Intro
Product information was key to business for Walmart and Sam’s club as a retailer. The availability of the correct product information significantly influences customers' in-store experience.
Apart from a few self-service-oriented users, most of them rely on our associates. They expect them to provide the correct product information they are looking for.
Pain points
It's important to understand our associate's perspective at that point.
- An associate could be new or experienced.
- They might be a floor, fresh, or a backroom associate.
- They might not be trained in the product and inventory.
Once they are in the spotlight, it is their role to help members find the right product and help them buy it.
Current alternatives they use
- Sams Club mobile app
- Sams Club website
- Contact another associate
Pain points in the current approach
- No club-specific info
- No accurate inventory data
- No information about out of stock items
MVP approach
Ask Sam being an associate-facing application, was a great platform to solve this problem. The conversational nature of the application added to its advantage.
We wanted to do an MVP as we wanted to launch fast and then evolve. Our first step was to make sure the item information is easily searchable and accessible.
Design goals
- Make it easy to search for a product using voice and text.
- Provide the key product information upfront (Carousal cards)
- Provide detailed information upon request (Product detailed card).
Next steps: Measure
Once the skill was launched we were able to collect a good amount of information from various sources.
Analytics data
User data from Club visit
Stakeholder interview
App feedback
Based on the data received, we identified the key areas to improve. The following where the two main initiatives taken as a part of this.
1. Product Lookup 360
Our MVP skill helped them to search for the product quickly. But when an item is out-of-stock, they must help members find that across the clubs. We integrated omnichannel inventory data to Ask Sam so that all the item inventory details are right at their fingertips.
2. Smart response
With improved Natural language processing and conversational design, Ask Sam could identify the specific intents of the users’ utterances and respond with more relevancy and clarity.
Outcome
With these two initiatives in place, we could see an overall improvement in skill usage. The following were the key metric we measured.
- Overall skill adoption increased with more sessions per week and users.
- Overall, session time got reduced with a minimum of carousel scrolls and back-and-forth navigation.
- Repeated questions were reduced, and unique questions increased.
- Skill-level feedback saw an increase in positive feedback.
Now our associates are more empowered and confident in serving our users.
New initiatives (Work in progress)
We are not done yet. Our next goal is to leverage the untapped potential of conversation design and AI to bring more power to the user. The following are the initiatives in progress.
Smart suggestion
Proactively suggest new navigational models for users to narrow down to the right product.
Type 3: Follow up actions
These suggestions will help users perform quick actions based on the response.
Type 2: Follow up with pivot points
These suggestions will provide quick alternatives based on the context of the conversation.
Type 1: Follow up filters
For any given set of search results, users should be able to add more specifics and get more tailored results using these suggestions.
Complex multi-intent questions
Now the user should be able to convey their specific requirements in a question and Ask Sam should be able to understand that and provide the correct answer.
This can help marginally reduce the search completion time.
If you want to have an in-depth walkthrough of these case studies, please contact me
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