Challenges & Benefits of Retailer POS Data Use

Table of Contents

Overview Understanding Brand Value & Trends

Challenges of POS Data

4-7

Benefits of POS Data

8-9

Next Steps How to leverage POS Data

10-11

Overview Understanding Brand Value & Trends

Retailers know the value of their data and are attempting to monetize with Brands.

Commercial structures for POS data are not tightly coupled with the data value brands can leverage

Merging Monetization & Data

Trends toward second party data, and first are bringing unique opportunities. These could bring unique data sets on a brand’s platform and unique calculations, integrations, and models, built from the data. This includes separate licensing tiers for a brand’s own products and competitor products as part of being a Category Advisor.

Visuals and media in presentations are key. Though video and audio aren't mandatory, you should create engaging campaign mockups. Seek out vector illustrations to highlight important points. Lastly, keep your presentation easy-to-read. Don’t make the page too wordy, and choose a color palette that won’t distract the audience.

Brands are creating their own competitive advantage

Cloud platform

Benefits of acquiring more direct Point of Sale (POS) data

Ease & interest in integrating data with other data sources

Licensing cost of third-party data

Cohesion & unity between Brands & retailers

availability, capability & cost

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Challenges of POS Data

Maximize the potential of POS data by solving the problems that dilute the data's benefit

POS data is generated by the retailers’ systems, often customized to their specific environment.

Retailer

Those receiving the files (including third party providers) could ask them to change the files and the data in it. However, the probability of that is low, so brands must work with what they have. Each Data Set is Typically Unique

Data Set

Unique Content Unique Format Unique Frequency ETC...

Make Data Useful to Brands on POS in...

Significant sizes of data can be repeatedly refreshed throughout the day, and historical period data could be restated; requiring stored data updates. If a brand's platform has adequate ownership and support, data may not get the custody required, which can lead to erosion of trust by the end user.

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Challenges of POS Data

POS data doesn't have to be hard to validate.

This can be accomplished via two primary mechanisms:

Validating against a known independent source In this case, retailer POS can be compared to a known third party source

Validating against itself

In this case, data in POS files is evaluated for anomalies

This type of validation is most useful once sufficient POS history for a retailer has been accumulated

This type of validation is most useful in the initial stages of POS usage

Market

Product Items must continuously be harmonized to generate accurate insights at the UPC, Brand, or Category level. The same item in two retailers may be coded differently due to use of Shelf Keeping Units (SKUs) vs. Unique Product Codes (UPCs) / European Article Numbers (EANs), over-stickering, errors at checkout, etc. Period Cutoffs for data processing vary. Differences must be defined to ensure correct interpretations of data. Retailers' Promo Weeks may vary. The period dimension doesn't change frequently, and can be aligned upon initial ingestion. Even if retailers provide information about dayparts, sufficient granularity usually exists for foundational alignments.

Market dimensions are constantly changing. Store-level info provided by a retailer must be aggregated for analyses.

This could be done by:

Creating store clusters around brand definition (e.g. consumer segmentation) Aligning it with definitions (e.g. zip code for Designated Market Area)

Fact

Filed retailer POS facts can differ across retailers. Beyond transaction data, some may include:

Data about the consumer Data about media publishing Data relating to the supply chain

Defined assessment across retailers is needed.

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Challenges of POS Data

Unusable retailer source data needs to be transformed.

Aggregations

Deriving calculated facts

Mapping of data to other sources

For example, sales across retailers’ banners might need to be aggregated, inventory might need to be calculated across all retailer nodes, or a retailer’s stores might need to be mapped to a brand’s support territory. Keep data relevant and open to applications and users by using a single data model. The primary use cases from the brand must dictate the data model employed. The data model should be revisited periodically to ensure relevancy to the brand and incorporate changes.

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Challenges of POS Data

Occasionally, brands might need to set user access permissions across data to filter which teams (Account, Executive, etc) get certain access to retailer data.

Raw POS data lets brands integrate this data with other data sets to derive unique insights. If a brand sponsors a sporting event, the event's data can be combined with retailer POS to better characterize purchase timing and location. Data Science teams tend to make joins across raw data sources to determine unique insights, oftentimes automated.

Retailer

Analytic Reporting

Analytic reporting may need to be created for users to get standardized views of insights. Though retailers may want the brand to present specific reports to manage the business, the brand should always consider additional reporting to derive competitive advantage. Each Data Set is Typically Unique

Standardized Insight Views

Account Team Finance Team Category Mgmt. ETC...

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Benefits of POS Data

Despite inherent challenges, brands can gain these benefits using retailer POS data:

Refining Analytic Use Cases The increased granularity in retailer POS data could be used to supplement data from a third party or the Brand’s own data.

Example:

UPC listing/delisting decisions could be recommended by stores that index on a specific consumer segment defined by the brand.

Causal Factor Improvements Even where third party data is still used, POS data could be used to improve the modeling of certain situations.

Example:

More granular time periods or media data in a POS file could be used to achieve higher model fidelity of cause/effect.

Precision Marketing The shopper information a retailer provides in their POS feed could be used alongside a brand’s consumer segmentation profiles or Customer Data Platform (CDP). A digital marketing campaign could target shopper demographics, and locations with the highest repeat purchase rate, or promotion spend could be better aligned with execution. Example:

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Benefits of POS Data

Alignment of Ad Budgets Ad spend must be applied most effectively. Retailer POS data can help direct ad spend to its greatest sellout potential.

Example:

Out-of-stock products in a retailer location shouldn't be advertised to that location's users. Ad spend should shift to locations with large inventories or at risk of perishability.

Operations Improvement If the retailer provides supply chain information in the POS feed, that data can improve production/shipment decisions, timing an speed.

Example:

Production and logistics decisions could be informed by the rate of sell out at a retailer’s distribution center vs. just the brand’s orders, inventory, and shipments.

Additional Use Cases The facts available through retailer POS data may include facts that have not been available before to the brand. Shopper-behavior data results from a digital campaign with the retailer by the brand, and payment information can help determine co-branding opportunities with the brand. Example:

The value of a retailers POS data will be determined in part by the commercial value proposition for the data.

If the retailer directly, or indirectly charges for the data, that cost must be made up by the benefit from using that data. Ideally, the benefit would significantly outweigh the costs. Though the opportunity cost of NOT having the data needs to be considered as well; including the potential for competitors to achieve some advantage from using the data.

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Next Steps How to leverage POS Data

A series of research spikes should be performed using an agile methodology whereby: To determine how best a brand could leverage POS information, an empirical assessment of retailer POS data available to a brand should be performed.

The potential for common use cases would be evaluated (e.g. store clustering) Greater time fidelity lets brands make more timely, relevant decisions

An estimate of market coverage would be performed

Use of POS data as causal factors would be explored

Operational improvements could be feasible

Client-specific use cases for POS data could be outlined

To perform these activities, slices of retailer POS data need to be made available for evaluation.

Should take a small team a couple months of time, on average Make employed analytics use cases available for context Stakeholders and Subject Matter Experts (SMEs) must collaborate on the evaluation Output must indicate the possibilities, challenges, and considerations associated with the use of that data POS Data Assessment:

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Next Steps

Enable better decisions with Fractal Who is Fractal? Fractal is one of the most prominent players in the Artificial Intelligence space. Mission: Fractal’s mission is to power every human decision in the enterprise and bring AI, engineering, and design to help the world’s most admired Fortune 500® companies.

Authors:

Chris Hansen Fractal Consulting Partner

Raj Kapoor Nigam Fractal Consulting Principal

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