Marketelligent

MOSAIC - Business Visualization

2013-05-23T11:33:00.001-04:00

In today's competetive world, when markets are constantly evolving and consumer preferences changing at a rapid pace, Organizations need to be equally dynamic in their decision making abilities to adapt to these changes.

MOSAIC is an interactive online dashboard which facilitates strategic decisions through business visualization. It links data with graphics, making information accessible in ways a spreadsheet never could. It provides visibility into an enterprise, and does it on a finer time scale. It is also capable of supplying real-time insights to help business decision-makers arrive quickly at complex decisions and optimizations.

MOSAIC can be leveraged in various industries ranging from tracking product performance for a Consumer Packaged Goods company, to tracking outlet profitability for a Retailer, to analysing profitability and risk for a Financial services firm. It aids senior management with a customizable view of the business at a macro level; line managers and functional executives can further drill down based on the product portfolio they handle.

Please contact us for a free demo.

Retaining Customers. Proactively.

2013-04-01T08:38:00.002-04:00

Owning an indispensable and uncontested product or service may be the ideal scenario for Customer Retention; however in most industries, a lot of grey remains between what you and a competitor is offering. Media engagement to influence a customer’s brand recall, promotions such as loyalty cards or discounts are effective but may not be the optimum utilization of a company’s budget to manage Customer Retention.

Armed with sufficient data, a Business Scientist would like to approach Customer Retention in two steps:

Segmentation of customers into 'Loyal' or Engaged, 'Lost' or Attrited, and Customers that are 'At-risk' or that lie in the grey area of decreasing 'Engagement'.
Developing an effective Retention strategy for 'At-risk' Customers.

To build a Proactive Customer Retention (PCR) Strategy, an analytical model is developed leveraging past customer data, historical attritions, identification of specific attributes of attriting customers, and the weightage that each of these attributes carry.

Depending on whether it is a B2B or B2C business, magnitude of data capture, number of customers, frequency of purchase, etc. will vary. Typically, a B2B business will have lower numbers of customers and less transactions of higher value or contracts for a longer period; however such businesses tend to capture more details for each of their customers, their usage and purchasing patterns. A B2C business on the other hand will primarily capture sales data. In such a scenario, building the model parameters around recency of purchase, frequency of purchase, tenure with the business and transaction value will be key.

Once the PCR model has been build and factors identified, current customers are scored based on the model results to understand their engagement level and 'at-risk' Customers identified. ‘At-risk’ customers may further be segmentated on basis of geography, transaction size or industry segment to develop segment specific retention strategies.

Next, a retention strategy needs to be developed. The retention strategy could be a periodic phone call to at-risk customers, offering limited period discounts, packaging two or more products/services together, customizing the product or service to suit a particular customer’s requirement, etc. In addition, the PCR model can be employed to determine overall influx and outflux of customers, expected uplift from retention initiatives, and to predict future sales. A robust PCR strategy will also ensure that the model is refined and updated with fresh data from time to time.

For further reading please click here for some of our recent work on Customer Retention.

Retail Analytics

2013-02-21T12:50:00.000-05:00

Organized Retail has exploded in the last decade. To keep pace with changing industry dynamics, consumer’s evolving needs, and a highly competitive landscape; Retailers need to constantly evolve and adapt themselves to new trends.

Marketelligent partners with Retailers in making faster and more informed business decisions using our realm of analytics capabilities that pans through different functions and teams.

More details can be found here. Please contact us for additional information.

Application of Decision Sciences to Solve Business Problems in the Consumer Packaged Goods (CPG) Industry

2013-02-01T07:24:00.002-05:00

Marketelligent partners with CPG companies to increase their revenues and margins by making faster and more informed business decisions using our realm of analytics capabilities that pans through the product life-cycle.

More details can be found here. Please contact us for more information.

“Driver Analysis” for Designing Products & Related Communication Strategies in line with Consumer Preferences

2013-02-01T00:47:00.000-05:00

Driver analysis as a technique is widely used by marketers to understand which brands or product/service attributes have the greatest influence on consumers’ purchase decision. For instance, consumers might rate a personal care product based on its colour, scent, functionality, price, brand ambassador, whether there is a discount offer, and so on.

All of this will eventually help marketers in:

- Identifying the right product offering for launching a new brand/variant

- Narrowing in on the right target group for their brand

- Designing the right sales & marketing communication strategy

Consumption is driven by different kind of needs. As per the Hierarchy of Needs by Maslow, people are motivated to fulfil the most basic needs before moving onto more advanced needs. At the lowest level are the most basic, physiological needs. As we move up, are safety needs, social needs, self-esteem and finally self-actualization needs at the top of the pyramid.
Driver analysis helps us identify the consumer needs which translates to purchase behavior - if it’s functional aspect (physiological or safety needs) or if it’s imagery in consumers’ mind (having an ipod is considered cool by friends and adds to the self-esteem) or if the customer is delighted with his experience and is extremely loyal to the product (“I know this is what I want, don’t really care what others say about it”--self-actualization).

Driver analysis is the statistical effect of many independent variables on the dependent variable, which is our focus. For instance, sales of a product might be driven by a variety of factors like functional attributes like price, likability, quality or emotional attributes like brand imagery.

How do we prioritise the Key Drivers?

There are many statistical techniques for the analysis, simplest being correlation analysis, to determine the association with a product’s overall performance & the perceived performance of brand on separate attributes. Though it is simple, it doesn’t discriminate between the most important & the least important attributes.

A second method is multivariate regression, which explains overall market performance as a function of ratings on separate attributes. However, due to multi-collinearity (correlation amongst the independent variables) they can cause aberrant results. For instance, it may appear that 2 attributes are very important, but due to multi-collinearity, we may not be able to measure their unique influence.

