Jul 22 2014

Analytics for eTail

We are glad to share that our team successfully organized the second meetup titled “Analytics for eTail” at IIT Madras Research Park. The meetup serves as a platform for the business firms to understand the relevance of analytics and how it can improve their day-to-day business operations. The meetup is alive at http://www.meetup.com/Analytics-for-Business/about/ .

Ms. Parvathy Sarath, Director of Aaum Research and Analytics introduced various eTail topics to the business firms. WP_20140721_014The various eTail topics discussed are

  • Monitoring campaign Performance
  • Price sensitivity analysis
  • Optimization techniques
  • Social media analysis
  • AB testing
  • Recommendation
  • Market mix modelling
  • Sales attribution analysis
  • Heat map generation techniques
  • Loyalty measurement and analysis and
  • Dynamic Pricing

Meetup 2 - All

The meet up spanned for two hours. The participants showed lot of interest in understanding the concepts and to see how those techniques could be adopted for their business. Click here to view the presentation delivered on our geniSIGHTS solution. The participants showed keen interest in attending future Meetups conducted by Aaum.


Jul 16 2014

The vital click!

Attribution modelling is a widely used tool in businesses that helps the marketers to understand the impact of various marketing channels have on the ROI of their businesses. The insights from this simple exercise allows the marketers to track and analyze the multiplied touch points in sales and their impact on the conversion value. This in turn helps the marketers in effective credit allocation of their marketing budget on the various credit channels.

With the behemoth amount of data, digital marketers are now shifting their focus on attribution for the purpose of increasing their conversion rates. Low online conversion is generally overcome by optimized customer service by personalizing customer experience management, web analytics and enhancing the use of feedback. Conversion path analysis is done to convert the normal website visitor to a paying customer or even a subscription to a newsletter may be considered as a conversion. A simple layout of a path conversion is shown as follow:


When the website visitor is on the landing page there is a requirement to enhance his user experience by providing relevant information about the products and by establishing an emotional connection to the brand. To avoid distraction, it is required to provide focused content and targeted conversation and providing highly flexible pages for easy operation by the users.

Conversions generally involve more than one channel and conversions generally travel down the multi-channel funnel.

The channels could be:

  1. Paid-search

  2. Organic-search

  3. Social media

  4. Referrals

  5. E-mail

  6. Direct

For example, one may first read about your product on a blog-post. Then, he may see a display-ad. Later, he may read a review on some website. Curious about your product, he will see your PPC ad. After that, he visits a product comparison website. He then clicks on an organic search result which then reaffirms a customer to buy your product and he finally purchases your product.

In the example, we see that a ‘buyer persona’ visits various channels and finally decides whether he wants to purchase a product or not. A marketer would now want to assign credit to the different channels which assisted directly or indirectly in the conversion process. This set of rules that govern the assignment of credit to the various channels is known as attribution. For this process there are various attribution models available. These are:

  1. Offline-Online attribution model: This model determines the impact that the digital marketing channels have on the offline marketing channels and vice-versa. This model understands the influence that digital marketing channels have on the offline marketing channels and how to assign credit to them.

  2. Multi-Device attribution model: This model determines the impact that various devices (Laptops, Desktop, Mobiles, Tablets, etc.) have on conversions and how credit is to be assigned for various devices.

  3. Multi-Channel attribution model: This model is the most common model that is used in the industry. It studies the effect that various digital marketing channels have on the other for conversions and how the credit is to be assigned for the various channels.

In the case study available here we have tracked and analyzed the various metrics driving to multi-channel attribution modelling that is commonly used in the E-Commerce market. The model also extends further to showcase a data driven hybrid approach to attribution modelling that combines the intelligence of more than one attribution model based on the customer profile to channelize the credit assigned effectively. The outcome of such an exercise would be similar to a plot below which depicts the conversion value attributed from each channel which is used in your conversion path. 


