In January 2022, the vice-president of technology for Nata Supermarkets was reviewing the company’s performance against its competitors for the 2021 calendar year. The company had been performing poorly both based on its internal metrics and against competitor growth rates. The vice-president also noticed that many competitors began revealing new data analytics initiatives in their annual reports. Many companies experienced industry-leading growth because of these changes and upgraded their guidance for the following year. To compete with an increasing number of data-driven competitors, Nata Supermarket created its internal data set to collect information on customer shopping habits and customer demographics such as age, educational background, and frequency of complaints. With the emergence of visualization tools and data analytics, the vice-president was wondering what useful insights could be drawn from its internal data set. Could this information be useful to resolve various issues such as targeting promotions and forecasting demand?
In July 2022, a senior data scientist for Guell Appliances was aggregating customer reviews from the company’s recent appliance launch. The previous month, the company had launched a revamped product line that required a significant amount of investment in research and development. The success of the product line was paramount because it defined the success of the company. The senior data scientist wanted to apply modern analytical techniques to thousands of product reviews to thoroughly understand the preferences of customers, based on what they were saying about their new appliances.
Kae Jonishi, the protagonist in the case, is a marketing analyst at Force Energy (Force). Her company has advertised with Major League Baseball (MLB) teams in the past and has gained moderate amounts of exposure from these marketing campaigns. However, Jonishi’s manager wants to increase the company’s exposure to MLB fans. One way to do so is to sponsor teams that are likely to advance into the MLB post-season, or playoffs. The top performing teams during the MLB regular season advance into the playoffs and increase their fan base, which results in greater marketing exposure for advertisers.<br><br>Jonishi has access to a data set that contains aggregate team statistics for offence and pitching performance during all MLB seasons from 1995 to 2019. With adequate knowledge of both baseball and data science, Jonishi begins by analyzing and visualizing the data. She then builds a machine learning model that can predict whether a team will make the playoffs. Using this model, Jonishi can input future offence and defence performance predictions to determine if the team would have advanced to the playoffs in previous years.
London Hydro, Inc. (London Hydro) must forecast the electricity demand of the firm’s clients. The Ontario Energy Board had just announced the results of a pilot program to introduce a new pricing scheme to residential energy consumers in Ontario. This was the origin of tiered pricing, which was based on overall monthly energy usage. In the past, consumers had all been on a time-of-use plan where energy was more expensive during peak hours and cheaper in lower-demand hours. Thus, London Hydro had to anticipate the potential change in client behaviour and predict the effects of the pricing shift.Using data it had gathered on individual household energy consumption, London Hydro hoped that by forecasting which consumers might shift to the new tiered pricing plan it could gain key insights that would help the firm understand what effects the plan might have on its revenues and on its clients’ consumption behaviours.
As a result of recent success and rapid growth, the Skyrose Marketing Agency team was becoming overwhelmed with significant variation in workload levels. The vice-president, who was responsible for managing the company’s clients from the beverage industry, wanted to smooth the team’s workload level to improve morale. She also wanted to remain attentive to her clients’ needs, which could increase as peak holiday seasons approached. The vice-president was considering using Google Trends to predict how popular certain beverage products would be in the future so that her beverage company clients could predict future sales volumes. She hoped that by forecasting proxy sales for three specific clients she could gain key insight to help her smooth the volume levels of her team’s workload, without having a negative impact on her client relationships.