Another approach used by industry experts which proves to be better than the above mentioned methods is path analysis. SEM or Structural Equation Modeling is a widely used Path analysis technique.

SEM identifies the relationship that an observable customer behaviour (like purchase intent, satisfaction levels etc.) has with latent constructs. These latent constructs cannot be measured directly, instead factor together variables that show similar customer behaviour. SEM however, is a confirmatory and not an exploratory approach. It requires a clearly defined hypothesis to begin with.

Another approach developed in the recent times, is Bayesian networks. It is a graphical model that encodes probabilistic relationships among variables. This is much simpler to execute as compared to SEM. Combined with Bayesian statistical techniques, it has the scope for exploratory analysis and doesn’t require a hypothetical model.

Case Study: A leading hair care manufacturer wanted to identify drivers of brand preference for the category which would aid them in designing the right communication strategy

Framework:

- Built a Structural Equation Model (SEM) to identify key equity themes and their hierarchy in terms of importance in driving purchase for the category, for each of the markets

- Identify the best pathway to improve brand’s equity in target consumer’s mind for each of the markets

Result:
The following recommendations were made and implemented by the business:

- Leverage brand’s strength on the “health” dimension which goes in line with brand’s equity pyramid

- “Ingredient” is one of the key category drivers on which the brand is performing very well--strengthen communication strategy to capture this

- Even though health benefit is the key, consumers eventually desire the beauty aspect – redesign communication strategy to convey this as the end benefit

- Currently “beauty” dimension is weak – build credibility on that with consistent communication

More details on the case study can be found here.

Pitch 'Analytics in Action' Conference

2013-01-08T21:32:00.005-05:00

Marketelligent is pleased to be the Knowledge Partner for the first Pitch 'Analytics in Action' Summit.

The objective of this summit is to 'DEMYSTIFY ANALYTICS' and demonstrate how it has helped solve business problems. The focus is more on the results and application of analytics than the analytics technique itself, making it more relevant to people running the business. It aims to answer a few questions pertinent to analytics generators, providers and users.

Through interactive sessions with marketers, planners and analytics practitioners, learn how businesses leverage analytics to build successful brands at the first Pitch 'Analytics in Action' conference in Bangalore on 16 January 2013.

More details here.

Helping Retailers Minimize Bad Debt

2012-12-01T22:47:00.000-05:00

Bad debt is an amount owed to a business or individual that is written off by the creditor as a loss (and classified as an expense), because the debt cannot be collected and all reasonable efforts to collect it have been exhausted.

If you offer credit to customers, you run a risk of not being able to collect it. Then why do companies give credit? Because by offering credit there is a greater opportunity to acquire more sales. The increase in profits from incremental sales should more than cover bad debts.

According to research done in the United Kingdom, SMEs (Small and Medium enterprises) in particular write off an average of £14,000 in bad debt per annum, which means that at a 5% profit margin they would have to make additional sales of £280,000 to make up for the loss. In any case, this shows that bad debt does not simply affect a company’s cash flow and bottom line performance, but sales and marketing efforts as well - a crucial issue for start-up's, or growing companies such as SMEs. Thus, effective management of credit is crucial for the success of any company.

Identifying Bad Debt is important

Minimizing bad debt adds value to both Business and Customers:
- Value to Consumers: reduced consumer prices and greater purchasing power, since consumers would likely be faced with higher prices if businesses were unable to recoup losses resulting from bad debt.
- Value to Businesses: It helps them keep costs down and reduce their risk of financial insolvency and bankruptcy that may be triggered by unrecovered bad debt.

Predictive Analytics as a tool to manage bad debt

Predictive Modeling can be used to build customer-level models that capture the likelihood that a customer will take a particular action. These models exploit patterns from customer attributes, and other historical and transactional data to identify risks and opportunities. In the case of bad debt, the objective would be to predict if a customer will pay back the debt. Probability of the customer to pay back his debt indicates the risk level associated with it. This probability is the output of the model which is given in the form of a risk score from 0 to 10 assigned to each customer. Inputs for these models can come from various sources like CRM and sales.

These predictive models are built using statistical methods to examine all variables relevant to default using regression techniques. They identify a select set of key variables and then weight the variables based on their importance to the outcome. These scores are very valuable in quantifying the forward-looking risk for a customer/prospect before offering credit.

Once Prospects with a high potential for bad-debt are identified, the retailer can take targeted actions - it can offer them pre-payment options only, or it can price differentially. And if Customers do default on payments, predictive analytics can help retailers maximize recoveries also.

For further reading, please refer some of our recent work that helped a B2B retailer minimize its bad debt.

Using MCDA to Evaluate Business Partnerships

2012-11-21T07:31:00.002-05:00

Strategic partnerships play a vital role for any firm that is into selling solutions. A lot of effort goes into joint marketing campaigns, product development and branding in order to emphasize the value of a solution provided in partnership with another firm. It is very important to partner with a firm that synergies with your Brand. In a large corporation with hundreds of alliances, there is a marked need for a framework to evaluate and prioritize partnerships. Recent survey/specific instances for a large Technology firm indicated that:

Sheer volume of partnerships dilute messages in the market
Some partnerships are detrimental to the firms Brand
Some partnerships are under-leveraged and do not drive maximum value
Partnerships need to be strategically aligned with evolving Line-of-Business (LOB) priorities

This would be a common scenario in many large corporations. To address this need, we need to be able to rank partnerships based on relevant criteria and draw actionable insights. MCDA (Multi Criteria Decision Analysis) techniques come across as very effective and transparent when used in structured decision making processes.

