Fashion Industry and Big Data

In this post and the next two posts, I am going to write about big data and how fashion industry leverages it.

In social media era, people opinions become very powerful and effective on many sectors such as the fashion sector. A lot of fashion brands pay attentions to people's comments, likes, tweets and other interactions and turn them into engaging sessions which actually give them strong support and inspiration of its audience. Through active social media, growing of content and people's constant engagement which gives the players in the fashion industry the opportunity to know about their customers and then create new ideas and fashion concepts by using Big Data.

In the following image, there are 14 fashion trends chosen by audience which describes how people can express their opinions about what they like or dislike about latest fashion trends, and how they engage with them.

Fashion Industry and Big Data ( Part 2)

There are huge sets of data that paly major role in the fashion industry; big data helps fashion industry to reveal patterns, associations and trends.

Whether data is structured or unstructured, it can be analyzed, divided into groups or categories and then data form a definition about the latest patterns and trends in the fashion sector.

Sources of Big Data

With digitization, modern fashion styles and trends have taken into popular social media platforms, in the digital sectors. Nowadays, people prefer to go online to talk about their favorite cloths and fashion styles. There is a statistic says that over 70% of the world being online, thus the digital mediums such as Twitter, Facebook, Instagram, Pinterest, and others are filled with likes, comments, tweets, pins, Instagram, LinkedIn shares. When it comes to fashion, almost every day in brand new day in this field, every day more number of people shares their ideas, thoughts and theory and concepts about fashion. in addition, trends are constant, it keeps changing and people in social media keep taking about what they like or dislike about those trends. Lately, designers, brands and retailers are growing in the fashion industry, and they tend to use online platforms, especially social media. The fashion industry is expanded through social media and it can be tapping into consumers, across the globe. people who are working or engaging in the fashion industry can easily add a lot of productivity in developing creative fashion ideas for the world. Designers, retailers, and fashion brand need to collect feedback from their audience such as, comments that express their opinions, likes, shares and other interactions. After they get such data, they spend time to understand it, analyze it and then take insights to fashion ideas forward, it all goes to big data that make it possible.

How fashion business leverages social media comments

People put in their opinion and give their preference after almost every fashion show that stream in YouTube, Facebook or any other social media platform. It is really helpful for the fashion industry to know their audience, and their likings, and show them a way-forward. Also, it is beneficial from the perspective, designers can sell their products, as desired by their targets. fashion magazines tend to use online platforms in order to hear what their audience have to say. By collecting information from Big Data, and analyze it ; designers, the fashion brand heads, and magazines elicit their next fashion trends.

For more on this click here

Fashion Industry and Big Data ( Part 3)

According to a team of researchers, “Big data may be the next new thing to hit the fashion industry's runways” University Park, Pennsylvania. Heng Xu, associate professor of information sciences and technology, Penn State claims that researchers were able to identify a network of influence among major designers and track how those style trends moved through the industry; by analyzing relevant words and phrases from fashion reviews. Heng Xu, associate professor of information sciences and technology, Penn State claims that researchers were able to identify a network of influence among major designers and track how those style trends moved through the industry; by analyzing relevant words and phrases from fashion reviews.

The availability of large amount of data that facilitate finding patterns, creating correlations and identifying trends that are emergent is becoming very popular, and it applied to various sectors and industries such as , politics and health care, it is known as data analytics.

"what we wanted to see is if data analytics could be used in the fashion industry," said Xu "We were drawn to the question of whether or not we could really trace a hidden network of influence in fashion design." December 18, 2014 The researchers presented their findings at the Workshop of Information Technology and Systems in Auckland, New Zealand, analyzed 6,629 runway reviews of 816 designers from Style.com, formerly the online site for Vogue, one of the most influential fashion magazines. The reviews covered 30 fashion seasons from 2000 to 2014

According to Xu, "the researchers team extracted keywords and phrases from these reviews that described silhouettes, colors, fabrics and other details from each designer's collections and added them to the dataset"

click here to read the full article

Google Analytics

Since we are studying Marketing Analytics course which is primarily based on analyzing data by using Google Analytics, I decided to look deeper into Google Analytics and its benefits, features and user satisfaction.

