How data is helping the Fashion industry

In an industry like fashion, where things go out of style just as quickly as they came in, data is extremely important. With the fashion industry being as unpredictable as it is, data can really help to show the trends in fashion as well as what customers like and dislike. With the internet and social media being used so frequently, fashion companies now are posting their exclusive lines on Facebook, Instagram, Twitter, Pinterest, before anyone else sees it. Then they use sentiment analytics from likes, shares, comments, re-tweets collected to understand the customer demands. As a fashion designer and a clothing company, it is difficult to be successful if you aren’t aware of the latest trends.

Julia Fowler, a fashion designer, struggled a lot with the lack of knowledge about the latest trends and she decided to do something to change this. She left her designer days behind and became co-founder of EDITD which helps fashion designers predict the latest fashion trends using big data. EDITD collects fashion trends and sales information from various different web platforms, social media, retail websites, designer runway reports, fashion blogs and then makes it available in real-time. The well known fashion brand Asos, increased their sales by 33% after they started using the data analysis services provided by EDITD. Julia Fowler said that “a product on sale/ discounted is the result of wrong decisions” but now that companies know the trends and their customer’s demands, sales willing increase and less will end up going on sale. The success of Asos is a great example as to how helpful this tool is to the fashion industry.      

http://fortune.com/2014/09/22/fashion-industry-big-data-analytics/

Data and Facebook

As we all know, Google has collected enough data about us to have detailed information about our lives. What other website do we all use just as much as Google…. Yep, you guessed right, Facebook. The Social Media Marketing Industry report in 2015 stated that Facebook was the number 1 social platforms for marketers. Facebook has a lot of detailed information about their users thanks to data. Facebook knows our lives, our likes and dislikes, our friends, where we are, what we are doing, what we look like and so on. Now, when you put a picture up on Facebook and go to tag someone Facebook already has them as a suggestion to tag, thanks facial recognition. Have you ever been looking through Facebook and then decide you want to do some shopping? You go open a new tab and begin shopping your favorite sites, but wait, I don’t really have $100 to spend on those beautiful pointed toe heels I came across. So you make the adult decision to exit out of the tab and return to your Facebook browsing. Well now those beautiful heels are haunting me on Facebook thanks to their tracking cookies they have installed to track my web activity while on a different tab. Facebook can apparently also accurately predict my satisfaction with life, intelligence, religion, sexual orientation, emotional stability, drug use and alcohol use, gender, age, relationship status, race, political views, and probably the sex of my future unborn baby. They do this through simply analyzing the things that you ‘like’ on the site. Facebook is like that crazy stalker girlfriend that just won’t let you forget anything in your life. Yes, I liked an article about the struggles of being single, thanks for reminding me everyday since that I am in fact single, appreciate the constant reminder of that Facebook. It really is crazy how sites like Google and Facebook can know when you are cheating on your spouse months or even years before your spouse ever finds out. However, like I have discussed in one of my previous blog posts, they must be smart about how they use all this data otherwise it is just plain creepy.

https://www.simplilearn.com/how-facebook-is-using-big-data-article

Monetization of Analytics Data

Data monetization is the process of unlocking the financial value behind data. With all of us turning to the internet everyday, companies have access to a ton of data that can really help them to make a lot more money than they did before. Monetization isn’t just a way of making money, it also allows companies to save money and even cut costs to make spending more effective. When companies collect data about customers, they can sell it or charge a fee for others to access it. In order to monetize from your company’s data, it is important to use the information gathered from the data correctly. The following are four ways on how a company can internally monetize their company’s data.

The first way to monetize data is targeted offers; this may be pretty obvious however for those of you who are new to data this is important to know. The overall purpose of data is to make it easier for companies to make smarter marketing decisions based off of the data they have collected. The best way to monetize your company’s data is to simply divide your audience into sections and target them based on their interests, demographics, browsing behavior, buying patterns and so on.  

The second way to monetize data is by having advanced analytics. As I am sure you can imagine, companies collect an overwhelming amount of data everyday which means that traditional methods no longer work as well as they once did. In order to get the most out of the data, it is important to have advanced analytics to really optimize the information. When it comes to data, speed and volume go hand in hand. The more amount of data that comes in, the faster it will need to be analyzed. Therefore, the more advanced the analytics are, the more you can utilize the data that is collected.

The third way to monetize data is by retargeting. When a customer comes to your company’s website, it is because they are already somewhat interested in what you do or sell. However, research has shown that before customers buy anything or become loyal to one specific brand, they have been influenced by the brand via multiple different touch points. Retargeting can help companies reach new customers.

