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Too much data and not enough time and resources to leverage it intelligently? Need someone to assist you in decision-making by mining the data you have on your Customers, Products, Employees or Operations? Have an urgent need for sophisticated data analytics? Or just plain data management? Get a 2-pager on us. And contact us .

October 12, 2014

Social Media Listening to improve Product Design

Social media monitoring, also known as social media listening, is the process of identification and assessment of what is being said about a company, individual, product or brand in the digital media space.

With the advent of social media, word of mouth about your business spreads faster than ever, is more public than ever, and is no longer just between two people. Having a robust social media monitoring set-up allows an organization to respond quickly to adverse situations, discover and promote positive reviews, and build a good rapport with members or customers.

Our client, a leading multinational computer technology company wanted to create a niche in the highly competitive Indian tablet market by launching a range of products. The company’s previous launch did not meet with great success though it was backed by good print and media advertising. It was also forced to kill a range of tablets because of poor global sales.

Marketelligent used its Social Analytics platform : SocialFellas and helped the technology company analyze buzz around the product by monitoring social media conversations during the launch period. Social media channels like Blogs, Microblogs, Facebook & Twitter were identified where conversations related to product launch were happening. Themes like pricing, design, performance & battery life were identified to pinpoint areas of concern, or highlight areas which consumers liked the most. Sentiment analysis was done on an overall basis to determine the opinions of general users. This helped the company in getting quick feedback of the product after the launch which otherwise would have taken a lot of time through alternative primary research methods.


Our holistic approach helped the company in the following way:
  • Sentiment analysis of a product or a brand helped the company in identifying areas of concern  providing them opportunities to plug in the loopholes
  • Share of voice with insights related to what is driving brand or product conversations helped them to better manage brand buzz
  • Social Media campaign analysis helped the business know whether their marketing communication was actually reaching to the end customers
  • List of social media influencers of mobile and technology domain helped the brand to engage with them more fruitfully & create positive word of mouth
Results: Based on our social media monitoring, the technology company was able to gain insights regarding their product launch:
  • Display, Build Quality and Battery life of the product were appreciated on social media. Recommendations were made for them to be the highlighting points in the next advertising campaign of the product launch.
  • Many buyers were disappointed with lack of micro HDMI port and low resolution screen, which were highlighted as areas of concern and were suggested to be included or improved in the next product launch.
  • Top Influencers with great knowledge of the product were identified and listed for better engagement before and after product launch.
More details can be found here.

August 28, 2014

Credit Risk Scoring using non-traditional data

Unbanked and underbanked Customers are regarded by creditors/investors as high-risk borrowers due to insufficient information about their assets and liabilities. Given lack of this information, traditional risk-evaluation methodologies do not work.  However, psychometric risk profiling has been growing in popularity for determining the credit worthiness of such individuals. This exercise utilizes a blend of psychology & statistics to come up with questions that are used for reliability & validity of attitudes and behaviors of people. We helped our client, a leading financial services company, measure risk and business potential for such customers in emerging countries through psychometric tests, which asked questions about their attitude and beliefs, financial acumen, problem solving skills etc. to generate a risk-score through proven statistical techniques.

Credit risk modeling is a very common statistical technique used in the Banking and Financial Sector which involves analyzing historical data of borrowers to identify certain characteristics that predict the likelihood of the borrower defaulting on his/her loan in the future. The data could include, for example - past transactions, credit history, default in months, years in business, etc. At Marketelligent, we leveraged this conventional modeling technique to come up with a predictive credit score for individuals based on the psychometric test conducted. The data included socio-demographic, situational, psychological and behavioral variables. The operational procedures for building the predictive model involved:
  • Analysis of characteristics of each variable and relations between them
  • Identify possible inconsistencies and missing cases
  • Individual comparison of variables with good and bad credits
  • Redefinition of some variables and creation of other new derived variables
Various approaches like Decision Trees, Neural Networks (Gradient Boosting), Genetic Algorithm and Logistic Regression were used with the inclusion of psychological variables and scales, in order to come up with a robust model that would help us provide a probability score on each individual based on their survey responses. The analysis was done by calculating the Area under Curve (AUC) of all the countries and emphasized on to improve the AUC’s of the 5 worst performing countries.  The predictive scorecard developed had strong ability to identify risky customers. Model also worked well across countries.  Credit risk score generated helped the issuer in lending to previously un-bankable but credit-worthy customers, thereby directly improving the lives of struggling customers across many developing economies.

More details can be found here.

July 16, 2014

OOMF – Order of Magnitude Forecast

In today’s fast paced business ecosystem, manufacturers are increasingly looking to expand globally and utilize vast opportunities available in emerging and rapidly growing new markets. In this scenario, it becomes extremely important to identify the right markets and the right entry strategy. One of the key factors in developing a robust entry strategy is in understanding the market structure. Market structure analysis helps companies in understanding how various products are positioned, their interactions and in identifying gaps in the marketplace.

Our client, a leading pharmaceutical and CPG manufacturer wanted to establish presence in a new market. We did the market structure analysis to help them understand the competitive landscape and their current position in that environment, and also to identify opportunity areas to enter the new market. This was done by doing an in-depth analysis of historical sales by products, segments, formats, ingredients, price, packaging, distribution, communication, etc. This was overlaid with macro-economic factors, trade regulations, demographic factors and other tactical parameters in forecasting how the structure will change over the years.

The gaps identified were mapped with the company’s products and portfolio strategy to develop various product portfolios to be evaluated for the launch. A simulator was developed to help the manufacturer get an Order of Magnitude estimate of sales for various scenarios of the launch, the sequence of launch, marketing investments required and various other parameters impacting the launch. The client was able to quickly evaluate the opportunity and identify the right portfolio to launch in the market. The market structure analysis and the simulator developed are detailed below.

Market Structure Analysis:
We conducted a Market Structure Analysis to assess the size of prize, right to win and an order of magnitude forecast for a new entrant which helped the manufacturer understand the key value drivers impacting a new market entrant. This understanding helped to simulate various scenarios and develop the best market entry strategy.
Market Structure
Using information from the strategic and tactical launch parameters, the leadership was able to design a launch plan for the product in terms of identifying target markets, target consumers, suggesting the order of entry strategy, establishing the right product positioning and the right marketing mix & distribution. Along with this, the leadership got an idea on the overall product performance in the new market with respect to customers, financials and technology.

Simulating ‘New Market’ entry:
After the holistic analysis of the ‘new market’, we developed a simulator to weigh in different launch strategy options to enter the ‘new market’ in a way that marketing spends were optimized and best returns on investment were derived for the manufacturer entering the ‘new market’. The simulator helped to understand the impact of the newly launched product over the years and estimated the size of prize for the product launched in the ‘new market’.With this simulator, the manufacturer was able to select from a basket of new products; the right product to be launched in the market and the right time to launch the product.

For multiple product launches, this tool helped time the market right and sequenced the launches in an optimal manner in order to maximize returns. The manufacturer was able to visualize the market impact of the product launch by predicting the value share of the new product in the market on an yearly basis for the next ‘x’ years and thus foresee the success/failure of a brand launch in the long term.

Based on the above analysis, we leveraged the simulator to evaluate scenarios and created an optimal portfolio to help build the launch strategy, sequence and time the new product launches in the market in order to uphold the long term financial goals and vision of the manufacturer.


OOMF - Order of Magnitude Forecast Tool
This robust analytical approach helped the company in significantly reducing the risk and fastened the entire launch process. 

More details can be found here.