Navigating the connection of ratings, reviews, and customer sentiment in Fashion — a Sales Forecast.
Introduction.
The fashion industry is extremely dynamic, and staying ahead of market trends and client/consumer preferences is crucial for business success.
This article focuses on a predictive analytic project that aims to leverage the power of product review and ratings to enhance sales forecasting — an imperative aspect of planning for fashion businesses.
By analysing consumer sentiments and purchasing behaviour, this project seeks to provide valuable insights for stakeholders or consumers in the fashion industry.
Project Overview.
This project revolves around the synthesis of predictive analytics and fashion retail, specifically focusing on the influence of product reviews on sales forecasting.
I aim to create a robust framework that integrates data-driven insights from customer feedback into traditional sales forecasting models.
Importance of Sales Forecasting in the Fashion Industry
Sales forecasting plays a vital role in the fashion industry, where trends are ever-changing and preferences constantly evolve. Accurate predictions empower businesses to align their production, marketing, and distribution strategies with market demands.
This, in turn, aids in minimising excess inventory, reducing costs, and maximising revenue. Therefore, understanding the significance of sales forecasting is fundamental to appreciating how this project addresses a critical need within the fashion sector.
Importance of rating and review in the fashion industry?
In the fashion industry, ratings and reviews are incredibly important. They go beyond just evaluation metrics and have a profound impact on consumer trust, what people choose to buy, and how they perceive brands.
Positive reviews act as social proof, building confidence among customers and influencing what they like.These reviews are powerful tools for discovering new products, fostering a sense of community, and engaging with others in the fashion world.
Moreover, reviews are a valuable source of feedback for brands, helping them identify areas for improvement and spot emerging trends.
In fashion marketing, ratings and reviews serve as a means of differentiation, contributing to a brand’s credibility and online visibility.
Their influence extends to social media, where good feedback can spread via word-of-mouth and influencer endorsements, reaching a bigger audience.
In conclusion, ratings and reviews are critical components of the fashion industry, impacting not just what consumers buy but also contributing to the overall success of fashion brands.
Project Objective
The main goal of this project is to create a predictive model that uses product reviews to predict fashion sales trends. It aims to provide fashion businesses with a tool to foresee sales, adapt to market changes, and enhance operational efficiency.
Tools
Python
Pandas
numpy
matplotlib.pyplot
sklearn.linear_model
sklearn.model_selection
Data Source
The data used in this project was an Adidas fashion dataset. It consists of 845 rows and 21 columns.
Here’s the link to view the dataset : http
s://www.kaggle.com/datasets/the devastator/adidas-fashion-retail-products-dataset-9300-prod.
Let’s take a look at the project and its results.
Analysis
- Relationship between Reviews Count and Sales Volume
- The correlation coefficient between Review and sales volume
- Which product attributes (e.g., category, colour) have the most influence on both average ratings and sales volume?
- Patterns in Customer Sentiment from Product Descriptions influence sales
Data Preprocessing
For analysis one, Relationship between Reviews Count and Sales Volume. we started by creating a sales volume column(data cleaning/ Formatting) to ensure we get our intended results The sales volume column will be a hypothetical metric.
A hypothetical metric refers to a calculated measure that is used for analytical or modeling purposes but may not necessarily represent a real-world, observed quantity.
From the code snippet above,
1. The columns used to create the sales volume are the Selling Price and Reviews Count
2. Hypothetical Metric: Sales Volume is calculated by multiplying the selling price by the reviews count, this is a hypothetical metric. It’s a numerical value that indicates an estimate of the overall sales generated by each product.
The term “hypothetical” is used here because the sales volume is not a direct, observed measure of sales; instead, it’s a computed value based on the assumption that the combination of selling price and reviews count somehow correlates with sales.
This metric is a made-up number used for analysis, sort of like a stand-in for actual sales. Creating this number helps us make more accurate predictions. Now, let’s see the questions and what we found.
