Retail Analytics Training & Certification

Learn analytics to boost your brand and sales, better inform business decisions and more seamless shopping experience. Based on industry use cases by Experfy in Harvard Innovation Lab.

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Average annual salary for a Big Data Scientist is $123,000

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190K data scientist jobs by 2018

McKinsey Global Institute

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190K data scientist jobs by 2018

McKinsey Global Institute

Data Science in Retail Industry Track

According to the McKinsey Global Institute, Big Data has the potential to increase net retailer margins by 60 percent, and is expected to drive up 1 percent of annual productivity growth in US retail alone.

Today's customers are constantly creating data in multiple platforms. This data contains valuable insights about their preferences and tastes. Coupled with transactional data and data from various touchpoints in a retailer, this repository can be an invaluable source of information. But there are a couple of challenges in making this dream a reality. In today's multi-channel, high-touch, always on hypercompetitive consumer markets, data from multiple sources need to be collected and analyzed, and it needs to be done fast. This is only possible if the team has the right skill set to both aggregate and analyze the data.

Understanding the customer is vital for a data-driven retailer, but it's not the only problem data can solve. Retailers can optimize their stock levels, decrease transportation costs, and analyze potential store locations using various types of predictive analytics methods. 

Some of the most common analytical methods used in the retail industry include:

  • Localization and Clustering: Electronic product tags and internet stores are providing retailers with deep insights on local buying habits. This data can then be integrated with data on leases, costs, store performance, and maintenance to find an optimal location to open a store on. The offerings of the store will be unique to the preferences of the local customers. This will increase sales and prevent retailers from keeping too much stock.
  • Market Basket Analysis: Market basket analysis uses affinity analysis methods to understand customer purchase behavior. If a customer is regularly purchasing cereal and milk together for example, offering discounts for both of the items is not very logical, but offering a discount for one of the items can drive the sales of the other. 
  • Price Optimization: Price optimization is the practice of using data on operating costs, inventories, and historic pricing and sales to come up with the price of an offering that will maximize profits. Understanding the customer and how they will react to a specific price point is of vital importance in price optimization.
  • Marketing Mix Modeling: Marketing Mix Modeling is the practice of using historical sales and marketing data to understand the impact of different marketing tactics on sales and then forecasting the impacts of future strageties. This type of analysis is especially useful when preparing marketing and advertising mixes that have positive impacts on sales and revenue.

Experfy's courses on Retail Analytics are designed to help retail industry professionals and managers understand and implement various data-driven solutions geared towards reducing costs and increasing revenue. The courses are designed to address industry-specific problems, and they include a lot of demos and case studies to make sure that the students not only understand the solutions presented, but also gain the skills to apply them in real life. 

According to Glassdoor, the average salary for a Retail Solutions Data Scientist is $113,436.

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