Optimizing Retail Sales Strategy with Predictive Modeling

Title: Optimizing Retail Sales Strategy with Predictive Modeling

Introduction:

In today's highly competitive retail landscape, businesses are constantly seeking ways to optimize their sales strategies and gain a competitive edge. With the advent of advanced data analytics and machine learning techniques, retailers can now leverage predictive modeling to make data-driven decisions and improve their sales performance. This project aims to explore the application of predictive modeling in optimizing retail sales strategies and provide actionable insights for businesses to enhance their profitability.

Objectives:

1. To identify key factors that influence retail sales performance using historical sales data and relevant variables.

2. To develop predictive models that accurately forecast sales based on identified factors and assess their impact on sales outcomes.

3. To provide recommendations for optimizing retail sales strategies based on the insights derived from the predictive models.

Literature Review:

Predicting and Defining B2B Sales Success with Machine Learning

The research team used statistical modeling techniques, including binomial logit, gradient boosting, and random forest, to develop a predictive model for a Fortune 500 company. They built models using data from the company's CRM system to predict the likelihood of winning sales opportunities. The best model, a random forest, achieved 80% accuracy in predicting win propensity and was chosen to provide insights for increasing revenue and profits by improving sales close rates, shortening sales cycles, and reducing costs. [1]

2. Sales prediction using Machine Learning Algorithms

The paper proposes using machine learning algorithms on grocery store sales data to predict sales patterns and quantities, addressing the importance of accurate forecasting for effective marketing strategies. By leveraging these techniques, businesses can gain insights through data analysis, enabling informed decision-making at critical stages of their marketing strategy, ultimately improving accuracy and efficiency compared to manual forecasting methods. [2]

3. Walmart's Sales Data Analysis - A Big Data Analytics Perspective

This literature review explores Walmart's store sales data to identify factors influencing sales performance, such as unemployment rates, fuel prices, temperature, and holidays. It highlights the use of big data techniques and machine learning algorithms, including regression models, HDFS, MapReduce, Apache Spark, and programming languages like Scala, Java, and Python, to analyze historical data, gain insights, and improve sales prediction accuracy. The review suggests that leveraging these techniques enables retailers to make data-driven decisions, optimize operations, and maximize profitability, contributing to existing knowledge by examining Walmart's case and emphasizing the importance of analyzing sales data for business success in the competitive retail industry.[3]

4. Forecasting of sales by using fusion of machine learning techniques

This paper compares various machine learning models, including ARIMA, Auto Regressive Neural Network (ARNN), XGBoost, SVM, hybrid models, and STL Decomposition, to forecast sales for Rossmann, a drug store company. The study finds that nonlinear models like Neural Network, XGBoost, and SVM outperform the linear ARIMA model, while composite models using hybrid and decomposition techniques further improve performance. The STL Decomposition model, which combines Snaive, ARIMA, and XGBoost, demonstrates superior results, concluding that the decomposition technique is more effective than the hybrid technique for this specific sales forecasting application. [4]

5. Predictive Analysis of Retail Sales Forecasting using Machine Learning Techniques

The authors discuss the challenges of accurate sales forecasting in IT chain stores and explore machine learning algorithms such as Back Propagation Neural Networks (BPN), Support Vector Regression (SVR), and Multivariate Adaptive Regression Splines (MARS). They highlight the strengths and limitations of each algorithm and propose using MARS for modeling complex nonlinear and non-parametric regression problems in large datasets.[5]

6. Predicting Future Sales of Retail Products using Machine Learning

In this paper, the authors emphasize the importance of accurate sales forecasting for organizations of all sizes and focus on Kaggle's "Predict Future Sales" problem, which involves forecasting sales using a time-series dataset with static and time-varying data. They split their team to study existing approaches and search for novel techniques, discovering that machine learning algorithms such as XGBoost with lagged features, Autoregressive Integrated Moving Average (ARIMA), and LSTM-based networks have provided quality results in similar tasks. The authors chose to evaluate the performance of these three algorithms using RMSE. [6]

7. Prediction Analysis for Business To Business(B2B) Sales of Telecommunication Services using Machine Learning Techniques

