Ask Sam is a conversational assistant designed for Sams Club and Walmart associates. Sams Club is a membership-based store of Walmart. Ask Sam, powered by NLU and conversation design, has helped us reimagine the in-store experience for Samsclub.
I have been leading the product design for the last three years. With a vision to bring all the information to users' fingertips, Ask Sam now has achieved the following:
In this initiative, we tried to solve a million-dollar industry problem on skill discovery. Our goal was to help our users effortlessly discover and learn our new skills.
Even though the overall usage of Ask Sam is high, the use across skills is inconsistent. High-performing skills contribute only around 30%, leaving behind 70% of low-performing skills.
How to improve adoption of low-performing skills
We, along with the product, investigated various reasons behind low-skill adoption. “Lack of awareness” emerged as a critical pain point that could be a potential opportunity if solved.
We carefully planned and executed a discovery phase to evaluate the same and build upon it. The goal was to collaborate and ideate together as an experience team.
The team brainstorming exercise helped us to generate more user pain points and solution directions. The team involved: Product manager | Design | Engineering | Business
We conducted stakeholder interviews with the operations team. We needed to understand how we handled this problem at the operations level.
“Apart from the generic training, there is no specific training given to the associate on using applications” - Club operations.
Knowing that discoverability is a universal problem among conversational assistants, it was essential to evaluate what measures have been taken across the industry.
Most assistants have dedicated skill documentation for users to explore and learn.
Featured skill cards, and skills suggestions, by the way, were some new initiatives.
We also had a similar explore skills page and “what’s new” feature to communicate new and available skills. But the usage was minimal.
As a next step, we did multiple club visits, in-person interviews, and observation studies to know our users better.
Our research goal was:
“Our users didn't know
that certain skills could exit ”
They had a very strong perception about the limited capabilities of Ask Sam
While we designed Ask Sam to perform various capabilities and features, some of our users continued to perceive Ask Sam as a tool to perform only specific essential skills like product look-up, schedule, floorplan look-up, etc. Neither did they know about other skills, nor did they expect Ask Sam to perform different things.
The product team's conceptual model of Ask Sam and users' mental model of Ask Sam turned out to be different. The expectation is for them to match to have the best experience.
Product team conceptual model. VS
User's mental model
Based on the information collected on the discovery phase we decided to go with the following approach.
We need to push the right information upfront to the user.
rather than waiting for the user to pull them
Our goal was to ensure the information was easily discoverable for the user. We must place them at the right touch points in the user journey towards that goal.
Cross application promotion
Bring users immediate attention.
The user might disregard if busy
Dependency on other platforms and teams
Both the options were effective but required close collaboration and integration with the other apps and service teams. We decided to narrow down the scope of our application and identify touch points within that.
The chat screen within Ask Sam was the page where our users land first and stay active to ask questions. So, we decided to meet where our users are, Chat screen. Within the options considered among various components, we decided on using a chat component, as it's more organic and scalable.
Our goal was to identify what part of the conversation should we push the skill information.
Before they start a conversation
The user always ignored the greeting as it was static content.
If written well, along with the right micro-interactions, a greeting can engage users before they start asking questions.
Follow up after a conversation
Following up with a skill suggestion after a conversation can also be an excellent opportunity for engagement.
Generic follow up
Follow up suggesting a skill which they have not used yet.
Contextual follow up
Follow up by suggesting a skill that is related to the previous conversation.
To keep them actively engaged, we must always provide value in the information we show. So, our goal was to make the skill suggestion relevant to the user group. To maintain relevancy, we created a skill map with respect to user groups. To reduce redundancy, we integrated with real-time skill usage so that users will only see relevant skills that are not used.
Information should be:
Based on the above criteria, we finalized four main information components.
We conducted prototype testing with the club users to finalize the design components. This helped us to understand the following.
Which design was:
Tested with variations in card behavious
Once we identified all the design components, we worked on creating individual assets for each skills card. That involved designing the content and the iconography.
Once we released the feature, based on user inputs we made a few changes that strengthen the intended experience.
Apart from skill discovery, I have worked on many others features. Here are few.
Walmart being a retailer, product information is very crucial to the business.
Here is a classic example of reimagining a content-heavy web-based promotion platform into a seamless conversational experience.
The new skill, enabled better suggestions and reduced the promotion search time by 70%.
Connect with me
Apart from design I sing, capture and drive. Connect with me if any of these passion aligns with your.