December 22, 2008
CPGmatters: Fisher-Price Leverages POS Data as Single Source of Truth
By Al Heller
Through a special arrangement,
presented here for discussion is an excerpt of a current article from the
monthly e-zine, CPGmatters.
Toy maker Fisher-Price
has found a way to leverage point-of-sale (POS) data as a single source
of truth for retail activity. The Mattel Brands division has used this
knowledge across the corporate organization to help generate better demand
insights and sales forecasts, optimize orders for its customers, plan and
manage promotions and categories, and execute in the field.
Since opting for a demand
signal repository (DSR), Fisher-Price has been able to overcome some inherent
challenges in its primary business:
- Extreme
seasonality. The lesser January to July season, and the prominent August
to December season, which accounts for 80 percent of sales. - Long
production lead times because so many goods are made overseas, which add
to the need for precise sales forecasting. - A
narrow window of just a few weeks to make actionable decisions for the
coming year, based on fast, accurate reads of POS data.
So described Steve Czajkowski,
senior manager of sales IT planning and e-business at Fisher-Price, in
a recent webcast, “Creating a Single Source of Truth with POS Data
Across the Organization.”
A demand
signal repository, according to Lora Cecere, vice president-consumer products
at AMR Research, is a robust centralized database that stores, harmonizes
and normalizes large volumes of demand data. These include POS, wholesale
distribution information, inventory movement, promotional demographics,
market demographics, third-party market content, and customer loyalty data,
all to support better decision-making.
Based mostly
on CPG pilots, she estimated a 14-month return on investment on DSR technologies,
and a six-month ROI on predictive analytics that use the data.
“When
data can be used to drive better response for seasonal products, a DSR
can often drive a 2 percent to 4 percent sales increase,”
Ms. Cecere said, while suggesting that the retailer-CPG trade has a way to
go.
“What should be easy is fundamentally difficult. The industry hasn’t
built processes to use downstream data. Most organizations have rewarded
sell-in, not sell-through, and have not aligned to the shelf.”
“It’s
extremely important for CPG to know how it is doing with an account in
order to improve forecasts, and we’re seeing an evolution of software applied
to this,” she added.
Discussion Questions:
What do you think of the potential of demand signal repository (DSR)
technologies? What questions would you have about such technologies given
the longstanding challenges of fully leveraging point-of-sale (POS) data?
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Ms. Cecere (AMR Research) hit the nail on the head by noting that most vendors still reward sell-in vs. sell-through and that one fact has a couple of repercussions: 1. Vendors over-value sales and under-value analytics (and they hire and compensate accordingly), 2. Vendors continue to pay lip service to wanting more data and insight tools while secretly worrying about not making the most of what they already have.
One of the most common complaints that we hear from retailers (when we conduct assessments on behalf of vendors) is that their vendors possess wildly varying skill sets and priorities around analytics. Lots of “information” (regurgitating information that is readily available to the retailer) but very little “insight” (calling out specific opportunities to drive sales). All of this to say that downstream data solutions are vital to the minority of vendors that stay a step ahead and they should be able to make sure of these resources full strength/without having them dumbed down. Hopefully, everyone will eventually catch on!
Over the past 30 years, CPG companies have been getting better and better data (more timely, complete, and accurate), and have been doing less and less with it over time. The analytical capabilities of today’s Nielsen/IRI can’t compare to the previous generation. They’ve drowned themselves due to their inadequate ability to drink from a fire hose.
They can change the name of it (“demand signal repository?”) but they seem to be unable to change their practices.
It’s very sad that the former leaders in the use of detailed databases are now distant laggards compared to telecom firms, financial services providers, and others. It’s not entirely their fault–these other firms have much better consumer-level data available to them than to CPG firms, but that’s the way it goes….
“Food for thought” from a solution provider’s perspective–building an effective DSR is not trivial. An effective DSR will anticipate support for decision-making functions throughout the organization, including category management, vendor-managed inventory (VMI), trade promotion management, supply chain management, and promotion management. Each function requires different data in order to address specific business issues. Obtaining, storing, maintaining and harmonizing multiple sets of disparate data into a cohesive, centralized database is, to put it mildly, challenging! To overcome this challenge, it’s important to identify business issues critical to each function, and understand the data required to address each issue … retail POS data is just the start. Data sources that should be anticipated for integration and harmonization in a DSR include:
• Retailer POS data – The ultimate “demand” data, POS data is store, SKU-level sales information. Many retailers now provide this data directly to their suppliers. Retailers may provide data via the internet, email, FTP sites or proprietary systems. Unless a vendor is a trusted category advisor/captain, however, POS data from retailers will likely include only the vendor’s own products, so 3rd-party data is often required.
