1/8 The brief
Who is ReturnRunners?
ReturnRunners (RR) is a Chicago based startup tackling the reverse logistics market through a white-glove service that returns customer’s retail items for a refund on their behalf. They aim to provide a convenient service that eliminates the hassles of returning items to stores, freeing up time for their busy and affluent target audience.
ReturnRunners pride themselves on their white-glove service built on personal relationships and a great customer experience. As a small company, most of the staff, including founder Fara Alexander, participate in runs — but they aspire to hire third-party runners (à la Uber) to do the heavy lifting. During our kickoff meeting we learned about RR's future ambitions:
- Grow the business to an on-demand service.
- Increase its value proposition with retailers through data collection.
- Collect logistics metrics to sustain their business in the future.
What they wanted from us
RR asked us to build a runners platform to sustain a potential workload increase of up to 200 orders per week due to a future partnership. With no system in place for runners, they believed this to be their next step.
2/8 Exploring our options
We used the following research methods to find the best way to ensure RR’s sustainability as they scaled in the future and determine if a runners platform was viable.
- Contextual inquiry
- Domain research
- Journey mapping
After the kickoff meeting, we looked to understand the runner’s process and determine if RR’s initial assumptions and insistence on a runners platform were valid. I felt this opportunity lent itself to a contextual inquiry, and asked Fara if we could shadow her on a run.
Fara excitedly responded - “What are you doing in about 20 minutes!?”
With the goal of uncovering opportunities for improving the runner’s journey and getting to know our client better, Mac and I shadowed Fara and her associate, Sammy, on a run. Upon entering Fara’s car, I immediately began documenting the experience. From my questions and observations, I learned to empathize with the runner’s needs and gained insight to further focus our research.
What I observed from my experience
1. Sammy pulled up an email on her phone containing the customer's order and contact information. She entered their address into Waze.
2. Fara directed Sammy to call the customer to communicate they were en route and confirm any special instructions.
3.Upon meeting with the customer, Sammy was handed bags of clothing for the return. She used the order details on her phone as reference and checked that all items were accounted for.
4. After the items were collected, we drove to Macy's.
5. Fara dropped off Sammy to do the return and stayed with the car to avoid parking issues.
6. Sammy returned items to Macy’s. She snapped a photo of the return receipt.
7. We walked across the street to Old Navy.
8. At Old Navy, a complication arose and, the refund could not be issued back on the client’s card. RR would need to schedule a way to return the item back to the client and refund their money.
What I learned from the experience
Everything is manual.
Orders are output as emails displaying the items and contact info of the customer. Runners use this text output to contact customers, input pick up locations into a GPS and, confirm items during pick up.
There are a lot of unknowns before a pick up.
RR is committed to creating a convenient service that strives to ask little from the customer. As a result, the required order information doesn’t paint a clear picture and runners arrive to pick ups unprepared.
The pick up process is inconsistent.
Runners must cross reference the items they are picking up with order details in an email. The mental load of this process can result in mistakes that lead to incomplete returns.
Route optimization relies on the runner's experience.
Without an integrated navigation, runners rely on their knowledge of the city and store locations to determine efficient routes.
Division of labor isn't sustainable.
Most deliveries are currently done as a pair to make life easier and increase efficiency. In an on-demand environment, this process would be difficult to sustain.
Runners can't remember every return policy.
Return policies greatly vary between stores. Even for an experienced runner it can be difficult to catch problematic returns during pick ups.
RR's current process is cumbersome and would be impossible to sustain as they grew, but RR’s reluctance to make customers provide more information during orders caps the efficiency of runners.
RR provided us with interview participants to find ways to improve the runner’s process and gain insight into the logistics space. Due to the natural relationship between customers and runners, we took this opportunity to learn as much as we could about customer behavior to determine their willingness to provide more information during the ordering process.
- Understand the perspective of inexperienced runners.
- Understand the logistics and reverse logistics space.
- Understand and confirm RR’s target audience.
- Understand the customer service industry.
What we heard
Online return habits are problematic for retailers.
“They’re buying online and returning it to our store, so it's an overall negative in our sales for that day.” - Andra G.
Information provides comfort.