The assignment of credit is essential to understand which channel plays a more important role in assisting the conversions. We can determine on which channel we would want to spend more money and time. Thus, at a minimal cost we would be able to improve the efficacy of conversion rate. Thus the attribution model is becoming an integral part in the lives of digital marketers.

Jul 7 2014

Validating “income levels” based on asset based “wealth indicators”

The income variables received from surveys will typically shows a relatively very high proportion of abnormal values due to insertion of false information, substantially reducing the number of valid cases in multivariate statistical analyses, etc. Such a discrepancyon a key predictor variable like income variable is undesirableas it alters the modeling analysis, insights. In this blog, we shall focus on an effective alternative mechanism to qualify “the wealthiness” of the customers based on their assets.

The survey questionnaires can be effectively constructed to collect information on household assets with the aim of obtaining more precise measures of economic well-being on the largest possible proportion of respondents. Indeed, the non-response or fraud rate associated with the household asset items in the application is much lower than the one for the income variable.

The estimation of relative wealth using Principal Component Analysis (PCA) is based on the first principal component. Formally, the wealth index for household is the linear combination, the first principal component variable across households or individuals has a mean of zero and a variance of λ , which corresponds to the largest eigenvalue of the correlation matrix of x .


The first principal component y yields a wealth index that assigns a larger weight to assets that vary the most across households so that an asset found in all households is given a weight of zero . The first principal component or wealth index can take positive as well as negative values. PCA analysis was performed on the asset information collected from the respondents. Each applicant was scored from 1 to 5 based on the computed wealth indices. The score 1 denotes the bottom 20 % or otherwise the poorest of our applicants. Similarly the top 20 % were denoted by the score 5 who were our richest applicants. use of PCA for estimating wealth levels using asset indicators to replace income or consumption data. A relative wealth index was computed using the methodology described based on the following items asked in the survey:

Wealth Index

The above exhibit is the normalized representation of the data. The analysts can now calculate the calculate principal component loading value that can be used as effective wealth indicators. The below exhibit qualifies the effectiveness of these indicators on the income level. The plot showcases the wealth index computed for the individuals who have undergone the survey at household level. Based on the wealth indicator computed from the asset analysis of the individuals, they have been scored and ranked as “Poorest”,”Poor”,”Average”, “Good” and “Richest”.Do note that the average income level rises as we scale to higher wealth indicator groups. This technique not only validates the income levels but also provides an alternative powerful variable for statistical analysis.

Wealth Index District Wise

Click here to view a case study demonstration on Validating “income levels” based on asset based “wealth indicators”.

Jun 25 2014

Marketing Mix Modelling from Multiplicative Models

E-Commerce or Digital Marketing has emerged as a separate and rapidly growing business domain with many businesses thriving solely on them. With internet acting as a strong channel to perform business, there also arises a simultaneous need to use this channel effectively to add value to your business.


2014 – The year of Digital Marketing Analytics

According to a popular study on the Forbes magazine, 2014 is the year of Digital Marketing Analytics.

DMA Businesses that use digital marketing analytics for improving customer acquisitions, increasing brand loyalty, increasing ROI from their marketing channels, etc have a better competitive advantage over other businesses who have not yet ventured out into this space. The internet search giant company Google Inc, has also created a massive impact in the field of E commerce analytics through their Google Analytics platform which provides a variety of KPI’s and statistics to help digital businesses track and measure their online sales and marketing. AdWords, E commerce reporting, real time analytics are some of the popular services provided in this platform.


Digital Media Analytics

While these services are good enough to track high level trends and provide basic directional metrics, there is often a need to dwell the past data much deeper with sound understanding and usage of advanced analytical techniques to get better insights. One such area in the field of digital market analytics which relies heavily on the use of econometric models for decision making is Market Mix Modelling. The Gartner IT glossary defines Market Mix Modelling as “analytical solutions that help marketers to understand and simulate the effect of advertising (volume decomposition), and to optimize tactics and the delivery medium”.

