Data in the right hands can be extremely powerful and can be a key element in decision making. American statistician, W. Edwards Deming quoted that, “In God we trust. Everyone else, bring data”. We can employ statistical measures to analyze data and make informed decisions. Tableau enables us to calculate multiple types of statistical measures like residuals, correlation, regression, covariance, trend lines and many more.

Today let’s discuss how people misunderstand causation and correlation using Tableau.

Correlation and Causation

Correlation is a statistical measure that describes the magnitude and direction of a relationship between two or more variables.

Causation shows that one event is a result of the occurrence of another event, which demonstrates a causalrelationship between the two events. This is also known as cause and effect.

Types of correlation:

1. 1 → Positive correlation.
2. -1 → Negative Correlation.
3. 0 → No correlation.

Why are correlation and causation important?

The objective of analysing data is to identify the extent by which a variable relates to another variable.

Examples of Correlation and Causation

1. Vending machines and obesity in schools: people gain weight due to junk food. One important source of junk food in schools is vending machines. So if we remove vending machines from schools obesity must reduce, right? But it isn’t true. Research shows that children who move from schools without vending machines to schools with vending machines don’t gain weight. Here we can find a correlation between children who were overweight and eating junk food from vending machines. In actuality, the “causal” point (which is the removal of vending machines from schools) has a negligible effect on obesity.
2. Ice cream sales and temperature: If we observe ice cream sales and temperature in the summer, we can determine that they are causally related; i.e. there is a strong correlation between them. As temperature increases, ice cream consumption also increases. Understanding correlation and causation allows people to understand data better.

Now let’s explore correlation using Tableau. We are going to use the orders table from the superstore dataset which comes default with Tableau.

Before going further let’s understand how to calculate the correlation coefficient ‘r’.

We can easily understand the above formula by breaking it into pieces.

In Tableau, we can represent the above formula as 1/SIZE() -1 where SIZE is function in Tableau.

We can use WINDOWSUM function for doing this summation in Tableau.

x_i is the sum of profit and x-bar is the mean of profit, which is window average of sum of profit, and s_x is standard deviation of profit. That means that we need to subtract mean from sum of profit and divide that by standard deviation.

(SUM([Profit])-WINDOW_AVG(SUM([Profit]))) / WINDOW_STDEV(SUM([Profit])))

This is similar to the formula above but we only need to swap profit with sales.

(SUM([Sales])-WINDOW_AVG(SUM([Sales]))) / WINDOW_STDEV(SUM([Sales])))

Now we have to join all these formulae to get the value of the correlation coefficient of r. Be careful while using parenthesis or you may face errors. Here is our final formula to calculate r.

1/(SIZE()-1) * WINDOW_SUM(( (SUM([Profit])-WINDOW_AVG(SUM([Profit]))) / WINDOW_STDEV(SUM([Profit]))) * (SUM([Sales])-WINDOW_AVG(SUM([Sales]))) / WINDOW_STDEV(SUM([Sales])))

Let’s implement this in Tableau to see how it works. Load superstore data into Tableau before getting started.

After loading the superstore excel file into Tableau, examine the data in the orders sheet. You can see that it contains store order details complete with sales and profits. We will use this data to find correlation between profit and sales.

Let’s get our hands dirty by making a visualization. Go to sheet1 to get started. I made a plot between profit and sales per category.

Now in order to find the correlation between profit and sales, we need to use our formula to make a calculated field which serves our purpose.

Now drag and drop our calculated field onto the colors card and make sure to compute using customer name as we are using it for detailing.

Here we can see the strength of the relationship between profits and sales of data per category; the darker the color, th he stronger the correlation.

Next we’ll add trend lines to determine the direction of forecasted sales.

These trend lines help demonstrate which type of correlation (positive, negative or zero correlation) there is in our data. You can explore some more and gain additional insights if you add different variables like region.