The main steps involved in a MCDA technique are :

Identifying the parameters involved in the evaluation/decision making process
Gather data on the parameters involved and score them
Assign weights for every parameter based on decision makers preference and evaluate the final score
Formulate priorities and recommendations based on the final scores

Consider the example of the Technology firm mentioned earlier. Let us use the MCDA technique to evaluate partners across this firm.

Identifying the decision parameters: First, we need to identify the parameters based on which we will evaluate every partnership. It is vital to identify every possible criteria for a more accurate result. After a discussion with all the stakeholders, it was agreed upon that the partners would be evaluated based on the following parameters:

P1. Brand Strength

P2. Financial Strength

P3. Strategic Alignment and Product fitment

P4. Marketing Spend

P5. Consumer perception

P6. Other preemptive criteria

Depending on LOB priorities, these parameters can be altered for different verticals.

Scoring the parameters: Second, all the partners that get flagged by the preemptive criteria would be highlighted for review. One would not want to partner with a firm that is bankrupt/scandal hit/etc.

The parameters involved would be rated on a quantitative and qualitative scale making it difficult to combine them all at the end of the exercise. To compare all the parameters on the same scale, we rank the outcomes of each parameter in hierarchy and score them on a scale of 1-10. Consider the example of a partner in the Healthcare vertical: When scoring the parameter - Marketing Spend as a % of revenue, the outcome could be anywhere between 0%-15% and above across the vertical. Segment this range into 5 buckets and assign them a score between 0-10 in hierarchy. The range may vary for different verticals and appropriate adjustments may be made. A similar approach can be used for qualitative criteria by scoring them relatively in hierarchy.

Scoring partners and prioritizing them based on final score: After every parameter is given a score, weights are assigned to each parameter based on the decision makers strategy to arrive at a final score for every partner. The final score(S) boils down to a weighted average of the parameters :

At the end of this exercise we have all the partners scored across an index across relevant parameters.

Ex: Decision scorecard on potential partners for a Tech firm in the Education Vertical

Ex: Scorecard to prioritize existing partners

Insights and Recommendations:

The marketing team compared the scores for existing partners against the marketing funding received from the partners for the joint marketing activities. When viewed from the strategic alignment perspective, some partners had a very high synergy but were not being fully leveraged in terms of marketing activities. This resulted in $20 M in increased partner funding towards marketing activities.
7 partners were identified as Brand diluting and Risky and are being reviewed.

For further reading on MCDA, refer these links. Link1, Link2

Towards Accurate Demand Forecasting

2012-10-25T14:54:00.000-04:00

Demand forecasting is critical in todays ‘Customer is King’ scenario. Businesses are juggling customer satisfaction with on-time delivery; larger and dynamic product portfolios on one side and lean inventory management and multiple supplier relationships on the other. It is a delicate balance - a sudden burst of demand can lead to stock-outs and complicate resource planning with suppliers, and a slump in sales could lead to excess inventory and capital constraints. The importance of accurate demand forecasting cannot be emphasized more than ever now, with every business trying to meet their customers ever changing demands, and competing with rivals in shortening delivery times while maintaining margins. Accurate demand planning to face volatile business scenarios is the need of the hour.

Some of the key questions that need to be answered during business planning are:

What is the expected demand for each of the SKU categories over the next few weeks/months (Demand Forecasting)
What is the minimal inventory, and the right reorder point for each SKU (Safety Stock and Replenishment Strategy)
How much inventory of each SKU do we need to maintain at a particular point of time (Inventory Optimization)

To answer all these questions, we first need to be able to understand and forecast accurate customer demand. A number of ways, both qualitative and quantitative have been tried and tested, to forecast demand.

Qualitative v/s Quantitative Forecasting

Most qualitative methods adopt use a top down approach where Management decides on targets, and based on marketing inputs, the forecast is broken down to the smallest unit required. Marketing inputs are primarily extrapolations combined with their current market knowledge and specific marketing plans for the particular time period.

An example is the Delphi method developed by RAND Corporation where a group of suitable experts, typically 5-20 members, are asked to give their forecasts and reasons for the same. Discussions continue till a consensus is reached. This method would then take a median or mode of the experts forecast. Another popular technique used is to conduct Customer surveys to evaluate demand based on customers purchasing patterns and preferences.

Most quantitative techniques and statistical models use a bottom up approach based on historical sales by SKUs and other independent metrics that impact sales to forecast demand. While the choice of forecasting method mainly depends on availability of data, statistical techniques are known to give far accurate results.

One of the main statistical methods used for forecasting is the Time Series technique. This method is extrapolative in nature, i.e. it uses trends in historical sales as an input. Time period based factors like seasonality trends that influence sales are also taken into account to help extrapolate sales.

Some of the Time Series methods used are:

- Moving Averages : In this method the forecast is calculated as a moving average of actual demands of ‘n’ number of past time periods. The number of periods to be chosen is a decision to be made based on data. If we have stable underlying pattern then a large value of ‘n’ is acceptable, but if there are short term fluctuations to be seen then a small value makes more sense. Weighted moving averages are also used where each time period is given a weight.

- Exponential Smoothening methods like Holt Winters method : This is a variation of the Moving Averages method. It incorporates time parameters like trend, growth, seasonality of historical data, and gives recent data more weightage. We have single exponential smoothening; double exponential smoothening (which is applied when the data shows a trend), and triple smoothening or the Holt Winters method. The Holt Winters Method is applied when the data exhibits both trend and seasonality.

- Another popular method is ARIMA : Autoregressive Integrated Moving averages method is another application of the moving average models. Subsequent outputs reflect autocorrelations from both historical data and previous outputs. This method predicts values as a linear combination of the data’s own past values and past errors. ARIMA models can also include other time series data as inputs and these models are called ARIMAX models. These models give best results when data is stable.

