In the fiercely competitive retail market, every player is compelled to undertake numerous activities, some of which yield positive results while others do not. Conducting regular category or brand review analyses is a standard practice for both retailers and suppliers to gain insights into shopper behavior and their objective is simple: identify the causes and take corrective actions. For the corrective actions to be efficient, they need to address the right root cause
In this article, we review how data patterns help identify the 5 main causes of sales drop so the right corrective actions can be setup and address the right pain effectively
Shoppers visit the stores to purchase the products, so range variety is the most critical factor for Shoppers to choose the stores. When a Retailer deletes the SKUs, what several scenarios can happen:
Shoppers Switches to other Retailer Stores
We can reduce the scenario by 2nd and 3rd if we customers well the Shoppers decision tree
Data pattern: How can data help you find out if this is the right scenario
Corrective Actions:
When Brands increase the price, Shoppers behavior's are often grouped in 5 main types, depending on Brand Loyalty and how important the category for shoppers is.
Data pattern: How can data help you find out if this is the right scenario
Corrective Actions:
When Shoppers face out-of-stock, 4 choices depending on brand loyalty.
Data pattern: How can data help you find out if this is the right scenario.
Corrective Actions:
It requires 4 success factors for Shoppers to purchase a promotion item:
If one of these factors fails, it might decrease sales due to the promotion in case we performed well last year.
Data pattern: How can data help you find out if this is the right scenario
Corrective Actions:
Therefore the data pattern is similar to availability issues
Corrective Actions:
If you want to make your next decisions based on such analyses, Hypertrade’s Retail Management Platform enables you to do it automatically across multiple retailers’ dataset.
Onuma Patthamakanokporn (nicknamed Bee) is Hypertrade’s Data Director. With a strong retail and data analytics background acquired with Tesco and Dunhumby in Thailand, she helps manufacturers in SouthEast Asia, Middle East and Africa make the most of their data sets to drive continuous and profitable growth.
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Bee can be joined at onuma.p@hyper-trade.com
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Hypertrade is a retail tech company in Category Management and Shopper Behaviors Analytics. Manufacturers use our platform to manage their multi-retailer data sets and to automate their category management and sales analytics. Retailers use our platform to improve their Commercial offering, collaborate with their suppliers and Engage their Shoppers. Building on Sales, POS & Loyalty data, some of the things we fully automate are:
• Brand& Category Score card
• Brand automatic Diagnostics
• Range Optimization
• Promotion Planning & Forecasting
• Shoppers baskets analysis