From this analysis we can understand how two or more variables are correlated with each other. We begin to understand how each region’s sales and profits are related.

Let’s see how a correlation matrix helps us represent the relationship between multiple variables.

A correlation matrix is used to understand the dependence between multiple variables at same time. Correlation matrices are very helpful in obtaining insights between the same variables or commodities. They are very useful in market basket analysis.

Let’s see how it works in Tableau. Download the “mtcars” dataset from this link. After downloading it, connect it to Tableau and explore the dataset.

The dataset has 35 variables where each row represents one model of car and each column represents an attribute of that car.

Variables present in dataset:

Mpg = Miles/gallon.

Cyl = Number of Cylinders.

Disp = Displacement (cubic inches)

Hp = Gross Horsepower

Drat = Rear axle ratio

Wt = Weight (lb/1000)

Qsec = ¼ mile time

Vs = V/Sec

Am = Transmission (0 = automatic, 1 = manual)

Gear = Number of forward gears

Carb =Number of Carburetors

Let’s use these variables to make our visualization. I made this amazing visualization showing correlation between models by referring to Bore Beran’s blog article, in which he explained how to make this visualization which helps us understand more about using Tableau to understand correlation.

Conclusion

We must keep in mind that if we want to measure the dependence between two variables, correlation is the best way to do it. A correlation value always lies between -1 and 1. The closer the value of the correlation coefficient is to 1, the stronger their relationship. We must remember that correlation is not causation and many people misunderstand this. There are many more relations and insights that can be unlocked from this dataset. Explore more by experimenting with this dataset using Tableau. Practice to be perfect.

The post Basic Statistics in Tableau: Correlation first appeared on Perceptive Analytics.

But do you think is it necessary to go to this level to segment the offerings?

I think the success of Netflix answers this question on its own. Netflix is considered to have one of the best recommendation engines. They even hosted a competition on Kaggle and offered a prize money of USD 1 million to the team beating their recommendation algorithm. This shows the sophistication and advanced capabilities developed by the company on its platform. This recommendation tool is nothing but a segmentation exercise to map the movies and users. Sounds easy, right?

Gone are the days when marketers used to identify their target customers based on their intuition and gut feelings. With the advent of big data tools and technologies, marketers are relying more and more on analytics software to identify the right customer with minimal spend. This is where segmentation comes into play and makes our lives easier. So, let’s first understand what is segmentation? and why do we need segmentation?

Segmentation, in very simple terms, is grouping of customers in such a way that that customers falling into one segment have similar traits and attributes. The attributes could be in terms of their likings, preference, demographic features or socio-economic behavior. Segmentation is mainly talked with respect to customers, but it can refer to products as well. We will explore few examples as we move ahead in the article.

With tighter marketing budgets, increasing consumer awareness, rising competition, easy availability of alternatives and substitutes, it is imperative to use marketing budgets to prudently to target the right customers, through the right channel, at the right time and offer them the right set of products. Let’s look at an example and understand why segmentation is important for marketers.

There is an e-commerce company which is launching a new service for a specific segment of customers who shop frequently and whose ticket size is also high. For this, the company wants to see which all customers to target for the service. Let’s first look at the data at an aggregate level and then further drill down to understand in detail. There are 5 customers for whom we want to evaluate the spend. The overall scenario is as follows:

Should the e-commerce company offer the service to all the five customers?

Who is the right customer to target for this service? Or which is the right customer segment to target?

We will see the details of each of the customers and see the distribution of data.

Looking at the data above, it looks like Customer 1 and Customer 2 would be the right target customers for company’s offering. If we were to segment these 5 customers into two segments, then Customer 1 and Customer 2 would fall in one segment because they have higher total spend and higher number of purchases than the other three customers. We can use Tableau to create clusters and verify our hypothesis. Using Tableau to create customer segments, the output would look like as below.

Customer 1 and customer 2 are part of cluster 1; while customer 3, customer 4 and customer 5 are part of cluster 2. So, the ecommerce company should focus on all the customers falling into cluster 1 for its service offering.

