Some may argue that it is because Warehouse clubs and Super stores are growing rapidly than Departmental stores but it is just not that,Warehouse clubs and Super stores are also taking away Departmental stores sales. If we examine yearly sales, the Departmental stores sales are in decreasing trend from 2000 while the total Merchandise industry sales and ‘Warehouse club and Super stores’ sales are growing implying Warehouse clubs and Super stores are eating away Departmental stores sales. In addition to that from 1997 to 2007 numbers of warehouse club and super stores have increased by 178% while Departmental stores have decreased by 18%. The facts that numbers of Super stores are increasing and sales of Departmental stores are decreasing indicate that Warehouse clubs and Super stores are replacing Departmental stores.

Beer, Wine and Liquor are no longer luxury goods. They have become more of basic goods. Look at the sales during dot-com bubble and the recent recession, sales have increased slightly instead of decreasing. This means people are not treating beer, wine and liquor as luxury which is the reason for not postponing their consumption of alcohol to save money during recession. Alongside if we observe sales during 1991-2010 they have increased from $21 billion to $42 billion, a 100% increase, at a steady rate without any sudden peaks and troughs – implying it is not much affected by by economic changes.

It is surprising that, even during recession people tend to keep up with their sports habits. According to US census data sporting goods sales have increased from $35 billion to $37 billion during recent recession.

From the above graph, it is also evident that sales growth is never negative indicating that Sports goods stores sales have never declined majorly. In addition to that, during recession sports goods stores sales has grown more than GDP in 2008 and maintained the sales i.e., 0% growth till 2009 end. This implies that even during recession people tend to keep up with their sports habits even though they may not take new ones.

Buy for everyone in a single trip seems to be new mantra in clothing industry. More People started to prefer family stores to exclusive stores. Look the transformation of market share of clothing industry from 1992 to 2010:

The market share composition of clothing  industry show that Family clothing stores market share has increased (from 44% to 66%) while the market share of women’s and men’s clothing stores has decreased (from 42% to 28% and 14% to 6% respectively) – a change in preference. The Compound Annual Growth Rate (CAGR) of Men’s clothing stores, Women’s Clothing stores and Family Clothing stores are -1.5%, 0.83% and 5.42% respectively. The facts that increasing market share and high CAGR implies that people are shifting their preference from exclusive stores to Family clothing stores.

Let us dissect further and look who among men’s stores and women’s stores are affected more.

Men’s clothing stores are majorly affected by family stores than women’s clothing stores. According to US Census data Men’s Clothing stores sales have decreased from $10 billion to $7 billion from 1992 – 2010 while women’s clothing stores sales have increased from $31 billion to $36 billion during the same period. In addition to that the growth rate trend of men’s clothing stores is decreasing while that of women’s clothing stores is increasing. This shows that men’s clothing stores with decreasing sales and sales growth are largely affected by family stores than women’s clothing stores. But something interesting is happening with women’s clothing stores sales, even though sales are growing its market share is decreasing, it means family stores are not replacing women’s clothing they are just growing more than women’s clothing stores while on the other hand in men’s clothing stores case family stores are replacing their sales.

An obvious yet a surprising fact is that Education is the broad theme emerging from the tweets to make India a land of equal opportunity free of prejudice and discrimination. Surprisingly, Politics and Government seem to have a lesser role to play. Money, Finance or other terms related to finance also did not figure prominently.

I hope you can review the word cloud and the tweets to come up with your own insights. Please do share them in comments.

What should we do to make India a land of equal opportunity for all, free of prejudice and discrimination?

— Ratan N. Tata (@RNTata2000) March 11, 2012

About the Author:  Chaitanya Sagar is the CEO of Perceptive Analytics. Perceptive Analytics is a Data Analytics Company focused on Web Analytics and Visualizations. Our approach is to develop deep domain expertise and do rigorous analysis so we can develop strategies to keep you ahead. Vaishnavi Kandala, A business associate with Perceptive Analytics helped in writing this article.

