Radha R would like to thank Rahul Mohandas for contributing this month’s column. (CPG) organizations have typically invested in inward-looking information systems, such as supply chain and finance, and have shied away from market information systems. While the existing information systems deliver results to a large extent, there are limited additional benefits that these systems can offer, which leads organizations to consider new focus areas . One area is marketing information systems.

This month’s column discusses the information needs of the marketing function in a typical CPG organization and various marketing metrics and sources of the information of interest. It will also discuss the kinds of challenges in putting together the information in a holistic manner.

The consumer packaged goods CPG industry, as the name suggests, attempts to produce and market packaged goods such as personal care, home care, food, clothes and beverage items to be consumed by the general public.

While the functions in a CPG organization, such as production, supply chain and customer development, focus on getting these items to the point of purchase as quickly and efficiently as possible, the marketing function attempts to understand what the consumers are looking for as well as:

  • Develop products that have the features and benefits consumers are looking for,
  • Sell products at a price consumers are willing to pay,
  • Promote the product for consumers awareness and trial and
  • Make the product available at a location where consumers are be able to buy them conveniently.

The marketing function needs to work seamlessly with the other functions in the organization such as R&D, product development, supply chain (including procurement, manufacturing and logistics), customer development and finance to be able deliver on their promise to the consumer.

Marketing function can only be measured by its success in the marketplace, and the only metric that can measure success in the market is the organization’s share of the chosen market.

An Analysis Framework and Approach

Just tracking market share is not sufficient because it will not provide insights into why the market share is increasing/decreasing or whether the changes are in line with the rest of the market. An example framework for analysis is shown in Figure 2.

To enable the example in Figure 2, marketing would need to track various metrics from external sources and correlate them with information from internal financial and distribution systems. Possible external sources include:

  • Retail audits and/or point of sale (POS) scanning data,
  • Consumer panels,
  • Consumer surveys and
  • Media spend tracking.

The information from these sources could be collected after validation and stored in a central/distributed data warehouse for reporting.


While it is typically easier for an organization to implement information systems to track information from its internal processes and systems, it is much more complex to build an information system that relies on collecting information from external sources and combining them with information from internal sources, as is required for the marketing function.

Differences in Definitions

One of the main challenges an organization will face when putting information together will be aligning definitions of the different dimensions and determining exactly what constitutes each of them.

For example, consider the product dimension. If the category is not clearly defined, none of the metrics make any sense. In the case of an iced tea vendor, whether or not the beverages category is defined to include beverages such as colas has a huge impact on all consumer-oriented measures including market share.

In addition, the different metrics need to be defined and the definitions communicated to all parties involved so that the suppliers know what they are expected to provide and the user analyzing the information understand what he or she is looking at.

There are also issues with aggregating data because not all data is at the same level of granularity. For example, not all surveys are conducted at a country level, and it does not make sense to aggregate data from multiple surveys done on different customer segments in different parts of a country.

Depending on the scale of the initiative, there is the potential for challenges in aligning metrics, including availability of information as required and variation in definitions due to multiple suppliers for the same information across either countries or categories.

One of the obstacles that an organization could face while trying to conform the definitions is the nature of its contracts with the data suppliers. Data buying contracts may vary depending on supplier, country and product category. In the case of an organization operating across multiple countries and categories, it may not be possible to have the same definition for a measure due to the differences in way the businesses are run.

Data Model Design

While modeling information, it is important to take into consideration the various dimensions and their relationships with each other. The main dimensions of analysis are shown in the Figure 3:

Retail data may be provided by different suppliers for different products in the same country. For the purpose of query resolutions and supplier efficiency tracking, it is important to capture this relation.

Modeling the information correctly is critical to allow changes in the way information is analyzed as the organization evolves and information needs change. In order to do cross-functional analysis at a later point in time, it is also important to consider the corporate data model. For example, this applies to analysis around raw material cost versus market share versus consumer perception on quality for different products catering to different customer segments in the same category.

Ensuring Data Quality

Another major obstacle the organization will face while embarking on such an initiative is ensuring data quality on an ongoing basis, because most of these measures will be coming from diverse external sources and a large number of metrics are likely to be qualitative from consumer surveys. It is of utmost importance to ensure data quality because the information will be used to make critical strategic decisions. Any doubts among the user community regarding the quality of information are likely to lead to a rapid reduction in system usage.

To ensure data quality, a variety validation mechanisms and processes are needed. Some of them can be simple reports that compare the latest data with the previous data submissions and highlight any changes greater than a predefined threshold. A feedback loop that allows users to raise queries and for data suppliers to resolve them needs to be set up as well. There must also be a mechanism for suppliers to explain valid reasons for partial availability of data as well as known differences with definitions so that users are proactively made aware of such instances and are able to factor into their analysis.

Business Benefits

Marketing system information can be used to understand trends in the market and also better position the product within the target segment. It can also be used to identify emerging trends and segments in the market and extend existing brands, or come up with new brands and products to address the segments and therefore drive superior financial performance in terms of both growth and profitability.

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