As retail companies continue to compete to increase market shares, business owners are seeking to implement technologies that they believe will boost their revenues. Because of the many technological systems in use, it’s impossible to analyze all accessible data generated by different resources, such as mobile phones, websites, social media, forums, emails, etc.
Fortunately, most businesses, including retailers, now understand that Machine Learning (ML) solutions can solve many of their problems and grow their businesses. If ML solutions are modeled properly, businesses can improve their processes to increase customer engagement by analyzing purchase data, inventory and stock information, competitor data, and customer experiences and information, e.g., their browsing history, their date and time information, questions they might have, the final products they purchase, their rates and reviews about products, and their posts on social media. In this way, ML methods can be used in many applications related to various industries. In fact, applications of ML are almost unlimited when it comes to retail. ML systems can enhance retail process performance by providing feedback related to marketing and product placement, customer recommendations, demand anticipation, anomaly detection, purchasing habits, search systems, etc. This article introduces the most influential machine learning applications used by retailers. The subsequent blog in this series will discuss alternative applications and the future of ML in retail settings.
Stocking and Inventory
Businesses that offer goods and products to customers must deal with inventory, which is either sourced from other manufacturers or produced by the business itself. The use of smart inventory systems directly influences supply chain efficiency. Managing raw materials and resources for production, packing products, and shipping orders all depends on knowing what’s in stock. The problem, though, is that the above-mentioned processes are extremely complex, especially in large-scale retail companies that service many territories. Moreover, other outside factors, such as supplier efficiency, service provider performance, and even adverse weather, impact inventory management. Therefore, managing retail stock in a smart way necessitates the use of ML models to interpret complex internal and external data. This has led to a lot of research on ML applications.1,2
Recommendation Systems (RS)
Product recommendation systems (RS) for sales is one of the most commonly and successfully applied uses of ML technologies because it increases both sales and customer satisfaction. RS formulate and present personalized offerings to customers, increasing purchase and growing business profits, thereby making them extremely useful in e-commerce. Additionally, RS can help customers find items relevant to their previously purchased goods.4 Check out our previous article that discusses this topic more in depth here. 5
Every retailer has the potential to employ ML methods to enhance performance by utilizing the insights that predictive analyzing provides. In fact, machine learning methodologies can provide solutions for daily retail problems, enabling manufacturers to optimize and update processes quickly instead of spending months experimenting with other approaches. ML methods optimize manufacturing processes by:
- -Refining maintenance, repairs and overhauls by using accurate fault-prediction models on an individual-component level
-Determining factors that impact the quality of services and/or products
-Increasing production8 by optimizing processes9 related to workforce, source, machine, and supply chain10 utilization.
Banks and other payment service providers already employ ML systems extensively, especially for credit card transaction monitoring. As a matter of fact, ML algorithms play an important role in the real-time authorization of transactions. ML models used in payment transactions enable companies to:
- -Track consumer behavior by evaluating large financial dynamic data sets
- -Identify and address data anomalies
- -Authorize high volumes of transactions and verifications in near real-time
- -Identify credit cards12
- -Detect credit card fraud and notify management13
- -Automatically recognize payment recipients
-Enable bill payment systems with voice recognition14
One of the primary uses of ML systems is to determine optimal dynamic pricing algorithms. There are many algorithms that make optimal pricing decisions in near real time, helping businesses increase revenues and profits. In this case, the aim of the dynamic pricing algorithm in retail websites is to decide whether to display the standard price or the discount price for specific users in real time. Recently, there has been an increased adoption of dynamic pricing policies in retail and other industries where sellers can store inventory. Some of the factors that contribute to dynamic pricing include the increased availability of demand data, the ease of changing prices with new technologies, and the availability of decision-support tools for analyzing demand data. The references that follow feature some of the research that has been conducted on this topic.15,16,17
ML algorithms are widely used for analyzing the results of promotions and improving sales in retail companies. Promotions planning is an important consideration in increasing retail profits. ML technologies can reasonably predict future promotion sales figures based on past promotion results, provided sufficient data exists. This allows retailers to evaluate the success of new promotion campaigns before launching them. For more information, see the following resources.19,20
Using Machine Learning to Improve Campaign Performance
The risk that retailers face when launching marketing campaigns with unknown returns can be reduced by using updated and less complex technologies. For retailers, the key to resolving this problem is the use of clear retail marketing analytics methodologies to track and predict the results of marketing campaigns. Creating accurate predictions, however, depends on employing ML algorithms with proper metrics and KPIs. Retailers should implement tools and products that let them use all their data to provide alerting, predictive analytics and machine learning capabilities. This approach allows companies to make faster and more successful decisions21.
Understanding Buying Habits of Customers
Buying habits are the tendencies customers have when purchasing products and services. These tendencies come from a variety of different factors, many of which seem obvious or unimportant. When examining buying habits, ML evaluates data related to both physical and mental patterns to build a model of prediction. It’s important to note that one of the biggest assumptions of this model is that customers with the same demographic information have the same buying habits. For example, algorithms might assume that young women will buy more colorful clothes than their elders. These assumptions generate automatic customer segmentation that personalizes presentation and advertising displays for customers within particular demographics.