In this study, the authors underscore the significance of accurate sales prediction analysis for businesses, particularly in the telecommunications industry, to survive market competition and achieve growth. They employ machine learning techniques on B2B sales data to improve the accuracy and reliability of future sales predictions, as traditional forecasting systems struggle with big data. The study compares various predictive models based on reliability, accuracy, estimation, evaluation, and transformation, ultimately recommending the Gradient Boost Algorithm as the best-performing model. This model exhibits the closest data fit from the beginning to the end of the target data and achieves the best MSE (24,743,000,000.00) and MAPE (0.18) results compared to other methods, demonstrating maximum accuracy in predicting and forecasting future B2B sales. [7]

I utilized PyCaret, a powerful machine learning library, to analyze sales performance. PyCaret facilitated the quantification of the impact of marketing spend, assortment variety, and pricing on sales, enabling a comprehensive understanding of their individual and collective contributions.

PyCaret is a Python library meticulously crafted to streamline the machine learning workflow. It automates diverse tasks ranging from data preprocessing to model selection, hyperparameter tuning, model evaluation, and deployment.

In this particular case, the methodology for leveraging PyCaret encompassed the following sequential steps:

1. Data Loading:

Load the subscription data from "full_joined_subscriptions.csv" into a DataFrame called `sub`.

Filter the data to include only "b2c" customer types.

Convert the "report_date" column to datetime format.

2. Data Visualization:

Group the data by different frequencies ('D', 'W', 'M') based on the "report_date" column.

Aggregate the "base_price_gross" column by summing the values for each frequency.

Rename the aggregated column to "total_sales".

Plot the total sales trends for each frequency using a line plot.

3. Data Transformation:

Group the subscription data by week frequency based on the "report_date" column.

Aggregate the data by calculating the mean of "sub_term", sum of "base_price_gross", and count of  "subscription_id".

Reset the index and rename the aggregated columns to "sales_count" and "total_sales".

4. Data Preparation (Cars):

Load the car data from "full_joined_cars.csv" into a DataFrame called `cars`.

Create a new column "age" by subtracting the "model_year" from the current year (2023).

Drop the "model_year" column.

Convert the "consumption_100km" column to numeric format by extracting the first part of the string.

Drop unnecessary columns like "power_hp", "exterior_engine", "model_name", and "model_line".

Fill missing values in the "ev_range" column with 0.

Drop rows with missing values.

5. One-Hot Encoding (Cars):

Identify categorical features in the `cars` DataFrame.

Perform one-hot encoding on the categorical features using `pd.get_dummies()`.

Merge the transformed car data with the subscription data based on the "report_date" and "finn_car_id" columns.

Drop the "finn_car_id" column.

6. Data Aggregation (Cars):

Group the merged data by week frequency based on the "report_date" column.

Aggregate the categorical columns by summing the values and the numeric columns by calculating the mean.

Reset the index.

7. Price and Marketing Spend Data:

Load the price stock history data from "price_stock_history.csv" into a DataFrame called `price`.

Load the marketing spend data from "marketing_spend.csv" into a DataFrame called `spend`.

Convert the "report_date" column in the `spend` DataFrame to datetime format.

Group the marketing spend data by week frequency based on the "report_date" column and sum the values.

8. Data Merging:

Merge the marketing spend data with the transformed subscription data and transformed car data based on the "report_date" column.

Remove the last two rows of the merged data.

Filter the data to include only rows where the "report_date" is greater than "2021-04-01".

Drop columns containing "id_" or "color" in their names.

9. Model Setup:

Set up the PyCaret experiment using the `setup()` function.

Specify the target variable as "total_sales".

Configure the experiment settings, such as fold strategy, normalization, multicollinearity removal, and date features.

10. Model Comparison and Selection:

Compare baseline models using the `compare_models()` function, excluding "lar" and "omp" models.

Plot the forecast and residuals for the best model.

Create a specific model (e.g., 'en') using the `create_model()` function.

11. Model Tuning and Evaluation:

Tune the hyperparameters of the selected model using the `tune_model()` function.

Plot the forecast and residuals for the tuned model.