Other than time series, Explanatory methods are also employed for forecasting; these methods predict sales (dependent variable) based on other independent variables. Examples of such models are regression models, Artificial Neural Networks (allows for non-linear relationships between the dependent and independent variables), predictive modeling and other econometric modeling techniques. These methods are used when sales history can no longer be considered as the sole predictor for future demand.

Recently other set of forecast model that use both time series and explanatory variables are being used these are known as dynamic regression models, longitudinal models or transfer function models.

Building a Forecasting Model

A number of factors need to be taken into account before building a model:

Interaction with key stakeholders – to answer questions such as what needs to be forecasted, time period for which forecast is required, current methodology for determining forecasts, etc
Collection of data – Available historical sales data needs to be collected in addition to other data such as sales targets for next month/year, marketing budgets and allocations, marketing plans, etc.
Available tools – Tools that are currently available in your organization, can be MSExcel, SAS, R or any other statistical tool.
Choosing the right model – A starting point can be to plot the available data to determine trends, presence of seasonality, outliers and correlation between available variables. The final model used depends on data availability, strong correlation between explanatory variables with sales, etc. It is common to use more than one model - the committee approach.
Evaluating a forecast model – evaluation of a forecast model is possible only after actual data is available for that time period, common metrics used are MAE (mean absolute error), MSE (mean squared error) and MAPE (mean average percentage error).

A recent forecasting exercise is shown below. In this case, the business objective was to forecast daily demand for the next 60 days, for a highy seasonal product. Forecast accuracy achieved was close to 92%.

A more detailed case study can be found here.

Other Forecasting Tools

There are a number of software packages available in the market that aid forecasting sales like SAP APO, Oracle Demantra, Forecast Pro etc. Each of these packages have their unique way of using the above techniques, treating outliers, and understanding seasonal patterns and trends.

However, it is important to understand how these packages work – what technique is being used, how outliers being treated, what corrections are being made to the data, a business check on the assumptions and how does it impacts the final forecast. Without this basic understanding some packages may seem like a black box that churn out numbers for the business to use.

This leads us to believe that Statistical models that are tailor made and build from scratch, along with business sense and other situation-specific business inputs may be more are reliable and holistic.

How Nestle's former executive Carlo Donati is grooming data analytics startup Marketelligent

2012-10-17T06:00:00.000-04:00

Reproduced from The Economic Times article, Oct 16 2012.

Please click here for article.

Having spent 34 years in Nestle, including six years as the head of its South Asia business, Carlo Donati knows a thing or two about who the consumer is and how a company can reach him. The former Nestle lifer also feels companies today are not pushing the right buttons to take decisions. "Data analytics is the missing piece in the top management dashboards," he says. "They can't rely on gut feel to take decisions."

For the 66-year-old Swiss, that intellectual gap is also a business opportunity, one he is tapping by being a mentor, client-builder and, in time, an investor in a $2 million Bangalore-based data analytics startup called Marketelligent. "Data analytics gives the Key Performance Indicators (KPIs) and Marketelligent has identified this niche," says Donati in a telephonic interview from Lugano in Switzerland.

It helps that when he was in Nestle Asia, Roy Cherian, the CEO and one of the two founders of Marketelligent, worked under him for three years. The 47-year-old Cherian headed the chocolates and confectionery business of Nestle India.

While the former Nestle duo knows much about consumer businesses and decision-making, what they don't know as well is the technology that can swallow a mountain of data and spit out pithy observations. And that's where Anunay Gupta, the other founder, comes in.

Gupta heads analytics of Marketelligent, and he comes with a raft of experience in this area. The 44-year-old set up Citibank's analytics back office in Bangalore to support the US bank's cards division. And the Citi global manager who forged the bank's analytics drive, Marcia Tal, will be advising Marketelligent on clients and sectors in which to offer services, as well as technical and business aspects of data management.

Tal, 53, spent 25 years at Citi. During this time, she started and headed Citibank's worldwide analytics division before forming her own consultancy, Tal Solutions. Gupta worked under Tal for five years. "Anunay was one of the first employees of the Bangalore Citi back office. In every sense, it was a startup - inside of a large corporation ( Citigroup)," she says. "The mindset, skills, commitment, tenacity and motivation I saw in Anunay are why I believe he will be successful in building his own company." Tal also advises Black Oak Partners, a US-based data analytics firm.

Marketelligent, formed in 2008, is positioning itself as a data analytics company servicing the retail, banking, energy and healthcare verticals. It will delve into aspects like consumption patterns, market trends and consumer behaviour, among other things. "The founders have competence and professionalism on the topics, which are fundamental for success," says Donati.

According to Cherian, data analytic techniques are transferable across sectors. "You need experts in statistics and forecasting." At the same time, they are looking to hire subject experts, for which they plan to seek help from their two global advisors as well. "We are in talks with an energy expert based in Texas," says Cherian. "He is my vintage (same age), an Indian, and we reached out to him from our IIM network."

Between the founders, Gupta is the techie, responsible for technology choices and delivery quality, while Cherian focuses on operations, marketing and funding. Says Cherian: "The duo - Donati and Tal - help us reach out to global customers." He adds that, since Donati came on board, the company has begun pursuing two client leads in Europe, but he declines to share their names.

While decorated executives as advisors to startups are common, a high degree of engagement in operations is not. Donati is prospecting for clients for Marketelligent - a rare thing for an executive of his stature to do. "Often a marquee customer can help bag business more than an advisor. However, if the advisor prospects for business, it's a different case," says Raman Roy, who has himself birthed four BPO startups.

