Case Study Name: CHAMPO CARPETS: IMPROVING BUSINESS-TOBUSINESS SALES USING MACHINE LEARNING ALGORITHMS
Questions:
1. With the help of data visualizations, provide key insights for the data and any intuition/conclusions you may derive. 2. What kind of analytics and machine learning algorithms could be used by the firm to solve its problems and for value creation? 3. Develop machine learning models to help identify features that contribute towards conversion or non-conversion of samples sent to the customers (R codes will be provided). Which one you would choose and why? 4. Analyze the data strategy for segmenting customers using clustering. What are the benefits that the firm can expect from clustering? What are the disadvantages of clustering in decision making? 5. Develop customer segmentation using k-means clustering. Discuss optimal number of clusters and cluster characteristics. 6. What is your final recommendation for the firm and why? What are the risks/disadvantages of your recommendation and your contingency plan?
[backcolor=rgba(var(--colors-gray-30),var(--tw-bg-opacity))]Answer & Explanation[color=rgba(var(--colors-green-50),var(--tw-text-opacity))] Solved by verified expert
[color=rgba(var(--colors-gray-80),var(--tw-text-opacity))]Answered by BrigadierIbex3223 on coursehero.com
1. Data insights like patterns, correlations, and trends can be seen in data visualizations. For instance, a straightforward scatterplot might show correlations between two variables like the conversion rate and the quantity of samples requested. Histograms and boxplots are two other visualizations that can shed light on the data's distribution. Data anomalies and outliers can also be found using data visualization techniques.
2.
The company might utilize a range of analytics and machine learning techniques to address its issues. For instance, models that forecast client conversion rates could be created using supervised learning techniques like linear regression. Customer segments could be created using unsupervised learning techniques like k-means clustering. Key words and phrases that customers use to make decisions about purchases may be recognized using natural language processing (NLP). Algorithms for reinforcement learning could also be utilized to improve their marketing efforts.
3.
For this task, I would go with a supervised learning approach like logistic regression. It would be possible to determine which characteristics are linked to customer conversion or non-conversion using logistic regression. Additionally, it is a fairly easy algorithm to use.
4.
Customers can be divided into many groups using clustering based on traits like age, geography, purchase history, etc. Then, using this information, marketers may target various client segments with more pertinent marketing messages. Better client targeting, more individualized message, and increased customer retention are all advantages of clustering. Clustering does have some drawbacks, though, such the challenge of figuring out the ideal number of clusters and the risk of overfitting.
5.
An elbow plot can be used to calculate the ideal number of clusters. The sum of squared distances between data points and the cluster centers are plotted on an elbow plot against the number of clusters. The graph's "elbow" designates the ideal number of clusters. Additionally, by examining the mean values of the variables for each cluster, the characteristics of the clusters can be discovered.
6.
The company should employ machine learning algorithms to identify client segments and target them with more pertinent marketing messages, which is my final piece of advice. This will aid the business in improving customer retention and conversion rates. This advice carries certain hazards, including the chance of overfitting and the challenge of figuring out the ideal number of clusters. The company should test and assess the efficacy of its models on a regular basis as a backup plan to make sure it is getting the outcomes it wants.
[backcolor=rgba(var(--colors-purple-10),var(--tw-bg-opacity))]Step-by-step explanation
Detailed explanation: My final suggestion is that Champo Carpets use analytics and machine learning algorithms to increase business-to-business sales. The identification of client segments and the targeting of various consumer groups with more pertinent marketing messages can be assisted by machine learning algorithms. This will aid the business in improving customer retention and conversion rates.
Models that forecast customer conversion rates can be created using supervised learning techniques like logistic regression and linear regression. Using unsupervised learning techniques like k-means clustering, it is possible to divide your clients into various groups. Key words and phrases that customers use to make judgments about purchases can be recognized using natural language processing (NLP). Algorithms for reinforcement learning can also be used to improve marketing strategies.
An elbow plot can be used to calculate the ideal number of clusters. The sum of squared distances between data points and the cluster centers are plotted on an elbow plot against the number of clusters. The graph's "elbow" designates the ideal number of clusters. Additionally, by examining the mean values of the variables for each cluster, the characteristics of the clusters can be discovered.
This suggestion comes with some drawbacks, including the potential for overfitting and the challenge of figuring out the ideal number of clusters. The company should test and assess the efficacy of its models on a regular basis as a backup plan to make sure it is getting the outcomes it wants.
Overall, using analytics and machine learning algorithms can benefit the company by increasing value creation and business-to-business sales. The company ought to think about using these tools to its benefit.
Key references:
Saleem, H., Muhammad, K. B., Nizamani, A. H., Saleem, S., & Aslam, A. M. (2019). Data science and machine learning approach to improve E-commerce sales performance on social web. International Journal of Computer Science and Network Security (IJCSNS), 19.
Ughulu, D. (2022). The role of Artificial intelligence (AI) in Starting, automating and scaling businesses for Entrepreneurs. ScienceOpen Preprints.
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