We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is est…
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48 results for “customer purchase activities”
Proposes dual product embedding for complementary product representation learning.
problem Detecting complementary relationships from noisy and sparse customer purchase activities.
method Knowledge-aware dual product embedding with multi-task learning and user bias terms.
result Complementary relationships are captured more accurately than simple similarity.
New policy optimizes product assortment in the presence of unpredictable customers.
problem Optimizing product assortment in the presence of outlier customers.
method Developed a robust online assortment optimization policy using an active elimination strategy.
result Established upper and lower bounds on regret, showing optimality up to logarithmic factor in .
Modelling customer churn for the retail industry in a deep learning based sequential frameworkstat.ML
Deep learning predicts customer churn in retail.
problem Accurately predicting which customers are likely to stop purchasing.
method Survival model parameters learned by recurrent neural networks.
result Individual level survival models for purchasing behavior.
Study uses causal machine learning to assess coupon campaign impact on retailer sales.
problem Assessing the causal effect of a coupon campaign on retailer sales.
method Causal machine learning algorithms, subgroup analysis, optimal policy learning.
result Only two coupon categories (drugstore and other food) have a significant positive impact on sales.
Study predicts purchasing decisions of online food delivery customers.
problem Understanding and predicting consumer purchasing decisions in online food delivery.
method Used machine learning techniques including CART, C4.5, random forest, and rule-based classifiers to predict purchasing decisions.
result C4.5 decision tree model outperformed others with 91.67% accuracy.
Firms miscount their customers who stop buying without saying goodbye.
problem Counting non-contractual customers accurately.
method Estimating repeat purchase probabilities and extrapolating to infinite time.
result The count of alive customers is only partially identified, with a wide range of estimates.
The DeepSurv model predicts purchase timing better than other survival models.
problem Predicting the exact purchase timing of consumers.
method Survival models (Kernel SVM, DeepSurv, Survival Random Forest, MTLR) were compared using various consumer attributes.
result DeepSurv model outperformed other models in predicting purchase completion.
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotionscs.AI
This study benchmarks AI agents for personalized retail promotions using simulations.
problem Optimizing coupon targeting for sparse customer purchase events.
method Comprehensive simulations of customer shopping behaviors; training RL agents on batch data.
result Contextual bandit and deep RL methods outperform static policies in sparse reward environments.
This paper monetizes customer load data to boost energy retailer profits.
problem Improving load forecasts to reduce energy imbalance costs.
method Cooperative game theory approach to quantify and distribute profits.
result Retailer gains significant profit from customer load data.
It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial for such predictions. However, their demographic data, preferences, and other information that might be useful for building loyalty programs …
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
In order to better engage with customers, retailers rely on extensive customer and product databases which allows them to better understand customer behaviour and purchasing patterns. This has long been a challenging task as customer modelling is a multi-faceted, noisy and time-dependent problem. The most common way to…
This research extends the Pareto/NBD model using neural networks for better out-of-sample predictions.
problem The limitations of the Pareto/NBD model in predicting out-of-sample data.
method A neural network-based extension of the Pareto/NBD model.
result The proposed method shows extraordinary predictability on repeat purchases at individual and aggregate levels.
Order Matters at Fanatics Recommending Sequentially Ordered Products by LSTM Embedded with Word2Veccs.LG
E-commerce system predicts customer interest in sequentially ordered products.
problem Modeling customer interest in sequentially ordered products.
method Embed purchased items with Word2Vec and model the sequence with LSTM RNN.
result The recommender system outperforms its predecessor in click-through rate.
A new pricing strategy learns customer valuations without noise distribution knowledge.
problem Setting optimal prices for products based on customer valuations with unknown noise.
method Developed a novel perturbed linear bandit framework to learn both contextual functions and market noise.
result Proved sub-linear regret bound and demonstrated superior performance on simulations and real data.
Improved product recommendations using deep learning.
problem Sparse customer purchasing data for personalized recommendations.
method Deep Collaborative Filtering (NCF) with latent variables and Bayesian Optimization.
result NCF achieved highest NDCG performance on proprietary dataset.
Paper develops privacy-preserving dynamic pricing policy for e-commerce.
problem Protecting customer privacy in dynamic pricing with personalized information.
method Uses differential privacy framework to develop a privacy-preserving policy.
result Achieves both privacy and performance guarantees in dynamic pricing.
Method identifies potential customers from limited data.
problem Efficiently market products to interested but non-loyal customers.
method Double Positive and Unlabeled (PU) learning approach.
result Proposed algorithm achieves efficient marketing.
The paper develops loss functions for pricing models using observational data.
problem Evaluating pricing policies directly from observational data with historical biases.
method Adapting machine learning techniques for corrupted labels to derive unbiased loss functions.
result Identifies minimum variance and robust estimators for contextual pricing.
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
problem Active liquidity management by mutual funds in India.
method Examined cash holdings and stock purchases of Indian equity mutual funds.
result Funds with active liquidity choices outperform, highlighting the importance of this strategy.
Improved conversion prediction for B2B products using collective online activity trails.
problem Limited information from single user activity trails for B2B ad targeting.
method Introduced relevant users concept and used distributed activity representations to build seed lists.
result Improved conversion prediction AUC by 8.8% using collective activity trails.
Binary choice forests model customer choices in retailing.
problem Estimating DCMs using transaction data is challenging and prone to misspecification.
method Random forest of binary decision trees to represent DCMs, interpretable and consistent predictions.
result Random forest can predict choice probabilities and assortments unseen in training data.
