Socializing Insights with end users: Analytics for masses – Amazon vs. LinkedIn

This blog post is about comparison of amazon.com and linkedin.com in terms of similarities across dimensions of analytic maturity & use of data shared by their customers. As Thomas Davenport mentions in his book “Competing with analytics”, amazon.com is one of the few companies which was built on the foundation of data, the so called “Analytically mature” company. LinkedIn has joined the list, with lot of new features available to their users.

              As customers interact with the site, they generate data about their liking towards certain products or feature. Companies like amazon.com and LinkedIn clearly understand how to leverage this information to make the interaction between the customer and the site even more valuable & relevant. Users who are ready to share more data with site about their likes/dislikes, the better would be the site’s recommendation for the user.  The companies need to instil this confidence in the customers mind, and hence have the users share data by will.

               Amazon.com & LinkedIn makes available every little fact about the consumer’s behaviour and interaction with other users or products to help change their behaviour in terms of decision they make to buy or not-buy a product or whether to look for an employer change, etc.

LinkedIn has amazing insights about the companies, profiles which is all available to the users freely. In a interview with linkedIn CEO, Reid Hoffman by Andreas weigend, Reid talks about every individual as a small business and every individual thinks of their reputation in terms of number of new connections, who viewed their profiles, how many times their profile came up in the search results and stats of similar kind.  Andreas Weigend, a social data expert talks behavior change brought about by features like ‘who viewed your profile in the last 15 days’ in end users and in the way companies like LinkedIn treats the users.

(i)                  Insights about companies( lets say we are researching the company mu-sigma):

• Employee switching patterns between companies. Employees moved from ‘xyz’ to mu-sigma.
• Employee switching patterns between companies: Employees  moved from mu-sigma to “abc”.
• Gender distribution: M to F ratio at mu-sigma.
• By years of experience, how does mu-sigma differ from other companies. Similar statistics is available by job function, educational qualification & university. Similar company benchmark is available for comparison.
• People who looked at “mu-sigma” also viewed – other list of companies?
• Where employees of mu-sigma call home
• Most recommended at mu-sigma.
• Time trend of employees who got a change in title.

(ii)                Insights about profiles/users

• Who viewed my profile in the last 15 days?
• How many times did your profile show up in search results?
• Recommendation about other profiles/users you  might know.
• Companies which user might be interested in following.
• Relevant jobs for every user with functionality to apply for it.
• Work recommendations by colleagues and customers.

LinkedIn

Here’s a look at what amazon.com offers. When purchasing a product at amazon.com, the user would be presented with stats related to
• How many users who searched for the book “The outliers by Malcolm Gladwell” (say) ended up purchasing it or ended up purchasing “The tipping Point” , “The Blink” , “What the dog saw”, etc.. in the same order. However I feel the need to quantify the same would help. I mean calling out that 80% of people who searched for “A” ended up purchasing “B”. Or 80% of people who searched for “A” ended up purchasing “A”.
• “Frequently brought together items” for a given product.
• Review statistics: How many rated 5-star, 4-star and so on, as a bar chart.

We are moving towards an era of socializing data with end users to make every little decision they possibly make is data driven. WordPress, Netflix, glassdoor, etc are some of the other companies geared towards this trend. The intention of collecting data has truly gone beyond marketing purpose.

“Integrate, Analyze, Visualize & Socialize” – Visualization Tools & Techniques

Turning raw data into insights often involves integrating data from multiple disparate sources (not just limited structured one), analyzing the data, visualizing it and socializing the results/insights to a broader audience to whom the results are of interest. In this cycle of turning data into insights, Visualization plays a vital role and hence would be the topic of my discussion in this blog post . Visualization could aid in analyzing huge data by identifying patterns which are easily interpretable visually as compared to tabular layout of numbers.Second, Visualization could help  represent the numbers using visuals which are easy for everyone to read and understand. One could easily convey the insights of the analysis by visuals, grasped in a minute or two, which might have possibly took 3-4 mins using textual aid/table of numbers.This is a important factor to consider especially when are you delivering the findings to the CEO/CFO/CXO/CIO of a company, as often they have limited time.