Sanjeev Aggarwal, MD of Helion Ventures, a venture capital firm, feels the value a person like Donati adds to a startup depends on the level of engagement. "An eminent global name can help in customer acquisition in Fortune 500 companies, which is extremely tough for a startup," he says. "A big name will help open doors, get audience quickly, but a deal is an entirely different matter."

Carlo Donati Joins Marketelligent Board of Advisors

2012-09-05T08:39:00.000-04:00

Marketelligent to benefit from Mr. Donati’s Global Leadership Experience

Marketelligent, a leading data analytics services provider, has announced that Carlo Donati has joined its Board of Advisors.

"We welcome Mr. Donati to the board and expect to benefit from his valuable experience as a Global business leader" said Roy Cherian, Co-founder and CEO of Marketelligent. "We anticipate that Mr. Donati will provide an invaluable perspective and strategic guidance as we continue to help clients grow their businesses."

"Joining the advisory board at Marketelligent presents an opportunity for me to be in touch with the new exciting world of leveraging data" said Mr. Donati. "Marketelligent has a solid team and in-depth expertise in data analytics which can be leveraged by companies. I look forward to being part of the Board and leveraging my experience to help guide Marketelligent’s growth."

Mr. Donati has had a very distinguished and successful career in the Nestlé Group, spanning 34 years and many countries and businesses in three continents. His prior leadership positions include Chairman and Managing Director of Nestle South Asia, Chief Executive Officer of Nestlé Waters, and member of the Executive team at Nestle SA.

SKU Rationalization

2012-09-03T10:46:00.001-04:00

SKU Rationalization is the process of re-looking at your product portfolio and depending on the business you are in, optimizing it on the basis of one or more parameters. The objective of a SKU rationalization exercise is to decrease business complexities that arise from managing too many items, various vendor relationships, inventory, business processes, product lifecycles, customer preferences, etc while ensuring there isn’t any substantial impact to the top or bottom line, and customer satisfaction.

Traditional SKU Rationalization: A SKU rationalization exercise usually starts with identifying parameters that need to form the basis of rationalization:

Identify and retain the high volume SKUs: whether low volumes SKUs need to be rationalized - not a good idea if product life cycles are short and they follow the bell curve, you may end up rationalizing something that is yet to perform
Identify and retain the high margin SKUs: whether low margin SKUs need to be rationalized - ideal for businesses with specific vendor issues, not so ideal for enterprises functioning mainly in low margin high volume SKUs, and high margin low volume SKUs
Identify and retain SKUs that have a higher shelf life - an important criteria in the perishable food business and businesses such as apparel, fashion accessories that follow market trends
Identify and retain SKUs that are in tune with current and future customer preferences - product categories that are likely to grow / retain their market share based on historical data, style and color preferences.

Most businesses would have atleast two key criteria that they would like to base their rationalization on; a good approach is to plot a 2 X 2 matrix with these criteria and then decide on the thresholds.

The above illustration plots margin vs. volumes to arrive at an appropriate threshold.

An alternative approach to SKU Rationalization: Although traditional rationalization exercises are carried out at a SKU level, creating segments of similar product types helps the overall objective of decreasing business complexities. Segments may be made at a style level, color level, or any other relevant dimension to evaluate a category performance. Eg; evaluation of style performance, followed by performance of colors within a style, etc. In this scenario, entire style families may be rationalized, followed by non-performing colors in some style families, and few entire style families may be retained.
The sequence of arriving at parameters for rationalization and creating segments may be interchanged; also the former may be tweaked depending on the latter’s results. Also parameters and threshold may vary in each segment.

Developing an evaluation framework model: businesses may like to incorporate various rationalization criteria, modify each, and measure impact; simulators may be built for the same. Such models may have multiple evaluation criteria with varying thresholds.

A SKU rationalization exercise should be supplemented with an impact study: ’what is the revenue impact associated with the exercise’ and how can it be minimized; ‘what is the inventory carrying impact’ and overall savings, 'will it result in Customer dissatisfaction', etc. Analysis on customer buying patterns of various product categories such as ‘what is the customer reactivation rate on rationalized SKU's’, ‘how many customers return on the same SKU’, ‘is the product seasonal’, ‘what is the ideal time frame to rationalize the category’, ‘what are the substitute products that the customer can be offered’ are also essential.

Please also find a case-study on some of our recent work on SKU Rationalization here.

Artificial Neural Networks ?

2012-08-25T08:19:00.003-04:00

What are Artificial Neural Networks (ANN)? : ANN's are programs characterized by a massively parallel but highly interconnected architecture. The strength of an ANN is pattern recognition and pattern classification, but these programs can also be used for predictive purposes. They use a series of neurons in what is known as the hidden layer (middle layer) that apply nonlinear activation functions in the data. ANNs are used in various fields such as industrial process control, speech recognition, financial market forecasts and chemical compounds identification, to name a few.

Two types of neural networks are used in data mining

1. Supervised: NN is presented with a target variable and fits a function to predict the target variable. Alternatively, it may classify records into levels of the target variable when it is categorical, analogous to statistical procedures such as linear and logistic regression

2. Unsupervised: NN does not have a target variable, but finds characteristics in the data, which can be used to group similar records together, analogous to cluster analysis

ANNs vs. the common Regression. Some pros......