Analysis And Visualization
- Relationship between Reviews count and Sales Volume.
- Understanding the relationship between reviews count and sales volume in the context of fashion can be valuable for several reasons, especially when aiming to enhance sales forecasting.
Interpretation of the Scatter Plot:
In analysing the scatter plot, it is evident that there is a higher concentration of data points in the lower ranges of both reviews count and sales volume. Specifically, a notable cluster exists around 0–2,000 reviews and 0–400,000 sales volume, indicating that products with moderate reviews tend to have lower to moderate sales volumes.
As the reviews count and sales volume increase, the density of data points decreases, and there are fewer products with exceptionally high sales volumes.
What does this mean for the brand?
The scatter plot shows that the brand has a mix of products with varying levels of popularity and sales performance. The concentration of data points in lower ranges indicates that products with moderate reviews contribute substantially to overall sales.
Understanding the non-linear relationship between review count and sales volume emphasises the importance of diversifying product offerings.
Business Recommendation:
Stakeholders should consider these insights strategically, recognising the variability in sales performance across different review count levels. The brand should strategically market and promote products in lower ranges to boost reviews and sales.
2. The correlation coefficient between Review and sales volume
The correlation between reviews count and sales volume is crucial for the business as it indicates that as the number of reviews increases, there is a significant tendency for sales volume to rise.
The correlation coefficient between reviews count and sales volume is calculated to be 0.904, indicating a strong positive correlation between these two variables.
What does this mean:
The correlation coefficient of 0.904 suggests a robust positive relationship between the number of reviews a product receives and its sales volume. As the number of reviews increases, there is a substantial tendency for the sales volume to also increase.
The high positive correlation signifies that consumer engagement, as reflected in reviews, is strongly associated with higher sales volume.
Recommendations
- Actively encourage customers to leave reviews, potentially through incentives or engagement strategies.
- Utilise positive reviews in marketing materials and product descriptions to enhance consumer trust and influence purchasing decisions.
- Pay attention to negative reviews and address product issues promptly. Resolving concerns can positively impact both reviews count and sales.
In summary, the business should recognise the value of consumer reviews as a significant driver of sales volume. By strategically managing and leveraging this relationship, the brand can enhance customer satisfaction, product development, and overall business performance.
3. Which product attributes (e.g., category, colour) have the most influence on both average ratings and sales volume?
Understanding the most influential product attributes, such as category and colour, is crucial for accurate sales forecasting. By analysing the correlation between these attributes and both average ratings and sales volume, the business can identify key factors that drive consumer engagement and purchasing behaviour.
This knowledge informs strategic decisions, allowing the brand to focus on product characteristics that positively impact sales.
In this analysis, a Random Forest Regressor model is trained on features such as category, color, reviews count, and sales volume to predict both average rating and sales volume. The importance of each feature in making predictions is evaluated through feature importance scores.
Two separate models are trained — one for predicting average rating and another for predicting sales volume — using features like category, color, and reviews count.
Feature importance scores are obtained, indicating the contribution of each feature to the model’s predictions.
The bar chart illustrates the feature importance for average ratings and sales volume. The x-axis represents different feature importance values ranging from 0.0 to 0.6, while the y-axis includes review counts and other variables. Notably, the review count bar extends to 0.6, whereas other variables fall within the range of 0.0 to 0.1.
The prominent position of the review count bar at 0.6 indicates that, among the considered variables, review count has a significantly higher impact on either average ratings or sales volume.
Other variables, represented by bars within the 0.0 to 0.1 range, have comparatively lower importance in influencing average ratings or sales volume.
What does this mean :
The high feature importance of review counts emphasizes their critical role in shaping average ratings and influencing sales volume over clothes categories and clothes colors. The business should strategically prioritize efforts to encourage and manage customer reviews.
While review count is dominant, understanding the specific variables within the 0.0 to 0.1 range is essential. It enables the identification of factors that, while less impactful, still contribute to the overall picture.
