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 need to go in depth on the impact of each of the ingredient 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. 1. TV – Measuring the impact by channels, time slots, programme genre and copy on sales/share/ mind measure
2. 2. Digital deep dive- Measuring the impact of each of the digital components in the mix
3. 3. Print deep dive – Measurement of impact by type of publication (e.g dailies vs weeklies vs monthlies), ad position and size
4. 4. Radio deep dive – Measurement of impact by time slots, programmes and duration
5. 5. Out of Home deep dives - Measurement of impact by size, location

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 is also establish 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

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.

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 View^TM, 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.

As one of Philip Kotler’s famous 4’P’s, pricing is one of a marketer’s biggest challenges. 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. 1. Estimating business outcomes based on different price scenarios by measuring the price sensitivity of the brand.
2. 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. 3. Pricing optimization by SKUs to get the right pricing strategy by to maximize sales.

In Marketing Analytics, it is the specific that is of value, not broad estimates. RainMan customizes its analysis to the brand at hand, instead of throwing up broad generalizations and ROIs which off the shelf or software packages tend to do.

This is especially so with regard to analysing the effect of promotions.

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.

Once the impact of promotions is estimated, RainMan uses its proprietary Non Linear Optimization process to understand the impact of each specific promotion. This analysis examines promotions not just from a macro level, but from drivers within the promotion – was it the discount offered, the free gift bundled, or the duration of the promotion that made it a success? With this knowledge, marketers can retain and fine-tune promotions with a high ROI and do away with unsuccessful ones.

RainMan’s proprietary software Octopus^TM is used by marketers to maintain a database for sales and marketing at a granular level. This can be used to improve sales efficacy by pulling preconfigured reports that focus on the key opportunity and problem areas at a market level.

Given that salespeople have limited time to meet and convert leads, automating sales data gives the sales force clarity as to which markets, stores or individual customers to target. They can access this data through a variety of devices, with processes and protocols in place to ensure data security.

So, say, in a given scenario, Octopus^TM predicts the fastest growing stores as opposed to those in decline. This can be cross-referenced, through a data drill-down, to match respective stores with key SKUs that the sales automation system predicts will sell best in them. This, then, gives the time starved salespeople a clear list of priorities to chase.

The unique differentiator that makes Octopus^TM such a powerful tool is that it can be customized for each client. So, rather than being a “product”, with many difficult-to-implement bells and whistles, RainMan studied each client business process, before key reports and models are integrated. This, in turn, results in a sales automation system that produces reports that are of indispensible value to the client.

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. Analyzing and managing them in equally quick time is therefore not just important, but critical.

RainMan’s approach to social media tracking combines innovative statistical technique 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
2. Brand perception analysis based on social media conversations
3. Time series social engagement data for future econometric analysis to examine its impact on brand measures
4. Customer profiling against those of the competition using depth of involvement, specific areas of interest within category, product usage, expressions of advocacy etc.

Good marketers never assume that a customer stays a customer forever. Instead, they continuously take steps to keep their customers.

In some cases, like that of credit cards, loyalty programs or magazine subscriptions the data generated by transactions and interactions with the customer can be used to improve the sales process and also to reduce the number of customers leaving the brand.

Such data, transactions and interactions (emails, phone calls, response to promotions) is a rich source of behavioural patterns. These can be used to improve sales, by determinind whether a related product can be cross-sold, or if the customer can be upgraded to a superior or more profitable product. Typically, promotions are tailor made for specific clusters, enhancing their success rate and leading to a better yield.

The same process can also identify customers who could leave the category or defect to a competitor. Called a churn analysis, this model profiles customers who have left in the past and identifies specific markers displayed before they stopped being customers. By picking up these markers in existing customers, strategies can be formed to retain them and reduce the marketer’s loss of franchise.