Prachi Pawar's Profile

Interesting read! Reverse logistics is a big pain point for many early stage companies esp. in emerging markets such as India which do not have the scale to optimise on logistics cost.
I really like what Optoro is doing. The nature of products returned is such that they have less depth in every product type, making it all the more difficult to create any value from the returned product. Enabling the retailer to repurpose the returned inventory which eventually would have anyway become dead inventory, is truly impressive. Apart from the benefits mentioned, carbon and waste savings can be achieved with optimised operations of these returned items.

Great points on connecting a customer’s offline and online date to understand and predict purchasing habits. Using omnichannel data to retarget customer would be very valuable. Walmart has a lot of physical touch-points with customers. If it can leverage those touch-points and target these customers online, it stands a chance at disrupting Amazon’s standing.
Also, I believe Personalisation – which Amazon currently does very well- with the help of AI/ML is the key to staying ahead in this battle. Weeding out irrelevant data will help the customer and shorten the purchase journey, making the product funnel a lot better. Machine Learning can also be used along with this data to improve customer service and enable round the clock support.

What an interesting application of Machine Learning!
I agree with mrrobot’s point of view and drawing of similarity between the Gap case and a Michelin star restaurant using AI. Given the reputation and expectation of innovation from such restaurants, AI could kill the novelty of the restaurant. Though insights from different parts of the world about trends would be helpful to a restaurateur – the role of the chef or the human element of innovation cannot be eliminated in an art such as cooking.

AI vs. the Human Heart – I agree with John Smith’s point here about machines being able to rightly capture the complexity of human emotions. Along with security concerns that you rightly pointed out I believe that Artificial Intelligence is not neutral. The machine will feed off of the biases of its users and generalise recommendations based on that. The predictability of the matchmaking options also reduces the concept of ‘chance’ which is probably the more exciting part of dating. I wonder if these apps also have processes where it suggests options not so aligned with past interactions/interests data.

You’ve made an interesting point in the article about winning back a customer being harder than maintaining a current customer – which leads to identifying patterns when a valuable customer could drop off.
This also makes me wonder whether there is a way to make an unprofitable customer profitable by using ML/AI and predicting better sizing and styling for customers. It would eventually reduce the number of returns if the customer experience is improved via personalisation.

Super interesting application of Machine Learning. Exhibit 3 with estimated costs due to congestion is a great way to look at the magnitude of this problem. I agree with Amina – data sanctity is really important here. Regarding the option of either working with the government or individually with consumers – I believe working with government would help them scale faster and get access to data quickly. Working with consumers could also mean a slower pace of growth like any other digital company and also tough competition from similar apps such as Google/Uber like you mentioned who can decide to enter the market.