The last way to monetize data is by simply getting closer. A big mistake that companies make is when they first set up their company’s website, they install google analytics or some other type of tracking software and then they never really look back. The data that is collected by these softwares are important and should be used. Not only can they help companies to expand to a bigger audience, they also help companies get to know their existing customers on a more in depth and personal way. Getting new customers to look at your site is easy, but getting them to stay is not as easy. The data that is collected gives companies an insight to who their customers are and what they want, and in order to monetize data companies need to keep their already existing customers.

There are many ways to monetize data externally however I think that it is more important to look at the ways to do it internally. It’s crazy to think about how much work it takes for a company to be able to use the data that is collected. The first steps are easy, create a website, have some type of tracking software attached to it and you are good to go, right? Wrong! There is so much more that has to happen beyond that. Although optimizing all the data collected is not easy, it is definitely worth it for companies to do because when it is analyzed and used properly, it can bring in a lot of money.

http://www.informationbuilders.com/blog/terri-white/20053

https://datafloq.com/read/4-ways-how-monetize-big-data-your-company/2020

Data and Privacy

Data and Privacy

It is no surprise that as data analytics continues to develop, companies will learn more and more information about consumers, including private information about them. Companies need to be really careful about using the data they collect appropriately, otherwise it can really be creepy. Every time I get into my car now at work, my phone comes up with an alert telling me how long it will take me to get home. It started doing this after my first week on the job, and honestly, it kind of creeps me out. I know very well that google has a lot of information on me, but I like to not think about it.

Every time you go shopping you allow companies to gather intimate details about your consumption patterns. Companies can use this information to their advantage but need to be really careful in doing so. The company that learned this lesson the hard way was Target. The marketing analytics team at Target correctly guessed the pregnancy of a teenage girl in Minnesota, based on a formula involving the rates of buying cotton balls, unscented location, and mineral supplements. From this information, Target started sending her coupons that were geared toward baby products. Which in turn is how her father ended up finding out about her pregnancy. Her father went to Target demanding to speak to a manager, saying “my daughter got this in the mail! She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

Although Target ended up being correct with the information they collected about this girl being pregnant, this is a lesson all companies can learn from. Just because they were correct that time doesn’t mean that each person who buys those products is pregnant. Data analytics has gotten to the point where companies now have enough data to figure out personal information about people based on patterns. How scary is it that, in this world of data, Target knew about a teenage girl’s pregnancy before her own parents, who live with her. Start shopping with cash my friends if you are buying things you are embarrassed about, or trying to hide.

https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#10dbdc626668

http://www.slate.com/blogs/how_not_to_be_wrong/2014/06/09/big_data_what_s_even_creepier_than_target_guessing_that_you_re_pregnant.html

Four Types of Data Analytics

Four Types of Data Analytics

1) Descriptive

Descriptive analysis, also known as data mining, is describing what happened. It looks at past events for insight on how to approach things in the future and to uncover patterns over time. Descriptive analysis is known as being the easiest type because it allows you to change big data into bite-sized pieces to make it easier to understand. Descriptive models analyze a variety of different relationships between products or customers.

2) Diagnostic

Diagnostic analysis looks at past patterns to discover or determine why something happens. Once a business figures out the why behind something, they can use that information to their advantage and make the changes necessary to be more successful. This type of analytics gives decision makers at organizations real actionable and difference-making insight.

3) Predictive

Predictive analysis looks at past data patterns and comes up with a list of different outcomes for situations. This is very helpful for setting realistic and reachable goals as well as planning and girding your expectations. Properly tuned predictive analysis can be used to help support sales, marketing, and other types of complex future events.

4) Prescriptive

Prescriptive analysis provides the actions that should be taken and what steps are recommended. Prescriptive analysis is like predictive analysis, expect it goes beyond just predicting, it gives the likely outcomes for several different scenarios.  

When combining these four different types of data analytics, the data turns from pointless numbers to important information that can tell a story. It can describe what happened, why it happened, what could happen, and what should happen. All four of these pieces are important for businesses to know in order to grow.

http://www.ingrammicroadvisor.com/data-center/four-types-of-big-data-analytics-and-examples-of-their-use

http://www.rosebt.com/blog/descriptive-diagnostic-predictive-prescriptive-analytics

http://www.smartshifttech.com/big-data-and-analytics-which-type-analytics-does-your-business-need

http://www.insightsquared.com/2014/11/4-types-of-analytics-you-cant-survive-without/

The Importance of Psychology in Data Visualization and Reporting

What is Data Visualization?

