Top two key techniques to analyze data:

There are many techniques to analyze data. In this post, we’re going to talk about two techniques that are critical for good data analysis! They are called “Benchmarking” and “Segmentation” techniques – Let’s talk a bit more about them:

1. Benchmarking

It means that when you analyze your numbers, you compare it against some point of reference. This would help you quickly add context to your analysis and help you assess if the number if good or bad. This is super important! it adds meaning to you data!

Let’s look at an example. CEO wants to see Revenue numbers for 2014 and an analyst is tasked to create this report. If you were the analyst, which report would you think resonated more w/ the CEO? Left or Right?

Benchmarking data providing context in analysis

I hope the above example helped you understand the importance of providing context w/ your data.

Now, let’s briefly talk about where do you get the data for benchmark?

There are two main sources: 1) Internal & 2) External

The example that you saw above was using an Internal source as a benchmark.

An example of an external benchmark could be subscribing to Industry news/data so that you understand how your business is running compared to similar other businesses. If your business sees a huge spike in sales, you need to know if it’s just your business or if it’s an Industry wide phenomenon. For instance, in Q4 most e-commerce sites would see spike in their sales – they would be able to understand what’s driving it only if they analyze by looking at Industry data and realizing that it’s shopping season!

Now, let’s shift gears and talk about technique #2: Segmentation.

2. Segmentation

Segmentation means that you break your data into categories (a.k.a segments) for analysis. So why do want to do that? Looking at the data at aggregated level is certainly helpful and helps you figure out the direction for your analysis. The real magic & powerful insights are usually derived by analyzing the segments (or sub sets of data)

Let’s a look at an example.

Let’s say CEO of a company looks at profitability numbers. He sees $6.5M and it’s $1M greater than last years – so that’s great news, right? But does that mean everything is fine and there’s no scope of optimization? Well – that could only be found out if you segment your data. So he asks his analyst to look at the data for him. So analyst goes back and after some experimentation & interviews w/ business leaders, he find an interesting insight by segmenting data by customers & sales channel! He finds that even though the company is profitable – there is a huge opportunity to optimize profitability for customer segment #1 across all sales channel (especially channel #1 where there’s a $2M+ loss!) Here’s a visual:

segmentation data Improve profitability low margin service offerings customers

I hope that helps to show that segmentation is a very important technique in data analysis!

Conclusion:

In this post, we saw segmentation & benchmark techniques that you can apply in your daily data analysis tasks!

#sqlpass webinar: “Data Analytics Explained for Business Leaders” on 1/15

A quick blog post to let you know about a #sqlpass webinar on 1/15.

Data Analytics Explained for Business Leaders

Thu, Jan 15 2015 12:00 (UTC-05:00) Eastern Time (US & Canada)

RSVP: http://bit.ly/PASSBAVC011515


Abstract:

Description: The world is becoming more efficient. Today, seventy percent of the companies that graced the Fortune 1000 list a mere decade ago have vanished. Agility and survival are function of innovation, culture, and the ability to predict the future. To that end, data analytics offers a lifeline, a means of survival that will drive productivity and continue to disrupt and redefine business. However, the resources available to today’s business leaders sit on two vastly different ends of the spectrum. On the one hand, highly technical academic resources and on the other largely fluffy overviews of value propositions and potentials. The state of the industry shouldn’t be surprising. The same dynamics played out in early years of the internet. Software providers, technical leaders, and consulting firms greatly benefit from mystifying the world of data analytics into something that is incomprehensible. That lack of conceptual understanding is incredibly risky and propels the cost of analytics initiatives upwards. This webcast aims to bridge that gap between the technical data scientists and business leaders. Ultimately, this understanding will help to: – Connect the strategic goals of business leaders with the capabilities of technical advisers – Focus investments and initiatives within analytics and technology – Distill immensely complex subject matter into comprehensible examples – Accelerate the path to value and increase the ROI of analytics initiatives


Speaker Bio

Alex is a Predictive Analytics Architect in the Oil and Gas industry with a passion for distilling complexity into insights and evangelizing data science. His work has been featured on KDNuggets and he was recognized by DataScienceCentral as a top 180 blogger in 2014.

RSVP: http://bit.ly/PASSBAVC011515

I hope to see you there!

Example of using segmentation to identify low-margin service offerings:

Example of using segmentation to identify low-margin service offerings:

Problem:

Need advanced data analytics techniques to analyze profitability data

Solution:

Here’s an example of how customer segmentation helped identify some low margin service offerings:

Improve profitability low margin service offerings customers

SQL Server Reporting Services: How to Add Tool Tips to charts?