All inexperienced runners had never been on a solo run and expressed they valued knowing what to expect on the job to feel confident.
A seamless customer experience is what builds trust.
“If there is more of a system, you can trust that your item is going to the right place.” - Sara U.
Ordering processes are inconsistent from customer to customer.
RR’s web and native app ordering process is inconsistent resulting in different information. I interviewed one customer who only called or texted RR directly to place an order.
From our interviews, we determined RR would need to have a consistent ordering process to make runners more efficient and establish a spoke-and-hub logistics model before growing into an on-demand service.
With the newly gathered insights from our interviews, we developed a runner’s journey map alongside Fara. This exercise provided us the opportunity to clarify anything we had missed during the contextual inquiry and keep the client engaged in our process.
- Find opportunities to improve the runner's journey.
- Find opportunities to gain data to improve RR's efficiency.
With a bit more focus, we further contextualized our work through domain research. Our objective was to clarify a RR road map that would be viable for their business to help us focus our future solution.
Prompted by what we learned from our logistics SMEs, we researched ways to help RR scale their service sustainably.
- Determine how to consider scalability.
- Determine what needs to considered for sustainability.
- Understand the nuances of a two-sided marketplace business model.
There are 4 drivers of scalability. The major drivers pertaining to RR’s service model are distribution and network. The team determined that in order for RR’s service to scale in the future, it would need to leverage these drivers to support the current point-to-point distribution model as well as the future spoke-and-hub model.
After researching into the downfall of companies like Shyp, the team determined the best route for sustainability was to allow data collection to determine their future and remain flexible so they could tweak their business if needed.
As a two-sided market, we determined that both sides of the service, customers and runners, needed to be built at a balanced rate as both are reliant on the other. As RR had no runner app, it was apparent that we would need to focus on a runners platform.
Our solution needed to focus on the runners in order help RR scale in a sustainable way by balancing the two-sided market, growing a network, and collecting data.
We determined that many of RR’s current tactics were valid. Even as we were unable to refute RR’s choice of target audience, their decision to grow a specific network was the correct decision according to our domain research.
We validated the choice to help runners in order to create a seamless customer experience that would elevate RR’s brand.
Next, we focused our efforts on designing a solution that helped RR scale in a sustainable way — by helping the runner perform their job better.
4/8 Focusing our efforts
With a better understanding of our approach, we determined that RR's problem was larger than just the need for a runners platform. Our problem statement and design principles were developed to help us focus on the runners while still helping us consider RR's business needs.
Service-minded runners need a digital tool that will aid in collecting, collating, and organizing the information they need, so they can confidently make informed decisions on-the-spot to provide excellent and personal service. This ensures a mutually beneficial relationship between runners, customers, and retailers.
Before using our design principles to start concepts, we used our research to develop a more in-depth road map to align with the client and clarify our concept intentions.
RR is currently practicing a point-to-point model (Phase 1) and ultimately wants to partner with a company (Phase 4) that can do the heavy lifting of returning so they can focus on pick ups, which can be done by 3rd party hires. Our road map expands upon RR’s and provides requirements for each step.
To get them to that point, we determined we would need to provide them with a runners platform and process to reach phase 2 in a sustainable way.
5/8 Testing what ideas stick
We developed concepts that would make runners effective at their job so RR could sustain themselves until they established a spoke-and-hub model. Our next step was to analyze companies that had succeeded with a two-sided model.
We chose the following services because of how they’ve dealt with the following subjects:
- Customer ratings
- Route navigation
- On-the-go interfaces
Sketch 'til it works
We sketched and tested ideas on paper to quickly gauge the desirability of our ideas with 3 runners. We focused our concepts on discerning the level of automation appropriate for the pick up and return process.
After each pick up, the runner rates the customer and provides additional customer data for future use by ReturnRunners.
The runner is provided with an itinerary and map, as set up by a “dispatcher.” This tool helps the runner provide the white-glove service by enabling them to be the interface for the customer.
The runner is provided with a simple checklist to mark the condition of an item during the pick up process. This checklist helps prevent declined returns at stores.
Runners are able to switch between picking up or dropping off returns. This gives ReturnRunners flexibility in their return process as they scale.
