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EXAMPLE: DynamicLake ended on a retailer's pattern of continual unexpected investment in their data lake by ensuring applications and work streams had dependable access to operate as required. Using DynamicLake frees resources and improves team productivity. |
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EXAMPLE: DynamicAction highlighted a women's shirt that was under-performing compared to similar shirts with the same price point. Within a few clicks the retailer learned that there were several product images missing and resolved the issue. |
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Highly Fragmented or Out of Stock EXAMPLE: DynamicAction identified falling conversion from product recommendations in the home decor category, alerting the retailer to an influx of bad reviews on a previously hot-selling item. The retailer rectified the issue and made adjustments to the business rules in their recommendation platform to prevent the recurrence. |
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EXAMPLE: DynamicAction recognized that product profit in a patricular product line was well below it's peers despite excellent online selling efficiency. The retailer realized they had included the product line in an online promotion in error and corrected the in-store promotion to exclude the line. |
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Highly Fragmented or Out of Stock
EXAMPLE: DynamicAction revealed that while a marketing campaign returned excellent view throughs, the products sold generated little profit. The retailer conducted a controlled test where they increased the selling price, monitored the effect of these changes with DynamicAction's Action Impact feature and increased profit per unit across the test group by nearly 40%. More in Heine Case Study (pdf) |
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High Lifetime Value Win Back
EXAMPLE: DynamicAction pinpointed customers that were habitual returners who also did not often purchase full price items. The retailer used this laser focus to exclude them from promotional emails. |
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EXAMPLE: DynamicAction diagnosed a series of overstocked shirts in the Dallas store. The retailer cleared the stock by selling the shirts to customers that matched the buying profile. |
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Demand Signals |
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EXAMPLE: DynamicAction’s reports revealed that a product on a retailer’s website had high views but very low stock cover, and suggested replacement with a SKU that had lower views, higher inventory, and a higher profit margin. The retailer redirected their in-site recommendations, increasing sales on the replacement product at a higher profit margin. |
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Adjust site search and sort order to provide additional views EXAMPLE: DynamicAction exposed that while a particular product did not have high sales volume, the profit per view was excellent. The retailer tested sending additional traffic to the product, and when profit held, expanded the strategy in other marketing channels, including offline. |
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EXAMPLE: The DynamicAction Analytics Platform showed a rise in customer unmet delivery expectations and that a few stores were responsible for the increase in delays. The retailer removed these stores from the Ship from Store program until additional training could be executed. |
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EXAMPLE: A retailer saw that while a particular product did not have high sales volume, the profit-per-view was excellent. The retailer tested sending additional traffic to the product, and when profit held, expanded the strategy in other marketing channels, including offline. |
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EXAMPLE: By using DynamicAction, a retailer was able to find out who their negative lifetime profit customers are and sent the list to their email platform to suppress them from their future promotional campaigns. |
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EXAMPLE: A retailer’s transition to DynamicAction data lake delivered a much more efficient and clean set of data, extracting more value from their existing applications. The improved data lake and associated dashboard resulted in a massive time savings, supplying instantly accessible reports that previously required a laborious 19+ hours per week.
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To see how DynamicAction empowers retail teams to achieve profitable growth, contact us for a demo. Additionally, read Client Case Studies and see how specific Retail Roles use DynamicAction.