# Coverage Analysis — Retail Tracking

Marketers sometimes struggle to bridge the void between the theoretical concepts and the practical real-world limitations.

Unlike financial statements where dollars and cents must add up, marketing research/analytics reports intended to impart an understanding of market dynamics, do not require complete, 100% coverage.

Moreover, research firms and their clients must make compromises between the costs of expanding services and the benefits of doing so. This remains a business decision — clients have budget limitations.

Like other forms of market intelligence, retail tracking data which meets acceptable accuracy standards, irrespective of coverage levels, remains useful, provided one is aware of what is covered and what is not covered, and one appreciates the strengths and limitations of the data. Utility, of course, does increase with coverage.

Contents

Coverage Gap
Pipeline Effect
Coverage over a Product’s Life Cycle
How Distribution Affects Coverage
Coverage Analysis — Case Examples
Coverage Analysis

### Coverage Gap

#### Exhibit .5   NielsenIQ’s RMS covers the more densely populated urban provinces of China.

The retail measurement service (RMS) tracks the sales of goods from retailers to consumers, at specific outlets in a predefined geographical area referred to as the retail universe. This universe is usually not the same as the supplier’s sales territory. For example in China (Exhibit .5), NielsenIQ tracks the densely populated urban provinces of China, leaving out the sparsely populated regions in the West.

#### Exhibit .15   Distribution network, shipments and covered sales.

Moreover, as depicted in Exhibit .15, within the same geographical territory covered by a retail tracking service, the supplier may sell to outlets at locations such as schools and military establishments that are not accessible to retail auditors.

There are several such inaccessible or non-participating outlets, and many of them are important contributors to sale of some product categories. For instance, stalls at enclosed construction sites sell a lot of cigarettes. Or for instance, non-participating outlets at tourist locations sell large quantities of impulse products like ice cream and chocolates.

#### Exhibit .16   Coverage gap is the shortfall between the agency’s estimated purchases by retailers and the manufacturer’s shipments.

The difference between the supplier’s shipments and the agency’s estimate of store purchases is the coverage gap (Exhibit .16). This gap arises due to the following reasons:

• The retail universe is not the same as the supplier’s sales territory.
• The pipeline effect, which arises from any increase or decrease in stocks within it. The pipeline comprises of all the intermediate locations that items pass on their journey from the supplier’s warehouse to the consumer’s shopping cart. Its existence creates a time lag between the moment an item is shipped from the supplier’s warehouse to the instance it is bought by a consumer.
• Export and imports or parallels as they are often referred to. If parallels constitute a large component of goods sold in the retail universe, then coverage may even exceed 100%. For instance, a large proportion of bar soap sold in Singapore is sourced from Indonesia.
• Inaccuracies in the agency’s estimates, arising due to sampling and other factors pertaining to method of tracking sales. This is referred to as pick-up error.

### Pipeline Effect

As mentioned above, the build-up or depletion of pipeline inventory affects coverage. Some of the many factors that cause these fluctuations are listed below:

• Irregular shipment patterns. This arises when there is any change in the intermediaries. For instance, when new distributors are appointed.
• Investment buying by distributor or retailers. This occurs when suppliers offer high trade discounts, and results in fluctuations in the pipeline flow.
• Trade and consumer promotions. In addition to trade discounts, other forms of trade promotions also influence stock levels within the pipeline. Similarly, consumer promotions lead to fluctuations in stocks and sales.
• Longevity of a product. Goods that take long to expire can linger for prolonged periods in the pipeline. On the other hand, products such as bread, pasteurized milk that have short shelf lives are likely to have shorter pipelines. They pass much more quickly from factory to retail store. In the case bread for instance, most manufacturers deliver directly to retail outlets on a daily basis.
• Distribution network. An elaborate structure of wholesalers, agents, distributors and concessionaires can lengthen the pipeline.
• Geography or size of the sales territory. Larger countries require more elaborate distribution networks to service the entire nation.
• Product life cycle. Coverage varies over the life cycle of products, as detailed in the next section.

### Coverage over a Product’s Life Cycle

#### Exhibit .17   Coverage varies over a product’s life cycle, particularly during its introduction and decline. (As there are many other factors affecting coverage, it will fluctuate more widely than shown in this illustration).

