AI – powered Anomaly Detection in Ecommerce for Category Managers

For an ecommerce company, each category is a mini business with its own P&L. Category directors assisted by their managers collaborate with the suppliers, exchange information, build and focus on common goals to shape the business. Suppliers work with the category managers, support promotions, suggest new product launches, exert towards meeting supply chain needs and aid business growth. AI analytics for category managers can help the ecommerce business in four key aspects of their job responsibilities.

  1. Driving Category Growth: While driving marketplace growth of the specific category mindful of customer and competition, category manager is bombarded with variety of information which is increasingly impossible to track. At times, the category managers focus on short term priorities and miss out on critical information that would have increased their revenue and margins in long term. Knowing the relevant information in real time is imperative for category growth. An artificial intelligence system that automatically filters the irrelevant information, correlates and delivers business incidents that are mission critical would equip the category manager to manage products better irrespective of their experience level.
  1. Trend Identification: Spotting early trends in the market and placing the right bets on the future is vital for the success of category managers. Real time anomaly detection products in the market are enabling this happen.
  1. Managing promotions: Category managers drive promotions plans and coordinate it with marketing teams, suppliers and supply chain teams. Often promotions are either super success or failures, which leads to extreme strain in the system. Having an insight into promotions while they are happening is essential to seal revenue leakages and increase business performance.
  1. Delivering P&L: The most important part of a category director’s job is to deliver on the P&L. There are times when the whole energy of the teams is directed to finding the money that will fill up the shortfall. Often this is an activity that happens in hindsight. Tracking key KPIs at granular level would indicate this shortfall before it happens and would help pin the responsibility on the right reasons for the shortfall. And the director can know when the budget is not going to be hit and address the shortfall before it happens. The warnings can be automatically sent by an anomaly detection system along with the contribution to that anomaly at the granular level.

Anomaly detection of the granular KPIs with AI analytics will assist the category managers do more with less. This is because managers assisted with the power of AI can manage by exceptions and use their time and energy into more important issues that are highlighted by those exceptions. The frequency of decision making comes down, saves invaluable time and energy and helps them focus on business priorities and deliver growth.

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