Use Cases

• Dependably deliver clean, high fidelity data to technology partners faster and cheaper.
  
  
• Conduct faster analyses that once eluded your organization.

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.

• Quickly identify which of your slow selling SKUs have out-sized profit potentials.
  
  
• Receive prescriptive alerts: increase exposure, increase frequency in the recommendation engine, adjust price, review page content.

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.

• Generate higher profit from each product view on your site and every product placement in your store.
  
  
• Reduce product recommendation, exposure, and placement for products with: 
  

Highly Fragmented or Out of Stock
Poorly Reviewed
Returned Frequently
Poised to Sell Through

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. 

• Keep as much profit as possible at every markdown decision.
  
  
• Evaluate AI prescribed pricing actions with respect to web vs. store price disconnects, competitor pricing and/or wholesale channel prices.
  
  
• Tailor your customer contact strategy to customer behavior and lifetime value.

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.

• Rationalize digital campaigns that promote products: 

Highly Fragmented or Out of Stock
Poorly Reviewed
Returned Frequently
Poised to Sell Through

• Shift and re-balance campaign investments to drive high profit per order and full price sales.

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)

• Identify customers prescriptively to fuel your contact strategies: 

High Lifetime Value Win Back 
Category and Channel Nudge
Cross Sell
Nurture to Next Purchase Program
Limit Loss

• Exploit AI generated analyses of channel isolation, historical purchases, promo usage, repurchase risk, profit history, return history, digital engagement and more.

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.

• Target customers with personalized promotions and loyalty incentives to clear overstocked SKUs that match the customer’s profile.

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.

• Adjust allocation and replenishment to stores and warehouses given:

Demand Signals
Conversion
Reviews
Returns

EXAMPLE: DynamicAction determined that a product was selling well in 20% of a retailer's stores and not as well in others. The retailer revised their inventory allocation of the product to favor the stores where it was selling well.

• Identify over and understocked SKU’s by store faster.
  
  
• Determine stores where product can be reallocated to sell faster.

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. 

• Find high converting, high profit per view products lacking exposure:

Adjust site search and sort order to provide additional views
Expand product range and paid search keyword buys to match demand signals

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.

• Understand delivery on promise at the category, product, and store level continuously.
  
  
• Adjust Ship from Store procedures and/or ship points given shipping profit and shipment timing.

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.

• Have complete clarity on category, brand, and product affinities from store and web purchases.  No black boxes.
  
  
• Place focus on products with strong “add to bag” support and higher lift indexing.

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.

• Stream highly distilled data and profit grounded insights to your tech platforms to enable intelligent automation.
  
  
• Unlock wasted marketing effort on your negative lifetime value customers in email, promotions and loyalty programs.
  
  
• Make every product recommendation and store allocation calculation more relevant by using profitability, returns, reviews, stock cover, and fragmentation to accelerate the value you get from your recommendation engines and strategies.

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.

• Leverage DynamicAction’s highly extensible retail data pipeline and data lake enrichment features.
  
  
• Avoid the time-consuming trial and error in connecting data sources, deriving valuable metrics and measures, and discovering clear customer and profit-centric patterns.
  
  
• Fuel your AI and ML initiatives with rigorously cleansed and normalized data pipeline of atomic and distilled data sets.
  
  
• Empower your data science team to drive your company forward.

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.