When a product is introduced, stocks start to fill the pipeline as they move from the supplier to retail outlets. While the pipeline expands, retailer purchases grow at a slower pace than shipments, and coverage is below norm. When the pipeline fills up and the expansion of the distribution network starts to level off, coverage starts to stabilize. (Refer illustration in Exhibit .17).

At a later stage, when the product is in decline, the pipeline starts to shrink, and pipeline inventories decline sharply. During this stage, shipments are lower than retailer purchases, and this raises the coverage level.

### How Distribution Affects Coverage

#### Exhibit .18   Product’s distribution level affects the accuracy of sales and purchases estimates.

The accuracy of the purchases and sales estimates is affected by the level of distribution of a product. When the distribution is low, the number of stores stocking the product in the agency’s sample of retail outlets is small. As distribution expands, the product is likely to be found in more stores in the retail sample. The resulting increase in the effective sample size improves the accuracy of the purchases and sales estimates. As can be seen from Exhibit .18, this reduces the volatility in coverage estimates, which is markedly high when the product’s distribution is low.

### Coverage Analysis — Case Examples

Differences between a supplier’s shipments and the agency’s estimate of store purchases can be difficult to comprehend due to the complexities of the distribution networks, pipeline effects, practices such as investment buying, impact of promotions and the peculiarities of a retail tracking service.

A coverage analysis can impart an understanding of the factors contributing to the discrepancies. And, as can be seen from the following case examples, the analysis can also reveal very useful insights into the dynamics of the supplier’s distribution network.

### Kraft Builder, Indonesia — Pipeline Effect

In the last quarter of 2007, a few years after Kraft Indonesia took over Danone’s biscuits business, the company transformed its distribution network. The previous network relied on one national distributor, encompassing 26 branches and 28 sub-districts. The new distribution network, named Builder, relied on 15 territory level distributors, encompassing 39 branches and 13 sub-districts.

Kraft estimated that retailer coverage increased from 87,000 (pre-Builder) to 165,000 stores, by end 2007.

The company’s shipments of biscuits surged by roughly 40% in 2008. This achievement was one of the reasons CEO, Irene Rosenfeld, made it a point to meet the Indonesian team on her visit to the Asia Pacific region.

The only dampener was that estimates by Nielsen Indonesia showed substantially lower sales. This discrepancy led to a coverage analysis and an in-depth investigation into the Indonesian retail tracking service.

One year later, in 2009, Kraft’s biscuits shipments plummeted, the decline largely compensating for the surge in 2008. This confirmed what was inferred from the earlier investigation.

The key reason for the surge in sales in 2008 was the creation of the parallel sales pipeline. The new Builder distribution network was larger, and importantly, the depletion of stocks in the old pipeline was occurring at a far slower pace than the build-up in the new one.

As a result, a large proportion of the 40% increase in sales was due to the increase in the pipeline inventory, which for a big country like Indonesia can be quite large.

When the expansion of the Builder distribution network levelled off, this coupled with depletion in stocks in previous network, resulted in the sharp drop in shipments in 2009. Shipments stabilized once stocks in the phased-out network depleted, and it ceased to exist.

During the transition, while shipments jumped up and down like a yoyo, the Nielsen sales estimates grew at a steady pace. The turbulence in the intermediary networks did not have an adverse effect on consumer offtake.

This case goes to show the importance of relying on a well-designed service to track consumer offtake. It also illustrates the use of coverage analysis to enhance our confidence in retail tracking data and improve our understanding of the dynamics in trade.

### Organics Shampoo and Campbell Soup — Investment Buying

Some years back, when the lower trade was big in Singapore, Organics shampoo year-end shipments to distributors surged from an average of 17 thousand litres per month to 53 thousand litres. This was primarily the outcome of a trade promotion, and though retailer purchases reflected a relatively small increase, consumer purchases remained flat.

Enticed by the promotion, distributors merrily stocked up, inflating the pipeline. And because they did not pass the discounts to the retailers, there was no incentive for them or their customers to purchase more than their usual requirement.

In another example of investment buying, the Campbell Soup Company sold huge quantities of canned soup to a dominant supermarket chain by offering a large trade discount. Subsequently the manufacturer’s country head was disappointed that there was no discernible increase in scanned sales even several months after the promotion.

