Reference no: EM131980051

Assignment

Great Clips Employs Spatial Analytics to Shave Time in Location Decisions

Great Clips, the world's largest and fastest growing salon, has more than 3,000 salons throughout United States and Canada. Great Clips' franchise success depends on a growth strategy that is driven by rapidly opening new stores in the right locations and markets. The company needed to analyze the locations based on the requirements for a potential customer base, demographic trends, and sales impact on existing franchises in the target location. Choosing a good site is of utmost importance. The current processes took a long time to analyze a single site and a great deal of labor requiring intensive analyst resources was needed to manually assess the data from multiple data sources.

With thousands of locations analyzed each year, the delay was risking the loss of prime sites to competitors and was proving expensive: Great Clips employed external contractors to cope with the delay. Great Clips created a site-selection workflow application to evaluate the new salon site locations by using the geospatial analytical capabilities of Alteryx. A new site location was evaluated by its drive-time proximity and convenience for serving all the existing customers of the Great Clips Salon network in the area. The Alteryx-based solution also enabled evaluation of each new location based on demographics and consumer behavior data, aligning with existing Great Clip's customer profiles and the potential revenue impact of the new site on the existing sites. As a result of using location-based analytic techniques, Great Clips was able to reduce the time to assess new locations by nearly 95 percent. The labor-intensive analysis was automated and developed into a data collection analysis, mapping, and reporting application that could be easily used by the nontechnical real estate managers. Furthermore, it enabled the company to implement proactive predictive analytics for a new franchise location because the whole process now took just a few minutes.

Questions for Discussion

1. How is geospatial analytics employed at Great Clips?

2. What criteria should a company consider in evaluating sites for future locations?

3. Can you think of other applications where such geospatial data might be useful?

In addition to the retail transaction analysis applications highlighted here, there are many other applications of combining geographic information with other data being generated by an organization. The opening vignette described a use of such location information in understanding location-based energy usage as well as outage. Similarly, network operations and communication companies often generate massive amounts of data every day. The ability to analyze the data quickly with a high level of location-specific granularity can better identify the customer churn and help in formulating strategies specific to locations for increasing operational efficiency, quality of service, and revenue.

Geospatial analysis can enable communication companies to capture daily transactions from a network to identify the geographic areas experiencing a large number of failed connection attempts of voice, data, text, or Internet. Analytics can help determine the exact causes based on location and drill down to an individual customer to provide better customer service. You can see this in action by completing the following multimedia exercise.

(Sharda 596-597)

Sharda, Ramesh, Dursun Delen, Efraim Turban. Business Intelligence and Analytics: Systems for Decision Support, 10th Edition. Pearson Learning Solutions, 12/2013. VitalBook file.

The citation provided is a guideline. Check each citation for accuracy before use.

Part 2:

Read Application Case 14.3, "A Life Coach in Your Pocket," on pages 601 and 602 of your textbook, and answer the following questions:

1. Search online for other applications of consumer-oriented analytical applications.

2. How can location-based analytics help individual consumers?

3. How can smartphone data be used to predict medical conditions?

4. How is ParkPGH different from a "parking space-reporting" app?

Your response to each of the seven questions must be a minimum of 200 words; therefore, the completed assignment must contain a minimum of 1,400 words. All sources used, including the textbook, must be referenced; paraphrased andquoted material must have accompanying citations and be cited per APA guidelines.

Application Case 14.3

A Life Coach in Your Pocket

Most people today are finding ways to stay active and healthy. Although everyone knows it's best to follow a healthy lifestyle, people often lack the motivation needed to keep them on track. 100Plus, a start-up company, has developed a personalized, mobile prediction platform called Outside that keeps users active. The application is based on the quantified self-approach, which makes use of technology to self-track the data on a person's habits, analyze it, and make personalized recommendations.

100 Plus posited that people are most likely to succeed in changing their lifestyles when they are given small, micro goals that are easier to achieve. They built Outside as a personalized product that engages people in these activities and enables them to understand the long-term impacts of short-term activities.

After the user enters basic data such as gender, age, weight, height, and the location where he or she lives, a behavior profile is built and compared with data from Practice Fusion and CDC records. A life score is calculated using predictive analytics. This score gives the estimated life expectancy of the user. Once registered, users can begin discovering health opportunities, which are categorized as "missions" on the mobile interface. These missions are specific to the places based on the user's location. Users can track activities, complete them, and get a score that is credited back to a life score. Outside also enables its users to create diverse, personalized suggestions by keeping track of photographs of them doing each activity. These can be used for suggestions to others, based on their location and preferences. A leader board allows a particular user to find how other people with similar characteristics are completing their missions and inspires the current user to resort to healthier living. In that sense it also combines social media with predictive analytics.

Today, most smartphones are equipped with accelerometers and gyroscopes to measure jerk, orientation, and sense motion. Many applications use this data to make the user's experience on the smartphone better. Data on accelerometer and gyroscope readings is publicly available and can be used to classify various activities like walking, running, lying down, and climbing. Kaggle (kaggle.com), a platform that hosts competitions and research for predictive modeling and analytics, recently hosted a competition aimed at identifying muscle motions that may be used to predict the progression of Parkinson's disease. Parkinson's disease is caused by a failure in the central nervous system, which leads to tremors, rigidity, slowness of movement, and postural instability. The objective of the competition is to best identify markers that can lead to predicting the progression of the disease. This particular application of advanced technology and analytics is an example of how these two can come together to generate extremely useful and relevant information.

Source: Institute of Medicine of the National Academies, "Health Data Initiative Forum III: The Health Datapalooza,".

Analytics-based applications are emerging not just for fun and health, but also to enhance one's productivity. For example, Cloze is an app that manages in-boxes from multiple e-mail accounts in one place. It integrates social networks with e-mail contacts to learn which contacts are important and assigns a score-a higher score for important contacts. E-mails with a higher score are shown first, thus filtering less important and irrelevant e-mails out of the way. Cloze stores the context of each conversation to save time when catching up with a pending conversation. Contacts are organized into groups based on how frequently they interact, helping users keep in touch with people with whom they may be losing contact. Users are able to set a Cloze score for people they want to get in touch with and work on improving that score. Cloze marks up the score whenever an attempt at connecting is made.

On opening an e-mail, Cloze provides several options, such as now, today, tomorrow, and next week, which automatically reminds the user to initiate contact at the scheduled time. This serves as a reminder for getting back to e-mails at a later point without just forgetting about them or marking them as "unread," which often leads to a cluttered in-box.

As is evident from these examples of consumer-centric apps, predictive analytics is beginning to enable development of software that is directly used by a consumer. The Wall Street Journal estimates that the app industry has already become a $25 billion industry with more growth expected. We believe that the growth of consumer-oriented analytic applications will grow and create many entrepreneurial opportunities for the readers of this book.

One key concern in employing these technologies is the loss of privacy. If someone can track the movement of a cell phone, the privacy of that customer is a big issue. Some of the app developers claim that they only need to gather aggregate flow information, not individually identifiable information. But many stories appear in the media that highlight violations of this general principle. Both users and developers of such apps have to be very aware of the deleterious effect of giving out private information as well as collecting such information. We discuss this issue a little bit further in Section 14.8

(Sharda 601-602)

Sharda, Ramesh, Dursun Delen, Efraim Turban. Business Intelligence and Analytics: Systems for Decision Support, 10th Edition. Pearson Learning Solutions, 12/2013. VitalBook file.

The citation provided is a guideline.