In marketing analytics, RainMan offers the full arsenal of techniques RainMan models unearth insights across marketing, sales, CRM & promotions using appropriate methodologies

Our services provide solutions to marketing issues across a variety of industries. We draw upon learnings from previous exercises, ensuring that our experience is constantly used to add value to your business.

End to End
Marketing Analytics Services

RainMan's advanced Marketing Analytics service uses cutting edge methodologies covering the entire gamut of Marketing and Sales opportunities.

Media Deep Dives

It is not only important to gauge the performance of media on brand outcome, but also the performance of the ingredients of each medium.

Marketing mix analytics help to dissect the impact of different marketing inputs on business outcomes. This technique measures the role played by each media in generating sales/share/mind measure. But, while these are important, one also needs to go in depth on the impact of each of the ingredients of the media. So, we don’t just, for example, study TVs impact, but the impact of each individual genre/channel.

Media deep dive analytics, based on RainMan’s advanced statistical proprietary techniques, help marketers fine-tune the impact of media strategy and improve its effectiveness manifold. Some of media deep dive components are:

1. TV – Measuring the impact by channels, time slots, programme genre and copy on sales/share/ mind measure

2. Digital deep dive- Measuring the impact of each of the digital components of platforms in the mix

3. Print deep dive – Measurement of impact by type of publication (e.g dailies vs weeklies vs monthlies), ad position and size

4. Radio deep dive – Measurement of impact by time slots, programmes and duration

5. Out of Home deep dives - Measurement of impact by size, location

Brand Equity Models

For most brands, advertising is an important component of the marketing mix. It’s also a major expenditure. Hence, it is important for marketers to be able to track how efficiently a brand’s advertising drives consumer mind measures and final business outcomes.

The Mind Measure Planner is a process which helps marketers to quantify the impact of media on various consumer mind measures. It leverages the availability of quality continuous tracking measures and media data.

In its simplest form, the Mind Measure Planner attempts to establish a relationship between advertising efforts (such as media weight in GRPs/ TVRs or digital spend and Brand Equity. It also establishes which mind measures most influence the business metric, giving marketers a direction to craft effective strategies to push the identified mind measure variable.

Price Sensitivity & Analysis

Price Sensitivity & Analysis

Pricing is the most critical component to maximizing revenue. Pricing can attract or deter people from a brand, but there are other nuances that have to be kept in mind, such as how pricing affects consumer behaviour based on SKUs, geographies and seasonality.
RainMan’s unique approach to pricing analytics estimates the price elasticity of the brand and deep dives into that of various SKUs, factoring in all marketing inputs. Our unique process results in the following outcomes:

1. Estimating business outcomes based on different price scenarios by measuring the price sensitivity of the brand.
2. Creating an optimal SKU mix by breaking the average SKU weighted pricing sensitivity into the pricing sensitivity of each SKU. This helps identify the most sensitive and less sensitive SKUs to create a right mix strategy for the future.
3. Pricing optimization by SKUs to get the right pricing strategy to maximize sales.

Product 1
Product 2
Product 3
Product 4

Promotions Analytics

While brand building is usually the focus of attention, much less attention is paid to promotions, whether direct to consumer or to trade. They are seen as tactical and one-time events. However, promotions are very effective in driving immediate sales and, when run year-round with a similar design, can demand large budget allocations. Besides, successful promotions can be repeated annually, making them valuable brand assets in the medium to long term.

RainMan compares the effect of promotions of different designs, scale, and durations, after accounting for all other factors. This is done by building comprehensive marketing mix models which estimate the impact of both marketing inputs (including promotions) and that of environmental factors.

Geo Lift Solution

Traditionally effectiveness of digital campaigns are measured using attribution models. But given the increased data privacy regulations, measurement through attribution models is becoming a challenge. Increasingly randomized control tests are considered to be a fine alternative to attribution models given the present day data privacy challenge.

This concept originated in the pharmaceutical industry, and has been increasingly adopted in marketing science. Typically, the experiment involves dividing the audience into two randomly selected groups – the exposed group which is served the ads, and a holdout group which is not exposed to the ads. When the campaign is run, we expect the sales of the exposed group to increase above its past average and the control group is expected to maintain its past average.

Sales After CAMPAIGN

Thus the holdout group provides the baseline, relative to which, efficiency of intervention is measured. Randomized control tests are performed either on individual customers if the data sample is large or on regions or markets for a group of audience. This is called a Geo Lift solution. RainMan deploys machine learning algorithms to implement Geo Lift solutions that help measure the effect of all contact points on incremental sales.

Mind Measure Models

Mind Measure Models

For most brands, advertising is an important component of the marketing mix. It’s also a major expenditure. Hence, it is important for marketers to be able to track how efficiently a brand’s advertising drives consumer mind measures and final business outcomes.

The Mind Measure Planner is a process which helps marketers to quantify the impact of media on various consumer mind measures. It leverages the availability of quality continuous tracking measures and media data.