London Cholera Outbreak visualized

Going back to history of visualization. The most famous, early example mapping epidemiological data was Dr. John Snow’s map of deaths from a cholera outbreak in London, 1854, in relation to the locations of public water pumps. The original (high-res PDF copies from UCLA), spawned many imitators including this simplified version by Gilbert in 1958. Tufte (1983, p. 24) says,”Snow observed that cholera occurred almost entirely among those who lived near (and drank from) the Broad Street water pump. He had the handle of the contaminated pump removed, ending the neighborhood epidemic which had taken more than 500 lives.”
The following pointers should help anyone analyze data and socialize finding by effective newer visualizations techniques:

1. Fusion Charts – involves basic chart types, all it needs is a data file, configuration file and can link the chart to the data file, flash based  & supports interactive charts, web supported.
2. Fusion Maps – contains maps of all counties and major cities world wide, interactive, flash based, involves data file and configuration file, web supported.
3. Fusion Widgets – involves coolest visualization techniques like angular gauge, spark line/column,gant chart, pyramid, cylindrical & thermometric gauge & bulb gauge. Some of these charts have power to do real time streaming generally used in stock market analysis.
4. Power Charts – contains some of the rare chart types like node chart, heat map, waterfall chart, multilevel pie chart, candlestick chart,etc. again flash based and hence web supported.
4. R – Revolution Computing – a powerful open source data mining/stat language which can generate stacked multi-combinatorial charts using a single line of command.
5. Google Visualization – javascript based, web supported, involves some of the coolest viz techniques like motion chart which can display data in 5 dimensions, geomap, word cloud, money pile, 3D chart, QR code, etc.
6. Google Charts – contains all basic chart types, from google.
7. Custom Flex Charts – Using customer written flex code and action script code.
8. Microsoft Excel – famous for its quick and ease of chart creation , latest version now has spark line chart support.
9. Tableau – Data Exploration- would recommend this tool for rapid fire analytics involving various dimension, it is just as easy as drag and drop to change views of the metrics by dimension hierarchy.
10. BI Report Tools – BOXI, Cognos – commercial BI tools with support for creation of various report type based on charts and tabular layouts.

Industry Trends involve real time streaming of charts – used in supply chain analytics, interactive charts, mobile supported charts, Creating alerts in charts(for example alert biz. users sending an email, as the sales of any product goes below $x on three consecutive days and so on..), video & audio supported charts.

Market Basket Analysis/Association Rule Mining using R package – arules

In my previous post, i had discussed about Association rule mining in some detail.  Here i have shown the implementation of the concept using open source tool R using the package arules. Market Basket Analysis is a specific application of Association rule mining, where retail transaction baskets are analysed to find the products which are likely to be purchased together. The analysis output forms the input for  recomendation engines/marketing strategies.

Beyond BI & Analytics

For the last 6 months, i have been closely following trends in information management. Below are few of my observations.

• Data source explosion: Business Problems are gaining complexity day by day, hence there is a huge demand for analyzing data from multitude of sources to help companies frame strategies for growth.  GPS data accumulated by Telecom companies offer insights into customers current location and provide context aware recomendations. Infact, some of the telecom companies have introduced location based pricing. Sensor data helps identify security threats to secure networks. Social network data has opened up as a channel for marketing services/product. Analysis of such closely knit data leads to behavioral & Contextual targeting. Traditional data analysis tools/algorithms fail to perform efficiently because such data are of huge sizes and needs newer datastructures for efficient analysis.
• Databases going beyond relational is gaining popularity. NoSQL dbs and Graph/Tree/XML based databases.
• Open Source tools continue to emerge.(R, RapidMiner, Weka)
• Growing need for massive dataset analysis.
• Artificial Intelligence(AI) and NLP gaining popularity among data analysts( in additional to ML techniques)
• Multimedia Analytics: Need for gathering critical metrics like customer footfalls, quantifying customers satisfaction by using facial expressions. All these applications demand high end signal processing( both Image & Video). There is a lot of scope for innovation in this area.
• Privacy preserving techniques for data analysis. This in turn encourages companies to outsource some of the critical data analysis to third parties.
• Agile Methodologies for Analytics Project to cope up with rapidly changing customer/business needs.
• Bio-Inspiration/Bio-Imitation: To learn from nature/natural processes and develop analogous techniques which could potentially solve a real-world problem. Some classic examples are development of Neural network inspired by working of a human brain, solving path optimization problem from Ant colonies, 280 degree view of honey bee(vision) etc.
• More and more data are made publicly available.
• Real Time data integration, insight generation and business decision.
• Complex visualization techniques through new technology like Adobe Flex , MS Silverlight,etc which are known for generating RIA.(Rich Internet Applications)

