{"id":28852,"date":"2018-11-12T20:50:25","date_gmt":"2018-11-13T01:50:25","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/cornershop-how-machine-learning-can-improve-customer-satisfaction-and-increase-accuracy-in-operations\/"},"modified":"2018-11-12T20:50:25","modified_gmt":"2018-11-13T01:50:25","slug":"cornershop-how-machine-learning-can-improve-customer-satisfaction-and-increase-accuracy-in-operations","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/cornershop-how-machine-learning-can-improve-customer-satisfaction-and-increase-accuracy-in-operations\/","title":{"rendered":"Cornershop: how machine learning can improve customer satisfaction and increase accuracy in operations"},"content":{"rendered":"

Cornershop: delivery of groceries to your front door<\/strong><\/p>\n

Cornershop is a Chilean startup that provides on-demand home delivery of groceries. Customers buy products from the Cornershop mobile app and receive the products the same day [1]. The app was launched in May 2015 in Chile and in July of 2015 in Mexico with an initial investment of $1 million.\u00a0<\/strong><\/p>\n

Machine learning at Cornershop<\/strong><\/p>\n

In the online retail business, delivery times is a key indicator that is reviewed constantly. In the case of Cornershop, a better estimation of delivery time estimates improves customer satisfaction and enable a better assignation of the delivery staff to the different purchase orders. The purchase process at Cornershop (Figure 1) relies on many factors that increase the variability of delivery times. The incidence of traffic and the distance between the location of the customer and the supermarket affect the transportation times (stages 3 and 5). Correspondingly, the mix of products ordered and their location in the store, the skill level of the delivery person and the congestion in the stores affect the speed of purchase (stage 4).<\/p>\n

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Figure 1. Purchase process at Cornershop.<\/h5>\n

Machine learning has emerged as a possible solution to improve the delivery time estimate. When Cornershop began its operations, the logistics team predicted the delivery time based on historical purchases with similar characteristics [2]. However, due to the large number of factors that affect the efficiency of the delivery process, the team decided to apply more sophisticated methods and train a model for learning from previous experiences.<\/p>\n

What the management team is doing and what it will do to improve the prediction of delivery times <\/strong><\/p>\n

In the short term, to improve the prediction of the delivery times and the assignation of delivery staff [3], Cornershop has strengthened the data science team and has started using machine learning methods. To illustrate, the company applied four machine learning methods. For the predictions of each method, the team calculated the standard error of the mean (SEM) and the mean absolute error (MAE). SEM measures the deviation of the predictions considering their directions and therefore positive deviations can offset negative deviations. In contrast, MAE measures the deviations of the predictions without considering their directions.<\/p>\n

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The method that obtained the best results was the neural network, which is a technique that simulates the neurons of the human brain through the creation of multiple layers that process information with the objective of transforming the input into something the output unit can use [4]. Through this method, the MAE was reduced by approximately 40% [2] versus the non-machine learning method. Since positive deviation canceled negative deviation, the SEM results were similar in both cases.<\/p>\n

While Cornershop\u00b4s operations were improving rapidly, Walmart, attracted by the opportunity to continue its expansion, offered $225 million to acquire Cornershop [5]. Cornershop accepted the offer in September 2018 and will be able to take advantage of Walmart\u00b4s expertise in machine learning to improve their predictions models and make the operations more efficient. Going forward, Cornershop will have to analyze which is the best way to integrate the data science team of both companies and will have to test the machine learning methods developed by Walmart. In addition, when expanding into new cities, Cornershop will have the opportunity to use both companies\u00b4 databases as a starting point for developing their predictions.<\/p>\n

Continuing the efficient expansion of Cornershop<\/strong><\/p>\n

In the future, the management of Cornershop should continue focusing on the expansion of the company and on cost reductions. Currently, the users are mostly in a high socioeconomic demographic [6]. Since the use of credit cards in the middle and low-income segments is low, Cornershop should offer other payments methods such as cash or electronic deposits. In addition, these segments will show completely different purchase behaviors, which will have to be used to train the model. To illustrate, the amount and the variety of the purchases will be lower, but the frequency of the purchases could be higher.<\/p>\n

With the objective of reducing the costs, Cornershop could use machine learning methods to optimize the purchases of the delivery staff. The company could use past purchase behavior to motivate delivery staff to be in zones with expected high demand, reducing the transportation times. Along the same line, Cornershop could manage the demand and distribute purchases throughout the day by charging different delivery prices to the customers.<\/p>\n

The improvement of the prediction models appears to be an infinite iterative process.\u00a0Which other parts of the process could be improved through machine learning? Which other machine learning methods could Cornershop\u00b4s team apply to increase the efficiency of the processes?<\/p>\n

(Word count: 797)<\/p>\n

[1] Cornershop website, \u201cNuestra historia,\u201d https:\/\/cornershopapp.com\/about, accessed November 2018.<\/p>\n

[2] Cornershop website, \u201cMejorando la estimaci\u00f3n de tiempo usando Machine Learning,\u201d https:\/\/tech.cornershop.io\/mejorando-las-estimationes-de-tiempo-parte-1-1af96d8afb4c, accessed November 2018.<\/p>\n

[3] Fedyk, \u201cHow to tell if machine learning can solve your business problem.\u201d\u00a0性视界 Business Review Digital Articles (November 15, 2016).<\/p>\n

[4] Forbes. \u201cWhat are Neural Networks \u2013 A simple explanation for absolutely anyone,\u201d\u00a0 https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/09\/24\/what-are-artificial-neural-networks-a-simple-explanation-for-absolutely-anyone\/#53cffad11245, accessed November 2018.<\/p>\n

[5] Forbes, \u201cWhy is Walmart Acquiring Cornershop,\u201d https:\/\/www.forbes.com\/sites\/greatspeculations\/2018\/09\/14\/why-is-walmart-acquiring-cornershop\/#717c6f233e32, accessed November 2018.<\/p>\n

[6] Economia y Negocios, \u201cLa historia y el modelo tras el despegue de Cornershop,\u201d http:\/\/www.economiaynegocios.cl\/noticias\/noticias.asp?id=495340, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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