A customer's interactions with a financial institution, whether using an ATM or a mobile app, are crucial touchpoints that establish their relationship with the bank. These interactions provide rich data for understanding behavior patterns, service needs, and areas for improvement in customer services. To provide this kind of tailored and optimized user experience, financial institutions must anticipate the needs of their customers, particularly about interactions via mobile apps and ATMs. By understanding and predicting customer needs, banks can proactively offer services and features that enhance convenience, foster loyalty, and increase customer satisfaction. Financial institutions and businesses can predict future purchases, recommend products, and tailor personalized marketing strategies by analyzing past transactions and identifying trends.
Here, we will discuss a goal-based approach to anticipate customer buying patterns using past transaction details with financial institutions.
Key Components in Predicting Customer Buying Patterns
- Transactional Data: This refers to the detailed history of all transactions made by a customer, including purchase amounts, dates, times, locations, categories (e.g., groceries, dining, travel), and payment methods.
- Behavioral Pattern: Patterns of behavior can be found by looking at recurrent behaviors, like the frequency of purchases, average spending amounts, and favored categories. For instance, a consistent spending pattern may be indicated if a customer spends a specific amount each month on dining out.
- Temporal Factors: The timing of transactions (e.g., weekend spending, holiday shopping spikes, or monthly recurring payments) can reveal patterns in customer buying behavior, such as seasonal shopping habits or payday-related spending spikes.
LSTM Approach to Goal Prediction
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), can model sequential dependencies in transaction data, learning patterns that record how past interactions influence future behaviors, and use this knowledge to customer goal prediction.
Why LSTM for Goal Prediction?
- Sequential Data Handling: Transaction data is inherently sequential. LSTM networks are designed to handle time-series data, making them ideal for capturing the temporal dependencies between past transactions and future customer behavior.
- Long-Term Dependencies: LSTM networks excel at learning long-term dependencies, which is crucial for identifying recurring or periodic customer behaviors, such as monthly bill payments, seasonal purchases, or habitual spending patterns.
- Ability to Handle Noisy Data: Customer transaction data is often noisy and irregular. LSTMs are more robust in capturing relevant patterns in such data, filtering out unnecessary noise.
How to build the LSTM Model?
Data Collection
- Transactional history including timestamps, amounts, categories (groceries, utilities, etc.), locations, and payment methods.
- Customer metadata, such as age, gender, location, and other demographic data.
- Any external factors that may affect transactions, such as holidays, promotions, or economic data.
Model Building
- Input Layer: The input to the LSTM will be a sequence of past transactions, where each transaction is represented as a vector of features.
- LSTM Layers:
- Add one or more LSTM layers. The LSTM cells will learn to capture dependencies in transaction patterns over time.
- Each LSTM layer will take the output of the previous layer and learn sequential dependencies, with memory cells capturing long-term patterns.
- Dropout Layers (Optional):
- To prevent overfitting, dropout can be applied between LSTM layers. This randomly disables some neurons during training, encouraging the model to generalize better.
- Dense Output Layer:
- The final layer will be a dense layer, where each neuron corresponds to a potential goal (e.g., making a purchase, saving for a vacation, paying bills). The activation function can be softmax for multi-class classification to predict the probability of each customer goal.
Model Training
Training an LSTM model for goal prediction involves defining the loss function, optimizer, and evaluation metrics.
Evaluation Metrics
- Use metrics like accuracy, precision, recall, and F1-score to evaluate the model’s performance in predicting customer goals.
Model Prediction
Once the LSTM model is trained, it can be used to predict future customer goals based on recent transactional data.
- Input: Feed the LSTM model with the sequence of recent transactions for a customer.
- Output: The model will output a probability distribution over potential goal. The goal with the highest probability can be considered the predicted next customer action.
For example:
If a customer has been spending on movies and entertainment over the past few weeks, the model may predict that people may want to maintain their discretionary spending and may decide to spend money on shopping next.
Applications in Anticipating Buying Patterns
1. Personalized Recommendations
Based on the previous spending of a customer, we can give curated recommendations. For example, tailored advertisements for fitness-related products can be sent to a consumer who purchases exercise equipment via email or mobile apps.
2. Targeted Marketing Campaigns
By predicting future buying patterns, businesses can send personalized promotions or discounts before a predicted purchase, increasing the likelihood of conversion. For instance, sending a discount on groceries just before the predicted purchase can drive sales if a customer regularly buys groceries at a specific time each month.
3. Cross-Selling and Up-Selling
Anticipating what a customer is likely to buy next provides an opportunity for cross-selling (suggesting related products) and up-selling (offering premium products). For instance, if a customer frequently buys electronics, the bank could offer a loan for purchasing larger items like home appliances.
4. Inventory Management
Retailers can benefit from these predictions by aligning their inventory with anticipated customer demand. By forecasting which products will be in higher demand, businesses can optimize their stock and avoid overstocking or shortages.
AUTHOR - FOLLOW
Namita Menon
Associate Manager
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