Difference between revisions of "Analysis - Final Analysis"
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<div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 16px"><font color="#cb181d">What are the distinguishing factor(s) between good, average, and bad restaurants?</font></div> | <div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 16px"><font color="#cb181d">What are the distinguishing factor(s) between good, average, and bad restaurants?</font></div> | ||
| − | + | [[Image:Ca.png|600px|thumb|center|Competitor Analysis 1]] | |
<div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 16px"><font color="#cb181d">Are there observable trends, or changes over time for restaurants and user preferences?</font></div> | <div style="background: #fee5d9; padding: 5px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #cb181d solid 32px; font-size: 16px"><font color="#cb181d">Are there observable trends, or changes over time for restaurants and user preferences?</font></div> | ||
Revision as of 12:04, 20 November 2015
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The geospatial component of the dashboard shows the distribution of bad (below 2.5 stars), average (between 2.5stars and 3.5stars) and good (above 3.5 stars) restaurants indicated by red circle, yellow circle and green circle respectively. This would allow the new business owner to identify the potential areas that are good for setting up new restaurants. For example, if the business owner is interested to open a bar restaurant in the AZ state, he or she should set up the restaurant in areas with low distribution of good restaurants as indicated by the geospatial component of the dashboard. For existing business owners, they can use the geospatial component of the dashboard to see the current distribution of the bad, average and good restaurant to gauge their current standing and competitiveness in the area.
The composition of the positive reviews for the Beach Pizza restaurant has decreased from 100% in 2010 to 60% in 2014 as indicated by the green smiley face. This implies that there has been an increase in the level of customer dissatisfaction with regards to the Beach Pizza restaurant in recent years. Hence, the restaurant owner has to examine and rectify these poor performing areas in order to attract more customers which would in turn lead to an increase in the revenue. Likewise, this time series analysis of the composition of the text reviews can be applied to all the other restaurants. In addition, the graph would allow the restaurant owner to compare the composition of the text reviews with its competitors. This would help to explain the difference in the amount of revenue between two similar restaurants located in the same vicinity.
