Overview About Beinf

Beinf helped optimize the marketing campaigns of a taxi service. Now, the company provides next best offer to customers that receive maximum response. Initial Challenge Taxi services represent a highly competitive market, where offering significant discounts is considered standard practice. As a result, customers tend to use the taxi service that offers the best price at the moment they need it. Many customers do not stick to one service and constantly switch taxi companies. The company wanted to implement the ability to provide the lowest discounts while remaining attractive to customers. To achieve this, it was necessary to determine the optimal discount level for each individual customer based on their level of interest in each offer. Our Solution Beinf deployed an ML model and trained it on client data so that it could assess which offer the client would use and with what probability. Gathered metrics that comprehensively describe customer behavior (taking into account previous offers, channels of delivery and response rate). Organized the training of a machine learning model on customer data. Diagnosed the key factors influencing the customer's decision to accept an offer, including non-material ones (additional services, available vehicles, drivers, etc.). Developed a model for preliminary assessment of the effect of communication. Outcome The company revised its discount policy. Thanks to customers who choose this taxi service with a smaller discount (due to the influence of other factors), the opportunity arose to win the “discount race” against competitors. Additionally, the diagnostic results allowed strengthening those areas of the customer experience that negatively impacted the choice of taxi service. This led to an increase in the share of loyal customers, the number of repeat orders, and the number of trips per user. ? 1,13 churn rate   ? 1,15 response rate

Customer-centric companies are often obsessed with their customers and go to great lengths to engage, retain, and interest them. New technologies and approaches often assist in this endeavor, but they are not a magic solution. Before their implementation, it is crucial to not only understand the uniqueness of each customer but also to comprehend what constitutes these unique traits. Our story clearly demonstrates how to achieve this through smart AI-based segmentation. Customer profile The Eastern European fashion e-commerce platform offers a selection of over 70,000 products and 1,800 brands across 8 countries. This includes both global bestsellers and products from local brands. The company has direct contracts with top brands and sells exclusively original products, verified by certificates. The retailer is committed to providing a smooth shopping experience while staying up-to-date with evolving customer experience management practices. Initial Challenge The company faced challenges in effectively segmenting its customer base and tailoring marketing efforts to individual tastes. Previously, the client used traditional approaches to segmentation. However, these methods, based on past purchases, failed to capture the nuanced preferences and changing behaviors of customers. There was a need for more accurate predictions of user actions, which led to the use of predictive and diagnostic models. However, this also brought about the challenge of interpreting the complex results. Our Solution To address the challenges faced by the fashion e-commerce platform, integrated micro-segmentation was implemented. BeInf offered a combined approach to micro-segmentation, leveraging descriptive, predictive, and diagnostic customer attributes. This approach provided the client with a comprehensive understanding of each customer segment, including their needs, preferences, and the impact of communication. To interpret the results of the ML models, BeInf developed tools to translate the predictive models and diagnostic analysis results into practical, understandable information that could be easily used by various departments.  Finally, a customer attribute map was created. BeInf conducted an analysis of all available and potentially valuable customer attributes, selecting the most valuable ones and categorizing them to facilitate their use. This enables the quick, easy, and flexible creation of micro-segments. Outcome Six months after implementing the new approach to customer segmentation, the fashion retailer achieved the following results: x 1,3 higher communication conversion rate; x 1,07 higher CLV; +7% in NPS for Communication; -2,1% in promo budget. The advanced micro-segmentation implemented for the fashion retailer enhanced marketing efficiency and led to increased conversion rates and improved ROI. Additionally, the company gained the ability to adapt more quickly to market conditions and trends, as well as changes in customer preferences, by making more accurate real-time decisions.