· ANNs present the main advantage of not being based on “a priori” assumptions, required in the conventional statistical techniques. Below are a few such assumptions

· The predictors are linearly independent, i.e. no predictor can be expressed linearly in terms of any other (Multi-collinearity)

· The variance of the error is constant across observations (Homoscedasticity)

· The errors terms are uncorrelated

· ANNs can fit data where the relationship between independent and dependent variables is unknown

· Self-organized neural networks can learn and update, based on the new data automatically

.....and some cons:

· ANN Model is generally a ‘Black Box’ i.e. neither the relationship nor the strength of the relationship between dependent and the independent variables is revealed

· ANNs sometimes tend to over-fit the given sample of training data

The final verdict : With all its advantages, there are still limits to where the ANN could be put to use.

USE: Where the prediction is the end result and the ANN can adapt itself to the changing customer behavior. Eg Credit Scoring, Fraud Detection, etc.

DON’T USE: Where the output needs to be supported by the relationship of the independent variables to the dependent variable. Eg Marketing Campaign Targeting, Response model, CRM etc.

Optimizing Acquisition Investments in the Insurance Sector

2012-08-10T10:46:00.000-04:00

Insurance industry belongs to the category of businesses where Analytics can help increase efficiency in each phase of the customer life cycle – from acquisitions to cross-sell to retention. Traditionally, the underwriting process was the one with maximum analytics exposure (and the realm of actuaries). But with high claims ratio and rising acquisition costs, the inclusion of analytics in other areas has become indispensable. Within the whole cycle, the customer acquisition process stands to gain the most with the help of Analytics.

Acquisition costs range from 5% to 25% of the premium for most Insurance firms. The scope of optimizing these costs using analytics is high and can be implemented in following steps:

1. Building a robust data management system: Information from internal and external sources should be collected and archived. Large organisations often have structured and unstructured information, spread across various platforms. It is essential to maintain cross-organisational data, along with data from external sources like credit bureaus, Govt. agencies, demographic and psychographic sources, etc. in order to harness the true capabilities of analytics.

2. Preparing prospective customer’s data: For completely new leads, external data would be the major source of analysis. This would include data from application form, as well as third-party sources like Acxiom, credit bureaus (if legally permissible), etc.

3. Developing the analytical framework: There are various modelling techniques like Logistic/ Linear regression, Neural Networks, Segmentation using CHAID/ CART etc., available in order to develop the appropriate analytical framework. These frameworks should be able to classify customers according to their probability of conversion.

4. Optimizing the expenditure: On the basis of probabilities and classifications obtained by using the analytical framework, acquisition investments can be strategically channeled in order to maximize returns.

Marketelligent recently helped a US Insurance provider reduce its acquisition cost by 25%, allowing for a significant impact on its profits. Please refer to this link for details.

Marcia Tal Joins Marketelligent Board of Advisors

2012-08-06T09:45:00.000-04:00

Marketelligent to benefit from Marcia’s extensive expertise in Decision Management

New York, NY (Aug 6, 2012) - Marketelligent, a leading data analytics services provider, today announced that Marcia Tal, Founder, Tal Solutions Inc., has joined Marketelligent’s Board of Advisors. Marcia, a seasoned executive, will bring business leadership experience from the Decision Management space to Marketelligent.

"We welcome Marcia to the board and expect to benefit from her valuable experience leveraging data for better business performance," said Dr. Anunay Gupta, co-founder and Chief Operating Officer of Marketelligent, Inc. "We anticipate that Marcia will provide an important perspective and sound guidance as we continue to grow our business."

"Joining the advisory board at Marketelligent presents an exciting opportunity for me," said Marcia. "Marketelligent is delivering significant value across industries, with its in-depth expertise in data analytics. I look forward to participating on the Board of Advisors, and leveraging my professional experiences to impact Marketelligent’s growth."

Prior to her founding Tal Solutions, Marcia spent 25 years at Citigroup, where she was recognized for creating and building Citigroup’s Decision Management function. Marcia was the EVP for Decision Management, where she institutionalized an analytics driven mindset across business decisions.

Marketelligent establishes presence in the US

2012-07-23T03:30:00.001-04:00

Marketelligent is pleased to announce that it has established an official presence in the United States, with the opening of an Office in New York City, and the on-boarding of Buck Chintamani as EVP, Strategic Initiatives and Business Development.

The US office will cater to local growth opportunities, and will be headed by Buck. Buck will be instrumental in developing new channels and business partnerships to help businesses leverage analytics for profitable growth.

Buck has significant experience working in start-ups, including Infosys when it was a start-up, and in companies like Yantra and Reva. Bucks speciality is in scaling up business by driving sales via leveraging channel partners and other innovative approaches.

Buck has done his Undergraduate from IIT Bombay and has an MBA from IIM, Ahmedabad. He can be reached at buck.chintamani@marketelligent.com and at 212-837-7827.

Trade Promotion Optimization - How to use fact-based business intelligence to increase ROI on trade spends

2012-06-19T03:27:00.001-04:00

CPG companies spend nearly 15-20% of their annual revenue on trade promotions; this is fast nearing a $700 billion figure world-wide. Trade spending is now the second largest expense item on most consumer manufacturers’ P&L's. In-spite of this significant investment, only 30% of trade promotions are estimated to be profitable.

Most companies are unable to improve the ROI of their spends due to lack of a structured internal mechanism that: collates market information in a timely manner, leverages insights from historical promotions, uses these insights to collaborate better with retailers and does a post-event evaluation through a built-in performance management system.

As of date a large number of CPG manufacturers have complex, conditional discount structures that they find difficult to implement & negotiate with retailers and expensive to audit & measure. The current economic recession and accompanied growth of private labels adds to the existing woes. Making a mistake in planning the right trade strategy can result in lost sales, declining market share and damaged retailer relationship.

