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.

The post Let Your Data Speak first appeared on Perceptive Analytics.

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.

The post Insights Using Twitter Information to Target Your Customers Better first appeared on Perceptive Analytics.

Search Engine Marketing is Great! But You Can End Up Spending Too Much!

In a budget-constrained world, it is important for advertisers to deliver their marketing messages or generate revenue in a cost effective manner. Using SEA, you can reach out to prospects far away, precisely at the time when they are likely to buy your products / services. Search engine advertising also provides far more tracking letting you know when, where, and for which search have your customers purchased the product.

Frequent Bid and Budget Changes Make Optimization Necessary

For marketers, it’s a challenge to pick the right keywords, allocate right budget to the relevant campaigns and to maximize ROI from all campaigns. Another challenge is that companies waste a great amount of budget on products not available, pitching customers who are not interested. Also, ad hoc changes to budget or keyword bids are not good enough. You have to frequently look at the overall spend and optimize budget so you can maximize ROI. It is also difficult to continuously learn from the data incorporating the learnings into bids and allocations. To address such issues our Ad Words Spend Optimizer (ASO) tool helps you decide keywords for a campaign, find ideal bids for keywords, and allocate budget to all campaigns based on expected performance

The post Generate higher conversions with AdWords Spend Optimizer first appeared on Perceptive Analytics.

Optimizing Marketing Spend with Marketing Mix Modeling

Marketers in CPG industry are constantly faced with the challenge of allocating their fixed marketing budget among various marketing channels like traditional communication mediums and digital channels.

To achieve effective budget allocation for marketing, companies have to rely on many of the marketing tools available today and create models that will show the impact each of this channel has on sales.

This case study will summarize the various marketing mix models available for marketers by analyzing the strengths and weaknesses of each of them. In particular, we focus on regression models, influence maximization models; agent based models and empirical methods being used by the marketers.

Check out the insightful case study!

The post Optimizing Marketing Spend with Marketing Mix Modeling first appeared on Perceptive Analytics.

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

The post Marketing Analytics: How to Multiply Results from Search Marketing first appeared on Perceptive Analytics.

How do you target less and generate more revenue?

The answer is in Analytics.

The post Target the Right Customers first appeared on Perceptive Analytics.

The post Understand Your Customers Better first appeared on Perceptive Analytics.