In its simplest form, the Mind Measure Planner attempts to establish a relationship between advertising efforts (such as media weight in GRPs/ TVRs or digital spend and awareness response (the proportion of target respondents who are aware of the advertising or associations with a particular attribute). It also establishes which mind measures most influence the business metric, giving marketers a direction to craft effective strategies to push the identified mind measure variable.

Mind Measure Planner works as follows:

1. Identify the importance of mind measure in driving the business metric: Identify the right mind measure and quantify its sensitiveness and contribution in driving the business outcome. Set this metric as a Key Performance Indicator (KPI)
2. Evaluate the performance of past or current advertising on the set KPI mind measure metric: This quantifies the impact of media on key mind measures in the relevant brand target group.
3. Simulate the impact of current and future advertising on key mind measures, resulting in a variant copy strategy of weights and schedules on media
4. For select categories, where digital presence is high, measure the interrelationship between paid, owned and earned media for actionable future strategy.
5. Create media planning guidelines: Using multimedia optimization technique results as a guideline, create a framework for optimal media deployment to influence the key mind measure KPIs identified and hence sales/market share.

The RainMan team passes the results through its benchmarking product called RainGauge TM, which stores the results of 4000+ model results implemented by RainMan across markets and categories. The result of each fresh analytic project compares with relevant market and category results and helps marketers to benchmark the results of their brand.


Rainman school of data science

The demand for advanced analytics candidates is exploding. In India alone, demand will triple to more than 200,000 by the year 2020. Organizations will benefit from equipping their people with advanced marketing analytics skill sets. Likewise, professionals will do well to seize the initiative and upgrade their abilities on their own.

Likewise there is a need to train marketing people to understand advanced analytics and use the analytics infrastructure optimally. 

Why Learn Advanced Marketing Analytics from RainMan School of Data Science?

● Stay relevant. Stay ahead.
Today, organizations including your own, recognize the importance of Marketing Analytics and are looking to reshape people with potential by equipping them with marketing analytics skill sets. At this juncture, a course in Advanced Marketing Analytics can become the turning point of your career.

● Supercharge your analysts and marketing 
Mastering Advanced Marketing Analytics will take your company to the next level— both in terms of your approach to analytics as well as the value analytics adds to your organisation. From a future point of view, this is an opportunity you just can’t miss.

Learn from industry experts
RainMan has for the last decade worked with leading organisations, (including Unilever, ITC, Wipro, Kellogg’s, among others) empowering them with big picture insights and the tools they need to effectively capitalize on opportunities. Learn from us and you’ll benefit from the real-world knowledge and the proven perspectives only experienced practitioners of the art of analytics possess.

What do you need to get started?
We customise all our programs to your organisational needs as each one is at a different level of evolution and in different industries. Our aim is to create professionals within an organisation, who can carry the torch of data based learnings.

Read More

Streamlining your data workflow

In today's data-driven world, organizations collect vast amounts of data from various sources. However, data collection can be a challenging task, as data can come in different forms and may be incomplete, inconsistent, or contain duplicates. This is where creating an analytical base table (ABT) comes into play.

An ABT is a consistent and cleaned dataset that has been prepared for analysis. It involves collecting, cleaning, integrating, transforming, and splitting data to create a reliable and consistent dataset suitable for analysis. The ABT ensures that the data is of high quality, free from errors, and provides a solid foundation for further analysis, making it easier to identify trends and patterns in the data.

RainMan Consulting is an analytics firm that specializes in creating analytical base tables and providing data-related solutions to businesses and organizations. Our team of experts is skilled in data cleaning, integration, transformation, and analysis, helping businesses ensure that their data is of high quality and suitable for analysis.

Here are some important points to keep in mind when creating an ABT:

  • Collect all relevant data: It is important to collect all relevant data from various
    sources, such as spreadsheets, databases, web APIs, or text files.
  • Clean the data: The data may contain errors, inconsistencies, or missing values.
    Cleaning the data involves removing duplicates, filling in missing values, or correcting errors.
  • Integrate the data: Integrating the data involves combining data from multiple sources or tables, and transforming the data to make it suitable for analysis.
  • Split the data: Splitting the data into training, validation, and test sets ensures that the model is evaluated on data that it has not seen before.
  • Creating an ABT can save time and resources, as cleaning and transforming data can be time-consuming and resource-intensive. By creating an ABT, the data cleaning and transformation are done once, and the cleaned dataset can be used for multiple analyses.
  • Keep the ABT updated, through a process, so that it is always available for analysis

Analyzing data can help businesses make informed decisions, and an ABT is an essential step in the process. By creating an ABT, businesses can ensure that their data is consistent and reliable, allowing for better analysis and decision-making.

In conclusion, creating an ABT is an essential step in solving data-related problems. It
ensures that the data is of high quality and provides a reliable and consistent dataset that can be used for making informed decisions. By following the steps outlined above and working with an experienced analytics firm, businesses can turn their data into a valuable asset and gain a competitive edge in their industry.