### Abbott Laboratories, Malaysia — Misleading Data

Abbott Laboratories, the manufacturers of infant formula, substantially increased their sales in Malaysia by expanding into newer channels of distribution. Unfortunately, for the Abbott team, these channels were not covered by Nielsen Malaysia, and so, it turned out that the Nielsen data reflected a decline in sales.

The decline was because, in addition to growing Abbott’s overall business, there was some cannibalization of the established channels covered by Nielsen Malaysia, by the new channels not covered by the agency.

Nielsen’s market share was a KPI for Abbott, and, despite explanations, the reported “decline” did not go down well with the regional/global bosses.

As can be seen from this example, an understanding of the strengths and weaknesses of a retail tracking service, particularly in the context of coverage, is of vital importance in the interpretation of the data.

### Coverage Analysis

Coverage analysis is an important exercise that helps the research agency and its clients assess coverage, and understand the reasons contributing to the gap. This understanding is used by the agency to prioritize improvements to their service. And as illustrated by the case examples, it can reveal very useful insights into the dynamics of clients’ distribution networks and guide them with their interpretation of the data.

Coverage analysis is based on moving annual totals (MAT) of reported retailer purchases and manufacturer’s shipments. It relies on three parameters:

• Coverage: The agency’s estimated retailer purchase volume as a percentage of supplier’s shipments.
• Expected Coverage: The proportion of supplier’s total shipments that go to the retail universe, based on supplier’s records and their judgement.
This takes into consideration known coverage gaps such as geographical regions, market breakdowns and chains/outlets that are not covered by the retail tracking service.
• Pick Up: The agency’s estimated retailer purchase volume as a percentage of supplier’s shipments that go to the retail universe.

It follows from the above definitions that: $$Coverage \,= \,Expected \,Coverage \,× \,Pick \,Up$$

Coverage reflects how the research estimates relate to the supplier’s total shipment and is dependent on the extent that the retail universe captures the manufacturer’s shipments (expected coverage) as well as the accuracy of the research methodology (pick up).

Pick up is a measure of the accuracy of the research methodology and is dependent on the sampling framework as well as non-sampling errors.

#### Exhibit .19   Expected coverage is the proportion of supplier’s total shipments that go to the retail universe. In this example, based on sales records and judgement, the manufacturer estimates that 80% of total shipments go through the outlets covered by the agency.

Example (see Exhibit .19) illustrating coverage, expected coverage and pick-up:

1. Total Shipment = 100 thousand units.
Supplier’s shipment of product in the sales territory is 100 thousand units.
2. Shipment into Retail Universe = 80 thousand units.
Based on sales records and judgement, the manufacturer estimates that 80 thousand units were sold to the retailers covered by the agency.
3. Expected Coverage = 80K/100K = 80%.
The estimated proportion of supplier’s total shipments that go to the retail universe.
4. Retailer Purchases = 72 thousand unit.
The agency’s estimated retailer purchase volume of the product.
5. Pick Up = 72K/80K = 90%.
The agency’s estimated retailer purchase volume as a percentage of supplier’s shipments that go to the retail universe.
6. Coverage=72K/100K=72%=Expected Coverage×Pick Up
The agency’s estimated retailer purchase volume as a percentage of supplier’s shipments.

Since coverage analysis is conducted on moving annual total (MAT) sales, it moderates pipeline effects and smoothens out the impact of promotions and seasonality. It also evens out the variances in the manufacturer’s shipments which can be volatile with peaks and troughs over months.

The sales territory in a coverage analysis should represent the total country. This reduces errors due to intra-country movement of goods.

Note also that coverage analysis is appropriate for brands with numeric distribution of at least 80%. This conforms with Nielsen’s global standard for sampling error, which applies to products that are available in 80% of the universe. Estimates for products with lower levels of distribution may not meet the agency’s global norms.

Manufacturers should review coverage for major brands once in every two to three years, or more frequently if there are concerns regarding the quality of the data. At the agency, the measurement scientists (or data scientists as they are now called) should be conducting the analysis on a regular basis as it provides critical information on how their service can be enhanced.

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