Problem:

Consider a chart like the one shown below (Sales Amount VS. State region).  When a user moves their mouse over one the bars then you want them to show them the value (sales amount) of that bar as well as show them the category grouping values (State region)

SSRS Tool Tip

(The Visualization is just for demo purpose. It’s not presentation-ready)

Solution:

Here are the step by step instructions to set up Tool Tip based on the requirements:

1. You’ll have to open the series group properties to add the Tool Tip. There are a couple of ways to open the series property

1A Select the Chart. Right Click on any of the bar and select Series Properties

1B Select the Chart. Click on any of the bar. You should see chart data pane. you can click on down arrow button on the series to open series property:

SSRS Reporting SQL Series Properties

2. Once you’ve opened the Series Property, here’s what you’ll do.

Make sure you’re on Series Data tab. You can either select one the fields or write an expression.

To meet our requirement of showing State & Sales Amount, I am going to write an expression.

From Tool Tip. Click on Fx

3. I wrote an expression that meets the requirements I stated earlier.

Expression SSRS state and salesClick OK. Click OK on series property too. And return to design view

4. Preview the report. Move your mouse pointer over one of the bar chart, you should see a Tool Tip:

REPORTING TOOL TIP CHART

This makes the charts a little more easier to read. I hope this helped!

SQL Server Reporting Services: Why am I not seeing every axis label in a chart?

Problem:

SSRS chart didn’t show all axis labels. Here’s an example.

Note: it does NOT show all country names:

axis labels sql server reporting services

Solution:

So what do you do if you want to show all axis labels in the report and do not want to skip the axis labels? Here are the steps:

1. Go to the Chart Axis properties

2. Under Label, change the value of Label Interval from Auto to 1

ssrs chart aix label properties

3. Preview your report to see if you see ALL axis labels now:

axis label ssrs issue solved

Conclusion:

The above chart is NOT perfect. There are other things that can be done but the goal of the blog post has been achieved! We have changed the axis label property so that all axis labels now show up on charts.

 

New Digital Marketing Analytics Report shows social media is not the best source of acquiring customers:

It’s great to see Insights that data can uncover. I saw a nice insight in a report I read about Analyzing customer acquisition channels for e-commerce sites and in this blog post, I am sharing it with you. So what are the top customer acquisition channels for Commerce sites? The Top channels are Organic Search, Emails & Paid Search.Here’s the report: E-Commerce Customer Acquisition Snapshot

It was not surprising to me to see Organic Search and Emails being among the Top customer acquisition channels but what surprised me was  relatively poor performance of social media in acquiring customers. Here’s the chart showing performance of various online channels for acquiring customers:

ecommerce analytics percentage of customer acquired vs. channel

Data Source: http://blog.custora.com/2013/06/e-commerce-customer-acquisition-snapshot/

Note #1: The post is NOT about devaluing the benefits of social media and it comes to down to understanding the goals of having a social media presence in the first place. While computing the ROI of social media, there are other factors like increased brand awareness, customer loyalty to be considered. But I posted this data because it’s a great way to show how data can uncover insights and sometimes it may surprise you

Note #2: The percentage of customers acquired does not add up to 100% for a year because the data does not include things like direct traffic. The author of the report confirmed it over an email w/ me.

That’s about it for this post. Your comments are very welcome!

Seven ways Analytics can create value in an Organization:

The value created by Analytics in an organization can be more than one and it depends on what an organization is trying to do with analytic’s – with that, Here’s the list that I compiled from my readings over the past few weeks:

  1. Increases revenue
  2. Creates strategic advantage
  3. Improved decision making (And increasing the speed of decision-making)
  4. Generates innovative insights
  5. Increases productivity
  6. Increases margins
  7. Reduces operational costs

The role of Sentiment Analysis in Social Media Monitoring:

I’ve posted tutorial/resources about the Technical Side of Sentiment Analysis on this Blog. Here are the Links, if you need them:

LingPipe (Java Based) | Python | R language resource | Microsoft’s Tool “Social Analytics

Apart from this, I’ve used other Tools per project requirements and It’s been fun designing and developing projects on “Sentiment Analysis” primarily using Social Media Monitoring. Having worked with clients on projects that use “Sentiment Analysis” – I reflected about the role of Sentiment Analysis in Social Media Monitoring. And in this blog post, I am sharing these reflections:

What is Social Media Monitoring?

Social Media Monitoring is a process of “monitoring” conversations happening on social media channels about your brand/company.

