Less Gut, More Analytics and Data Science for Merchants
Rahul Shrivastava
on Feb 04 2017

Merchandising is the engine driving most retail businesses. Merchants are directly responsible for the performance of their product categories and collectively steer the business towards growth opportunities while they watch and combat competition. These “Merchant Princes” [and “Princesses], as the Wall Street Journal calls them, are overwhelmed and need help from analytics.

Traditionally, retail merchants have been considered experts of their domain. They mastered their categories with near-magical buying decisions, based on their deep experience of shopper behavior, accumulated during years on the job. They work from a bevy of reports – most with stale data — indicating business performance, making decisions on where to focus or invest and what action to take.

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All this is changing rapidly

Consider a merchant whose category sales are declining. This merchant is not alone! Retail markets continue to become more dynamic and cutthroat, because of high-speed and nonstop competition, inventory issues, traffic trends, and consumer knowledge that approaches real-time and absolute 100%.

You’d probably laugh – and correctly so – at anyone claiming a static report can consistently point the merchant to where sales or margin problems hide. That belief would be unrealistic. Root cause analysis is part of a merchant’s routine and rarely is it a straight path from detecting a profit gap to pinpointing the cause. Instead, it requires exploration into all potential causes.

The only way to get accurate answers quickly: combine the expertise of a merchant with big data and state-of-the-art analytics. Today, that means prescriptive analytics. As Suzanne Kapner’s recent article in the WSJ explains, “Companies increasingly are relying on number crunching rather than top merchant’s instinct as they try to combat sluggish sales and changing shopping behavior.”

In a highly dynamic environment, an individual merchant’s ability to execute his/her own analysis — and quickly understand what the mass quantity of data is saying — has taken on great importance.

This is giving rise to a new kind of merchant – one who can execute and decipher reams of analyses to find insights and still have a strategic perspective based on instinct and experience. The evolution of the merchant has been enabled by new software tools that help analyze millions of data points and offer actionable recommendations in real-time.

It’s not only pricing that needs to be largely overhauled. Boomerang recently introduced its Assortment Optimization solution, which gives specific guidance to merchants, in near real time and at large scale, in the key areas of:

  • Identifying gaps in merchandise selection – alerting the merchant to missed margin opportunities where competitors are profiting on hot products.
  • Prioritizing new SKUs for onboarding and forecasting demand and profitability
  • Calling out profit opportunities that lie unnoticed within the existing product assortment Boomerang Assortment Optimization also tracks and makes recommendations to reduce cannibalization, and helps to improve sourcing.

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At Boomerang Commerce, we are convinced that over the next five years, merchants will become significantly more fluent and trusting in analytics. This can happen in two different ways: either through training and shifting of profiles, or through addition of decision science support within their teams.

This drives our product development philosophy of empowering merchants to make better decisions in a completely white-box manner so they can augment their heuristics with data insights, instead of having to choose between the two.

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