AdiTalk
AdiTalk is a chatbot built to address customer concerns such as long wait times on customer requests. AdiTalk is also built to resolve issues that the existing chatbot doesn’t solve.
Problem Statement
How might we optimize the Adidas chatbot with AI to effectively resolve a wider range of customer inquiries (ex: order tracking, returns, exchanges, promotions), reducing the need for human support and increasing first contact resolution rates to improve the customer support experience?
Problem Background
The existing chatbot delegates most of the requests to human agents. Human agents do not operate a 24/7 schedule so this creates a limitation for customers to access support in a timely and reliable manner. For example, inquiries about product availability, sizes, prices, and many more. It operates as a means to direct customers to human agents but it doesn’t necessarily use AI capabilities to provide faster and accurate responses on its own.
Research Insights
Based on our research, we discovered that the main area to optimize was customer care services. We also identified that 75% of our clients are dissatisfied with the current chatbot regarding customer care services. We also discovered that the current chatbot redirects most of the customer care inquiries to human agents who are unavailable 24/7 making the whole customer experience time-consuming and dissatisfying.
Supporting Data
I quantified the results of my research to identify the part of the Adidas experience with the biggest opportunity for improvement:
- According to my research, 75% of customers were dissatisfied with the current chatbot regarding resolving customer care questions which implies that customer support experience has the biggest opportunity to optimize with AI.
- The majority of participants provided high scores for other parts of the Adidas shopping experience:
- Overall shopping UX ranked high ( 90% score) with the top areas being:
- 100% reviews and ratings, which implies that customers are very satisfied with the reviews and rating system.
- 95% for product availability - produced the second-highest score of
- 80% for availability of information on product pages
- 95 % for ease of product search on adidas.com
- Overall shopping UX ranked high ( 90% score) with the top areas being:
User Pain Points
- Limited chatbot resolution for basic questions: The current chatbot provides limited solutions to frequently asked questions like product availability, product sizes, product pricing, and many more. Irrespective of the system being available online 24/7, it redirects users to human agents
- Delayed resolution time: Reliance on human agents who may take long to respond or may be unavailable since they don’t work 24/7.
- The available chatbot doesn’t address questions about performance, comfort, and product recommendations tailored to the user’s needs. Hence redirecting users to human agents who may take long to respond or may be unavailable since they don’t work 24/7
Landing on the Solution
Based on our target users’ pain points, we knew we wanted to work on fixing customer care-related challenges by building a more efficient chatbot (AdiTalk).
AdiTalk will mainly focus on resolving customers' pain points in real time by leveraging natural language processing. This will free human agents from dealing with these customer care inquiries and concentrate on more complex issues. AdiTalk will provide these features.
- Make it easy for customers to get assistance with payment-related errors, including billing discrepancies.
- Make it easy for customers to get help with tracking orders, delivery times, returns, and updates.
- AdiTalk will provide fast answers and reduce wait times for customers.
Explanation of Solution
After determining our solution AdiTalk, we knew it had to solve the following pain points for different clients.
Adidas customers often seek immediate assistance on product information, order status, returns, and general queries, which leads to long wait times, inconsistent support, and inefficiencies in handling routine tasks. This not only reduces customer satisfaction but also places a strain on human customer service agents.
AdiTalk will handle multiple customer interactions simultaneously, providing near-instant responses to customer inquiries. This will reduce wait times and improve the overall customer experience.
With access to previous interactions, customer prompts, and customer profiles, AdiTalk will offer personalized recommendations, such as suggesting products based on previous purchases or preferences. This will enhance the ability to tackle more complex customer inquiries.
Customers could inquire about the status of their orders, tracking numbers, shipping details, and return/exchange procedures directly via AdiTalk, reducing the need for manual customer service interactions.
AdiTalk will answer questions about product sizes, colors, materials, and availability, helping customers make informed purchasing decisions. It will even offer sizing advice to ensure customers choose the right fit.
By automating repetitive tasks, AdiTalk will reduce the workload of human agents, allowing them to focus on higher-value customer service activities. This can lower operational costs for Adidas.
AdiTalk will leverage AI & Machine Learning Enhancements to make interactions feel more human-friendly, conversational, and engaging.
AdiTalk will improve query response accuracy by using better Natural Language Processing.
User Flows/Mockups
Future Steps
We shall carry out additional research to find more information about the challenges of using the existing chatbot to refine AdiTalk and identify future opportunities.
After building AdiTalk, we anticipate that it will have the following capabilities shortly.
- AdiTalk will automatically request customer feedback after resolving issues or answering queries.
- Helps Adidas gather insights on customer satisfaction and areas for improvement.
- AdiTalk will be integrated into Adidas’ website, mobile devices, and social media for a seamless experience.
Images
Learnings
Product Manager Learnings:
Tinyefuza Gordon
I would like to appreciate my experience with Co.Lab and what they have taught me as a Product Manager. I have learned that the most important areas that a Product Manager should focus on are the following.
- The overall goal of a Product Manager is to guide the success of a product and help teams improve it. This can be achieved by solving customers’ pain points as well as addressing the business objectives which is mainly through enhancing revenue.
- The other goal of a Product Manager is to understand the market to stay ahead of the competition. In this case, we need to know what Nike and Under-Armour are doing better so that we can have our products improved as well.
- I was also exposed to a variety of AI tools which I have started using in a number of my projects. These include bolt.new, Whimsical, and Canva.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.