In highly commoditized retail sectors (like electronics, groceries, or basic apparel), consumers are extremely price-sensitive. A difference of a few dollars can shift massive amounts of volume from one retailer to another.

Oracle Retail provides a Competitive Shopping module to track rival pricing. While modern retailers often use advanced third-party web-scraping tools, that data eventually must flow back into the core ERP to drive actual price change events. This guide explains the data model used to house this competitive intelligence.

1. Competitor and Store Setup#

Before you can track prices, you must define who you are tracking. RMS allows you to create Competitors (e.g., Target, Walmart, Best Buy).

Because pricing is hyper-local, RMS also requires you to define Competitor Stores. You then link a specific Competitor Store to one of your own Stores. This creates a 1-to-1 or 1-to-Many mapping.

• My Store 200 (Downtown) is linked to Competitor Store 99 (Target Downtown).
• If Target Downtown drops their price on milk, the pricing analyst knows to evaluate a price drop specifically for My Store 200 to defend that local market.

2. Competitive Shopping Lists#

Retailers carry millions of items, but they only competitively shop a small subset—known as KVIs (Key Value Items) or Traffic Builders. These are the items consumers know the price of by heart (e.g., gallons of milk, 65-inch TVs, diapers).

In RMS, these KVIs are grouped into Competitive Shopping Lists. These lists are generated and assigned to store associates or third-party auditors. The auditors walk the competitor's store, find the items on the list, and record the prices.

3. Capturing Competitor Prices#

When the auditor returns, the observed prices are uploaded into RMS (often via an integration layer processing the third-party data).

The data captured includes:

• The Item.
• The Competitor Store.
• The Date of observation.
• The Regular Retail Price.
• Any Promotional Price (and the promotion type, like BOGO or % Off).

Once this data is housed in RMS, it is fed into the Pricing engine (RPM) or an advanced analytics platform. RPM allows analysts to write rules such as: "If Competitor X drops the price of KVI Item Y by more than 5%, automatically generate a Regular Price Change in RPM to match it."

4. Core Tables Reference#

Competitor Price Intelligence (COMP_PRICE)

5. SQL Deep Dives#

Finding Instances Where We Are Being Undersold

This query compares your current UNIT_RETAIL against the most recently observed competitor price for linked stores, highlighting items where you are priced higher than the local rival.

SELECT 
    csl.loc AS my_store,
    csl.comp_store AS rival_store,
    cp.item,
    il.unit_retail AS my_price,
    cp.reg_retail AS rival_price,
    (il.unit_retail - cp.reg_retail) AS price_disadvantage,
    cp.shop_date
FROM comp_store_link csl
JOIN comp_price cp ON csl.comp_store = cp.comp_store
JOIN item_loc il ON csl.loc = il.loc AND cp.item = il.item
WHERE cp.shop_date = (
    SELECT MAX(shop_date) 
    FROM comp_price 
    WHERE comp_store = cp.comp_store AND item = cp.item
)
AND il.unit_retail > cp.reg_retail
ORDER BY price_disadvantage DESC;

6. Official Oracle Resources#

For further reading, consult the official Oracle documentation:

• Oracle Retail Merchandising System Operations Guide: Competitive Shopping
• Oracle RMS Data Model: Competitive Shopping (COMP_PRICE)