In 2020, the year of the pandemic, online shopping increased 32.4%. Retail sales in the US are expected to increase by 2.3% in 2021 and 4.1% in 2022. And the e-commerce boom will likely continue long after the pandemic is behind us, as consumers and retailers adapt to new shopping norms.

However, in the era of social media, online reviews, and global pandemics, success in retail is no longer just about product and pricing. Customer experience and support are essential, which is driving retailers to innovate and differentiate by offering modern ecommerce experiences like rebate and rewards programs.

These and other digital touchpoints throughout the buying journey generate vast amounts of data, and thus the need for data processing and data enrichment. Data enrichment, or turning data into quality data, gives retailers another potential differentiator: the ability to train AI models and power predictive analytics that drive informed decision-making and give customers the personalized experiences they crave.

Opportunities abound with quality retail data

For example, with quality retail data, retailers can deliver highly targeted marketing campaigns and personalized shopping experiences based on touchpoints like previous purchase history, wish-listed items, and consumer reviews.

Quality retail data can also help retailers improve customer service. Macy’s uses an AI-powered virtual agent to answer customer queries. According to Microsoft, which powers the solution, the agent was answering 25% of customer requests soon after it went live.

On the predictive analytics side, retailers with quality data can better manage inventory, discover optimum prices, figure out which channels to invest in, and forecast revenue with accuracy.

The proliferation of data has also created a different opportunity for differentiation, this time through retail back-office operations, for instance, sales order processing, order fulfillment, and rebate processing. Each of these tasks requires real-time exception handling work in addition to typical data entry and enrichment support. Our client Ibotta, a cash-back shopping app company, relies on CloudFactory’s data analysts to process exceptions to the tune of around 12,000 hours per month.

Many retailers initially try to handle these processes in-house. Some succeed. Others struggle, primarily because in-house teams tasked with such work often find themselves at odds, trying to juggle added processing priorities while maintaining focus on high-value projects. And because a lot of retail data is time-sensitive, in-house teams feel even greater pressure.

Another problem retailers face stems from the fact that data processing work is tedious and time-consuming, leading to high employee turnover rates and notoriously hard-to-scale in-house operations. Attempting retail data processing in-house is costly, risky, and inefficient, especially post-pandemic when labor demand is growing alongside surges in ecommerce.

Outsourcing your retail data processing work is the way forward

There are other options. On one hand are traditional outsourcing models for retail data processing and transcription, such as crowdsourcing and hiring freelancers or business process outsourcers. The first two of those methods tend to create oversight and quality control issues, whereas the latter typically requires you to use their technology. A managed workforce, on the other hand, serves as an extension of your team to augment or even lead your team’s current data processing workflows.

A managed workforce is a great fit for retail data processing because such a workforce can scale rapidly and accommodate sudden changes to workloads and requirements—things impossible to do when you’re relying on contracted employees alone. A managed workforce also brings a higher degree of professionalism to the table, resulting in higher accuracy rates in retail data transcription and processing. Our client Ibotta increased its accuracy rate by 15% and reduced data processing times by 50% through a partnership with CloudFactory.

In the end, a managed approach to outsourcing can only make your retail business more agile and adaptive, crucial in these times of constant change. Agility and adaptability are especially vital now because consumers are far less forgiving about retail service disruptions than they were during the height of the pandemic. In the words of Jack Welch, former Chairman and CEO of General Electric, “Change before you have to.”

Our managed workforce serves as an extension of your team by tackling text annotation and retail data processing at scale so you can focus on building the next generation of AI-powered solutions for your business. Speak to one of our experts today to learn more.

Download a copy of Ibotta's case study.

Data Transcription Automation & Back Office Support Retail Exception Processing

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