In simple terms Market Mix Modelling refers to the estimation of statistical models to measure and analyze how various marketing channels are effectively contributing to your sales. If is often used to find the optimal mix of the market channels and forecast future mix which would help one to maximise the ROI from various marketing channels and sales. In particular it helps the business user address key questions such as:

  1. How do we decompose our sales to key drivers to understand the combination of these channels which would contribute to sales, market share or profits?
  2. How are these key drivers impacting sales over time?
  3. How do we calculate the efficiency or ROI from these market channels?
  4. What happens when we change the budget by channel


Market Mix Modelling – Methodology and Illustration

Market mix modelling relies on three popular model forms to understand the impact of various market mix variables on sales. Users use either of the three based on their requirement from the analysis.


Table 1. Summary of Market Mix Models functional forms


Additive models tells us how much of a change is generated in the response variable in unit terms with one unit increase in the explanatory variable and is only used in scenarios where the impact of each additional unit of the explanatory variable is identical. For example this model is not suitable in determining sales decomposition of seasonal brands such as yogurt or ice creams. However the estimation of additive models are easy and sales decomposition can be directly implemented to understand the variable contributions. Semi logarithmic models on the other hand show how much the absolute level of the response increases/decreases proportionately with the independent variable. An even better logarithmic model gives us the variable elasticities which exactly measures the responsiveness of the independent variable to the dependent variable. This model comes very handy to understand how price and promotion discount elasticity impact sales and hence the most preferred model in industry standards. However modelling and decomposing logarithmic or semi logarithmic models which are in the multiplicative form are indirect and quite challenging. A good understanding of the data and techniques is very essential to model market mix variables in the multiplicative form and to decompose it to arrive at something similar to the below figure.


Figure – Decomposing sales to key drivers

Market Mix Modelling from Multiplicative Models

In the above figure sales of a popular ice cream brand has been decomposed to understand how the key drivers such as its own price, the competitors spend, and its market channel spend from Press and TV impact the sales volume of the product. Sales volume has been decomposed here to its base effect (shown in blue) which usually can be interpreted as the sales derived from the brand value of the product and the incremental effect derived from the sales drivers. Impact of the market mix variable TV can be seen clearly in the months of Jun/July where the TV spend (shown in yellow) is made. A multiplicative model has been implemented here to capture the right effects of the variables and to understand the seasonality of the product sales as the product has a very seasonal demand.


A link is available to experience the true value from Market Mix Modelling here.


Right information at the right time through the right means

Adoption of modelling techniques in business and the insights gained from it are vital to the survival of any organisation. Here in this article I have showcased how the concept of market mix modelling developed in the right fashion could add much value to your business and help you foresee things which you could not have before. Right information at the right time through the right means is really worth your investment of time and money!







Jun 20 2014

How can you maximize your ROI through Targeted Marketing Campaigns

Every consumer company that exist in today’s world have vast amount of transactions data, containing information about every product that is sold and about every individual who has purchased it. Propensity and Uplift modeling would help in answering questions like:

  • Which marketing campaign will allow you to reach more customers?
  • Which is the customer segment on which the marketing campaign should be targeted?


aaum logoPropensity’ which literally stands for ‘change in behavior’ helps in finding which of the marketing campaign has the most positive impact on your ROI. This model works on the basis of score matching technique and then comparing the customer group who were exposed to the campaign (treatment group) and the customer group who were not exposed to the marketing campaign (control group). Comparison is made with respect to the underlying characters of every transaction between the treatment and the control group.


Uplift modeling helps you to identify and target the customers who want to hear – isolating the ones who don’t. Many marketers overlook the pricey consequences that can result from sending mass, untargeted emails or marketing communication messages to their entire audience.

