Method and medium for customer product recommendations

Abstract

Examples described herein relate to a system consistent with the disclosure. For instance, the system may comprise a data lake including information relating to an in-store activity of a customer and an online activity of the customer, a processing resource, and a non-transitory machine-readable medium storing instructions executable by the processing resource to identify the in-store activity and the online activity of the customer, aggregate and store the in-store activity and the online activity of the customer in the data lake, reduce an amount of products in a product portfolio, compare purchases including in-store purchases and online purchases, and recommend a product of the plurality of products based on the comparison.

What is claimed: 
     
         1 . A system comprising:
 a data lake including information relating to an in-store activity of a customer and an online activity of the customer;   a processing resource; and   a non-transitory machine-readable medium storing instructions executable by the processing resource to:
 identify the in-store activity and the online activity of the customer; 
   aggregate and store the in-store activity and the online activity of the customer in the data lake;
 reduce a number of products displayed in a product portfolio offering to the customer based on revenue coverage optimization (RCO), wherein the product portfolio offering comprises at least a plurality of products having interdependencies; and 
   recommend at least one product from the product portfolio offering based on a combination of affinity of products, similarity of customers purchasing the products, and digital customer intent.   
     
     
         2 . The system of  claim 1 , including instructions to rank the plurality of products in the product portfolio offering based on an ability of each product to link to a sale of another product. 
     
     
         3 . The system of  claim 1 , further comprising instructions to determine which products associated with the data lake of the customer are substantially similar to the purchases of another customer. 
     
     
         4 . The system of  claim 3 , wherein the instructions to compare include instructions to compare an intended use of the products associated with the data lake of the customer with the intended use of the purchased products by the another customer. 
     
     
         5 . The system of  claim 3 , wherein the instructions to recommend include instruction to recommend products purchased by the another customer to the customer based on a product affinity between the products purchased by the another customer and the products associated with the data lake of the customer. 
     
     
         6 . The system of  claim 1 , wherein the digital customer intent is based on the similarities between a task of the customer and a task of the another customer. 
     
     
         7 . A non-transitory machine-readable medium storing instructions executable by a processing resource to:
 identify a product activity of a first customer;   compare products associated with the product activity of the first customer to products purchased by a second customer,   rank products related to the product activity of the first customer based on revenue coverage optimization (RCO);   recommend at least one product to the first customer from a product portfolio offering based on the comparison of products, affinity of products, similarity of customers purchasing the products, and digital customer intent, wherein the product portfolio offering comprises a plurality of products having interdependencies; and   aggregate and store data of the first customer into a data lake based on the product activity of the first customer and global purchases of the first customer.   
     
     
         8 . The non-transitory machine-readable medium of  claim 7 , further including instructions to reduce a number of products displayed in a product portfolio offering. 
     
     
         9 . The non-transitory machine-readable medium of  claim 7 , further including instructions to recommend the ranked products related to the first customer based on the product activity of the second customer. 
     
     
         10 . The non-transitory machine-readable medium of  claim 7 , further including instructions to recommend related products to the first customer based on online purchases of the second customer and in-store purchases of the second customer. 
     
     
         11 . The non-transitory machine-readable medium of  claim 7 , further including instructions to analyze an interdependence of products purchased by the second customer. 
     
     
         12 . The non-transitory machine-readable medium of  claim 11 , further including instructions to recommend products purchased by the second customer determined to be interdependent to products associated with the product activity of the first customer. 
     
     
         13 . The non-transitory machine-readable medium of  claim 7 , wherein the products are ranked based on the ability of a product to complete a task of the first customer. 
     
     
         14 . The non-transitory machine-readable medium of  claim 7 , wherein products are recommended based on similarities between the intended use of the products associated with the product activity of the first customer and the intended use of the products purchased by the second customer. 
     
     
         15 . The non-transitory machine-readable medium of  claim 7 , wherein the products are recommended to the first customer on an online menu, at an in-store register, at an eCommerce portal, through a call center, through an email campaign, on an offline campaign, or a combination thereof. 
     
     
         16 . A method comprising:
 identifying an in-store activity and an online activity of a first customer;   determining products to recommend based on substantially similarity between products associated with the in-store and online activity of the first customer and products purchased by a second customer, an intended use between the products associated with the in-store and online activity of the first customer and the intended use of the product purchased by the second customer, or a combination thereof;   ranking products based on revenue coverage optimization (RCO), the ability of the product to complete a task of the first customer, or a combination thereof;   recommending products from the product portfolio offering to the first customer based on the products associated with the in-store and online activity of the first customer, affinity of products, similarity of customers purchasing the products, digital customer intent, or a combination thereof; and   aggregating data of the first customer based on the in-store and online activity of the first customer.   
     
     
         17 . The method of  claim 16 , further comprising comparing the products associated with the in-store and online activity of the first customer with the products purchased by the second customer. 
     
     
         18 . The method of  claim 17 , further comprising recommending products based on the compared products associated with the in-store and online activity of the first customer and the products purchased by the second customer. 
     
     
         19 . The method of  claim 16 , further comprising recommending the first five products of the ranked products. 
     
     
         20 . The method of  claim 16 , further comprising aggregating and storing data of the first customer into a data lake.