• Market and account level data – 3rd-party data providers offer high-level market and account level data. This data includes competitor information as well as causal information structured by industry standard product characteristics. This data has high strategic value, and in some instances, can be provided at store level to complement retailer direct POS data.
• Internal data – A manufacturer’s in-house systems enrich a DSR by providing shipment and financial data. This information is critical for addressing supply chain and availability issues.
• Reference data – Making sense of disparate data requires industry standard reference points such as demographics, clusters, store characteristics, product characteristics, etc.
• Other data – Loyalty data, panel data, and other data — such as weather — can further enhance the DSR.
In summary, functional areas within a CPG manufacturer contend with business issues that may require specific variations of each data type to capitalize on market opportunities. For example, sales and category reporting issues require all data sources, but internal data is less critical than other sources. For supply chain management and forecasting issues, retailer POS, internal, and reference data are critical sources, while market and account-level data may not be as important. While a complete DSR is the ultimate, desired goal, much value can be obtained along the way by taking a practical, phased approach to its development…a Nielsen white paper, available upon request, provides additional recommendations to guide consumer goods manufacturers toward immediate value.
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Ms. Cecere (AMR Research) hit the nail on the head by noting that most vendors still reward sell-in vs. sell-through and that one fact has a couple of repercussions: 1. Vendors over-value sales and under-value analytics (and they hire and compensate accordingly), 2. Vendors continue to pay lip service to wanting more data and insight tools while secretly worrying about not making the most of what they already have.
One of the most common complaints that we hear from retailers (when we conduct assessments on behalf of vendors) is that their vendors possess wildly varying skill sets and priorities around analytics. Lots of “information” (regurgitating information that is readily available to the retailer) but very little “insight” (calling out specific opportunities to drive sales). All of this to say that downstream data solutions are vital to the minority of vendors that stay a step ahead and they should be able to make sure of these resources full strength/without having them dumbed down. Hopefully, everyone will eventually catch on!
Over the past 30 years, CPG companies have been getting better and better data (more timely, complete, and accurate), and have been doing less and less with it over time. The analytical capabilities of today’s Nielsen/IRI can’t compare to the previous generation. They’ve drowned themselves due to their inadequate ability to drink from a fire hose.
They can change the name of it (“demand signal repository?”) but they seem to be unable to change their practices.
It’s very sad that the former leaders in the use of detailed databases are now distant laggards compared to telecom firms, financial services providers, and others. It’s not entirely their fault–these other firms have much better consumer-level data available to them than to CPG firms, but that’s the way it goes….
“Food for thought” from a solution provider’s perspective–building an effective DSR is not trivial. An effective DSR will anticipate support for decision-making functions throughout the organization, including category management, vendor-managed inventory (VMI), trade promotion management, supply chain management, and promotion management. Each function requires different data in order to address specific business issues. Obtaining, storing, maintaining and harmonizing multiple sets of disparate data into a cohesive, centralized database is, to put it mildly, challenging! To overcome this challenge, it’s important to identify business issues critical to each function, and understand the data required to address each issue … retail POS data is just the start. Data sources that should be anticipated for integration and harmonization in a DSR include:
• Retailer POS data – The ultimate “demand” data, POS data is store, SKU-level sales information. Many retailers now provide this data directly to their suppliers. Retailers may provide data via the internet, email, FTP sites or proprietary systems. Unless a vendor is a trusted category advisor/captain, however, POS data from retailers will likely include only the vendor’s own products, so 3rd-party data is often required.
• Market and account level data – 3rd-party data providers offer high-level market and account level data. This data includes competitor information as well as causal information structured by industry standard product characteristics. This data has high strategic value, and in some instances, can be provided at store level to complement retailer direct POS data.
• Internal data – A manufacturer’s in-house systems enrich a DSR by providing shipment and financial data. This information is critical for addressing supply chain and availability issues.
• Reference data – Making sense of disparate data requires industry standard reference points such as demographics, clusters, store characteristics, product characteristics, etc.
• Other data – Loyalty data, panel data, and other data — such as weather — can further enhance the DSR.
In summary, functional areas within a CPG manufacturer contend with business issues that may require specific variations of each data type to capitalize on market opportunities. For example, sales and category reporting issues require all data sources, but internal data is less critical than other sources. For supply chain management and forecasting issues, retailer POS, internal, and reference data are critical sources, while market and account-level data may not be as important. While a complete DSR is the ultimate, desired goal, much value can be obtained along the way by taking a practical, phased approach to its development…a Nielsen white paper, available upon request, provides additional recommendations to guide consumer goods manufacturers toward immediate value.