Uplift modeling has helpe dUS Bank better target its customers, and as a result, it has reduced its mailing volumes by up to 40% and has achieved a five-fold increase in campaign return on investment compared with existing programs”

There is a fundamental segmentation that separates customers into the following groups:

The Persuadables : customers who only respond to the marketing action because they were targeted

The Sure Things  : customers who would have responded whether they were targeted or not

The Lost Causes  : customers who will not respond irrespective of whether or not they are targeted

The Do Not Disturbs or Sleeping Dogs : customers who are less likely to respond because they were targeted

LoyalSIGHTS helps you to identify the persuadables, the only segment that provides true incremental response.

Jun 10 2014

Crouching “NoSQL” Cautious “SQL”

“According to analysis by Wikibon’s David Floyer (and highlighted in the Wall Street Journal), the NoSQL database market is expected to grow at a compound annual growth rate of nearly 60% between 2011 and 2017. The SQL slice of the Big Data market, in contrast, will grow at just a 26% CAGR during that same time period.” NoSQL adoption will go big bang but will it impact all the organizations? Will SQL lose this battle? Does everybody need to care about NoSQL? First of all, there is no battle to begin with. NoSQL doesnt mean NO SQL. It simply means “Not only SQL”. Wikipedia defines NoSQL database as

                  “A NoSQL or Not Only SQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. The data structure (e.g., key-value, graph, or document) differs from the RDBMS, and therefore some operations are faster in NoSQL and some in RDBMS. There are differences though and the particular suitability of a given NoSQL DB depends on the problem to be solved.”


Let us understand SQL and NoSQL a bit more better!



Clearly each one has its own merits and shortcomings. There is no standard solution that fits to all business requirements. Sometimes it is more shocking to see how people react/make decisions on this front. See for yourself one such fight.



Platforms, technologies should be chosen to fit business and not vice-versa.  While there are problems SQL isn’t suitable for, it still got its strengths. Lots of data models are simply best represented as a collection of tables which reference each other. Therefore SQL atleast for now should be good enough for most of the organizations. Right now, it’s fair to say NoSQL is only relevant to a very minor proportion of businesses. But that proportion is very very significant! The changing data arena would sooner see a technical innovation driving business innovation very similar to what we have seen with smart phones?

There wasn’t a need for smart phones but the technical innovation provided business opportunities. Likewise, big data promises a huge opportunity for NoSQL.

May 18 2013


AAUM Proudly Presents GeniSIGHTS .SnB - Genisights logo 2.0

We have launched our one of its kind reporting product GeniSIGHTS on Friday, May 17th of 2013. What is GeniSIGHTS and why is it important if one may wonder, GeniSIGHTS is a highly customisable, extensible product hosted on cloud to help the business achieve insights without huge investments.


Apr 24 2013

Analytics for Retail Success and Excellence

April 18th is yet another day of enthusiasm, energy and exhibition of our expertise for AAUM.  We have conducted yet another workshop for “Analytics for Retail Success and Excellence”.

The key delegates varied from jewellery, food and beverages, multiple chain retail stores, travel services providers, FMCG manufacturing companies etc.


Mar 20 2013

Safe Drinking Water and its policy Framework

Collage 2

We have organized a  workshop on “Discussion on safe drinking water and its policy framework” on 5th March 2013 at our IIT Madras Research Park office premises by in which more than twenty five leading government and non government organizations working in the field of water and sanitation across the world participated.  We have collaborated with LEAD (League for Education and Development) and EAWAG (Swiss Federal Institute of Aquatic Science and Technology or EAWAG, Switzerland).LEAD had come up with the draft policy on HWTS (House hold Water  Treatment and Storage), and approached AAUM to facilitate a workshop inviting relevant experts from Government, NGOs and research organizations to discuss the need for safe drinking water and the role of HWTS.


Oct 22 2012

Analytics for Business Success & Excellence

On 20-oct-12, we conducted a work shop on Analytics for Business Success & Excellence which spanned along four sessions with focus on

1.Business Analytics for competitiveness, Innovation and sustainability

2.Reaping benefits from Analytics(case studies with demonstrations)

3.The right reporting Framework, doing it the BIRT way

4.Future of Analytics