Google Analytics Overview:
It is one of the most popular web analytics' in the world which uses by thousands of companies all over the world. It has been imposing standards of business intelligence, uniting a variety of premium analytical features both for traditional and mobile users, since it was launched in 2005. Google Analytics concentrated on functionality and quality. it works with various number of funnel visualization techniques, and it summarizes data on high quality dashboards where users can read different types of reports. Google Analytics works with tracking codes, which load larger JavaScript files from the web server, and set variables for each of them. This is the main feature that makes Google analytics unique. The code is automatically loaded and collects relevant data from the browser, once the users starts browsing their website. Cookies will be sent by the activation of the code to users’ devices in order to gather anonymous Client ID information, also examining the actions that is performing on the website by users.

Google Analytics becomes available for mobile usage; moreover, it offers a specially developed Mobile Package with mobile site-ready tracking codes, including such that work with PHP, or JavaScript Pages.

There are so many benefits of Google Analytics, one of the most important benefits is how this platform helps users to understand their visitors and find out the reasons of visiting their website and most significantly is to determine why your visitors did or did not convert. Google Analytics gives the users the opportunity to base decisions on empirical data before starting on analyzing the real business to avoid throwing money away. Google Analytics makes this possible in different ways, with its four Advanced Reporting areas.

-        Google Analytics users will get to know who are their audience

-        How did those visitors come to their website?

-        What did they do while exploring the website?

-        Finally, figuring out if the visitors converted or not

Once the data classified on the dashboard, users can start using that data for example to optimize their marketing campaign, devote time to activities that help their business. Users can also leverage the dashboard information to analyze the content and understand which elements on your website are not performing so well. Google analytics offers reports for conversions and specific behavior, which explain the reasons of why the website is doing better or worse than before; that help to prevent spending money to examine these matters later.

Some Google Analytics’ features:

-          Advertising Reports

-          Campaign Measurement

-          Cost Data Import

-          Mobile Ads Measurement

-          Advanced Segments

-          Content Experiments

-          Dashboards

-          Custom Reports

-          Real-Time Reporting

-          Audience Data & Reporting

-          Flow Visualization

-          Social Reports

-          Filters

-          Multi-Channel Funnels

-          Event Tracking

-          In-Page Analytics

-          Site’s Speed Analysis

How much dose google analytics cost?

Google Analytics offers for small business life-time free packages. they can monitor a single mobile app or website, adding a tracking code and research their audience. "In terms of enterprise pricing, the company offers a suite of advanced applications, including Analytics 360, Tag Manager 360, Optimize 360 (beta), Attribution 360, Audience Center 360 (beta), and Data Studio 360 (beta), which can be purchased together or separately, and are priced on quote basis. Make a request to the company to obtain your price."

Dose Google Analytics users satisfied?

It is important to know if the buyers either companies or people satisfied of with the product or not, and not only depends on how experts evaluate it in their reviews. Google Analytics created behavior-based Customer Satisfaction Algorithm which gathers customer reviews, comments and Google Analytics reviews across a wide range of social media sites.

After the information has been gathered, it shows the negative and positives people experiences with Google Analytics. thus, it facilitates the purchase decision.

For more on this click here

Analytics with Crazy Egg

Crazy Egg is one of the analytics tools that collects visitors’ information such as their clicks on a web page and represents data on a visual manner. In Crazy Egg, there is something called “snapshot storage” which is the first step after signing up. It is like a moment in time for a given page. After a Snapshot has been created, there is a code that should be entered in the footer of a web page and Crazy Egg then starts tracking. Crazy egg is not a free tool and there is a plane starting with $9 / month.

Some of Crazy Egg Reports:

Heat Map

It is the most popular data reports of Crazy Egg. Heat map is created based on the actual clicks of visitors, it shows how they engaged with a website. The areas that are glowing are the popular click areas.

Scroll Map:

It represents the amount of time that visitors spend in viewing particular sections of a website, which helps to re-prioritize sections on the site based on the more popular page section.

