USE CASES

Build Strategy + Take Action with Confidence

 
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Retailers around the world use DynamicAction to operate with a readied focus on increasing profit

Get more value, faster, from your data lake 

datalake
    • Dependably deliver clean, high fidelity data to technology partners faster and cheaper

    • Conduct faster analyses that often elude your organization

EXAMPLE: A retailer was needing to make continual unexpected investment to ensure applications and work streams dependent on the data lake could operate as required. Using DynamicAction Smart Data Lake, resources were freed up, and productivity of the team utilizing the data lake improved


Profitably clear slow selling products

clearance
    • Quickly identify which of your slow selling products relative to its peers have out-sized profit potentials

    • Be alerted to the actions to take: increase exposure, run more frequently in recommendation engine, review page content, adjust price

EXAMPLE: A women's shirt was under-performing compared to similar shirts with the same price point. Upon investigation, the retailer learned that there were several product images missing

Win more profit from every product placement 

USE CASE PROFIT PER VIEW
  • 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
         High return rates
         Low SKU Availability
         Poor review ratings
         Low stock cover and limited profit potential

EXAMPLE: Conversion from product recommendations in the home decor category was falling. The retailer saw that there had been an influx of bad reviews on a previously hot-selling item. The retailer made adjustments to the business rules in their recommendation platform while rectifying the issue

Be surgical in product pricing actions

PRICE
  • Keep as much profit as possible at every markdown decision

  • Evaluate AI prescribed pricing actions with respect to web-store price disconnects, competitor pricing and/or wholesale channel prices

  • Amplify your customer contact strategy with customer behavior and lifetime value

EXAMPLE: While selling efficiency was excellent in the online channel for a particular product line, the retailer saw that product profit % was well below it's peers. The retailer realized they had included the product line in an online promotion in error, and the in-store promotion correctly excluded the line

Increase your digital marketing returns immediately

MARKETING
  • 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: A retailer realized that while a marketing campaign returned excellent view throughs, the products sold generated little profit

More in Heine Case Study (pdf)

Amplify your customer contact strategy with customer behavior and lifetime value

SHOPPERS-1
  • Identify customers prescriptively to fuel your contact strategies

         -High lifetime value winback
         -Category and Channel nudge
         -Cross sell
         -Nuture 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: A retailer pinpointed customers that were habitual returners who also did not often purchase full price items and excluded them from promotional emails

Utilize Customer and SKU matching to clear overstocks

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

EXAMPLE: A retailer cleared a series of overstocked shirts in the Dallas store to customers that matched the buying profile

Align distribution with demand

DISTRIBUTION
  • Adjust allocation and replenishment to stores and warehouses given

         -Demand signals
         -Conversion
         -Reviews
         -Returns

EXAMPLE: A retailer saw that a product was selling well in 20% of their stores and not as well in others. They increased the allocation of product for the stores where it was selling well

Strategically allocate stock down to the SKU and store

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

EXAMPLE: A retailer used DynamicAction to quickly identify which products sold well in certain stores and not others.  They modified allocation of that product to stores where there was higher demand today

Focus attention on the most profitable products still in stock

USE CASE PROFITABLE PRODUCT
  • 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: 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

Keep Customer Experience high for BOSS

USE CASE SHIP FROM STORE
  • 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

Win even more profit from your cross-sell & up-sell programs

outdoor-gear-1-1
  • 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

Make the move to intelligent automation

retail-analytics-solution
  • 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, promo’s 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: A retailer sent a list of negative lifetime profit customers to their email platform to suppress them from your promotional campaigns

Accelerate your AI intiatives

shopper_analytics-1
  • 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

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.