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Walker Advertising: Los Defensores and 1-800-THE–LAW2
Albert, Michael; Yemen, Gerry; Yeaton, Saul Case QA-0912 / Published October 11, 2019 / 8 pages.
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Product Overview

While organizations have been collecting data for years, not all businesses have the talent, culture, and systems in place in order to take advantage of their data. This case uses Walker Advertising, a company that partnered with law firms to advertise their services, to discuss broader themes around the role of data science in a traditional business. Predictive modeling is a crucial function of business to gain insights around allocating and utilizing resources. The material in this case allows for an exploration of building a digital team, leveraging data collection, and optimizing business processes through data science tools to better inform decision-making. It includes data for students to work through. The case begins in 2019 when Walker Advertising plans to grow a national presence, a shift that meant developing an attorney base outside of California. To do this, the company would need to shift from a subscription-based revenue model toward a pay-per-lead model. An important piece of the growth effort was building a digital team. Management wanted to forecast how much the business needed to spend to drive a certain number of leads. While Walker Advertising had been collecting data since its inception, there was not a process in place to leverage those data. Yet the jump to data analytics and machine learning in a firm with a strong brand and purposeful mission was not simple. CFO Quentin Kluthe understood the importance of gaining acceptance for a major change in how the business ran. It was crucial to provide clarity and transparency so others in the organization would embrace the shift from the old approach to new methods. How could a business that traditionally had not been data focused make a pivot to use data science in decision-making?


Learning Objectives

Examine the shift from a non-data-driven to a data-driven organization; explore predictive modeling in a growing organization; understand benefits and challenges of investments in machine learning; build comfort and familiarity with predictive modeling for time series; understand the attribution problem in multi-channel advertising; practice data analysis on a real company's data set.

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  • Overview

    While organizations have been collecting data for years, not all businesses have the talent, culture, and systems in place in order to take advantage of their data. This case uses Walker Advertising, a company that partnered with law firms to advertise their services, to discuss broader themes around the role of data science in a traditional business. Predictive modeling is a crucial function of business to gain insights around allocating and utilizing resources. The material in this case allows for an exploration of building a digital team, leveraging data collection, and optimizing business processes through data science tools to better inform decision-making. It includes data for students to work through. The case begins in 2019 when Walker Advertising plans to grow a national presence, a shift that meant developing an attorney base outside of California. To do this, the company would need to shift from a subscription-based revenue model toward a pay-per-lead model. An important piece of the growth effort was building a digital team. Management wanted to forecast how much the business needed to spend to drive a certain number of leads. While Walker Advertising had been collecting data since its inception, there was not a process in place to leverage those data. Yet the jump to data analytics and machine learning in a firm with a strong brand and purposeful mission was not simple. CFO Quentin Kluthe understood the importance of gaining acceptance for a major change in how the business ran. It was crucial to provide clarity and transparency so others in the organization would embrace the shift from the old approach to new methods. How could a business that traditionally had not been data focused make a pivot to use data science in decision-making?

  • Learning Objectives

    Learning Objectives

    Examine the shift from a non-data-driven to a data-driven organization; explore predictive modeling in a growing organization; understand benefits and challenges of investments in machine learning; build comfort and familiarity with predictive modeling for time series; understand the attribution problem in multi-channel advertising; practice data analysis on a real company's data set.