Retailers who possess industry intuition and innovation are aware of the benefits of data science in retail and are focused on ways to create an environment that creates a probability for financial increase.
A Brief History of Data Science
Data science is the organization and the study of invaluable data. The retail industry has been utilizing data science by collecting information about consumers since 1923, when Arthur C. Nielsen created AC Nielsen and began the practice of consumer data mining and analytics. Since then, the data has been used for many aspects of retail, including product placement, supply chain management, and customer experience, just to name a few.
Ever wonder why the candy bars are placed right in the check out aisle? Because they are easily accessible and while you wait, chocolate is the most ready-to-eat and appealing snack. Data scientists learned the behaviors of consumers and began to package and locate items according to what the consumers were looking for. This is called product placement and is a strategy developed as a result of data science in retail.
Catalog companies used data harvesting from catalog sales, mail information, and phone calls to grow an enormous amount of data on purchases. Barcodes came onto the scene in the 1970s and retailers began to scan items using the POS (point of sale) system. In the 1980s and 1990s, corporations revamped their internal supply chain data, using the data to significantly lower costs. In came automatic updates, outsourcing, and software engineering that helped corporations stay ahead of the competition.
Why is Data Science in Retail So Important?
The desire to grow within an industry is critical for CEOs and business owners and has created a great demand for data analysts, also known as data scientists.
Data science in retail and the utilization of it results in no meager turnout. In the scholarly book, “Data Science for Business: What You Need to Know about Data Mining and Data-Analytical Thinking,” author Tom Fawcett explains that data science in retail is the current trend in subtle but aggressive competition, effectiveness and efficiency. Retailers are hard pressed to reduce expenses and to increase their assets in order to gain a competitive advantage in their industry. Fawcett makes it clear that implementing data science in retail could more than double a company’s profit margins in the first year alone.
Data Science Informs the Customer Experience
Customer feedback, reward programs, concessions, exclusive discounts, customized consumer recommendations, point systems, trade-in programs, and membership programs are all designed to cater to the consumer while providing a way for companies to gather important data. Through these programs, business owners find out what does and doesn’t works. Any programs put in place that are not seen as a benefit to the company are either generally revamped or eliminated.
In addition to providing valuable feedback to the business owners about how to positively enhance their bottom lines, data scientists also help keep customers happy. Recorded phone calls for quality assurance, social media engagements, and product reviews assist the data scientists in preparing analyses that continue to improve customer engagement, their experiences, improve predictability and decrease buyers’ remorse.
Data Science in Goods and Services Promotion and Advertising
Eye-catching photography, displays, promotional arrangements, and customer cart and browsing analysis all contribute to the future trends and cross-advertising opportunities that exist in the current market.
In addition, the boom in mobile devices and users created a significant paradigm shift in the industry, creating a huge benefit to companies. Consumer compulsion coupled with the internet ensures that sales can now be made from anywhere and any time around the world. GPS and location-based offers now pop up on mobile devices, all thanks to data science in retail.
Data Science in Supply Chain Logistics
One-day shipping and shipping clubs have become the highlight of big box companies. From browsing, to shopping, to delivery and customer service, companies want deliveries and all purchases to go off without hitch. Warehouses are carefully organized to improve the data input from all angles. Purchases, returns, replacements and critical packages are carefully sorted and recorded for tracking and shipping domestically and internationally.
Data scientists use a number of tools including consumer identification, supply chain and inventory data, competitor pricing, benchmarking, forecasting, and market and consumer behavior data to help retail business owners reduce costs and/or bulky packaging to speed up delivery times. Carriers also now use GPS tracking systems, digital scanners, weather tracking and forecasting systems, and much more to help ensure expected and guaranteed delivery dates formulated by those corporations.
By investing in personalized techniques, companies are able to sell more and even get more customers to subscribe to tailored programs for monthly reoccurring deliveries.
Data Science and Predictive Analytics
Data algorithms track consumer purchase history. With these algorithms and other scientific methods continuously developing, more analysts are utilizing predictive models to estimate customer desires. Retailers want to know what you want before you want it. But predictive analysis can sometimes work too well.
If you browse knee-high socks on Amazon, you will see similar products pop up in your Facebook news feed. No, it is not a coincidence. These kind of tactics offer benefits to the retailer as well as convenience to the consumer, but can understandably make some customers quite uneasy, like Big Brother is watching them too closely. Consumers are beginning to complain and back away from companies who use too much predictive analysis, so data scientists should not take this lightly.
Retailers now deal with a huge amount of consumer information. Personal information like credit cards, names, addresses, social security numbers, and even the products consumers purchase can be compromised, stolen, and used for financial or fraudulent gain. This leaves retailers and their consumers extremely susceptible. The largest data breaches are continuously attributed to the retail industry, running close behind financial services.
Data science is critical in protecting consumer data. The Federal Trade Commission is diligent at protecting those in the Cloud, but everyday new viruses and malware penetrate corporate and personal accounts. Data scientists are deployed to create modules designed to protect commercial emails, online marketing efforts, and see to it that consumer privacy is honored.
The Future of Data Science in Retail
Moving forward, retailers must continuously study and invent new ways to analyze customer behaviors and needs without being invasive. Business intelligence and SEO methodology will undoubtedly play a major part in future predictive analytics. Each retailer’s data team must combine various aspects of the company including retail experts, data scientists, business experts, and marketing moguls. These people will be the holy grail of data management for retail in the years to come.
Related resource: Data Science in Financial Services