Paper proposes a method to estimate consumer valuations from bundle sales data.
problem Estimating consumer valuations from bundle sales data using classical methods is challenging.
method Proposes an approach using EM algorithm and Monte Carlo simulation to estimate consumer valuations from bundle sales data.
result The approach can recover the distribution of consumers' valuations and is robust to unobserved no-purchases and clustered market segments.
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
We present a model of credit card profitability, assuming that the card-holder always pays the full outstanding balance. The motivation for the model is to calculate an optimal credit limit, which requires an expression for the expected outstanding balance. We derive its Laplace transform, assuming that purchases are m…
Study analyzes player churn and purchasing behavior in games.
problem Retaining players and predicting churn in video games.
method Deep behavioral analysis and ensemble learning models.
result Discarding certain churners improves prediction models.
A cost-effective approach to label acquisition using active learning markets.
problem Improving model fitting and training for predictive analytics.
method Formalizing market clearing as an optimisation problem, integrating budget constraints and improvement thresholds, using two active learning strategies with distinct pricing mechanisms.
result Superior performance with fewer labels acquired compared to conventional methods.
A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attributionstat.ME
New framework for attributing online marketing touchpoints.
problem Fine-grained attribution of individual touchpoints' effects.
method Graphical point process framework for studying conversion effects.
result Proposed methods allocate proper credit to touchpoints for each customer's path.
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.
Deep learning predicts fit for fashion e-commerce.
problem Predicting correct fit for customer satisfaction and cost reduction.
method Deep learning content-collaborative approach using customer and article embeddings.
result Significant improvement over state-of-the-art methods.
Paper uses pre-trained models and active learning to analyze customer reviews quickly.
problem Automatic review analysis with limited labeled data and time.
method Pre-trained language representation and active learning framework.
result Fully automatic review analysis achieved at a faster pace.
Paper aims to improve relevance of e-commerce search results.
problem Improving relevance of online product search results.
method Combines machine learning, NLP, and IR techniques to predict relevance scores.
result Deep learning models outperform conventional IR models in relevance prediction.
Study examines retailer responses to stockouts in wholesale environments.
problem Effect of stockouts on future demand in wholesale settings.
method Statistical analysis of historical customer order and delivery data over 4 years.
result Stockouts negatively impact future demand frequency but not value, with effects being short-term.
Study predicts customer data sharing in Open Banking and explains key factors.
problem Predicting and explaining customer data sharing in Open Banking environments.
method Hybrid data balancing strategy with ADASYN and NEARMISS, XGBoost models, SHAP, CART.
result 91.39% accuracy for inflow and 91.53% for outflow predictions, revealing influential features.
Automating the customer analytics process is crucial for companies that manage distinct customer bases. In such data-rich and dynamic environments, visualization plays a key role in understanding events of interest. These ideas have led to the popularity of analytics dashboards, yet academic research has paid scant att…
We consider the problem of segmenting a large population of customers into non-overlapping groups with similar preferences, using diverse preference observations such as purchases, ratings, clicks, etc. over subsets of items. We focus on the setting where the universe of items is large (ranging from thousands to millio…
Proposes a new model to predict irrational customer behavior.
problem Irrational customer behavior in decision-making.
method Nonparametric choice model using decision trees and probability distributions.
result Decision forest model accurately predicts non-rational customer behavior.
Deep neural network predicts product returns before purchase.
problem High costs of handling returned fashion products.
method Bayesian Personalized Ranking (BPR) embeddings and skip-gram model for user and product features.
result Reduced overall returns through real-time return probability prediction.
In e-commerce, content quality of the product catalog plays a key role in delivering a satisfactory experience to the customers. In particular, visual content such as product images influences customers' engagement and purchase decisions. With the rapid growth of e-commerce and the advent of artificial intelligence, tr…
The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.
problem High dimensionality and sparsity in user-item data, especially in terms of revenue contributions.
method The approach encodes revenue contributions in the user-item matrix and computes customer similarity using suitable distance measures.
result The method segments users based on revenue-based similarity and supports recommendations aligned with profitability objectives.
Study examines pricing strategies in competitive supply chains with discrete prices.
problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.
Data-aware activation function customization reduces neural network error.
problem Current neural networks lack consideration for specific activation functions.
method Linear algebraic explanation and Diaconis-Shahshahani Approximation Theorem criteria for activation functions.
result Using an even activation function like seagull can reduce neural network error by orders of magnitude.
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecastingstat.ML
Access to a large variety of data across a massive population has made it possible to predict customer purchase patterns and responses to marketing campaigns. In particular, accurate demand forecasts for popular products with frequent repeat purchases are essential since these products are one of the main drivers of pr…
Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of t…
Study finds farmers are willing to pay higher premiums for higher coverage in agricultural insurance.
problem Determining the demand factors and WTP for agricultural insurance.
method Conducted a survey of 200 farmers to analyze the impact of socio-demographic variables and premium on insurance purchase decisions.
result Farmers are willing to pay higher premiums for higher coverage in agricultural insurance.
Study online pricing with contextual elasticity and heteroscedastic valuation.
problem Online contextual dynamic pricing with customer decision based on features and price.
method Introduced a novel approach to modeling customer demand with feature-based price elasticity and heteroscedastic noise. Proposed an efficient algorithm called Pricing with Perturbation (PwP).
result Proved an regret bound for the algorithm, matching a lower bound of .
Model dynamic customer sensitivities across categories.
problem Dynamic heterogeneity in customer sensitivities to marketing elements.
method Hierarchical dynamic factor model with Bayesian nonparametric Gaussian processes.
result Dynamic heterogeneity can be explained by a few global trends.