How Data Science Gives Retailers a Competitive Edge

Data Science for retailers

This article is written by Emily Newton.

Retailers worldwide must adapt to volatility by necessity. However, the last few years have brought more fluctuations in retail expectations. With the COVID-19 pandemic and hybrid shopping bolstering the world of e-commerce and TikTok popularizing the new faces of mass influencer advertising, it’s a puzzle to predict how sales in any niche will unfold. Retail data science could be the key.

Brands that leverage data science could overcome some of the uncertainties they have been guessing about to stay afloat. Questions about market trends and customer behaviors are all hidden in numbers and charts, ready to be revealed if your retail outfit embraces this technological resource. Implementing data science technology to understand your customers and sales will nudge your business into a perpetual competitive advantage.

Predictive Analytics Eliminates Worry

Retailers know confidence from a good day’s sales is a fleeting emotion, championed only by the dread and uncertainty of how their products will perform tomorrow in a world of ever-changing trends and global influence.

Retail data science with predictive analysis could dissolve those concerns by gathering data from countless internet nooks and first-party sources as customers purchase items in-store. What’s the point of relying on data from years prior when decisions can happen with data from then and now? Resources like machine learning interpret this data to make determinations based on historical and modern data and predict how to operate your retail store best to stay profitable.

Well, over 70% of companies may employ at least one artificial intelligence to gather data in the future, so it’s critical to get ahead now to stay competitive. It could collect everything from trending hashtags, historical anomalies and competitive insight. Data points can inform every facet of sales, including:
  • Current pricing with tips for increasing over time to match inflation.
  • Products to phase out based on trending keywords losing potency.
  • Creating an app with a loyalty rewards program and more order customization.
  • Branding strategies to try based on increased profits from competitors.
  • Advertising styles based on engagement and reach from social media.

Additionally, predictive analytics benefits everyone in the business. It will impact the bottom line for the better, but it will also inform recruitment on training clerks to mold to ideal communication standards based on customer reviews. It can inform accounting departments to allocate more funds to eco-friendly or more well-designed packaging because of corporate social responsibility trends.

The democratization of data will enlighten departments in different ways, regardless of their data science proficiency. Whether schooled in data science or not, every mind can glean creative or technical information from data, making it a highly approachable tool for diverse creative ideation for retail progress.

Identity Resolution Increases Customer Empathy

Data collection and predictive analytics make retailers think about the future with assurance, but they must consider customers in the present with equal gravity. Identity resolution (IDR) happens when data reveals individual customers by combining information like phone numbers or email addresses into a centralized hub to show retailers how well strategies work.

It can reveal the average amount spent based on customer engagement with the brand, allowing companies to become more intimately familiar with their audience. These bytes of information allow for centralized marketing segmentation, making it easier to get a holistic view of customer demographics, psychographics and behavioral data.

For example, suppose a customer has chosen to receive an email receipt every time they’re at the register. Retailers could improve that customer’s experience by making that automatic after so many times, expediting the purchasing experience. It may seem minuscule, but a streamlined process saves customers valuable time and they appreciate a more tailored shopping venture.

It also helps with data management. IDR can show data scientists and database managers when duplicate or outlying data is present, allowing them to curate the system to make strategies like predictive analysis more accurate.

IDR will also highlight customer commitment and brand loyalty, such as how much customers use the newly implemented loyalty program or made purchases online, in-store or both in the last quarter. Retailers wishing to hire social media influencers could reach out to already dedicated customers for easy buy-in, all because of IDR. Understanding customers more will make them feel more connected to the brand, increasing the chances of positive word-of-mouth advertising and repeated purchases.

Automation Creates Bottom-Line Resilience

Collecting data doesn’t just create lists of percentages and bar graphs to incentivize your teams to generate new business ideas. It can also feed customer relationship management software and other business assets to guide automation. Automation is the not-so-secret sauce to every industry’s success, saving retailers countless dollars with an upfront investment into technology that can do everything:
  • Schedule and post on social media
  • Submit orders to multiple departments, such as packing and marketing, for their unique uses
  • Improve quality control analyses for product consistency
  • Inform procurement for ordering materials
  • Implement sales or adjust pricing according to the market or seasonal fluctuations
  • Notify third-party vendors or supply chains of company-wide changes with immediate notifications
  • Updating security compliance and notifying of changes to data privacy standards

Retailers — mainly those maximizing profits from online spaces — have more processes they could use retail data science to automate. You can still see the information and institute automation based on insights if you don’t have reactive programs that shift according to incoming data. These revelations lead to process discovery and storefront improvements that outpace industry trends. It’s beneficial internally and externally.

Perhaps the data reveals the accounting team wastes numerous hours sifting through emails containing invoices. In that case, automation could help with email management and sending invoices into accounting programs by default, reducing money invested in labor. Additionally, data might show customers are more likely to purchase items in the front of the store versus the back, allowing staff to adjust inventory to meet sales metrics.

Using Retail Data to Come Out on Top

Data science gives retailers the power to predict the future, connect with customers, and automate parts of the business they never thought possible. The benefits all stack into an inarguable competitive edge, creating more loyal consumers, aware employees, and innovative sales strategies.

Every department benefits from what data shows, adjusting operational behaviors and expectations that lead to increased sales, customer satisfaction, and stakeholder investments. The future will prove retailers ignoring the competitive boons of retail data science will see declines in performance faster than any other retail business asset in history.

About the Author: Emily Newton is the Editor-in-Chief of Revolutionized, an online magazine that explores innovations in science and technology. She loves seeing the impact technology can have on every industry.

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