What’s on your retail “must-do” list? Whether a new pricing strategy or loyalty program, AI-powered checkouts or a virtual store, or even personalized cashback promotions, change and innovation are on the minds of retailers everywhere. It’s no surprise, as success in retail today depends on delivering novel and personalized customer experiences. As the cost of customer acquisition continues to rise, retailers are focusing on developing new capabilities, seeking out new revenue streams, and forming lucrative partnerships to create more profitable omnichannel experiences.

The inputs and outputs of these strategies are data – lots of data. And what matters even more than the amount of data is the quality of that data. Most retail data is valuable, but you have to structure and standardize it if you want it to power automation, analytics, and more personalized user or customer experiences.

The problem with existing OCR solutions

To turn raw data into quality, valuable data, many retailers rely heavily on optical character recognition (OCR) for transcribing written data—invoices, purchase orders, packing lists, and product descriptions—whether printed, scanned, or handwritten. In its raw form, such data is of little use. But if transcribed and enriched correctly, it can help you optimize business-critical processes through automation, personalization, and data-driven insights.

Those benefits aren’t automatic. Although OCR can help overcome the challenges of scaling data processing operations, retailers can’t rely on it entirely because of its notoriously high error rates, ranging from 5% to 20% in most applications. Edge cases also get in the way, for instance, smudges on sales receipts and non-standard product names that use special characters: Chips Ahoy!, YUM!, and ¢ł☺υ∂ƒα¢т◎я⑂*. (*That’s CloudFactory written in symbols.) Humans can figure out most edge cases, but automated solutions cannot.

Combine edge cases with traditionally high error rates, and the effects are not pretty. Employees spend too much time on rework. Productivity takes a hit. Customer-facing services suffer. And retailers still don’t get the level of data quality they need to woo, win, and retain customers.

The key to lower error rates is...

The key to lower error rates, successful retail data processing, and the benefits you’re looking for is finding a balance between automation and manual work. That way, you can streamline your operations and reap the benefits of both approaches.

Solutions like automated data labeling and OCR are great for handling large workloads because machines can carry out routine tasks like transcribing receipts and invoices at speed. When the OCR software outputs a data element with a low confidence score, it flags that element as a case for manual review. But given high error rates, such flags can burden and even overwhelm data processing teams. For each flagged case, a human has to manually review and correct the error and, where possible, feed the new data back into the system to improve future accuracy. This approach applies in any automation-related edge case where the algorithm fails to recognize a particular element. It could be a single problematic word or character picked up by the OCR software or, in the case of automated image labeling, an object in an image.

You need a scalable approach for the best outcomes, which might be nigh impossible for an in-house team. A managed workforce is the answer. The cash-back rewards app Ibotta used to spend vast amounts of time processing data and correcting errors. By partnering with CloudFactory, they decreased processing time by 50% and increased data accuracy by 15%. In a world where 20% error rates are standard, a 15% improvement is significant.

Partnering with a managed data-processing workforce also brings domain expertise and practically limitless scalability into the equation. And unlike with traditional outsourcing models, you maintain complete control and oversight over the process and can align your outsourced workflows to the unique needs of your business. When quality control and accuracy are paramount, a managed workforce will prove vastly more effective than outsourcing workloads to anonymous crowdsourced workers.

The benefits of automation plus a managed workforce for retailers

By enriching unstructured data, a managed workforce skilled in data processing can help you automate and derive insights from analytics. On the automation side, you might feed the accurate transcription of invoices, receipts, and purchase orders into an AI model to automate routine accounting processes with high accuracy. With ongoing refinement, the model will continue to improve. And, eventually, your operation will reach a point where edge cases are rare occurrences. On the analytics side, you can use quality data to manage marketing campaigns, promotions, shopper targeting, and pricing.

Want to deliver targeted, personalized customer experiences? Intelligent data capture through a blend of automation and humans in the loop can help with that, too, by enabling automated inventory management, customer onboarding, and even customer review collection. And to better align customer experiences with expectations, the output of data extraction and enrichment processes can automatically populate other retail systems, such as your customer relationship management and recommendation engines.

Your business can make omnichannel retail a reality quickly and cost-effectively by standardizing and scaling your processes with the help of a managed workforce. In times like these, when traditional retailers are at risk of getting left behind, the need to develop new capabilities and more efficient processes is paramount.

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 Retail Exception Processing

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