And I am sure these are just few items in the list and really not exhaustive. Feel free to share your comments.

Visualization Techniques

Visualization is considered to be one of the valuable tools in data mining. Visual analysis helps to understand data with minimum effort. Using graphs/ 3D plots to visualize data is more effective way of understanding massive sets of data. Ofcourse, we have other ways of exploring data like for example using statistical functions namely mean which depicts the average value, standard deviation / variance which depicts the spread of data and correlation which depicts the relationship between attributes. Some of the classical visualization techniques include

(i) Histograms (ii) Scatter plots (iii) Pie charts

              A frequency histogram displays the distribution of values for attributes by dividing the possible values into bins and showing the number of objects/records falling in each bin. Scatter plot is a great way to visualize paired numeric attributes. For example, you have two attributes height and weight. Scatter plot can visually represent the correlation of height wrt weight. It may indicate facts like “As the height increases, weight also increases” or “As the height increases, weight decreases”. Data mining techniques use scatter plots to identify redundant attributes which can be dropped from analysis.
             Newer visualization techniques are evolving with growing business needs and need to minimize efforts for decision making. Scientists use visual analysis to explore previously unknown patterns in their research/simulation data. Hence visualization has gained wide acceptance across all spheres of life. Some visualization techniques which I really admire about are the geo-spatial visualization and word clouds.

• Geo Spatial Data: Consider the average energy consumption per person data for various regions of the world. In this scenario, it is more meaningful to visualize the energy data against the geography to get some quick facts about data. The figure below shows the visualization. The bigger the circles and the darker the shade, indicates that energy consumption is high. On contrary, lighter shade with smaller circles indicate relatively low energy consumption.
  

  geo spatial Data Analysis : Energy consumption data

 

 

• 
   

 

 

 

 

• Word Cloud Analysis: I am sure, you must have seen this visualization when you visited any site like torrentz, pdf-geni, rapidsearch,etc. A word cloud indicates the frequency of word usage as a function of font/ color of the text. Bigger the font and darker the shade, the more frequent it appears in a given data set. The figure below shows the word cloud analysis of Lincoln’s speech. As seen from the visual, Lincoln more frequently used words like people, government, constitution, etc as these are relatively bigger in font. The figure on the right shows the word cloud analysis for this blog. There are some online tools to get the word cloud constructed for you blog/document – http://tagcrowd.com/

Word Cloud Visualization

Word cloud analysis of this blog

There is a classical example where visualization technique helped identify the reason for cholera outbreak in London. On visualizing the chorela affected houses, it was evident that those people nearer to a pond had developed cholera and those away from this pond had lesser probability. On inspection of the water samples collected from the pond, it showed contamination. Hence using visualization a major problem was solved.
                      In a recent article published in leading magazine, i had learnt that expert systems(AI) are visually repesenting the probable reason for health problem by highlighting the affected organ(s) in 3 dimensions with ability to drill down to the microscopic levels( based on medical test data). All these facts, prove visualization to be effective tools.
                   Please add your suggestions(if any) in the comments section. If you find this blog interesting, please subcribe to this blog.