CASE STUDY 1
Comsense helped the largest retail group of New Zealand adopt dynamic pricing, thus improving their revenue via net new customer acquisition by 7% within 3 months and lifting sales by 6%
The client was keen on leveraging its goldmine of data of suppliers, store locations, website, social media, customer purchasing and lifestyle behaviors, to drive better business outcomes. There was no tracking mechanism on what is currently selling and what will sell in future. The client was struggling to uncover what customers would like or buy next and how to turn one-time shoppers into brand loyalists.
The Comsense mar-tech team created a data lake to consolidate all customer data from various sources like portal data, in-store Point of Sales data, CRM transactional data, website and app login behavioral data, marketing campaign data, which includes email opens, click through rates and coupon codes redemption data. Customer profiles/personas based on advanced segmentation were created integrating the transactional, demographic and behavioral data. Applications such as basket analysis, data-driven tree analysis, retail promotions tool and CLTV modeling were used by data scientists that enabled:
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