A marketing firm specializing in retail-customer engagement improves their data management processes with automation and machine learning enhancements.
Our client was spending too much time and too many resources generating promotions and marketing to its end customers. This manual process meant that the client could not easily tailor its services to the unique needs of the individual customer. Customers were consistently providing feedback that the offers were not driving the desired level of personalization and engagement. Due to a lack of responsiveness, the manually generated offers were quickly became obsolete and failing to deliver the overall outcomes for their customers.
Reduced manual hours of data science team by 90%
Apex designed and delivered an end-to-end solution for producing granular and targeted offer recommendations. This solution entailed technology selection, solution architecture, implementation and deployment. The solution leveraged internal and external data APIs for efficient data exchange as well as a robust analytical engine that leveraged user configuration and machine learning to deliver optimized offers to its customers. The machine learning models benefitted from the growing dataset as well as a sophisticated management console that allowed for subject-matter experts to tweak the final output.
As a result of Apex's work, the client was able to provide more sophisticated, impactful analytics to its customers in near real-time. No longer limited by manual, siloed data processes the client could meet the increasingly-complex needs of their customers without growing technical debt or overhead due to dramatically increased efficiency. Not only was the client more agile and adaptive, but it was able to increase the success of its campaign-management and offer-recommendation which facilitated maturity and business growth.