How retailers can use Big Data analytics to optimize operations

Published July 21, 2022

What can Big Data tell you about retail customer expectations

Big data analytics and predictive analytics can isolate how an individual customer shops through customer data analytics or data science. This relates to customer data including but not limited to the amount spent, location, time of the month, and brand preferences. It helps retailers find a better market fit and predict how each product may perform, that is, improve customer service. Hence, retailers can plan for any potential shortages or excess inventory.

"As put by Oracle, 'Big Data' can be fractured into three core pieces: volume, velocity, and variety. The fourth 'V' that may deserve an addition to that existing trifecta is value. By harnessing the power of large datasets, retailers can use big data analytics to optimize their operations now and into the future."

There are numerous benefits to this practice for retailers, such as:

  • Optimizing and expanding margins
  • Limiting the need for discounting caused by mismanaged inventory levels
  • Creation of tailored customer profiles
  • More effective customer journey mapping
  • Stronger customer retention
  • Higher customer satisfaction
  • Lower churn rates
  • Cross-selling through recommendation engines

Inventory management is one of the most important aspects of this. As witnessed recently with Target, even the biggest corporations can make miscalculations resulting in inventory challenges leading to mass-discounting and narrower margins for months to come. Using data analytics, retailers can ensure the smooth functioning of the supply chain for better customer service.

How to exceed retail customer expectations with Big Data

Big data is a long-term game in the retail industry. Only when companies have built strong historical datasets that absorb a variety of factors, can algorithms begin to work efficiently. But, when that point is finally reached, it unlocks incredible utility.

Businesses can start to predict how often consumers make purchases, how much they're spending, and what product SKUs should be given preferential treatment. This method identifies windows of and for promotions and discounts as well as cross-selling opportunities.

With a large amount of data—full of information on consumer behavior—businesses will get the opportunity to personalize each and every customer experience.

A retail business will also need to factor in every channel for a marketing campaign. What age are they? What location are they shopping at or from? Is it an in-store or online purchase?

The marketing strategy of any business needs to be tailored depending on the audience and channel targeted. Big Data Analytics comes into play in both the online realm and in-store. So, unique strategies should be developed for both types of shoppers.

Related: The state of ecommerce in 2022

For example, one of the most important channels for retail marketers in the present day is mobile devices. There are more than 10 billion connected mobile devices in use globally. Roughly 79% of smartphone users have made an online purchase in the last six months, which showcases how vital it is for generating sales. If we factor in key shopping holidays such as Black Friday, the impact on companies not incorporating multi-channel marketing strategies is quite obvious.

One company that takes advantage of online opportunities is Ulta Beauty. Previously, it used big data to partner with creators in all 50 U.S. states that had some form of relationship with cosmetics. They were able to expand their reach and awareness, increasing brand loyalty by collecting 14 million impressions.

Examples of how Big Data exceeds retail customer expectations

The North Face, an outdoor fashion retailer, integrates big data by analyzing weather conditions. This adjusts their advertising spend depending on the weather and analyzes the effects it has on customers' purchasing decisions. While it may seem extreme, these are the data points that differentiate leading brands from the rest.

The weather directly affects how consumers will make purchasing decisions. Using data points like this gives retailers actionable insights to adjust stock requirements as necessary. For example, if a hurricane is expected, consumers will rush to stores to stock up on merchandise. Using predictive analytics, retailers can prepare as per the locations affected.

How to incorporate data into your consumer-led growth strategy

One dynamic shift over the last decade has been the use of mobile phones to make purchases. One of the most important, and sometimes underappreciated, mobile channels for new market development is social media. To add context, TikTok became the most visited site in the world in 2021, outpacing both Google and YouTube for traffic.

More than 50% of the global population is using social media, and the average time spent on these platforms amounts to almost two and a half hours each day. This poses an opportunity for retail digital transformation, especially when targeting younger demographics. For example, the most popular platforms for  millennials and gen Z are Facebook, Instagram, Snapchat, and TikTok, which have 57%, 70%, 55%, and 44% usage, respectively.

By understanding which social networks customers frequent, retailers have two options. They can employ paid advertising services to target customers more effectively. Alternatively, they can develop a unique digital marketing strategy for each channel to foster customer loyalty.

Predictive analytics on these platforms will present clear insights into what is and what isn't working. So, retailers can adjust their strategies as needed for product-led growth to increase market size.

WEBINAR REPLAY: Top 5 lessons from the pandemic that are key to the future of eCommerce

Learn how to boost your sales – quick.

Need more information?