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I’ve spent quite a few years in the world of “markdown optimization” helping retailers implement solutions that determine the “optimal” price point that would give a sales lift while maximizing gross margins. From a store ops perspective taking markdowns too often is expensive as it means re-ticketing the items, moving merchandise to the back of a store and/or discounting merchandise shelved in proximity of the items that were marked down. Not to mention the fact that continuously changing prices also causes “paper shrinkage” from incorrect pricing or missed markdowns.
In determining the optimal retail price there is a whole bunch of retail analytics at play behind the scenes which involves statistical modeling of the historical sales and application of seasonality and promo effects, competition etc. In the recent years we’ve seen most fashion retailers move away from the more traditional markdowns at end of life towards “lifecycle pricing” reducing prices early on for slow selling merchandise. With all the powerful analytics at their fingertips the buyers and planners should ideally be able to plan ahead for the “in-season” merchandise and reach all of their inventory goals. Why then don’t we see that happening ever so often?
One of the reasons being the predictive analytics, behind most forecasting solutions tends to have an underlying “forecast error”.“Prediction is very difficult especially if it’s about the future” Nils Bohr. This forecast error is a product of a number of external factors, for example, mild winters would result in lower sales of coats and if they were forecasted using previous three years of seasonality model there will be “forecast error”. Most if not all retailers also do periodic reclassification of merchandise (move products around in the merchandise hierarchy) which could result in this forecast error. Eventually the lower sales would result in a markdown recommendation but it would come too late. They would already have inventory built up in the DC/stockroom by then. The last thing for any retailer today is to have to deal with taking the merchandise down to clearance all at once and take a bigger hit on the margins.
What the buyers/planners need here is a solution to analyze the sales trends on a daily basis as a reality check. They need to look at the effects of climate, promotions, holidays in order to gauge the effectiveness of their current markdown decisions. This sales analysis would give them an idea of consumer price sensitivity as they breakdown the total sales by temporary markdowns, permanent markdowns and full price. An additional insight into the shrinkage to analyze trends for most damaged/stolen products against their OSA would empower them to make the most effective planning decisions. The evaluation could be done at several levels from Division right down to the color/SKU level by season or promotion (or any product attribute for that matter!).
This profit amplification solution will also enable the merchandisers to monitor vendor performance not only for quality but also for how long they take to fill the order, especially last minute orders. The pattern recognition for vendor shipments can also be applied to inventory flowing from warehouse to stores to identify ambiguous patterns and apply corrective actions. This will drive up the accuracy of virtual allocations and help them achieve their target sell-throughs. The buyers/planners could do a trend analysis for store performance either across the store cluster or the region right up to the chain level. The tool has powerful built-in retail analytics that will enable alignment of KPI across departments allowing greatest visibility to the causes for profit leakage.
Thus more power to the merchandiser would result in consumer driven planning and allocation decisions in turn driving up sales and margins.
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