6 answers
Genevieve
Student
Cedar Rapids
78
Questions
Asked
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How would data science be used in retail?
Would they analyze sales?
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6 answers
Patrick Storey
Country Management
4
Answers
Seoul, South Korea
Updated
Patrick’s Answer
DS is used in may applications in retail. Here are a few examples:
1. Identify groups of customers and needs - clustering can identify groups of similar shoppers and be useful in analysing needs.
2. Establish better pricing models - with testing and analysis firms can set more efficient pricing
3. Improve forecasting across the business - this is particularly useful if you can bring 3rd party data into your models (like weather, fx rates, etc)
4. Identify locations for new retail locations - understand where your customers are coming to you from
5. Measure the effectiveness of campaigns and promotions - understand if a marketing campaign or promotion was effective at getting customers to change behaviour and if that campaign made financial sense (+MROI)
1. Identify groups of customers and needs - clustering can identify groups of similar shoppers and be useful in analysing needs.
2. Establish better pricing models - with testing and analysis firms can set more efficient pricing
3. Improve forecasting across the business - this is particularly useful if you can bring 3rd party data into your models (like weather, fx rates, etc)
4. Identify locations for new retail locations - understand where your customers are coming to you from
5. Measure the effectiveness of campaigns and promotions - understand if a marketing campaign or promotion was effective at getting customers to change behaviour and if that campaign made financial sense (+MROI)
Thank you so much Patrick! I'm really excited by the possibilities!
Genevieve
Keith Rodgers
Sales Director
108
Answers
Phoenix, Arizona
Updated
Keith’s Answer
Customer segmentation. Retailers can use data science to analyze customer data and segment customers into different groups based on their buying behavior, demographics, and preferences. This can help retailers create targeted marketing campaigns and improve customer experience.
Inventory optimization. Data science can be used to predict demand for products optimize inventory levels, and minimize waste. Retailers can use data analytics to identify which products are selling quickly and adjust their inventory levels accordingly.
pricing optimizations. Retailers can use data science to analyze pricing trends and optimize pricing strategies to maximize profit. By using data analysis, retailers can identify optimal price points for their products, which can improve sales and revenue. Fraud detection. Retailers can use data science to detect fraud and prevent losses. By analyzing transaction data and customer behavior, retailers can identify potential fraud activity and take appropriate action.
Inventory optimization. Data science can be used to predict demand for products optimize inventory levels, and minimize waste. Retailers can use data analytics to identify which products are selling quickly and adjust their inventory levels accordingly.
pricing optimizations. Retailers can use data science to analyze pricing trends and optimize pricing strategies to maximize profit. By using data analysis, retailers can identify optimal price points for their products, which can improve sales and revenue. Fraud detection. Retailers can use data science to detect fraud and prevent losses. By analyzing transaction data and customer behavior, retailers can identify potential fraud activity and take appropriate action.
Thanks for the detailed answer!
Genevieve
jie peng
financial services
3
Answers
Roselle, Illinois
Updated
jie’s Answer
Yes data science in retail analyzes sales data and other data to provide insights on acquiring new customers, preventing customer attrition, boosting sales, and improving on revenue .
Thank you!!
Genevieve
Micheal White
Reimbursement and Quality Metrics Management
18
Answers
Panama City Beach, Florida
Updated
Micheal’s Answer
Patrick had a great answer. My capstone project for MIT DS Program was unsupervised learning using clustering in a retail setting with K-means and K-mediods. And more. This was to segment customers based on demographics, purchases, and marketing effectiveness. DS is very useful, dare I say essential in a successful retail organization.
Thank you!
Genevieve
Sarah Danenberg
4
Answers
Irvine, California
Updated
Sarah’s Answer
Absolutely. I actually came from a sales background and because I was naturally analytic I would find myself keeping track of peak sales days, hours, customers, types of items purchased, what age bracket typically likes which items, etc. Knowing these things can tremendously help out sales employees, but it takes a data scientist to figure out how to put this all together and uncover the stories.
Thank you Sarah!
Genevieve
david kirk
Retired Computing Manager
474
Answers
Syracuse, New York
Updated
david’s Answer
Yes, this could be done to analyze sales. This process is often called 'data mining' and consists of evaluating not just what a person bought, but what else the person bought. For example, there may be an obscure product on a grocer's shelves that few people buy, and people may wonder why the store sells it with such low volume. The answer, though, might be that people who make significant purchases of other items also buy that item. The grocer keeps it on the shelf, not for any profit it brings, but because that product keeps the customers returning who buy the larger items. On the flip side, data mining can be used on an aggregate level to evaluate marketing trends and typical customers. Data science is a vital component in today's highly competitive retail world. All the best to you.
Cool! Thanks David!
Genevieve