Let’s take another example and understand the concept further.

We will try to segment the countries in the world by their inbound tourism industry (using the sample dataset available in Tableau). Creating four segments we get the following output:

There are few countries which do not fall into any of the clusters because data for those countries is not available. Looking at clusters closely, we see that the United States of America falls in the cluster 4; while India, Russia, Canada, Australia, among others fall in the cluster 2. Countries in the Africa and South America fall in the cluster 1; while the remaining countries fall in the cluster 3. Thus, it makes it easier for us to segment countries based on certain macro-economic (or other) parameters and develop a similar strategy for countries in the same cluster.

Now, let’s go a step further and understand how Tableau can help us in segmentation.

Segmentation and Clustering in Tableau

Tableau is one of the most advanced visualization and business intelligence tool available in the market today. It provides a lot of interactive and user-friendly visualizations and can handle large amounts of data. It can handle millions of rows at once and provides connection support to almost all the major databases in the market.

With the launch of Tableau 10 in 2016, the company offered a new feature of clustering. Clustering was once considered a technique to be used only by statisticians and advanced data scientists, but with this new feature in Tableau it becomes as easy as simple drag and drop. This feature can provide a big support to marketers in segmenting their customers and products, and get better insights.

Steps to Becoming a Segmentation Sniper

Large number of sales channels, increase in product options and rise in advertisement cost has made it inevitable not only for marketers but for almost all the departments to analyze customer data and understand their behavior to maintain market position. We will now take a small example and analyze the data using Tableau to understand our customer base and zero-in on the target customer segment.

There is a market research done by a publishing company which is mainly into selling of business books. They want to further expand their product offerings to philosophy books, marketing, fiction and biographies. Their objective is to use customer responses and find out which age group like which category of books the most.

For an effective segmentation exercise, one should follow the below four steps.

1. Understand the objective
2. Identify the right data sources
3. Creating segments and micro-segments
4. Reiterate and refine

We will now understand each of the steps and use Tableau, along with, to see the findings at every step.

1. Understand the objective

Understanding the objective is the most important thing that you should do before starting the segmentation exercise. Having a clear objective is the most imperative thing because it will help you channelize your efforts towards the objective and prevent you from just spending endless hours in plain slicing and dicing. In our publishing company example, the objective is to find out the target age group which the company should focus on in each of the segments, namely philosophy, marketing, fiction and biography. This will help the publishing company in targeting its marketing campaign to specific set of customers for each of the genres. Also, it will help the company in identifying the target age group that like both business and philosophy or business and marketing, or similar other groups.

1. Identify the right data sources

In this digital age, data is spread across multiple platforms. Not using the right data sources could prove to be as disastrous as not using analytics at all. Customer data residing in CRM systems, operational data in SAP systems, demographic data, macro-economic data, financial data, social media footprint – there could be endless list of data sources which could prove to be useful in achieving our objective. Identifying right variables from each of the sources and then integrating them to form a data lake forms the basis of further analysis.

In our example, dataset is not as complex as it might be in real life scenarios. We are using a market survey data gathered by a publishing company. The data captures the age of customer and their liking/disliking for different genres of books, namely philosophy, marketing, fiction, business and biography.

1. Creating segments and micro-segments

At this stage, we have our base data ready in the analyzable format. We will start analyzing data and try to form segments. Generally, you should start by exploring relationships in the data that you are already aware of. Once you establish few relationships among different variables, keep on adding different layers to make it more granular and specific.

We will start by doing some exploratory analysis and then move on to add further layers. Let’s first see the results of the market survey at an aggregate level.

From the above analysis, it looks like fiction is the most preferred genre of books among the respondents. But before making any conclusions, let’s explore a little further and move closer to our objective.

If we split the results by age group and then analyze, results will look something like the below graph.

