Beymen, ilk mağazasını 1971 yılında İstanbul’da o yılların en prestijli alışveriş semti olan Şişli’de açtı. 1987 yılında bir ilke imza atarak, Türkiye'de henüz tüketiciyi koruma yasasının bulunmadığı ve tüketici haklarından söz edilmediği bir dönemde “Koşulsuz Müşteri Mutluluğu”kavramını hayata geçirerek sektörde bir devrim yarattı.

Türkiye’de lüks moda perakendeciliği Beymen ile başladı. Beymen, 1990’ların başında dünyaca ünlü birçok lüks moda markasını aynı çatı altında buluşturarak önemli bir yeniliği daha hayata geçirdi. Beymen, Beymen.com açılışı ile başlayan omnichannel yatırımlarına ağırlık vererek müşterilerine sunduğu hizmet kanallarını ve kalitesini daha da geliştirmektedir.

What was the need?

Beymen wanted to show special product recommendations and offer a personalized experience to its customers who visit its online sales website and online channels via mobile applications.

As a result of the analysis we conducted on this need, we made suggestions to make the necessary improvements on online sales channels in order to collect the necessary data set. Among these suggestions, the 3 most basic needs were identified;

More detailed and accurate storage of user movements in a complete and accurate way,
Storing the obtained data and processing it with Artificial Intelligence when necessary,
Providing customer-specific product recommendations processed with artificial intelligence to online sales channels in an uninterrupted manner.

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Solution process and technologies used

The customer's data sources were examined and all data that might be needed were collected. User data without KVKK approvals were excluded. The data to be modeled was taken on a special dataset. As a result of the work carried out with the business units, an agreement was reached on the data to be processed according to the needs and a data pipeline was designed.

The data storage process was designed to delete the expired data automatically. Integration processes were completed in collaboration with Beymen's expert teams.

An artificial intelligence model was developed to process the data and display customized products for each customer, and this model was deployed to the service on Google. This model was designed to provide scoring and weighting for the 3000 most popular products according to the purchasing and visiting activities of all customers.

Kullanılan Teknolojiler

For secure connection; Google Cloud VPN
For data transfer; Cloud Cloud Pub/Sub
For running the artificial intelligence model; Vertex AI
For scheduling data transfer jobs; Google Cloud Composer (Managed Airflow)
For data processing and ETL jobs; Google Cloud DataFlow
For storing and querying data; Google BigQuery

Beymen, Google Cloud sayesinde sağladığı faydayı en üst düzeye çıkararak toplam sahip olma maliyetini (TCO) sektördeki diğer örneklerine göre minimumda tutmayı başardı.

Beymen was able to maximize its benefits and keep its total cost of ownership (TCO) to a minimum compared to other examples in the industry thanks to Google Cloud.

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One of the common problems encountered in data collection is data loss during transfer between systems. With Google Cloud Pub/Sub service, data loss was minimized.
The infrastructure required for artificial intelligence modeling and model training becomes obsolete very quickly due to today's rapidly changing technology trends, and this poses a problem for the entire retail industry. With the Google Vertex AI service, this is no longer a problem and has become a service provided by Google Cloud.
The infrastructure and development needs required during the storage and processing (transformation) of the data obtained could be easily provided in the most up-to-date way with Google Cloud's manageable services and expandable infrastructure.
With the 'pay-as-you-go' model, it was possible to obtain the infrastructure and services required for the needs at the most affordable price without compromising performance and scalability.

Project outcomes

We were able to increase the average transaction value for recommended products. This allowed us to discover visitors' interests, while enabling them to reach the products they were looking for faster, increasing sales potential.
A flexible and high-performance product recommendation system was established that instantly meets the demand regardless of the number of customers visiting online sales channels.
Customized product recommendations and experience increased visitor/customer satisfaction and loyalty.
Conversion rate and profitability of products increased.

Tüm bunları herhangi bir ön satın alma yapmadan, toplam sahip olma maliyeti minimumda tutarak gerçekleştirdik.

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