Challenges of Big Data Analytics in Retail

Updated: May 11
By: Admin

Big Data Retail



The retail industry is one of the largest and most important industries in the world. In the US alone, it employs millions of people and accounts for many trillions of dollars of spending each year.


As such a crucial and established industry, retail is also among the most affected by technological change, and recent years have seen huge advancements in the ways that retail companies use technology.


One of the more significant developments in terms of technology has been the explosion of big data analytics. In this post, we’ll look at how retailers are using big data to their benefit and some of the accompanying the challenges posed by the migration to analytics technology.


What is Big Data?

Big data is the term used to describe data sets that cannot be easily handled using traditional methods. There is no set definition, but it generally refers to the types of huge data sets produced by large modern organizations.


The sheer scale of big data makes it necessary to use powerful computer-based technology to collect, organize, and analyze. This technology has become more and more affordable over time and is now realistically accessible in some form to companies of almost any size.

How do Retailers Use Big Data Analytics?

Retailers have always used data to gain insight into their marketplace and to help inform important decisions. However, it is only in the last decade or so that the use of big data has become widespread. This growth has been particularly apparent with the online retail giants such as Amazon, who can easily collect huge amounts of data for later analysis and have used it to dominate the marketplace.


Some common applications of big data are:

  • Gaining insight into customer behavior. For example, the US retailer Target used a customer’s shopping habits to automatically deduce that she was pregnant, then send her baby-specific advertisements.
  • Setting prices more effectively, for example by tracking buying habits at different price points.
  • Better supply chain management by tracking sales, monitoring stock levels, and automating supply chains.
  • Optimizing store layout to encourage the purchasing of certain products. This may involve scrutiny of video footage, as well as the trialing and monitoring of different layout patterns. It can even translate to an e-commerce environment, where certain products may be showcased prominently on the homepage for particular customers, for example.
  • Demand forecasting of particular product lines by modeling past and current data, helping to manage stock more reliably.

Here is a case study discussing how our team was able to provide granular forecasts for a leading telecom provider in Germany, to better allocate SKU level handset inventories, increasing the sell-through rate by 18%, minimized purchases of low-demand handsets and reducing inventory cost by 7%.


Download Now: Demand Forecasting Case Study

Challenges for Retailers Using Big Data

Big data can bring about big challenges for retailers. Here are a few things that need careful consideration when making use of large data sets in a retail environment.


Collecting Accurate Data


When collecting any amount of data, it’s important to ensure you target the right types of information and use the optimal methods to collect it. Bad quality data leads to inaccurate conclusions, so it can actually be more of a hindrance than a benefit. In the context of retail, the main types of useful data will be things like sales volumes, customer footfall, profit margins, stock levels, the effectiveness of advertising campaigns, and so on. The variety of data sources adds to the complexity, as different methods need to be used to collect it. 


For example, when a retailer wants to track warehouse stock, they may need to install ‘smart’ Internet of Things-connected sensors to automate the process and link them to other departments such as purchasing.


Collecting and compiling the data in a meaningful way also requires specialized software, which is likely to require extra training and time investment to implement effectively.



Complying With Data Protection Standards and Laws

There is a growing movement to protect the right to privacy of customers. This is illustrated by the recent introduction of various laws, such as GDPR in the EU.


As the laws and regulations become more complex, it is becoming more of a challenge for retailers to comply with everything. For this reason, any attempt to gather analytical data by retailers should be done with careful adherence to the law.


Beyond the legal requirements, there is also the challenge of handling data in an appropriate way to prevent it from being shared with any other parties. This could involve things like storing it securely, having a robust company policy and staff training about handling data, and so on.


Building Enough Trust From Customers


If a retail business wants to capture customer data on a large scale then it must build a certain level of trust which can be done by providing exclusive benefits for a customer in exchange for customer data (e.g discount coupon etc. Part of this process involves effective branding and advertising, helping customers build a relationship with the brand. Other important elements include storing the data safely, never using it improperly, and being clear with customers about what will be collected.


Using the Latest Technology


Technology is a fundamental part of big data analytics. Modern technologies such as machine learning and artificial intelligence have a huge influence on the effectiveness of analytics outcomes, so advancements are improving these outcomes all the time.


In order to ensure the more accurate insights are gained from big data, retailers need to keep up with the constant march of technology, something that can be challenging without the right help and investment.


Keeping Pace With the Competition


If retailers fail to address the challenges listed above, then not only will they be collecting subpar data, but they risk falling behind the competition. As the retail industry is getting more competitive, this is not a situation that any company wants to be in.


Keeping up with the competition in the fast-paced world of retail - both physical and online - requires proper investment into big data technology and expertise. 


How Does Your Business Make Use of Big Data?

Big data has the power to transform the way retailers operate for the better, improving efficiency across every department of the organization. Your business already has all the information necessary to gain incredible insight into your customers, the wider marketplace, and your own internal operations - you just need to unlock it.


Lynx Analytics is here to help you unleash the power of your data. We specialize in working with businesses to help them collect, collate, and analyze their data more effectively, using cutting-edge software and a wealth of expertise built over years of industry experience.