When it comes to holiday shopping, Americans continue to increase their spending. Since 2002, shoppers in the United States have spent more in the current year than in the last. The lone exception was 2008 – it took a recession to knock down holiday spending by 4.7%. However, it went up again in 2009 and by 2010 had surpassed pre-recession dollar figures, according to the National Retail Federation (NRF). It’s increased every year since. The NRF projects spending in 2018 will again hit record levels at about $720 billion. That’s a lot of gift buying. It creates an incredibly large market for retailers to tap into. And data analytics for retail has created another career opportunity for professionals with graduate degrees in data science and business analytics. That’s because retailers are increasingly incorporating data and analytics into their holiday business strategy.

Data Analytics for Retail Operations

In a report published in Logistics, a peer-reviewed journal published by the Switzerland-based MDPI (an acronym used by the Molecular Diversity Preservation International and Multidisciplinary Digital Publishing Institute), researchers from the United Kingdom and China identified four main themes in application of big data for retail: availability, assortment (or customer segmentation), pricing, and layout planning.

A major part of this effort involves the creation of “micro-segments” of customers based on factors such as the correlation between items purchased as well as purchase patterns dependent on time and location. Understanding the details of purchasing patterns helps retailers better understand their customer base. It also supports the forecasting of future purchases.

The Logistics report suggested the following use of data analytics for retail:


  • Using data from customers in loyalty programs to gain consumer insights, which can lead to better-targeted marketing
  • Using transaction data with delivery time windows, inventories, and weather forecasts to create accurate demand forecasts and to better manage stock replenishment and procurement
  • Making pricing decisions based on margins and the actions of competitors
  • Heat-mapping in-store traffic to improve store layout planning and increase exposure of less popular items
  • Using the knowledge gained from consumer insights data to identify long-term buying trends

Omnichannel Strategy

During the holidays, the competition among retailers is fierce. The smallest edge can lead to substantial gains. Several years back, the Think With Google team wrote that an omnichannel approach can provide retailers with a leg up on the competition.

Omnichannel refers to the idea of a cohesive marketing strategy that integrates multiple shopping channels that include traditional stores, websites, and purchases made through smartphones.

Shoppers who make purchases from the same retailer through multiple shopping channels can provide 30% higher lifetime value than those who purchase through one channel, according to a study cited by Think With Google.

Optimizing Sales

Big data plays a role in this Omni-channel strategy by providing a way to combine all the data from an operation, including inventory and shipping data.

This bigger picture supports the optimization of sales. For example, having operations-wide data allows retailers to better allocate inventory to the right store or channel where it has the best chance of being sold.

It’s a needed change in how retailers approach sales, according to a BRP study quoted in Forbes. That study found 71% of retailers do not have formal processes in place to manage omnichannel demand from consumers. That same study found 44% of retailers indicated that improving analytics is a top priority.

All of this comes into play this holiday shopping season, which officially starts with Black Friday after Thanksgiving and runs through Christmas Eve. Retailers want to work smarter and get a bigger piece of the massive holiday shopping pie – with the help of skilled data and analytics experts.