In simple terms, data visualization is presenting analytical data in a graphical or pictorial format to make difficult information easy to read, as well as make it easy to identify new patterns. Translating data into visual has become cheap and easy. Data visualization tools display detailed pie charts, fever charts, bar charts, geographic maps, heat maps, sparkline, dials and gauges, and infographic. The two biggest benefits to visualizing data is one, reducing the time to insight and two, improving the accuracy of insights. Psychology plays a huge role in data visualization because of the way you perceive things.

Psychological Schemas

Kendra Cherry defined schema as “a cognitive framework or concept that helps organize and interpret information. Schemas can be useful because they allow us to take shortcuts in interpreting the vast amount of information that is available in our environment.” People do not like to have to use cognitive thinking and try and find shortcuts to anything they can in life. This is important for business to know so that they can take advantages of psychological schemas to improve data visualization.

Shapes, Icons and Symbols

The first schema that can be used to improve data visualization is the use of shapes, icons and symbols. A very basic example would be adding a “+” or “-” sign before a number on a dashboard. This eliminates the words and makes it faster and easier to read. Another way to easier show positive or negative would be to use up and down arrows. Icons are also useful to use instead of words because it is more visually appealing. Icons also help to reduce and even eliminate any language barriers that could get in the way of misinterpreting data. For example, if I am talking about the most popular fruits I would present icons for bananas, apples and oranges instead of writing out the words. No matter what language you speak the icons for these fruits are all the same and therefore there is no way to misinterpret data.  

Colors

The second schema that can be used to improve data visualization is the use of colors. We all probably associate the color green with good or go and the color red for stop or bad. No one really knows where this schema came from but if i had to take a guess I would say it is originally because of things like traffic lights and red reminds people of blood which in most contexts is associated with something bad. Knowing the associations that your audience already formed, can be helpful to decrease the time of insight and increase the accuracy of the insight. You also must be aware to not mess with people’s schemas, meaning that if you are giving visuals of vegetables, don’t make broccoli purple and lettuce pink.

Spatial Context

Maps provide spatial context that helps us when analysing data. For example, anyone who has ever been to a concert or any type of sporting event has seen a map like the image shown below. Someone can use the schema that they have developed throughout their years of going to concerts or sporting events or even just common sense, that the seats on the lower level are more expensive the the seats on the upper deck. The first time I saw a map like this one was when I was buying concert tickets and it was very easy to run the cursor over the different seat colors to figure out the price instead of looking at an excel spreadsheet that would have taken more time to read and probably would not have made sense without the visual.  

If these three schemas are used properly, they can definitely help to get the most out of data visualization and to maximize the biggest benefits which is to decrease the time of insight and increase the accuracy of insight. Understanding the psychology behind data visualization can help to give you a step up in better knowing your target audience.   

 

https://www.journalism.co.uk/news/the-psychology-of-data-visualisation-beyond-the-numbers/s2/a557258/

https://www.sas.com/en_us/insights/big-data/data-visualization.html#

https://www.tableau.com/about/blog/2017/1/3-ways-psychological-schemas-can-improve-your-data-visualization-64600

https://www.verywell.com/what-is-a-schema-2795873

http://blog.jinfonet.com/psychology-of-data-visualization-techniques-and-reporting/

Future of digital analytics

My generation is known as the Millennials, which are described as the generation that has an increased use and familiarity with communications, media and digital technologies. Everything revolves around the internet, and therefore it has become more and more important for companies to keep up with their online sites.

Digital analytics is a fairly new industry that has come a long way in a short period of time. It amazes me how much technology is always changing and as I learn more and more about digital analytics, I find myself wondering what it will be like in the future. However, before we can figure out the future of digital analytics, we must first understand the past.

Digital Analytics was originally known as “Web Analytics”, but in 2012 when companies started tracking all things digital (i.e. websites, Facebook, Twitter, loyalty cards, SEO, display ads, mobile applications etc) they decided to change the name. Web Analytics was developed in the beginning of the 2000s, when the Internet started to take off. Marketers were able to finally see if people saw a message thanks to what is known as “log files.” This is when marketers really started using web analytic tools, and there were many to choose from. At this point in the analytics world, the main two companies in digital analytics are Google Analytics and Adobe Analytics. Digital analytics is now really starting it get big, and over the next few years it has been predicted that it will become a necessity to everyone in an organisation.