The customer churn rate is very important for business because it directly affects revenue: retaining customers is always more expensive than attracting new ones. Customers may leave the company for various reasons, and this indicator strongly depends on the specifics of the business. However, stopping this process is quite realistic. We will explain how to do this using machine learning further in our case study. Customer Profile In this case, we are bound by an NDA, so we cannot disclose the details of the client's business. However, we will share those details that will certainly interest you. Initial Challenge At the time of contacting Beinf, the company described its problems as follows: No clear understanding of customer base diversity, key segments, and customer profile. As a result, the communication strategy could not be personalized and precise. A high churn rate and short lifetime resulted in rapid customer base attrition. The churn identification period takes 3 months, leading to the loss of valuable time to retain customers. The lifetime value (LTV) of the customer was decreasing, and the company was losing revenue, so they decided to implement new approaches to working with customer data. Our Solution At Beinf, we decided to seek answers to our client's questions through Advanced Segmentation and Analysis. To achieve this, we created an approach for distinct segmentation, allowing for personalized communication. A comprehensive assessment of the customer base was conducted, digging deep into data and trends to uncover hidden insights. We developed a predictive model by creating and testing over 500 business metrics to describe the full customer experience, customer behaviors, and potential red flags. Based on these metrics, an ML model was launched, providing results on a daily basis. After this, we identified churn factors by developing a diagnostic engine to measure the personalized impact of each metric on churn probability and identify key churn factors. This allowed us to develop a Churn Reduction Strategy. Using the individual key churn factors, we provided the client with a list of actions to specifically combat and reduce churn. The communication strategy was completely transformed—it is now entirely data-driven. It ensures that each customer receives relevant and compelling messages. Outcome The work performed by Beinf enabled the client to achieve the following results: Insightful Metrics: 1200 individualized customer segments and over 500 business metrics ensured a deep understanding of the customer profile. High-level of Risk Identification: The predictive model is able to identify more than 90% of potentially churned customers in the early stages. Churn Reduction: In just two months, the Churn rate decreased by up to 4%, reflecting enhanced customer satisfaction and improved retention strategies. CLV increasing: During two years avg. life time increased up to 40% because of the Churn rate reduction and CLV up to 18% due to personalized offers using. Customer churn is a problem that most companies face. If this is your story too, let's work together to develop and implement a strategy to reduce churn risk and increase the customer lifetime value using your data, machine learning, and advanced analytics.

The electronic brokerage services market is highly competitive, featuring both major players with longstanding reputations and relatively young companies. Many of these companies use advertising to attract new clients. In this case, we want to share a story that will offer a fresh perspective on the possibilities of machine learning and data analytics specifically in the context of optimizing advertising campaigns. Client Profile This time our client is a brokerage firm whose services are used by both passive investors and active traders. Client information is protected by an NDA; however, we will share some details to inspire you with our success story. Initial Challenge: The Client company struggled with optimizing their advertising campaigns aimed at driving first deposits. The existing strategy faced the following barriers: Long decision-making period: The time it took from a user's first visit to making a deposit exceeded the storage limits of advertising platforms, causing crucial data gaps  Data Loss: Essential user data, crucial for understanding customer behavior and segmenting audiences, was either getting lost or wasn't captured effectively  High Branded Traffic: A significant portion of their website visits was direct or branded traffic due to data loss, which doesn't provide insights on advertising effectiveness  Reliance on Sign-Ups: The company's optimization strategy was primarily focused on user sign-ups, due to the shorter period between the first visit and the targeted event  Low conversion rate: However, a very small portion of these sign-ups actually converted into first-time depositors. This approach led to targeting a broader user base, most of whom weren't depositing. Our Solution To address the client's issues, we developed an ML model capable of predicting the likelihood of a newly registered user making a first-time deposit. This model provides a more accurate targeting mechanism than merely relying on sign-ups. The proposed model is characterized by a high percentage of identification. It boasts a recall rate of between 84-89%, ensuring that a vast majority of users likely to deposit are identified.  Additionally, we advised the company to shift its optimization strategy. Instead of targeting users based on sign-ups, the focus was shifted to users with predicted FTD (First-Time Deposits). Outcome Our solution helped the broker more effectively utilize the advertising budget to attract new clients. Specifically, it allowed for adapting targeting to the profile of potential clients considering the likelihood of their first deposit. Could your success story be next? Let's evaluate the potential of your customer data to solve your specific marketing challenge.