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Business Analytics��

All photos taken prior to COVID-19

Sudipta Dasmohapatra

sd1285@georgetown.edu

Session 1

July 12, 2022

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Learning Objectives

Develop a broad understanding of business analytics concept
Examine applications of business analytics and data science to draw customer and business insights
Describe how business analytics can be used across various business functions within the organization

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Poll

Have you worked in the data and analytics space?
How would you characterize your experience with data and analytics?

Heard about it
<1 year of Experience
1-2 years of Experience
3-5 years of Experience
5-10 years of Experience
10+ years of Experience
Other�

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Business Analytics Implementation: Microsoft Workplace

Source: forbes.com

Source: Microsoft.com

The Workplace Analytics effort translated into a combined 100 hours saved per week across all relocated staff members, and an estimated savings of $520,000 per year in employee time (HBR article 2016)

Both Business analytics and data science applications are far and wide. In recent times, a large number of businesses have begun realising the potential of business analytics. So let us have a look at some successful examples.

At technology giant Microsoft, collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by cutting down on the number of employees per building and by reducing the distance that staff needed to travel for meetings. In an article for the Harvard Business Review, the company’s analytics team shared the outcomes they observed as a result of the relocation. Through analysis of metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members, and an estimated savings of $520,000 per year in employee time.

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Business Analytics Implementation: Forecasting Orders and Recipes at Blue Apron

Source: blog.blueapron.io

Multinomial Recipe Model

Identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings = Improved user experience

Yet another example is a success story of Blue Apron. For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents its subscribers with a fixed menu of meals available for purchase and employ predictive analytics to forecast demand, with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, and these typically fall into three categories: customer-related features, recipe-related features, and seasonality features. By employing predictive analytics to better understand customers, Blue Apron has been able to improve its user experience through identifying how subscriber tastes change over time, and recognizing how shifting preferences are impacted by recipe offerings.

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Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s recipe preferences from the past, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine and forecast when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between customer, recipe related features and seasonality variables—Blue Apron’s engineering team has been able to measure the precision of its forecasting models for future orders including which meals are likely to be ordered and when order rates will be higher or lower.

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Business Analytics: Amazon Fresh and Whole Foods Campaign

Source: wholefoods.com

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Business Analytics: Fraud and Anomaly Detection

Feedzai, a fintech company, claims that a fine-tuned machine learning solution can detect up to 95 percent of all fraud and minimize the cost of manual reconciliations, which accounts now for 25 percent of fraud expenditures
Capgemini claims that fraud detection systems using machine learning and analytics minimize fraud investigation time by 70 percent and improve detection accuracy by 90 percent

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State of the Analytics in Organizations

TOP Benefits Companies get from using Analytics (2021/2022)

Company Culture and Data Quality are the most challenging aspect of analytics implementation (Dresner’s Business Intelligence 2021, Microstrategy 2022)

Source: 2021/22 Market Share Analysis and Data, Financesonline (Microstrategy)

Data Quality Issues: Is it trustworthy, relevant and actionable?

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State of the Analytics in Organizations

Organizations spent $215 billion in 2021 on analytics - 10% increase over 2020 (IDC Analysts 2022)

By 2023, data literacy will be key to driving business value (Gartner 2021)

69% of business leaders plan to use data in 2022 to shape the customer experience

(Unsupervised 2022)

96% of those investing in Big Data and AI are seeing positive results (NewVantage 2021)

95% of employers say that data science and analytics are skills that are hard to find (MicroStrategy 2020)

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