Recommendation
- Given the dominance of review count, the business should enhance strategies to generate more reviews. This could involve incentivizing customers, improving user experience, or actively seeking feedback.
- Despite their lower importance, a deeper analysis of variables in the 0.0 to 0.1 range can reveal insights into factors that, while less impactful individually, might collectively contribute to positive outcomes. Consider investigating how these variables can be optimized.
- Develop an integrated approach that considers both the quantity and quality of reviews. While quantity (review count) is essential, maintaining a high average rating is equally crucial for sustaining positive consumer perception.
In summary, the feature importance analysis underscores the paramount importance of review counts in influencing average ratings and sales volume. The business should strategically leverage this insight to enhance its review generation strategies and consider a holistic approach that incorporates other variables for a well-rounded understanding of customer satisfaction and sales performance.
4. Patterns in Customer Sentiment from Product Descriptions influence on sales
The script aims to analyze customer sentiment in product descriptions using the Natural Language Toolkit (NLTK) SentimentIntensityAnalyzer. The relevant columns extracted for analysis include ‘average_rating,’ ‘reviews_count,’ and ‘description.’ The sentiment analysis produces a ‘sentiment_score’ for each product description.
A weak negative correlation (-0.073) suggests that there is a slight tendency for products with lower sentiment scores to have higher average ratings, and vice versa. Another weak negative correlation (-0.108) indicates that products with lower sentiment scores might have slightly higher review counts, and vice versa.
The positive correlation (0.024) implies that, on average, products with higher ratings may also have slightly higher review counts.
What does this mean:
The correlation analysis between customer sentiment, average ratings, reviews count, and potential implications suggest several considerations for sales:
While positive sentiment is beneficial, the analysis suggests that customer engagement and sales are influenced by a combination of factors, including average ratings and review counts. The business may benefit from a strategic approach that considers the nuanced relationship between sentiment, ratings, and customer engagement.
Recommendations:
Enhancing product descriptions to convey positive sentiments could contribute to a more favorable perception, potentially influencing both average ratings and customer engagement. This optimization may positively impact sales.
Regularly monitoring customer sentiment trends, average ratings, and review counts allows the business to adapt its strategies based on evolving customer preferences. This adaptability contributes to maintaining and potentially increasing sales performance.
Conclusion
For this specific business, the analysis underscores the significance of customer engagement in driving sales. While positive sentiment in product descriptions is beneficial, the emphasis should be on encouraging a diverse range of reviews, including those with moderate ratings. The business should adopt a coherent approach that considers both the quantity and quality of customer feedback. Strategic marketing efforts should focus on enhancing product descriptions, actively managing customer reviews, and promoting products strategically. Continuous monitoring and adaptability to evolving consumer preferences are essential, with feedback from reviews guiding ongoing improvements. Sales forecasting should integrate both quantitative and qualitative aspects for a more accurate prediction of future trends.
In summary, ratings and reviews exert a substantial influence on sales forecasts in the fashion industry. The positive correlation between the number of reviews and sales volume highlights the pivotal role of customer engagement in driving purchasing decisions. While positive sentiment in average ratings is crucial, its direct impact on sales is less pronounced. The concentration of products with moderate reviews contributing significantly to overall sales emphasizes the importance of customer engagement, even without exceptionally high reviews. The analysis of product descriptions through sentiment analysis provides additional insights, underlining the need for a holistic approach in interpreting customer feedback. Strategic recommendations include encouraging and managing customer reviews, enhancing product descriptions, and adopting an integrated approach that considers both the quantity and quality of customer feedback. The dynamic nature of consumer preferences underscores the importance of adaptability, continuous monitoring, and feedback-driven improvements for a more accurate and responsive sales forecasting strategy. Ultimately, a nuanced understanding of the interplay between ratings, reviews, and sales allows businesses to refine strategies and stay competitive in the evolving fashion industry.