In the next few years there are some things that will happen to shape the future of analytics. In the next couple of months, it is expected that there will be small business analytic tools that are easy to use and don’t need the help of experts to set up. Analytics is extremely important for small business because exploring with different types of marketing tactics takes a lot of time and money and isn’t realistic for small businesses to be able to do.  

The Internet of Things (IoT) is the concept of connecting any device to the internet with an on or off switch. It is predicted to have the biggest opportunities but also one of the most difficult. With more and more devices being connected to the internet, it opens up the opportunities for new analytics. If this is utilized properly, it could help the overall user experiences and products.

Throughout recent years, wearable technology has become extremely popular with items such as Fitbits and Apple watches, however these new items bring about new challenges to marketers and analytics. Wearables are slowly becoming the future of technology which means that marketers need to find out the best way to analyse human behaviour through all these different wearable items.  

The amount of data that is collected from all these different kinds of digital devices is overwhelming and companies can’t keep up with all the data coming. The vice president of analytics at MassMedia, Chris Meares wrote an article about his opinions about the future of digital analytics that I thought was pretty interesting. He said he believes that “No longer will there be a need for a digital analytics tool, such as Adobe or Google Analytics. Instead, companies will collect the data on these digital devices and ship it directly to a data warehouse that is maintained and stored by the organization.” When I read this I thought it was a crazy thought but as I continued to learn more and more about digital analytics and how much is collected everyday from digital technologies, I think that it is a great idea. I agree that 10 years from now once digital analytics has been perfected, the amount of data will be too overwhelming for a company to handle.

Although a lot is predicted to happen in the next couple of years within digital analytics, many agree that 10-15 years from now it will be a lot more advance than it is right now. Technology is constantly changing and we are always coming up with new ideas and ways of doing things. Take phones for example, when the cell phone was introduced in 1973, who would have thought that today we would have touch screen phones that are voice activated that searches the internet and texts for you? Technology is constantly evolving.

http://www.digitalrepublicrecruitment.com/4-things-that-will-help-to-shape-the-world-of-digital-analytics-going-into-the-future/

https://www.linkedin.com/pulse/future-digital-analytics-chris-meares

https://www.clicktale.com/academy/blog/the-future-of-digital-analytics-by-vassilis-tourtouropoulos/

http://www.smartdatacollective.com/amandamork/67096/future-digital-analytics-big-data-and-marketing

https://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/#5b25aae61d09

Disadvantages of Analytics

In my last post I discussed the advantages of analytics, but like anything else there are also disadvantages. The amount of data that Google Analytics collects can be overwhelming, especially if you are someone like me who is not exactly tech savvy. Technology has come a long way and now companies have a variety of tools in order to find out exactly who the target audience is which in turn helps companies to better market themselves.

One of the biggest disadvantages of analytics is that it is time consuming. If you own a business and do not know how to read all the data from the analytics you may not have enough time to spend analyzing the data, and then have time to fix the website according to the data.

With that being said another disadvantage is that it can be expensive. As a result of not wanting to waste your own time or based on lack of knowledge, you will most likely have to hire a team that is well educated in analytics. Hiring a staff is expensive and small companies especially may fear that it is not worth it.

  Another big disadvantage to analytics is that it only factors in quantitative factors and ignores other factors such as qualitative, emotional, intuitive, and social. When people make decisions these are important factors to keep in mind however, with analytics they are overlooked.

Advantages of analytics for business

Companies in the past would have to buy very expensive tools if they wanted to do analytics on their company websites. What Google has done is create a tool called Google Analytics, which has made all of these tools free and easy for companies to collect visitor data and use it to increase traffic to their website. Like anything, there are advantages and disadvantages to Google Analytics. This post will cover the advantages of analytics.

The first advantage of analytics is it collects data about the traffic to your company website and information about the visitors. Analytics is able to tell where the visitor of the website came from, how they found the site, and how long they are on the website for. It can also give information about how many visitors go beyond the home page of the site, which is important information. Companies can utilize this information by seeing how a majority of their visitors came across the website and then investing more money so that there is more visitor traffic to the site.

Analytics also make it easy for companies to measure the amount of views there are on each page of their site. Not only can it measure how many people visit the site each day, it can also tell which pages are most popular. This data is extremely important to companies so that they can adjust their company website based on the interests of their audience. 

Another advantage to analytics is that companies can use the data collected to increase advertising. Many websites depend on advertising to make an income and with the help of analytics they can increase the amount they can earn by bringing the data to potential advertisers.