This is the 2nd part of a 2-part series. Read my previous blog Measuring Pricing Effectiveness: Key Metrics
Deciding what metrics to measure leads to the next big issue: How to Measure. Often the problem is not so much in the measuring but in the comparison. Measuring the revenue is only useful if you can compare it to a baseline of revenue to know whether the result is good or not. Summarized, there are four basic techniques, each with advantages and disadvantages:
Pricing Solutions have been around for many years in retail, and commonly the question is asked: Well, do they work?
Unfortunately, the answer is not easily determined because it has been challenging to decide what is meant by success and even harder to measure success. Measurement needs to be built into pricing projects from day one, and these measurements need to align with the pricing activities.
Among other reasons we’ve talked about, many retailers are looking at AI-powered pricing solutions because they feel confined by strict sets of pricing rules and policies, often put in place many years ago—long before modern data science was even a concept.
Retailers are already facing their next inventory crisis—too much stock on hand, with too few buyers.
Not an Ideal Predicament
Depleted revenue, compressed margins, a shortage of labor, plus an infrastructure not designed for an online volume consisting of 40-60% of total sales. Not ideal for fashion retailers. And beyond that, retailers experience up to 8 percentage points of margin loss on a digital order.1
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