Trade Promotion Optimization (TPO) uses advanced econometric modelling techniques to help manufacturers refine their promotion strategies, identify the right price & discount point that maximizes sales lift and ROI, meets their volume and profit targets and eventually help retailers and manufacturers increase consumer penetration and build bigger consumer baskets that have a long-term sustained impact on baseline sales. For a deeper look into how TPO's can help you, please refer this whitepaper.

To highlight the savings, let’s take an example:

For a $2B CPG manufacturer who invests say 15% of its sales (~$300MM) on trade promotions, a mere 2% improvement in returns can yield $6MM incremental revenues which can be ploughed back to the business!!!

Data Visualization

2012-05-31T07:57:00.000-04:00

It looks like everyone loves experimenting with the latest in Business Intelligence (BI) Products. We have Business Objects and Cognos – the powerhouses of enterprise-wide BI platforms. And more often than not, the CTO signed off on these multi-million dollar implementation solutions, only to see partial utilization by the rank and file. These platforms are basically backward looking - telling Managers what had already happened. More recently, platforms like Tableau, Spotfire and Qlikview have taken center-stage. Their ability to do drill down on the fly along with the promise of quick implementation makes them more appealing. Corporations are forever being sold such packaged BI solutions that promise to deliver the moon. And many have taken the bait.

However we regularly come across Clients who somehow still see a gap, and want to get more customized and easy-to-use reports; that are rich in insights and visuals. To this end, I think that none of the existing off-the-shelf BI solutions - large and small - meet the requirements. A few of them come close to providing this capability, but however get a bit too complex for Senior Managers to use (who often have no time to learn new packages, or detest coding). As such, there exists a clear need for someone to fill this gap and provide a BI-like service.

We have always felt that a service wrapped around a simple and customizable BI solution is the need of the hour. As such, please refer these simple illustrations : Demo1 and Demo2. They give you quick insights, and allow for insights discovery in a manner customized to your needs. Most likely, you will also find them very appealing.

Marketelligent launches SocialFellas - a Social Media Intelligence Platform

2012-02-25T11:55:00.001-05:00

Marketelligent announces the launch of SocialFellas, a social media intelligence platform for social media monitoring and customer engagement.

The world today is experiencing a social media revolution, and increasingly an age of digital relationships between brands and its consumers. With ease of accessing the internet, people are communicating online about businesses, products and brands 24X7, and this is creating tremendous amounts of data and ample opportunities to manage and influence perceptions. SocialFellas helps you make sense of all this by providing real time access to social media conversations across the digital space on one simple-to-use platform. SocialFellas will transform the way you understand and engage with your customers on the web.

Key features of SocialFellas:
1. Brand Monitoring: Add relevant keywords to dig out valuable insights from social mentions about your brand
2. Influencer Monitoring: Identify key influencers for the brands based on their propensity to influence others on the web
3. Competitive Intelligence: Compare your brand presence vis-à-vis key competitors
4. Campaign Management: Integrated approach to campaign management by managing campaigns across channel and tracking their performance real time
5. Engagement Console: Provides scope for one-to-one interaction with your customers across the web

“SocialFellas distinguishes itself from other products in the social media analytics arena on 3 counts:enables a quick set-up, provides a single dashboard for monitoring brand performance across the web, and a well-equipped team for doing customized analysis to answer business questions.” – says Roy K. Cherian, CEO and Co-Founder, Marketelligent.

For more information about SocialFellas, please visit us at www.socialfellas.com or email us at info@socialfellas.com

Analytics in Action - Building Value Through Targeted Solicitation

2011-08-23T03:16:00.022-04:00

The Challenge: Managing customer experiences while improving their participation.

We live in a mass customized society – one where consumers want it their way! Global market forces are driving the continual evolution of the food and beverage industry. Changing consumer preferences are dramatically impacting business strategy. In this fiercely competitive marketplace, one must consistently and cost-effectively innovate to meet consumer demand.

One such CRM strategy is the Loyalty Program. Loyalty programs are structured marketing efforts that reward, and therefore encourage, loyal buying behavior; behavior which is potentially beneficial to the organization.

The loyalty industry in United States has a perceived value of $12 billion in 2011, of which the retail sector accounts for 40% of total loyalty program memberships but just 25% of the perceived reward value issued. Retail in particular, therefore largely presents an opportunity to mine buried treasure.

Our client is a leader in food & beverage category, and has more than 10,000 outlets. The client has also, like many others, built a ‘loyalty program’ with multiple reward cards and has around 80,000 members enrolled. Earlier, the client had commissioned a “Usage & Attitude Study” to help them understand motivations behind customer purchase decisions.

The client wanted Marketelligent to develop an actionable customer segmentation framework on the basis of perceptual & behavioral data of these card holders. Our immediate objective was to review the transactional data of customers who had signed for the client's loyalty program. We observed a particular trend in the customer activity, akin to the “Long Tail” behavior. Almost 35% of customers consistently purchase products from one or two categories, while the remaining customers tend to experiment amongst the categories available.

The Approach: Develop an actionable customer segmentation framework.

The focused research data was tied in with customer transactional details using a technique called Data Fusion (see Exhibit 1). Having understood the transactional data & identified the drivers of customer behavior, we segmented the customers on the basis of their “needs”, visits frequency and average value of a transaction.

"K Means" clustering technique was employed to create the segments (see Exhibit 1). As a result, customers were placed into four segments, differentiated on the basis of 18 behavioral & 12 perceptual variables (see Exhibit 2).

Targeted strategies were then rolled out within marketing & sales division of the client organization; and the marketing team in particular realigned its role to focus mainly around segmentation-driven

Exhibit 1

Exhibit 2

The Result: Managing customer experiences while increasing their involvement.