Today we discussed some of the most commonly employed machine learning applications and provided examples of each. Each case is supported by valuable references for curious readers seeking more detailed information. Our upcoming blog will discuss related applications as well as the future trends of ML implementations.
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1 "Case-based reinforcement learning for dynamic inventory control in a ...." http://www.sciencedirect.com/science/article/pii/S0957417408005034. Accessed 2 Jan. 2018.
2 "Application of machine learning techniques for supply chain demand ...." 29 Jan. 2007, https://pdfs.semanticscholar.org/1edf/af279f01d6ea3469b20c57cff6fa69dec219.pdf. Accessed 2 Jan. 2018.
3 "Amazon to open London hub | London Evening Standard." 23 Jul. 2012, https://www.standard.co.uk/business/business-news/amazon-to-open-london-hub-7965332.html. Accessed 5 Jan. 2018.
4 "Recommender systems in e-commerce - IEEE Conference Publication." http://ieeexplore.ieee.org/abstract/document/6935763/. Accessed 2 Jan. 2018.
5 "How to Build A Recommender System In Less Than 1 Hour." 30 Nov. 2017, http://blog.avenuecode.com/how-to-build-a-recommender-system-in-less-than-1-hour. Accessed 5 Jan. 2018.
6 "Netflix Recommendations: Beyond the 5 stars (Part 1) - Netflix TechBlog." 5 Apr. 2012, http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html. Accessed 5 Jan. 2018.
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9"Intelligent scheduling with machine learning capabilities : the ...." 3 Aug. 1998, https://www.ideals.illinois.edu/bitstream/handle/2142/29867/intelligentsched1639shaw.pdf?sequence=2. Accessed 3 Jan. 2018.
10 "Application of machine learning techniques for supply chain demand ...." 29 Jan. 2007, https://pdfs.semanticscholar.org/1edf/af279f01d6ea3469b20c57cff6fa69dec219.pdf. Accessed 3 Jan. 2018.
11 "Netflix Recommendations: Beyond the 5 stars (Part 1) - Netflix TechBlog." 5 Apr. 2012, http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html. Accessed 5 Jan. 2018.
12 "Machine-learning algorithms for credit-card applications." https://academic.oup.com/imaman/article-pdf/4/1/43/6764690/4-1-43.pdf. Accessed 3 Jan. 2018.
13 "CARDWATCH: a neural network based database mining system for ...." http://ieeexplore.ieee.org/document/618940/. Accessed 3 Jan. 2018.
14 "Patent US5893902 - Voice recognition bill payment system with ...." 13 Apr. 1999, http://www.google.com/patents/US5893902. Accessed 3 Jan. 2018.
15 "Dynamic Pricing in the Presence of Inventory Considerations ...." http://www2.isye.gatech.edu/people/faculty/Pinar_Keskinocak/dynamic-pricing.pdf. Accessed 3 Jan. 2018.
16 "Dynamic Pricing in Retail Gasoline Markets - University of California ...." 8 Apr. 2003, https://are.berkeley.edu/~sberto/boren_shep.pdf. Accessed 3 Jan. 2018.
17 "Dynamic Pricing on the Internet: Importance and Implications - jstor." https://www.jstor.org/stable/27750982. Accessed 3 Jan. 2018.
18 "Dynamic pricing - Lynda.com." 22 Jul. 2017, https://www.lynda.com/Data-Science-tutorials/Dynamic-pricing/573130/622265-4.html. Accessed 5 Jan. 2018.
19 "Can Uncertainty Improve Promotions? | Journal of Marketing Research." http://journals.ama.org/doi/abs/10.1509/jmkr.47.6.1070. Accessed 3 Jan. 2018.
20 "In‐store trade promotions ‐ profit or loss? | Journal of Consumer ...." http://www.emeraldinsight.com/doi/abs/10.1108/07363769610115401. Accessed 3 Jan. 2018.
21 "The Role of Ad Likability in Predicting an Ad's Campaign Performance ...." 4 Mar. 2013, http://www.tandfonline.com/doi/abs/10.2753/JOA0091-3367370207. Accessed 3 Jan. 2018.
22 "Why Do Customers Buy? How to Identify Customer Buying Habits ...." 16 Jun. 2016, https://thrivehive.com/why-do-customers-buy-how-to-identify-customer-buying-habits/. Accessed 3 Jan. 2018.
23 "8 Millennial Car Buying Habits Your Dealership Needs to ... - AutoRaptor." 6 Mar. 2017, https://www.autoraptor.com/millennial-car-buying-habits-dealership-needs-know/. Accessed 5 Jan. 2018.
Hossein Javedani Sadaei
Hossein Javedani Sadaei is a Machine Learning Practice Lead at Avenue Code with a post-doctoral in big data mining and a PhD in statistics. He works mostly with machine learning and deep learning in retail, telecommunication, energy, and stock. His main expertise is developing scalable machine learning and deep learning algorithms using fuzzy logics.