Enabling local stores to sell through WhatsApp more efficiently
WhatsApp for Business allows businesses to engage with their customers and drive sales. One of these business segments is local stores and Kirana shops. After the pandemic, it is not uncommon for us to deal with our local stores through WhatsApp.
However, the whole communication needs constant attention from the shop owner while they run an offline business simultaneously. Hence, the current version of WhatsApp for Business is not viable for these stores.
Design an experience that helps local stores sell items to their customers through WhatsApp more efficiently.
- Shop owners cannot use the catalog feature as they have a vast and varied inventory. Even if they somehow catalog everything on the app, there’s a new problem of adding new stock and deleting it when it’s out.
- Texting customers and waiting for their replies throughout the ordering process is not the best use of their time and attention — especially when they also need to attend to in-store customers.
- There is a scope of product intervention to make the process faster and seamless and save both customer and shopkeeper time.
- The target group for this problem is WhatsApp users who order from their local stores by messaging them on the app. The burden of the problem lies on them, so it made more sense to approach this from the customer side.
- This group prefers to get items delivered possibly because of the following reasons:
i) They’re busy and cannot find the time to go to the store (homeowners, people WFH)
ii) Their physical condition (old-aged, differently-abled)
iii) Covid anxiety
iv) No means of transportation available at the time of need
When and where
- The users connect with the app when they need grocery items. Assumption here: They’re specific (to an extent) and know what they want. In stores, customers are exploratory.
- Another assumption: Since they prefer local shops, they’re at their place of residence (home, hostel, PG).
- They need to get items quickly delivered as they ordered from a local store. In contrast, if it were a BigBasket order, the user could likely wait for it to arrive as the delivery options are slower.
Let’s look at the user journey of a user from a CX perspective. Richa aunty is your jolly neighborhood mom who likes to bake on weekends. She’s in the mood for a cake today, but she’s out of baking powder. Sandeep Bhaiya to the rescue!
At almost every step of the journey, some information must be exchanged back and forth between Sandeep and Richa, which makes the whole process time-taking.
Why is the exchange of information manual and time taking?
- Sandeep cannot catalog so many items at the store, and as a result, Richa has to ask him directly in chat about the item’s attributes (price, quantity, brand, and more)
- Both parties wait for each other to respond. For example, Richa might make changes in her order that require further confirmation and waiting.
- Richa might have non-order-related queries. E.g., if Sandeep is accepting deliveries at that time + the delivery time.
- Sandeep might have to switch between apps to confirm if payments are received.
- Richa sometimes sends payment screenshots to confirm it.
- Richa’s mental model isn’t aware of the in-store customers that might be present. So, she expects attention and fast response rates.
A few more assumptions:
- In an online conversation, shopkeepers generally do not ask specifics if the customer didn’t mention them (like quantity or brand).
- Customers are accustomed to directly texting a list of items because it’s faster and easier to mention specifics.
Customers are generally unaware of the different attributes like size/quantity, so they use generalized terms like chota packet, bada packet.
Let’s try to minimize this manual communication between customers and shopkeepers. To do that, I listed the information exchange we can expect in a typical conversation between a shopkeeper and a customer.
I knew I had to eliminate these details from conversations. So if not conversations, then where? I combined all customer-side information together into one unit and called it a shopping list. Similarly, all shopkeeper-side details can exist as ‘order status’.
- Shopping List — A list created by customers that provides all the required order info by shops upfront.
- Order Status — A dashboard-like experience to get the status, pay through, see the delivery time, etc.
- Shopping list modifications — Done by shopkeepers in cases of unavailability.
IA and user flows
After incorporating shopping lists, this is how the CX flow looks like.
Whatsapp already has live location, voice notes, payments, an in-app camera, and your name. The shopping list takes advantage of these pre-built features to make the ordering process more seamless.
- The Shopping List feature has been designed to fit seamlessly into the existing chat ecosystem of WhatsApp. As it can be sent in the chat, it made sense to have it as a new kind of attachment.
- A shopping list only requires the items to be entered (in an ideal case). Customer’s name, address, and payment method are pre-filled with the help of Live location and WhatsApp Payments.
- Automated messages also come into the picture. These are triggered when the shopkeeper performs actions like modifying the shopping list, accepting the order, or marking it out for delivery. Again, the aim is to demand the lease of their time and attention.
- The shopkeeper can modify the list in certain cases where the item(s) are unavailable. Automated messages are sent for each modified item. Customers can then acknowledge this change by continuing with the payment. Or, they can choose to remove the item altogether.
Visit this link if the prototype doesn’t load properly.
This was an interesting design challenge by Razorpay’s design team that got me the internship. I liked how different pieces of WhatsApp came together (like geo-location, in-app camera, voice notes, and payments) to form this feature. As I was constrained to WhatsApp’s design system, I also learned more about iOS guidelines. If you made it this far, thank you for reading!