In order to create growth opportunities, we followed the segmentation exercise with affinity analysis to design up sell/cross sell strategies for each segment.
To lift the monthly transactional value of the “Sweet tooth” segment, we had recommended our client to add packaged offers around dessert & snacks, as we found the product categories to have high affinity. Within 3 months, the monthly transaction for the test group increased by 30% (Exhibit 3).

Exhibit 3

Also, the client expanded its newsletter from one to two, basis the segments; one focused specifically on the products available, and the other on lifestyle & new product information. Specific sections such as “Chef’s Favorites” & “Make your own coffee” now have clickthrough rates three times more than the previous general newsletter.

Big Data is Yesterday’s News.

2011-08-02T03:55:00.000-04:00

'Big Data' is already here. Over the past few years, businesses have been able to put in place massive ERP systems, storage devices and data warehouses. In the process they have spend billions of dollars. On top of this, they have spent extra millions by putting in place ‘Business Intelligence’ systems to tell Managers what has happened in the past. So today, a Manager can see exactly which Customer has spent how much, on what date, at which store and on what Product. Essentially Business Reporting.

Which brings us to the next stage in the information continuum – now that we have all this data and know what has happened, what do we do next? How can we leverage this data to influence consumer behavior, to manage growth, to predict the future, to optimize strategy, to take actions in almost real-time, etc in a forward-looking fashion.

In effect, to go from the ‘what’ to the ‘who-why-when-where’. And in the process become smarter about business decisioning.

“Real-time Customer-level Decisioning” will become increasingly important. Real-time because you want to act on fresh information in the most expedient manner; lest your competition take advantage. And Customer-level because each Consumer in unique and wants to be treated uniquely. Technologies exist today that can deliver on the promise of both. We have algorithmic trading where actions are taken in milliseconds. And we have predictive analytics that can deliver on customer-level decisions.

Unfortunately, most typical businesses are not there yet. For Consumer-focused companies, “Analytical CRM” is the next phase in the evolution of big data, and will be increasingly leveraged by them to manage their competitive positioning. The “Information” in the IT revolution will then become a reality.

Analytical CRM can help a business increase revenues by helping design the right products; by identifying those customers most likely to buy a particular product/service, and getting their product to them before the competition acts; by increasing Customers engagement with their Brand by having them buy more of that Product or by cross-selling their other products. It can also help them optimize expenses by managing advertisement, marketing and operational expenses in the most efficient manner.

Big data is yesterday’s news. Now let’s leverage this data for business optimization.

Marketelligent in the Middle East

2010-11-30T22:57:00.000-05:00

Marketelligent now has a voice in the Middle East. This has been made possible through our partnership with Jacobsons - a Dubai based firm that has been providing Direct Marketing and CRM Solutions across GCC and Levant for more than 25 years.

The Middle East has been a significant economic zone and will continue to expand. As businesses across industries grow and mature, competitive pressures will necessitate the need for advanced analytical solutions and services - something that Marketelligent has at its core.

We are looking forward to our partnership with Jacobsons and the opportunities to serve Clients in this area.

Analytics in Action

2010-10-04T00:29:00.005-04:00

Case studies not only showcase how we work in real business scenarios, but also help one understand the process behind what we do and how we go about addressing the business problem.

A few case studies illustrating how Marketelligent has helped various business situations in the CPG/Retail, Banking and Media domain are illustrated. These are real life case studies based on direct experience from projects led by Marketelligent team members. Though the case studies are for specific industries, related principles can be adapted and applied across industries

How Marketelligent helped a Lender lower its Default Rates
How Marketelligent helped a Bank retain its Profitable Assets
How Marketelligent helped a Card Issuer Combat Transaction Fraud
How Marketelligent helped an Auto OEM identify 'Hot' Leads
How Marketelligent helped a US Manufacturer Improve Demand Forecasts
How Marketelligent Helped A Leading Alcobev Manufacturer Forecast Sales
How Marketelligent Helped A Leading OTC Manufacturer Track Market Development
How Marketelligent helped a CPG Company optimize its Media Planning

Unlocking Value in Merchandise Returns

2010-09-09T12:45:00.000-04:00

Over 8 – 15 % of a Retailers Sales are returned for various reasons. This can cause both a negative sales impact, and an increase in expenses. However smart strategies leveraging transaction information
and the best of analytics can help manage Returns leading to a significant boost to the bottom line.

As an illustration - for a $1B annual revenue retailer; the impact to the bottom line in terms of increased revenues and lower expenses can be as much as $20MM.

Click here for more details.

A few areas where analytics can positively impact Returns:

1. Treat Returning Customers Differentially

Handling product returns should be an important component of a retailers CRM strategies
Retailers should capitalize on product return occasions and treat them as additional touch points to strengthen their relationship with customers

2. Encourage Cross-channel synergies

A cross channel return policy will create cross-selling opportunities. For example, retailers can encourage customers to shop in stores when they come to return online and catalog orders

3. Minimize Fraudulent Returns

Fraudulent and abusive returns cost retailers approximately $10-15B annually, accounting for nearly 10% of the return dollars
30% of the top 100 retailers actively use a returns solution that can verify if the receipt is valid, but cannot intelligently advise the store about the validity of the return

4. Minimize additional expenses related to returns

Handling returns costs US retailers over $165B each year
Expenses related to returns such as product repackaging, markdowns and item disposition cost retailers even more

Marketelligent at the 2010 Customer Show in Dubai

2010-08-25T04:35:00.000-04:00

Marketelligent Head of Analytics Dr. Anunay Gupta will be discussing how businesses can maximize revenues by better understanding Customer Behavior and applying the principles of 'Customer Lifetime Value' at the 2010 MENA Customer Show in Dubai Nov 22-25 2010.

Click here for more details and registration