Panasonic is in electronics manufacturing business. Panasonic's division than makes LCD TV$\text{'}$s is under

pressure from the CEO to increase sales. The Chief Marketing Officer and the Chief Operations Officer of

the company are strictly against changing the price of the TV$\text{'}$s as they believe that on average Panasonic

sells its TV$\text{'}$s cheaper than its competitors which include Samsung, Sony, Toshiba, Vizio, LG and Philips.

After brain storming with his team, the TV division head Hiro Ito is determined that the only way

forward is to improve the product quality. However, given the economic downturn the funds available

for investing in R&D are quite limited$.$ And the cost of improvement of any feature is exponentially

increasing in the extent to which the feature is improved. Further, there are differences of opinion

among his team regarding which aspect of their product they should improve.

After intensive discussion amongst themselves, they feel that they should try to gauge how they

consumers thought of them in comparison to their competitors. They approached the new UGC sub$-$

division within the marketing division. The UGC division was set up to capture what their customers

were saying about them on the web. While this division was collecting a lot of data generated regarding

Panasonic by its customers, UGC division head Yuji Hukiri was struggling with a way to show the

business value of his division. This opportunity arrived at the right time for him and he was quite happy

to help Hiro's team in the endeavor. They quickly decided that they would focus only on product reviews

and collect data not only themselves but also on their competitors.

Yuji$$s team quickly created an exhaustive list of all websites which provided customer generated product

reviews including Amazon, Epinions, etc. They also decided to collect review information from experts

$($e$.$g$.,$ consumer report, etc.$)$ regarding their and competitor's products. Using text mining and sentiment

analysis tools they identified six features which the consumers talked the most about in their reviews:

Video quality, Sound quality, Appearance, Number of Features, Ease of set up$,$ and Life span. Yuji's team

converted the text data regarding features into a numeric data on a scale of $0-5,$ where $0$ meant lowest

satisfaction and $5$ meant highest satisfaction for consumers. For example, if Panasonic got a rating of $4$

and LG got a rating of $3$ on video quality it would mean that on average customers are more satisfied

regarding video quality of Panasonic than LG$.$ Hiro's team was excited at getting this data but now they

faced the issue of how to analyze this data. One of the team members suggests to focus on that feature

of the product where they lag their competitors according to their consumers. She argues that this is

how they have done in the past. However, Hiro is not fully convinced with this approach. He believes the

data the UGC division provided is much richer and he may be able to get information on not only how

they are rated in comparison to their competitors on various features but also on what features the

consumers value the most. Hiro's team has approached you to analyze the data collected by the UGC

division and help them in devising the appropriate strategy forward.

They want you to figure out where they stand in each feature $($each user rating category$)$ relative to

their competitors, and if investing in the feature where users rate them lower than their competitors is

the best strategy. If not, what would you recommend? They want to get a ranking for features in term of

where they should consider investing first. Assume that the cost of improving the feature is same across

all features. For example, the cost of improving the video quality so that the customer rating for video