In the above graph, we get further clarity on the genre preferences by respondents. It gives us a good idea as to which age group prefers which genre. Fiction is most preferred by people under the age of 20; while for other age groups fiction is not among the top preference. If we had only taken the average score and went ahead with that, we would have got skewed results. Philosophy is preferred by people above the age of 40; while others prefer business books.

Now moving a step ahead, for each of the genre we want to find out the target age group.

The above graph gives us the target group for each of the genres. For biography and philosophy genres, people above the age of 40 are the right customers; while for business and marketing, age group 20-30 years should be the target segment. For fiction, customers under the age of 20 are the right target group.

Reiterate and refine

 In the previous section, we created different customer segments and identified the target segment for publishing company. Now, let’s say we need to move one more step ahead and identify only those age groups and genres which have overlap with business genres. To put it the other way, if the publishing company was to target only one new genre (remember, they already have customer base for business books) and one age group, which one should it be?

Using Tableau to develop a relation amongst the different variables, our chart should look like the one below.

Starting with the biography genre, age group 30-40 years comes closest to our objective, i.e., people in this age group like both biography and business genre (Biography score – 0.22, Business score – 0.31). Since, we have to find only one genre we will further explore the relationships.

For fiction, there is no clear overall with any of the age groups. For marketing, age group 20-30 year looks to be clear winner. The scores for the groups are – marketing – 0.32, business – 0.34. The relation between philosophy and business is not as strong as it is for business and marketing.

To sum it up, if the publishing company was to launch one more genre of books then it should be marketing and target customer group should be in the range of 20-30 years.

Such analysis can be refined further depending on the data we have. We can add gender, location, educational degree, etc. to the analysis and further refine our target segment to make our marketing efforts more focused.

I think after going through the examples in the article, you can truly appreciate the level of segmentation that Netflix has done and it clearly reflects the reason behind its success.

The post Tableau for Marketing: Become a Segmentation Sniper first appeared on Perceptive Analytics.

Business heads often use KPI tracking dashboards that provide a quick overview of their company’s performance and well-being. A KPI tracking dashboard collects, groups, organizes and visualizes the company’s important metrics either in a horizontal or vertical manner. The dashboard provides a quick overview of business performance and expected growth.

The post Tableau Sales Dashboard Performance first appeared on Perceptive Analytics.

Financial Modeling to Determine Customer Tariff

The client decided to spin off its gas division and focus independently on it. The newly formed entity provides two types of services viz., gas distribution and gas filtration.A major challenge in gas distribution business is fixing the tariff rate.

Faced with this challenge, the client approached Perceptive Analytics to build a model that could fix the tariff rate to be given to its end users.

We helped client assess the financial viability of the spin-off decision. The model presented a comprehensive control of key variables affecting the tariff rate. It calculated key metrics like ROI and IRR for a given tariff rate and also vice-versa enabling the client to set a competitive per gallon price.

Check out the model we developed!

The post Financial Modeling to Determine Customer Tariff first appeared on Perceptive Analytics.

Analyzing Text: Word clouds is a great way to analyze underlying patterns in Text. Here’s an example of the word cloud we generated for the Titles of our customers. This helps us understand better who our clients are!

This is a word cloud of Customer Titles. It shows most of our customers are CEOs, Presidents, Owners, and Directors.

This word cloud clearly shows that our customers are mostly:

• Presidents
• CEOs
• Owners
• Directors
• Founders
• Managers

Word Clouds help you to analyze the words that are next most important as well. All you have to do is remove the largest words and draw the word cloud again. Check out this word cloud after I removed the above titles.

Word Cloud redrawn removing top level titles

After you remove the first level titles, it’s clear that the second level titles involve:

• Consultants
• VPs
• Sales (as in related to sales)
• Principals

Write your own thoughts and any further learning or insights you can draw from these visualizations.

The post Analyzing Text Using Word Clouds first appeared on Perceptive Analytics.