Is it NEW? Not really. The idea of monitoring or gathering data about what is being talked about the brand/company is not new. Earlier, it was newspapers and magazine-articles and now, it’s the social media channels including online news, forums and blogs and thus the name given to this process is “Social Media Monitoring”

brand monitoring social media

What is Sentiment Analysis?

Analyzing data to categorize it under a “sentiment” (emotion).

Example. Is this review saying positive, negative or neutral thing about our product.

sentiment analysis positive negative neutral

side-note: Sentiment analysis is often categorized under “Big Data Analytics”.

What’s the Role of Sentiment Analysis in Social Media Monitoring?

We’ve seen that in social media monitoring, we gather all online conversations about a brand/product/company. Now wouldn’t it be great to take the data that we have and bucket it under “Positive”, “Negative” or “Neutral” categories for further analysis?

So few questions that can be answered after we have results from sentiment analysis:

1) Are people happy or sad about our product?

2) What do they like about our product?

3) What do they hate about our service?

4) Is there a trend or seasonality in sentiment data?

Among other business insights that may be not be easily answerable with just plain text data.

Thus sentiment analysis is one of the step in social media monitoring that assists in analyzing sentiment of all the conversations happening on the social web about a brand/product.

That’s about this for this post. Here’s a related post: Three Data Collection Tips for Social Media Analytics

your comments are very welcome!

Business Metrics #2 of N: Customer Retention Rate

In this post, We’ll explore a Business metric called “Customer Retention Rate”

What is it?

It is a metric that helps an organization monitor the % of customers retained.

Let me give you an example:

Year Number of Customers Retention Rate
0 100 100%
1 85 85%
2 70 70%
3 65 65%
4 61 61%

Do you notice the third column that keeps a tab on the percentages of customer retained? This is the basic Idea behind customer retention rate.

How is it used?

This metric correlates with other key business performance measures like: customer service, product quality, customer loyalty. Think about it. If the customer retention rate is higher than the organization must be doing “something” right – that something could be: great loyalty program, great customer service or great product quality! If it’s low then it requires some action from decision makers – they would want to know the reasons so that they could fix the situation.

In earlier post, we talked about Customer Lifetime Value – now higher customer retention rate would also help us have a higher customer lifetime value.

Also it’s important to realize that the cost of acquiring a new customer is typically higher than keeping existing customer – and so organization that sells products/service like to measure the customer retention rate.

Also, if you customer data then you can drill down to find trends in the retention rate. Questions like: Which Age group has the highest retention rate? or which has lower? Retention rate for male customers? And also predicting customer retention rate of a new customer?

Conclusion:

In this post, we learned about a business metric “customer retention rate”.

And as a reminder, This series is meant to understand Business Metrics from Analytics Perspective.

Three Data Collection Tips for Social Media Analytics

Data integrity is important especially if critical business decisions are based off on data. To that extent, in this post, I’ll write about five data collection tips to help you have accurate data for “social media analytics”. So here are the tips that are applicable to social media analytics irrespective of the tool you are using:

1. Social Media Platform

social_media

Select the right social media platform for capturing data. You do not want to select few such that you miss data.And you do want to select irrelevant social media platforms because if you do, then you’ll introduce noise in the data. Let me take an example. If your project needs to be based on USA only then you do not need to add “sina weibo” (Chinese social network) in your social media sources.

Now, Based on your business need for “social media analytics” campaign, you should test all possible social media platforms – you never know who might be talking about things that you are interested in. After you have selected the right social media platforms for your project, let’s go the next step:

2. “Search Keyword” Selection

Some of the social media platforms let’s you collect data via “search keywords”. Like twitter allows you to collect data via “hashtags” and/or keywords. So if you want to collect data about all social media posts having “american airlines” then you should not collect data using:

AMERICAN OR Airlines:

If you select the above rule, then it will introduce a LOT of noise because we’ll collect data people talking about just “American” PLUS data about people talking about just “airlines”. That’s bad!  What you want is rules like these:

1. American AND airlines

2. “American Airlines” (as a phrase)

american airlines social mediaNow, I can’t stress the importance of selecting the right search keywords enough. Choosing wrong keywords will add noise that would be bad for analytics. So choose keywords such that you are not adding noise as well as not missing on conversations. There’s no secret formula here, continuous improvement is the way to go!

3. Language & country Filtering

global-social-network

Social networks are GLOBAL in nature and so it’s important to filter (or include) based on the project that you’re working on. Not doing so would add noise in your data. And also remember to include country and language because you do not want to miss out on conversations either.

Conclusion:

Three Data Collection Tips for Social media analytics that I shared in this post are:

1. Select Right Social Media Platform

2. Select Right search keywords

3. Select Right Country and Language.