Headquartered in Turkey, Aydinli is a retailer of children and adult clothing. It is the licensed brand distributor in Asia, the Middle East and south Europe for brands including Pierre Cardin, Cacharel and U.S. Polo Assn. The company has approximately 5,000 employees.
Like many retailers in a hyper-competitive market, Aydinli’s marketing execs were looking for any competitive advantage and were turning to data to find it. The company’s marketing execs wanted a system that would allow them to quickly and accurately create audiences for their targeted campaigns, allowing them to perform a deep dive into customer insights.
Aydinli knew which retail-focused customer metrics mattered, but its capability to analyze customers based on those metrics was quite limited. Additionally, the company lacked a system that would empower its marketers to create meaningful segments and audiences, then easily deploy email and SMS campaigns based on those segments. With a desire to benefit from the value of artificial intelligence on its business, Aydinli began searching for a solution.
After considering other solutions, Aydinli ultimately chose Acquia CDP due to its powerful, self-improving prediction model capabilities. Using Acquia CDP’s Likelihood to Buy machine learning model for discount optimization allowed Aydinli to approach twin audiences with two separate discount offers and split them based on the likelihood-to-buy segments. This allowed the team to more efficiently manage its discount budget.
Additionally, Acquia CDP’s User2Product Recommendations machine learning model and product-based clusters gave Aydinli’s marketers the power to reach out to related segments in a smart and easy way. And machine learning also indicated behavioral-based clusters, allowing Aydinli’s marketers to target or eliminate segments with specific characteristics like high-returners, digital-only buyers or omnichannel customers. This helped the team achieve better conversion rates and avoid revenue cannibalization.
Since leveraging Acquia CDP’s machine learning models, Aydinli has improved its campaigns’ effectiveness and its company’s bottom line. Specifically:
- In a four-month period, the net impact of using machine learning models in campaigns across the company’s three brands was ₺125,000 in additional revenue per campaign.
- Aydinli has achieved an ROI of more than 3,500%.