of accepting nearly any data type (customer, locations, products, content, etc) in a way that can be used for personalizing experiences and fueling the machine-learning algorithms.
I led research and design on this project.
The primary user of the Monetate platform is a non-technical marketer. Understanding the relationship between this marketer and technical data feed manager was important to ensure we created features that supported their relationship and spoke to each user in a way that they understood.
Nearly all clients that send us automatic updates use this data onboarding feature for personalizing experiences and have seen significant impact in machine-learning powered experiences.
In order to truly personalize the experience for the end consumer, brands need to stitch together the behavior of people across browsing sessions and different devices. For example, you might start shopping on your phone, but finish your purchase journey later on your laptop. In order to power this, I helped deliver a feature that enables our clients to tie behavior back to people, creating a single view of the customer that allows brands to leverage cross-device and historical behavioral data.
I completed supplemental research and led design on this project.
The largest challenge was helping brands to internally align around a universal means of identifying their customers. Some brands have a universal customer ID whereas others do not. Helping to select this ID involved working closely with our services team and creating supporting documentation that lived outside of our platform to help clients realize the value of this feature and support them through this process. As with other features in our platform, there is an initial setup to this feature to unlock its value that requires a technical user’s support. Catering the experience to this technical user was key to ensure it was as simple as possible.
Roughly 40% of the client base uses this feature and we have seen up to a 30% increase in relevant experiences being delivered based on either cross-device or historical data.
My work at Monetate is covered by a confidentiality agreement (and therefore can't be shown here), but I'd love to talk with you about my design process.