Big Data in Marketing
We build state-of-the-art data management and analytics platforms that allow your Marketing and Business Intelligence teams to obtain a 360-degree view of your customers in real time.
Using Big Data, you can optimize marketing investments for the best ROI, increase customer loyalty and retention, and serve your customers with personalized product and content recommendations to maximize engagement.
Increase Customer Engagement and Loyalty with Real-time Personalized Recommendations
Amazon.com sells 25% of its products due to its recommendation engine. 80% of content watched by Netflix subscribers come from automated recommendations.
In a video content setting, personalized recommender systems help the company provide interesting content to a customer within a few seconds of starting a session, decreasing the likelihood that the customer will leave for another entertainment option.
The boost in customer engagement brought by personalized recommendations results in significantly increased video hours watched and lower subscription cancellation rates. The gain in streaming hours expands the available ad inventory, directly contributing to revenue in ad-monetized platforms. On the other hand, the decrease in subscriber churn rate increases the lifetime value (CLV) of existing customers and reduces the number of new customers needed to replace cancelled members for subscription-based platforms. Netflix estimates that it has achieved $1B of savings per year through personalization, while investing $150M per year on related R&D .
Fikrimuhal develops state-of-the-art recommendation systems by combining the latest developments in academic research and industry practices. We follow and contribute to the recommender systems literature, while at the same time ensuring that our work is guided by the same KPI’s and business metrics used by the world’s leading companies that generate a significant portion of their revenues from algorithmic systems, including Google, Amazon, and Netflix.
Our team members have vast experience in developing large-scale machine learning systems that serve millions of customers around the world. We build and deploy high-performance real-time content and product recommendation systems that create personal experiences for each and every customer.
 Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (December 2015), 19 pages.
Increase Customer Lifetime Value (CLV)
Birchbox has increased profits per customer by 70% by adapting marketing efforts based on the lifecycle stage of customers.
Using behavioral analytics, you can segment your customers based on their lifecycle stage. This allows you to convert members into buyers, one-time purchasers into repeat customers, and reconnect with those customers who are at risk of leaving you.
Churn (customer attrition) prediction systems use statistical techniques to automatically identify those customers who are likely to leave for your competitors. By reaching these customers with compelling discounts before they leave, you can keep them and ensure future revenue from them. Keeping your customers from leaving is important, because according to research firm Market Metrics, on average, the probability of selling to an existing customer is 60 – 70% while the probability of selling to a new prospect is 5 – 20%.
Churn prediction and prevention systems are used not only by e-commerce companies, but also in finance and telecommunications sectors. We have deep expertise in building advanced churn prediction and customer lifecycle management systems.
Optimize Marketing Budgets
Using historical purchase data from each customer, you can use more accurate data-driven attribution models in omni-channel campaigns. This helps you allocate your budget to the right channels and customers, increasing the ROI significantly. We have seen algorithmic budget allocation provide more than 2x increases in the ROI of ad spend.
Fikrimuhal’s team members have developed data-driven marketing optimization systems for a large number of clients.