Pricing Rules
For eCommerce And Retail Companies

As is evident from the name, pricing rules are rules for price calculations.

A pricing rule is a set of formulas, if-then conditions, constraints and priorities.
Typically, variables in such formulas / conditions / constraints are:

✓ competitors' prices,
✓ cost prices,
✓ stocks,
✓ inventory turnover,
✓ supply dates,
✓ acceptable range of markups,
✓ demand / sales forecasts,
✓ ...

Example:

In a city A, in this segment of goods, the price of an item is equal to the maximum of the two following values:
value 1 = the second minimal price of the market, compared to local competitors,
value 2 = (cost price + 10%).

Example:

Your company manufactures premium aluminium bumper frames for iPhone. It offers 10 types of frames and sells them through the website and Instagram.
In your country nobody sells similar frames.
You can calculate optimal prices with spreadsheets easily.

Example:

Your company is a drogerie retail chain with an online store. It operates in 100+ cities and has 10,000 SKUs in the assortment range. Competitor lists, their assortment ranges and their prices vary greatly among locations. KVIs lists, cost prices, costs, logistics differ based on location.
With poor automation, such as "spreadsheet level", your prices wouldn't be optimal. Your company would lose a part of profits, and it would be hard to retain customers' loyalty.

➜ Example.
A hypermarket chain analysed clients data and discovered a segment of consumers with high-quality expensive products preferences.

The company launched a new loyalty program for this segment, offering to such clients good deals about top-branded olive oil, salami, cheese, truffles, vine, caviar, etcetera. The chain provided these offers via email and mobile app. To get the special price, customers had to use loyalty cards at a cash-desk.

➜ Example.
An online store of garden machinery has launched dynamic personal pricing in customers' personal accounts, named "Secret Prices With A Promo Code".

The website displayed the regular price, so competitors couldn't be informed about the "Secret Low Prices", and the promo action didn't create a danger of a "price war". Clients saw personal special offers only in their personal account, after entering the promo code from the invitation email.

A dynamic pricing solution was recalculating "Secret Special Prices" fully automatically, according to complex and agile pricing rules set by the store pricing experts.
For example, when inventory of a product ran out and the store got a higher-cost batch of goods, the "Secret Price" was recalculated based on the new cost of goods, to prevent the company's losses.
If the new price no longer looked attractive compared to the competitors, then the dynamic pricing solution automatically removed the SKU from the "Secret Sale". That was preventing conflicts with customers: "You offer me a regular price, not a special one! You are trying to charge me high by pretending it's a sale."

Key competitors are retailers whose price changes have a significant impact on your store sales.

Analysing data with machine learning algorithms, modern dynamic pricing engine is able to automatically identify key competitors for each SKU. You can find more information about such algorithms here: Key Competitors Automatic Identification.

➜ Example.
In a city A, for certain SKUs, a retailer wants to maintain the second-lowest price, compared with the retailer's local key competitors. If these competitors change prices, the retailer's price has to adjust in order to become the second-lowest price again.

Modern advanced dynamic pricing systems perform this task easily.

Efficient rule-based competitive pricing doesn't require sophisticated algorithms, but agility of settings is crucial.
In reality, any retail / ecommerce business operates with quite complicated pricing scenarios; you can study a few common examples of such scenarios here: COMPETITIVE PRICING SCENARIOS.
And here is an example of a dynamic pricing solution with high agility of settings: COMPETITIVE PRICING SOLUTION.

➜ Example.
An online store of garden machinery had a warehouse only in a headquarters city, but delivered goods within a lot of geographic areas. The retailer began offering discounts to living far away customers, to compete more efficiently with their local stores. As a result, regional sales increased to 70% of total sales.

Complementary goods and impulse goods often have the potential to raise the margin.

The approach is essential for stores which have a wide assortment with a low turnover ratio and overstocking.

➜ Example.
The retail company analysed its inventory balance and detected excess stock.

✓ To get rid of overstocks, one store ran a "total sale", with discounts of 70% of the primary cost.

✓ In another store, they implemented several gradations of discounts, depending on the period the item was in stock.
This store sold 85% of the items with discounts of 20%, 30% and 50%. They had to sell only the remaining 15% of the goods at the maximum discount of 70%.

Thus, the first store lost profit on 85% of the items.

This type of partnership provides a lot of advantages and benefits, but there are some difficulties also.

✓ If the items are available from several suppliers, it can be difficult to quickly choose the optimal under current conditions shipment warehouse.
✓ When the product stocks of one supplier are over, and the store starts to ship from another supplier's warehouse, prices of the first and second suppliers can differ. If the second supplier price is higher, and the store remains its prices for consumers unchanged, it can significantly reduce the margin, up to selling at a loss, especially during promotional offers.