In this article series we explain how to analyze and find right keywords from Google Ad Words and Google Analytics search terms data.

We run ads on search engines like Google and often, we pay for the web traffic which may not be relevant. The reasons could be using wrong keywords or not knowing right keywords to use for Ads and SEO (Search Engine Optimization). But the good part in this problem is that, there are already keywords related data collected as search terms in Google Ad Words and Google Analytics. All we need to do is to analyze the search terms dump and find the gems – the right keywords. From Ad Words search terms data we can find keyword which will help us to improve ad performance. And from Google Analytics search terms data we can find right keywords to get more relevant traffic through SEO. Along with finding right keywords, this analysis will provide insights that will help us maximize revenue.

To help you understand this keyword analytics easily, we have divided this article into three parts.
Part 1: Organize keywords for better clarity.
Part 2: Analyze Google Ad Words search terms to find the right keywords.
Part 3: Analyze Google Analytics search terms to find the right keywords.

Part 1: Organize keywords for better clarity
Let’s take real life scenario of an ecommerce company, which sells products online. Google Adwords or Google Analytics has search terms data related to all the products. As-is, you cannot generate insights from it or act on it. To draw some clarity from search terms data, we need to group the search terms based on products.
For example let’s take books, for the Alchemist book the unsorted search terms data will be scattered as below:

We can sort the search the scattered search terms using filter function in Microsoft Excel. First go to Data tab and click on ‘Filter’

Once the filtering is turned on, click on the Arrow in the column header, click Text Filters Arrow and select ‘Contains…’

Now type the book title which you want to sort in empty text box besides ‘contains’ box and click ‘OK’

After sorting search terms based on different book titles the search terms data will look as below:

After the search terms are sorted based on a specific product, there is better clarity because the data is organized. In the same way we should organize search terms related to other products and make it ready for analysis.

In the second part of this article series I will explain how to analyze the Google Adwords search terms data. You will know how to find the right keywords, and also other useful tips you can use to maximize revenue. See you then!

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About the authors: Venkat is a Senior Marketing Associate at Perceptive Analytics and Chaitanya Sagar is the CEO of Perceptive Analytics .Perceptive Analytics is a Data Analytics Company focused on Sales & Marketing Analytics along with Web Analytics. Our approach is to develop deep domain expertise and do rigorous analysis so we can develop strategies to keep you ahead. You can contact us by email: ca [at] perceptive-analytics.com or call on 305.600.0950

How can you use twitter to know which geographies to target your customers through twitter?
And if the ones you are using are effective? or not?

We had the same question and we analyzed twitter ‘location’ data to find the answers. The data we obtained had approximately 3.5 million records which had the location field from each user’s twitter account.

How to Benefit From This Analysis?

1. Check twitter usage in your target geographies.
2. Which target geographies do not use twitter as much as you would like? (means that your message through twitter is not reaching your target audience)
3. Which new geographies can you reach using twitter?
4. How do you reach your target geographies that twitter does not have a presence in?

Word Cloud of the Entire Database

From the cloud we can clearly deduce that USA has the largest users followed by Brazil, UK, Germany, Canada and Australia. When it comes to US states, CA, NY, Texas, Florida have more users.

More Insights at the Country Level

As we have already seen in the above picture that USA, Brasil, UK and Germany visibly dominate the word cloud, we dissected the data further for each country.

USA

Cities and States in USA

While it is not surprising to see that CA (and “California”) has a huge influence on the cloud. This is followed by NY, Texas, Florida, Pennsylvania (PA), Illinois which is the next most populous state in the US has lesser influence on the cloud than Ohio, Georgia and North Carolina.

The ten most populous states in US are consistent with the top 10 states by frequency in the word cloud except Illinois.

UK

Cities and states in England

UK too follows a similar trend. Though Midlands has a greater population than Greater Manchester, Manchester has more users in the cloud than Midlands while the frequency for other counties or cities like Yorkshire, Essex and Liverpool is in sync with its population.

