August 1, 2017 by
Data Visualisation has never been easier, all thanks to Tableau. Give a read to my comprehensive blog on the same and explore the horizons you have never before :

July 6, 2017 by
Original post: Line graph is simple, neat and one of the most popular charts that I use in my work. Today, I want to create a line graph to compare daily revenue with the same day last year. The challenge is that Tableau does not have Month/Day date format. See below: I have been tried several ways. Now, I will show you the simplest way, which are only two steps. Step 1: Create a Month/Day field. This is actually not a date, but a string. Step 2: Drag Month/Day to Columns shelf, and Revenue (anything you want to compare) to Rows shelf. Graph type should be Line. Then drag YEAR(Date) to Color. It is basically done! Really simple huh?! This method also take care of 2/29/2016. You may see a gap between 2/28/2017 and 3/1/2017 because 2017 does not have 2/29. This is what we expect to see. Some people use Lookup function, which may have issue when we have different number of days between two years. For this graph in my work, we show year over year average based on rolling 7 day average, which uses a table calculation. I will show you how to do it by two steps as well. Step 1: Right click the field on Rows shelf, then select Add Table Calculation. Step 2: The Calculation Type is Moving Calculation. Summarize values using Average. Previous values should be 6 and next values should be 0 because I need rolling 7 days. Compute using Table (across) based on Month/Day because Month/Day is on Columns shelf. One more thing: by doing this, the line graph will show up until 6/26/2017 (Today is 6/21/2017) due to moving 7 days average table calculation. I would like to show data until yesterday (6/20/2017). We can use a Lookup function to control the end date. Drag Lookup Date function to Filters shelf. Select True. Then the line graph will be: Now, revenue year over year trend by rolling 7 day average is done!

July 6, 2017 by
Original Post: Bar chart is probably one of the simplest charts that every data analyst uses all the time. It is simple but very effective. I am working on a web traffic report, which needs to compare website visits by device category (desktop, mobile and tablet) over time. I think a stacked bar chart could be applied to this case. Instead of comparing total number of visits, I care more about the percentage of visits by device category. Today, I will show you how to create a stacked bar chart that adds up to 100% in Tableau. Step 1. Drag Date to column shelf. Make sure you choose Discrete Exact Date by right clicking Date on column shelf. Step 2. Drag Visits to row shelf. Graph type is Bar. Step 3. Drag Device Category to Color. Step 4. Instead of showing total visits comparison, I would like to present % of visits per device every day. Right click Sum(Visits) on Row shelf, then click Add Table Calculation. Calculation Type is Percentage of Total and Computing Using is Table (down). Step 5. Command (or Ctrl) + drag Sum(Visits) table calculation to Label mark in order to show the percentage of total visits per device on the stacked bar graph. It is done! The key to create a stacked bar chart that adds up to 100% is the usage of table calculation. Tableau provides powerful functions of table calculations and window calculations. I will show you more in my next blogs.

June 23, 2017 by
Let’s look at few insights related to social and digital media: - 22% of the world’s total population uses Facebook (Source) - Over 50 million businesses use Facebook Business Pages. (Source) - 2 million business use to Facebook for advertising (Source) - 88% of businesses with more than 100 employees use Twitter for marketing purposes (Source) - 38% organizations spend 20% more than their advertising budgets on social channels (Source) There is no doubt that social media is an essential driver for a brand’s success. However, not many brands are using analytics and data science to gain social media benefits. Even if they are using it, are not aware of key areas of analytics. In this article, I will talk about eight aspects in which brands can leverage social media analytics for their advantage. 1. 360 degrees Brand Tracking Brand tracking is the process of monitoring the presence of a brand - their activities, their competitor activities, consumer trends, customer behaviors etc.  across the entire social landscape. This cross-platform and in-depth monitoring of a brand provides invaluable insights which give them a competitive advantage as well as keeps them up-to-date with their audience needs. For example, using brand tracking, detailed data-driven insights can be used by the brands for an added advantage such as: What is the right platform, right time and right method to reach out to specific customers, How good is their promotional and offers strategy as compared to multiple competitors in same space. 2. Target Audience/Customer/Followers Analysis The audience is an integral part of a brand’s success. Audience analysis includes a deeper analysis of a brand’s followers, fans, and customers in different verticals such as audience engagement, audience sentiment, and audience influence etc. Audience analysis requires different sources of data such as social profiles, timelines, survey records, transactional records and demographics data. Audience analysis unearths insights related to segmentation, demographic, mindshare, sentiment, top questions, top needs, top queries and themes associated with the customer. 3. Content Analysis Content analysis of social media posts (such as tweets, posts, emails, blogs etc.) of a brand could provide myriad of recommendations and facts that will ensure higher engagement and traffic. For example, by using natural language processing and machine learning on historical, present and competitor data, one could suggest right keywords and hashtags, optimal length and post type (pic, video, link), best times to post, so that a brand would garner higher and effective response. 4. Influencers Identification - Global and Brand Specific Influencer analysis includes identification and ranking of celebrities and personalities across different dimensions such as - engagement rate, overall digital presence, followers acquisition and engagement rates. Influencers are important entities that share and endorses the brands and their products, analytics helps to segregate right influencers for right brands with associated insights. 5. Promoted Post-Detection Using classification models and sophisticated feature engineering techniques on the data of competitor social media posts, it can be predicted that which of the tweets or posts are promoted by a brand. Promoted post detection can help in demystifying competitor’s promotional, boosting and monetary strategies. By understanding these strategies, a brand looks closely into their own approaches and optimize the practices they follow. 6. Impression Burnout Optimization Advertisements are the key part of brand’s marketing strategies online, However, every posted advertisement is not completely effective, people start losing interest in an Ad with time. The likelihood of people clicking an ad decreases with time - this is called impression burnout. Analytics can help to optimize the features and content of an ad so that the burnout time of an ad is higher. Data Science can predict what is the true burnout period for an Ad, so that brand can either monetize the good ads and discard the bad ads. 7. Engagement Prediction ML models can be trained to predict what is the likely engagement of their posts, if a post is expected to gain higher engagements, a brand can promote it more, or vice versa - if a post is performing expected to perform too bad, they can either alter its content or completely discard it with a new post. 8. Cross Platform Correlation Analytics and Data Science can be used to identify which of the cross-platform variables are correlated with each other. For example, a restaurant in the US saw higher positive reviews on when they started promoting personalized offers on Twitter. Another example is the increase in email opening rates of a brand when they initiated a social media sales across Facebook, Twitter, and Instagram. With the massive boom in social media interactions, brands have to identify right patterns using analytics and data science so that they can adopt a proactive and intelligent approach towards meaningful, significant social media success. Feel free to share your thoughts in the comments section and share this blog. :)