How To Use Predictive Data Like Wawa and McDonalds

Two Case Studies In Predictive Analytics


For large companies, introducing a new product or marketing strategy always carries some uncertainty.

Will existing customers like the changes? Will they draw in new clients? Will they create enough revenue lift to be worth the costs of implementation? How accurately can the actual effect on income be measured?

Today’s predictive analytics can reduce much of this uncertainty, enabling firms to make better business decisions. Wawa and McDonalds are two of the latest companies to leverage big data predictive analytics, with the help of specialized companies.

When Wawa introduced a new flatbread sandwich, at first, it seemed like a roaring success. Customers were buying a lot of the sandwiches, but on closer analysis, its popularity was cannibalizing sales away from other, more profitable products. APT’s assessment identified the adverse effect on revenue, and the flatbread sandwich was pulled.

APT also assisted McDonald's with researching the decision to serve their breakfast menu all day. Their testing software tracked trials of the all-day breakfast service and concluded that it both drew in new customers, and led to existing customers spending more per visit. The decision to introduce an all-day breakfast service drove sales growth and helped to boost McDonald’s stock price.

Here are four tips to help your company reap similar successes by harnessing the power of predictive analytics.

1. Invest in Professional Data Scientists and New Technologies

Whether firms employ internal analysts or consult an outside firm, it’s well worth the investment to study business data using the latest analytics tools like the Hadoop software framework. In fact, there’s evidence that data-driven decision making might just be too rewarding to dismiss.

A Harvard Business Review study interviewed 330 North American companies, measuring both their use of data-driven decision-making and their performance metrics as reported by independent sources. Results showed the companies who were rated in the top 33% for using data-driven decision making of their industry had 5% higher productivity and 6% higher profitability than their competitors.

2. Use the RACI Framework to Make Agile and Inclusive Business Decisions

With the rapid growth of predictive data analytics in popularity and importance, firms must adapt their decision-making processes to take full advantage of the insights they gather. The challenge of today’s business world is that customers and the market demand more agility and rapid decisions, while at the same time, the use of more and more data means firms must include more stakeholder groups in the decision.

The RACI framework is a useful tool for identifying and tracking all the required interested parties and their roles in the decision-making process. RACI stands for Responsible, Accountable, Consulted and Informed. Each stakeholder is designated in one of these categories, allowing project managers and leadership to identify critical individuals for a quick response when needed. Different groups can also opt in to participate in the RACI, which creates an inclusive process.

3. Don’t Rely Solely On HiPPOs

Many companies choose to develop a unique decision-making process that relies on the experience and instincts of senior leaders and outside consultants. This joint source of decisions is referred to as a 'HiPPO' or 'Highest Paid Person’s Opinion.' Given the demonstrated advantages for businesses that rely more on data analytics, it’s advisable for firms to de-emphasize the HiPPO and focus on data analysis. Although expertise in a given industry is still highly valuable, it’s much more efficient when paired with valid data. When data contradicts a leader’s instincts, they should be willing to defer to the conclusions it offers.

4. Set the Tone for a Data-Driven Company Culture

Becoming a data-driven organization calls for changes, including moving away from HiPPO-based decisions. Old habits can be hard to break, and many companies fall prey to the mistake of using data to justify decisions after the fact rather than using data to make decisions.

Leaders must commit to the data-driven approach and create organizational buy-in for new initiatives, from investing in new technologies and roles to navigating legal and privacy requirements around data use.

The trend toward leveraging predictive analytics is here to stay, and it’s been proven to deliver results. Industry expertise is still critical, but leaders who can’t blend this knowledge with data to make business decisions will eventually be left behind by their competitors who can.


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