Confetti:

It is similar to what the heat map represents, but much deeper. According to visitors clicks, it shows the most popular sections’ clicks. In every single click, it holds various information, categorized by browser, used devices, country, etc. Clicks or dots with the same category have the same color.

For more information about Crazy egg click here

Google Analytics vs. Crazy Egg

Google analytics works with numbers of techniques of funnel visualization. It summarizes data into high-level dashboards which gives users the ability to creates different types of reports.

Crazy Egg focuses on visualizing data reports. It creates visual representations of the information that show the popularity of every sections and visitors clicks on a website.
cost:
Google Analytics offers free package for small business. It allows them to add tracking code, monitor a website or mobile add and reach their audience. For the enterprises, Google analytics offers a set of advanced applications which should request from the company to obtain the price.
Crazy Egg offers four plans based on monthly payment, basic plan $9, standard plan $19, plus plan $49, pro plan $99, also it offers annually subscription.
Features:
There are more than 30 features available of google analytics such as, dashboards, real-time reporting, advertising reports, campaign measurement, cost data import, mobile ads measurement, remarketing, search engine optimization and advanced segments.
Crazy Egg features are scroll map tool, heat map tool, confetti tool, simple setup, design testing, In-page analytics, conversion rate optimization, CRM.
In terms of available languages, Google analytics available in USA, UK, Canada, International, China, Germany, India and Japan while Crazy eggs only in USA, UK, Canada, International.

click here to read the full comparison.

Predictive Analysis – McDonald’s

In the previous post, I wrote about JMP statistical software and how McDonald’s leverages it. In this article, I am going to write about McDonald’s and its predictive analysis.

According to Cramer, JMP software allows his team to get better understanding of data through visual representation and use data visualization to predict customer behavior and trends successfully.

Senior Industry Advisor of Retail, Hospitality and Consumer Goods Industry Strategy in EMEA at Intel, David Dobson said: “Today's retailers all understand the importance of analytics but the biggest challenge is having the correct tools to collect information and having a skilled workforce who understand data and extract value from it”

McDonald’s uses analytics and it relies on its data to measure restaurant performance, and make decisions related to equipment, location, human resources, and the supply chain.

McDonald’s uses various technologies and techniques in order to obtain successful predictions such as:

-        Video Analytics, helps to track time that customers spent in the store on drive-through for use in process and conditions measurement

-        Quantitative video ethnography, in order to learn the behavior of people using its drive-through lanes

-        Eye Tracking, by tracking customers’ behavior in store and try to answer some questions for instance, what are their interactions with the order-takers? After they place orders, what are they doing?

For more on this, click here

Delicious Discoveries with JMP Software - McDonald's

McDonald’s and how they use JMP software :

McDonald’s Corp. the well-known restaurants chain which almost everyone in the world have tasted their food.

It founded in 1955 by Ray Kroc when he opened his first McDonald's restaurant in Des Plaines, Illinois, United States; $326.12 was the first day sales. After three years from opening, McDonald’s sold its 100 millionth burger. In 1959, McDonald's opened its 100th restaurant in Fond du Lac, Wisconsin, United States.

McDonald’s todays have over 30,000 restaurants in more than 100 countries spreading in all around

the world. McDonald’s has expanded their menu to reflect more health-conscious tastes, and in 1975,

the dive through introduced in Sierra Vista, Arizona, United States which all contributed in

increasing its share of sales consistently in the US.

McDonald’s challenge is to rise to the evolving demands of a global market. One of the solutions is JMP software that is used by McDonald's operations team. JMP works by gathering historical and current data which help to predict future trends also to present results to the clients all over the world in efficient and effective way. Mike Cramer who is the Director of McDonald’s operations, his job is to predict and monitor trends, identify and examine any opportunities in operations. Also, he gives advises to store owners and others within McDonald's family on enhance customer service constantly. the director Cramer and his operation's team are considering conditions of current operations and the future predictions in doing their responsibilities such as designing and developing McDonald's restaurants around the world. Operation department of McDonald's is responsible for everything that the customers experiences starting from entering the parking lot until they leave “It’s the equipment, information systems, job designs for the crews, the man-machine interface.”