GERMANY

Cities and States in Germany

Similar look at places in Germany shows an interesting insight. NRW also known as North Rhine-Westphalia leads the way followed by Berlin, Bavaria, Cologne, Hamburg and Frankfurt. It is also interesting to see that people who live in Germany and also USA are significant in number as they have listed down both Germany and USA in their location field.

California

We can also further delve deep into the data to find the cities in a particular state rather than a country. The word cloud below shows the cities in California.

Cities in California

As expected, Los Angeles dominates the cloud followed by Bay Area, San Diego, San Francisco and San Jose.

We have provided you a PDF version of all the images, created under Creative Commons License, which will give you the ability to zoom in and see the all the locations that are in the cloud. The more you zoom in, the more words you will be able to see.

World US UK Germany Brazil California

How we did it?

We obtained a total of 3.5 million twitter users’ ‘location’ records for our analysis. Because there was a lot of unwanted data in the database that we obtained (a lot of the user locations have been filled with the latitude and longitude positions via iPhone and other mobile service providers), we cleaned the data to remove any redundant items. We finally retained data without any numbers or character symbols.

Quick Facts

Number of words in the cleaned records: 7.99 million
Number of records after cleaning: 3.64 million
Initial number of records: 3.87 million

Sample Snapshot of the data we obtained

Snapshot of the data we obtained initially

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About the authors: Chaitanya Sagar is the CEO of Perceptive Analytics and Raj Nihar is a Senior Business Associate at Perceptive Analytics. Perceptive Analytics is a Data Analytics Company focused on Sales and Marketing and Finance. Our approach is to develop deep domain expertise and do rigorous analysis so we can develop strategies to keep you ahead.

Every business would have silos of business data in its marketing/sales department. This data has hidden treasures. It contains information that can help target right audience more effectively, bring in more efficiency in the sales process and also forecast the future of business. To generate these insights from the large unorganized databases we use business analytics. These are some of the top applications of analytics in Marketing/Sales:

Analytics on Consumer Behavior

Analyzing consumer behavior would help us understand when, why, how, and where people do or do not buy a product. By understanding this we can bring in changes to our product and marketing strategy helping us attract more consumers.

Marketing Mix Analytics

Analyzing returns on marketing expenditure across various channels would help us evaluate the effectiveness of each of the marketing activities. These insights will help us to reallocate resources from a less effective to a more effective channel.

Sales Force Analytics

Analyzing sales process and team will help us diagnose the barriers to sales force performance. It will enable us bring more efficiency in sales force by providing us insights on issues like optimum sales territory size, optimum product bag size, the quality of leads and monthly target forecasts.

Sales Pipeline Analytics

Analyzing the flow of sale through several stages would help us find any loopholes in the sales cycle. It will help us evaluate an optimal time period for each stage to occur so that you can make sure your customers experience a quick and efficient transaction. It also helps determine the capacity of your sales process.

Analytics on Communication Content

Analyzing consumer behavior on the communication content helps us observe how consumers react to our marketing material. This gives us insights on how to draw consumer’s attention towards our products and will enable us convey our message clearly.

Web Analytics

Web analytics helps us understand user behavior on the web and consequently generate more leads and sales. It will provide insights to enhance the look and layout of your website and make it more user-friendly. With these insights we can also asses and strategize the effectiveness of marketing campaigns in order to get more valuable customers.

Hope this gave you a brief idea on how analytics is applied in marketing and sales. In the next few posts I will write about the above applications in detail. See you soon!

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.

Word cloud is a powerful way to analyze patterns in text, particularly keywords in Internet marketing context.

We have made a word cloud of all the customers of p2w2 to check how it will be and here it is!

Word Cloud of p2w2's Customer Names

We have not given any weight to words (for obvious reasons!). If we have more customers who have John as first name, then John is bigger in the cloud.

Are you listening to it?

Each of the visualizations will have a story to tell. Can you listen?

How do you target less and generate more revenue?

The answer is in Analytics.