What ideas worked
What ideas didn't work
What we learned from testing
Trust the runner's training.
Runners would be hired and trained in this current phase, so our solution wouldn’t need to handhold them through every process.
Data collection shouldn't affect the pick up process.
Attempts to collect data can't disrupt the runner’s efficiency and ruin the customers experience.
There's a difference between the pick ups and returns.
The pick up and return process should be treated differently to prevent confusion and offer specific actions.
Our attempts to gain data in the field were rejected and we came back to the same issue as before: we needed to gather info from the ordering process to provide runners with enough information to increase their efficiency.
6/8 The other half of the equation
Our focus was on the runner, but an understanding of the entire service was necessary for success. We determined that information regarding the return date, store, and original payment methods could allow for a flagging system that rejects or confirms the viability of a customer's return during the ordering process. This would reduce the possibility of return conflicts, but would require more information from the customer. By acquiring more data from an order, runners could anticipate the pick up process and increase the success of returns. Beyond the personal contact and store information RR already acquired through the ordering process, we suggested the following:
- Item type
- Picture of receipt
- Original payment method
- Preferred refund method
- Date of original purchase
- Reason for return
The relevance of our concepts and proposed interactions depended on the information provided by users during the ordering process. Before continuing on to mid-fidelity prototyping, we needed to make sure our ask of the customer was viable.
On a Saturday afternoon, the team went to a Chicago mall and interviewed 20 participants from various backgrounds in order gain a diverse understanding of the general public’s mental models for returns.
We learned our ideas weren’t crazy
The inconvenience of returns outweighs inputting more info.
From a lack of return policy knowledge to being too busy, there are a lot of reasons people forget to return an item. This return paralysis results in items never being returned. With a reasonable order flow, customers would be open to divulging the needed information.
The information we need is a reasonable ask for the service.
Overall, participants were not concerned with their information, as they were familiar with how most online platforms worked. In the context of a return service, the ask fit with what participants felt was appropriate for a return process.
Now understanding that the information we needed from customers was reasonable, we were confident our solution was viable as long as RR invested time in developing the customer journey in the future.
7/8 Our solution
Our team create a mobile platform that carries the mental load for the user so they can focus on the human aspect of being a runner.
The checklist process (shown below) allows runners to keep track of the items they’re picking up, while being simple enough to no affect the customer's experience.
8/8 Future recommendations
Due to our short time with the project, the team left RR with many recommendations to ensure their success in the future.
Phase 1: Current state
For RR to succeed as a two-sided market, they would need to invest more time into their customers. We suggested further development and test of the ordering process to make the customer experience more enjoyable.
We suggested that the data collection from this process, whether through a native app or website, needs to be consistent. This will improve data inconsistencies in the future and cause less confusion for the customer.
During this phase, RR would also need to determine the process for the dispatcher who provides the jobs to the runner. The team recommended implementing a human dispatcher at this phase and transitioning to an automated dispatcher as RR scales.
Phase 2: Spoke-and-hub
When RR implements the spoke-and-hub model, runners would no longer return items directly to the store, but to a central hub. We suggested RR implement a tagging and tracking system to reduce item loss. This phase also provides RR the opportunity to find other ways to collect data that could increase their value proposition in the future.
Phase 3: On-demand
Once RR reaches a larger audience through this phase, the team suggested considering different pricing models. From our guerrilla interviews, pricing for the service was a concern for some participants. In general, individuals want a net positive from returns, so more interviews would be necessary to understand a viable pricing option for a wider audience.
The process of developing both an app and strategy to ensure RR’ success in the future was extremely rewarding. Considering the business needs of our client was a difficult challenge, but I’m fortunate to have had it. Prior to working with RR, my projects leaned more towards understanding the user. This project required a healthy consideration of both the user and product viability.
What made the process even more rewarding, was our interaction with the client. I was able to think of the client as a part of the team and enjoyed keeping them engaged in our work. This experience gave me more confidence to speak and work alongside clients.
I’m lucky to have had the opportunity to work with a such a passionate client and be able to provide solid work that went beyond their expectations. They were even nice enough to send the team gifts to express their appreciation for our hard work. Success!