Trial test before the development:

   

This trellis view created with Graph Builder summarizes performance data of a dozen time intervals split by two grouping factors, weekday-weekend and store number

 Cramer says. “It’s everything associated with that experience for both customers and employees.” JMP has been used for about four years by Cramer, he impressed with JMP capabilities of making strong visualization, Cramer says “I had been struggling with communicating statistically relevant topics to audiences that had limited exposure to statistically oriented problems,” and he says. “I thought JMP might bridge the gap. I was pleasantly surprised with how well it was structured and how easy it was to use.”

operations research :

There are three categories of operations research

1-     Predictive modeling

2-     Rapid validation

3-     Predictive analytics

Cramer says, “We do as much analysis and mining of that data as possible.” by Using JMP and its ability of visualization, Cramer's operations team is increasingly collaborating with, for example, growth strategists from one of McDonald’s major markets.

To sum up, by using JMP software, efficiencies have been built by McDonald’s Innovation team into its operations all over the world, by predicting changes in local market conditions for each geographic region.

To read the full article click here

How McDonald’s Leverages Big Data

McDonald's is a huge food service retailers which spread in almost every country in the world. their daily customer traffic is over 60 million customers, and about 75 burgers are sold every second. McDonald’s is a massive company with $ 27 billion annual revenue and has over 700.000 employees. One billion pounds of beef is consumed by only Americans at McDonald's in a year. McDonald's is obviously generating huge amount of data, lets discover together how they leverage that data?

McDonald's in the past years became an organization of information- centric that making data-driven decisions. McDonald's created a development model project were analytics forms a major aspect of the teams, but it is not the central part.

McDonald’s established teams from different disciplines, their job is discovering, developing and placing new solutions across the organization. In terms of discovery, the team's job is trying to rapidly find ideas and develop them. they have a few skill sets involved, For instance, operations, IT, analytics and engineering. In order to get the right decisions and develop and new projects, McDonald's add extra skills in development phase, such as HR, Finance and training. There are a lot of different departments have involved in development phase like, design or marketing department. According to the Director of Operations Research, Mike Cramer "advises a cross-functional approach with a business focus to achieve a great level of success, especially in the big data and analytics area"

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In the past, McDonald's had a problem which is the local stores was provided the data to the executive leaders depending on average metrics. Thus, it made it difficult to compare the stores and come up with appropriate actions that needed to take place in order to improve results.

In order to provide a lot more visions of what was happening and at which store, McDonald’s has moved to use trend analytics instead of using averages.

To better understand the cause and affect, they combined datasets and visualized it in the differences between stores. e.g. multiple graphs have combined to understand the correlation. These correlations were used to create more clear, relevant and actionable actions, resulting in saving money and time across the organization.

How McDonald's uses big data to optimize drive-thru experience is one of the interesting examples of combined metrics. There are three different factors have taken into account when they analysis and optimizing that experience. Design of the drive-thru, Information that is provided to the customer during the drive-thru and the people waiting in line to order at a drive-thru. When a single customer is waiting too long to get a coffee in the line because of the large family in a van ordering a large menu in front of him; he probably has a negative experience. Hence, McDonald's analyses the demand patterns in order to predict it.

To read the full article click here 

New Revenue Streaming

Moving from Analytics to Data Monetization - New Revenue Streaming

How do companies increase their revenue by leveraging data analytics?

what are the perfect ways to do that?

what are their competitors doing around that?

lately, companies are focusing on leveraging of the volume of current transactions which done which done by mining data and asset that are valuable and underleverage in order to create new revenue sources rather than looking simply to increase the volume of transactions. According to  Ravi Kalakota, analytics outcomes increasingly become one of the main revenue sources in healthcare industry. An example of (ACOs) Accoubtable care organizations which are improving the health status, in efficient way and has experience of care for a defined population by connecting group of providers who take responsibility for that. This all done by large investments in data and monetizing capability.