Other Services


To come up with an effective portfolio strategy, it is often crucial for marketers to use existing data to predict the growth of a category (and any sub-categories) over time.

RainMan uses a number of methods to forecast category growth. These take into account the ebb and flow of the category in the past and also factor in leading indicators. These include growth per capita, segment growth due to changes in demographics as well as correlations with other categories a bit ahead in the evolution. For instance, growth of the noodle category will indicate growth in the sauces category over time.

Forecasting sales growth for a brand, on the other hand, needs to take into account not only the category level influencers but also the trend in inputs for the brand. A combination of these two factors, overseen by domain expertise and seasoned statisticians, is the recipe for a good forecast.

RainMan has successfully conducted forecasts for a number of categories and brands in personal care, beverages, and feminine care. We have a number of methodologies at our command, including ARIMA, ARIMAX and VAR, and often use more than one method to triangulate a forecast.

YS Volume Sales: Actual vs Predicted
YS Volume Sales: Actual vs Predicted
YS Volume Sales: Actual vs Predicted

Sales Team Optimization

The sales force is a scarce and expensive resource for any company. This holds true whether it is a direct sales force selling real estate or automobiles, or an indirect sales force selling to intermediaries like grocery stores or general merchants in the case of FMCG products. Marketers therefore need to effectively track and optimize the efficiency of their sales people. RainMan has extensive experience in improving sales expertise for all types of companies.

For direct sales, the data of all past sales to individual customers (and their profiles) is used to build models that predict the probability of a customer purchasing a product. Since seasonality and time of last purchase are also important parameters, probabilities can be worked out for a specific time period, too. Using these probabilities, a Sales Manager can better deploy salespeople to focus on selling a specific product to customers at a specific time. This reduces the time spent in dry (unproductive) calls, leading to higher sales realizations.

In the indirect segment, the customer is no longer an individual but a business. As such, this is a continued relationship between the two parties. In this case, RainMan profiles stores by culls data at an individual invoice level. Logistical models then segment stores into different categories, based on preassigned objectives. Using this information, instead of using a similar approach for all stores, leads to separate strategies being evolved for significant segments. This in turn leads to better relationships and sales. Moreover, algorithms can predict individual store sales at an SKU level. This makes order-taking and inventory control much easier, besides helping identify profitable cross sells.

RainMan’s Sales Data Automation model creates business and KRA based reports that track important parameters. This enables the sales team to make tactical decisions on the go.

One ViewTM - Cross Media Attribute and Optimization

The digital revolution has changed the way consumers engage and interact with brands. Earlier, the process was quite linear, but consumers today interact with brands through a plethora of media connect points. Thus, a marketer's task is to maximise the effectiveness of each connect point that is deployed.

It may seem that the last mile impact is due to the media that delivers the sale. But in reality, many media connect points would have played significant roles in driving the consumer to the last media connect point through which the sale is generated. Not measuring the impact of these could lead to an unrealistic over-reliance on the final connect point.

For example, TV may have a direct impact in driving the sales or brand measure. But, TV could also lead a consumer to a website or Facebook page, thus enhancing the sales impact of search or social conversation. As can be seen, measuring the direct and indirect effect of each medium is essential.

One ViewTM, RainMan’s proprietary technique, helps marketers get a unified view of the entire cross-media impact on a brand. It helps marketers track the real impact of a brand’s media connect points by factoring in their direct and indirect effects on sales. That done, through statistical optimization, the best combination of media to deliver maximum outcome can be derived.

Social Media Brand Trackers

Consumer experience is one of any brand’s biggest business drivers. The advent of social media, though, has resulted in millions of consumer comments – positive and negative - being posted on social media daily, in real time. Analysing and managing them in equally quick time is therefore not just important, but critical.

RainMan’s approach to social media tracking combines innovative statistical techniques with linguistic principles to help marketers read the consumer pulse and use it as a positive driver for growth.

RainMan’s algorithm first converts unstructured data to structured data. Then, applying linguistic principles to the structured data, it generates a Social Engagement Index. This is a metric derived by converting textual data on the brand to a measure of engagement. It uses linguistics to infer the degree of positive or negative opinion, and the emotional and personal connection to the subject of the conversation, to arrive at a true measure of consumer engagement.

The social media brand tracker analytics thus provide the following core   benefits:

  1. Routine reports
  • Word clouds
  • Time series of Sentiment Ratios
  • Deep dives of Sentiment Ratios based on
    ◦ Category
    ◦ Brand
    ◦ Variant
    ◦ Aspect
  1. Brand perception analysis based on social media conversations
  2. Time series social engagement data for future econometric analysis to examine its impact on brand measures
  3. Customer profiling against those of the competition using depth of involvement, specific areas of interest within category, product usage, expressions of advocacy etc.