Digital Retail Hyper-Personalization
The client needed to reframe their digital ecosystem and have a more engaging and personalized online user experience to drive higher revenue. The global motorcycle manufacturer had a digital landscape consisting of over 30 unique systems.
2X Profit on Targeted Emails
The implementation of hyper-personalized recommendations resulted in a significant increase in the sales value of each email sent – with a surge of 10-20%. Additionally, the use of personalized product SKUs saw a marked boost with a 40% increased sales rate over the most popular SKUs. The cherry on top was the near doubling of profit from each targeted email sent, which bore the stamp of carefully crafted, personalized offers.
4X Return on Advertising Spend
By creating and engaging new segments of potential customers identified through this project, the client saw their return on advertising spend (ROAS) quadruple. The testament to the project’s success was the substantial addition of $250,000 to revenue within just six weeks of scaling these data-driven customer segments.
5% Increase in Click Through Rates
Enhanced website category sections, powered by insights from the project, saw a 5% increase in customer click-through rates. This displays an encouraging trend of customers exploring the website more extensively, bolstered by a more personalized, engaging browsing experience.
An integrated platform delivers
Dialexa normalized the data, creating a 360 view of the customer and transforming this data web into an integrated Customer Data Platform (CDP) mapped into nine data domains. Data pipelines were created for over 80 legacy data sources, including customer identities, ownership data, point-of-sale records, and third-party templates.
Harnessing the power of Azure Synapse Analytics, Azure Purview, and PySpark notebooks, Dialexa developed an omni-channel solution powered by a data lake. This platform was utilized to identify unique customers, predict purchasing behavior, and reduce customer churn. Dialexa set up an A/B testing framework and tested many different personalization options to target and adjust the messaging for end users. Equipped with AI/ML models, the personalization process involved crafting dynamic recommendations prior to purchase and post purchase, personalized SKUs, and targeted offers.
Global motorcycle manufacturer
- Data Engineer
- Data Architect
- Quality Engineer
- Data Strategy
- Data Engineering
- Hyper-Personalization
- AI
- Consumer Testing
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