One of the other common use of data monetization is cost savings from better inspection scheduling and preventive maintenance. This causes in massive savings because they are not using costly and experienced sources in responding to emergency repair calls.That happened in the case of a large ATM manufacturer “by monitoring various assets in the ATM (cash dispensers, printers, cameras etc.) via log analysis they were able to substantially reduce maintenance downtimes.”

Click here to read more interesting information about data monetization in this article.

Monetizing Data Analytics

In previous post I wrote a brief introduction of data monetization. In this post I will add some details 

about data analytics monetization.  

Companies and retailers are working together with wireless carriers in order to gain insights into geo-location data, customers movements at shopping stores are getting tracked and companies are using that data to make marketing campaigns programs that are loyalty designed on relevant and frequency.

Nowadays, data is becoming easily available and gathered in digital world than ever before. Companies or retailers have the ability to track customers’ transactions and interactions across  different channels and devices and invest in their data in new and innovative ways.

According to Chris Twogood, usually, monetizing data is indirect action. starts by making the operation run in efficient way, '' incentivizing certain types of behavior, or revealing the true value of an asset.'' Each company has their own way to monetize their data. and he identified five essential ways that can move companies to monetize their data:

1.      Start with Questions: companies usually start by looking to the data. They need first asking their staff about questions that give the right level of detail in the right time which give the most impact performance. by asking these questions would help the companies evaluate whether they have enough data or they need more data.

2.      Look for Patterns: Professor Russell Walker of the Kellogg School of Management at Northwestern University identifies trends of big data that indicate to patterns for monetizing data. At the Teradata Partners conference, Walker examined how the velocity of data, new forms of precision, and opportunities for fusing different data sets can lead to data monetization.

3.      Search for External Data: According to Chris Twogood, large organizations should dedicate one team member to searching for valuable external data.

4.      Sharpen the skills of analytics: by using traditional methods, big data becomes impossible to analyze data probationary, companies must comprehend the nature of machine learning and advanced analytics. monetizing data will become easy to apply by understanding of machine learning and advanced analytics.

5.      Understand the identity of data monetization

6.      In order to find ways for data monetization, organization should understand the role they play. an expert consumer of data, an aggregator, or the creator of a new data product. Each organization has their own goals and objective which lead them to find the right way to monetize data.

" the two researchers from Gartner, Doug Laney an Olive Hung define monetizing data as actions as direct like traded or data sold or indirect like data that becomes the foundations for new product or services offerings "

Examples of companies that is successfully built data monetization opportunities.

- Wal-Mart by Retail Link trading portal

- Alibaba by targeted personal finance offerings

Barbara H. Wixom, principal research scientist at the MIT Center for Information Systems Research mentions in her research "Cashing in on your Data," 2014 three methods of data monetization which are selling, bartering or wrapping.

retailers have been selling data for years with data of loyal customers or point-of-sale which is new income flows to the company.

retailers have been selling data for years with data of loyal customers or point-of-sale which is new income flows to the company.

According to Wixom, when bartering, reports or benchmark metrics are services for data exchanged.

To sum up, according to Wixom's report "companies that include data offerings with their products at no added charge are wrapping products in data. The desired outcomes of wrapping include increased market share, wallet share, switching costs and prices."

Data Monetization - Introduction

Monetizing Data Analytics

Monetizing data is the process that turning data into money which can also indicate to data used as a product or services enhancement or bartering device. Other definition of data monetizing is generating revenue by using available data sources or data that streaming in real time by discover, storage, capture, analysis and use of that data.

Companies realized that having huge amount of data becomes very valuable assets. They have been looking to ways to increase that value of data. There are some conditions for monetizing data: having huge amount of structured and unstructured; decreasing costs of storage; create relevant customer experiences by using marketing campaigns that depends on data and finally applying data analytics in order to improve business intelligence and processes.

Companies need to understand their data to know the value behind it. Data is stored in different forms such as texts or posts in social media. whenever data is easily accessible and in a scalable format, companies can easily get advantage of it. Also, companies need to ensure that their data is structured in order to extract marketable and relevant perceptions.