Evaluate the model's performance using the `evaluate_model()` function.

Conclusion:

Optimizing Product Portfolio: The company should focus on investing in the production and marketing of vehicles within high-impact body types and segments, such as SUVs, Sedans, and premium segments. Aligning product offerings with consumer preferences can potentially lead to increased sales.

Brand Strategy and Marketing Allocation: The company should strategically allocate marketing budgets and efforts towards brands that exhibit significant positive coefficients, such as Tesla and Audi. Strengthening brand positioning, highlighting unique features, and leveraging consumer perceptions can be pivotal in driving sales.

Segment-Specific Marketing: Tailored marketing strategies should be employed for different vehicle segments. Intensive marketing for high-impact segments like Mid Premium SUV and Large Premium SUV can capitalize on consumer preferences. For segments with negative coefficients, such as Mid Premium Estate&Co, a reevaluation of marketing strategies or product positioning may be necessary.

Drive Type and Power Considerations: The company can highlight features like Front-Wheel Drive, Rear-Wheel Drive, and higher engine power in marketing campaigns, emphasizing the performance and drive experience. Investing in the development of vehicles with these specifications may align with consumer preferences.

Sustainable and Eco-Friendly Focus: Given the negative coefficients for CO2 emissions and consumption, the company should emphasize sustainability and promote eco-friendly features in their marketing strategy. Investing in electric or hybrid technologies can align with evolving consumer values.

Marketing Investment: Increased investment in marketing can yield positive results, as evidenced by the positive coefficients for marketing costs.

Continuous Innovation and Adaptation: The company should prioritize continuous innovation, adapting to changing consumer preferences, and incorporating the latest technologies to enhance competitiveness. Regularly updating product offerings to align with current trends is crucial for sustained sales growth.

Diverse Market Approach: Recognizing the diverse impact of different brands, fuel types, and features, the company should adopt a diverse market approach. Understanding and catering to the unique preferences of different consumer segments can contribute to a more resilient and adaptable market strategy.

References:

1. S. Mortensen, M. Christison, B. Li, A. Zhu and R. Venkatesan, "Predicting and Defining B2B Sales Success with Machine Learning," 2019 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, 2019, pp. 1-5, doi:10.1109/SIEDS.2019.8735638. keywords: {Companies;Predictive models;Decision trees;Data models;Prediction algorithms;Analytical models;statistical modeling;decision tree;machine learning;process improvement},

2. Singh, M., Ghutla, B., Jnr, R.L., Mohammed, A.F. and Rashid, M.A., 2017, December. Walmart's Sales Data Analysis-A Big Data Analytics Perspective. In 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (pp. 114-119). IEEE.

3. Bajaj, P., Ray, R., Shedge, S., Vidhate, S. and Shardoor, N., 2020. Sales prediction using machine learning algorithms. International Research Journal of Engineering and Technology (IRJET), 7(6), pp.3619-3625.

4. Gurnani, M., Korke, Y., Shah, P., Udmale, S., Sambhe, V. and Bhirud, S., 2017, February. Forecasting of sales by using fusion of machine learning techniques. In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) (pp. 93-101). IEEE.

5. Bohanec, M., Borštnar, M.K. and Robnik-Šikonja, M., 2017. Explaining machine learning models in sales predictions. Expert Systems with Applications, 71, pp.416-428.

6. Lee, G., 2023. Exploring Predictive Variables Affecting the Sales of Companies Listed with Korean Stock Indices through Machine Learning Analysis. IEEE Access.

7. Swami, D., Shah, A.D. and Ray, S.K., 2020. Predicting future sales of retail products using machine learning. arXiv preprint arXiv:2008.07779.

8. Bajaj, P., Ray, R., Shedge, S., Vidhate, S. and Shardoor, N., 2020. Sales prediction using machine learning algorithms. International Research Journal of Engineering and Technology (IRJET), 7(6), pp.3619-3625.

This project outline provides a comprehensive structure for exploring the application of predictive modeling in optimizing retail sales strategies. By following this outline, you can develop a robust and insightful analysis that offers valuable recommendations for businesses to enhance their sales performance and profitability.