5 ways data quality adds value to your retail business

The retail sector is highly competitive and changing swiftly when compared with other industries around the world. Every year, new market entrants create a more competitive business environment. Like every other business vertical, data is playing m its part here too as a core enabler for success. Because in today's world, every touchpoint generates data, its optimisation is necessary for success. 

The role of data quality is instrumental in achieving retail success. Clean, accurate, and reliable data is the foundation upon which retailers can build their strategies, operations, and customer experiences. 

Bad data can ruin your business 

For all those retailers and brick-and-mortar businesses, developing business strategies based on insights can be fatal without ensuring the quality of the data. According to Gartner research, the typical financial repercussions of subpar data quality for organizations amount to an annual average of $9.7 million. Meanwhile, according to an IBM report, businesses in the United States suffer annual losses reaching  $3.1 trillion solely due to inadequate data quality. 

Accommodating bad data is both time-consuming and expensive. Data might have plenty of errors and sometimes play a critical role in specific business requirements. So, ensuring proper data quality mechanisms is necessary; otherwise, it results in hindering the entire process. 

Data quality is such a persistent requirement that a report from Fosters states that close to 30% of analysts spend over 40% of their time to verifying and validating analytics before these insights can inform critical strategic decisions in the business. 

So, here are the top ways data quality adds value to retail customers  

     1. Seamless customer experience 

Customer retention is the ultimate goal of any retail business. They have a plethora of options to buy products, as they are always in search of a seamless customer experience at every touch point. Epsilon suggests that when brands provide a personalised shopping experience to customers, 80% of them are most likely to purchase products. 

Through the recommendations made by modern AI and ML models, data quality plays a vital role. The data is fed to models in order to offer Frequently bought-together and other intelligent recommendations. 

Clean and accurate customer data allows retailers to gain valuable insights into customer preferences, behaviour, and purchase history. By analysing this data, retailers can create detailed customer profiles and segments, enabling personalised marketing strategies. Targeted promotions, tailored recommendations, and personalised offers based on accurate data lead to improved customer engagement, loyalty, and ultimately increased sales. 

     2. Pricing strategy  

Data quality is critical for pricing strategies. With accurate data on product costs, margins, and market trends, retailers can optimise pricing to remain competitive while maximising profitability. 

Accurate data on product costs, competitor pricing, and market trends empowers retailers to develop data-driven pricing strategies. By analysing pricing data, retailers can set competitive prices while maintaining profit margins. Additionally, data quality enables retailers to identify price elasticity and adjust pricing strategies accordingly, maximising revenue potential. To gather accurate, current, and valid data to support solid pricing decisions and maximise ROI, a complete data quality framework plays a pivotal role for any retail business. 

     3. Shipping and deliveries 

Shipping and deliveries are another area where data quality is highly desirable for any retail business. Failed or late deliveries cause a significant loss to any business, resulting in customer loss. A primary reason for late or failed deliveries is the address data. 

Bad address data can be the result of multiple inputs like a mistyped web form, weak connectivity, incorrect entries, and many other factors that vary according to geographic location. So, standardisation and validation might take some time to ensure data accuracy, but they result in more efficient cost management and more satisfied customers. 

     4. Optimal supply chain management  

Data quality ensures that retailers have reliable information about their inventory levels, product demand, and supplier performance. With accurate data, retailers can optimize inventory management, avoiding stockouts and overstocking. Timely and precise insights facilitate efficient supply chain management, reducing lead times and minimizing carrying costs, which contributes to improved operational efficiency and cost savings. 

     5. Better marketing campaigns and sales performance 

With accurate data, you can tailor your marketing campaigns to specific customer segments, improving their relevance and effectiveness. Furthermore, reliable data allows you to track sales performance accurately, identify top-performing products, and optimize pricing strategies, contributing to increased revenue and profitability. 

Why invest in data management? 

Data quality is not just a nice-to-have for retail businesses; it is a fundamental requirement for success. By leveraging accurate and reliable data, retailers can gain deeper customer insights, optimize operations, enhance customer experiences, and make strategic decisions that lead to sustainable growth and competitiveness in the retail market. Retailers that prioritize data quality will position themselves to thrive in a rapidly changing and competitive retail landscape. 

Without a modern-day approach to data quality, customers can leverage the overall data quality architecture to save valuable time and money. Make informed business decisions to build deep, long-lasting relationships with customers. Visit our data management and business intelligence page for more offerings.