Automating item onboarding and intelligent attribution

Master data is a foundation on which retailers build and run their businesses. Item onboarding and attribution are critical merchandising processes that build master data, which is the lifeblood that enables insights and helps the business make optimal decisions. These two processes, however, have historically been cumbersome, manual, error-prone and time-consuming.

Imagine a future model in which processes (or elements) within onboarding and attribution occur in minutes versus weeks—and at +95 percent accuracy. Productivity improves by at least 25-50 percent, freeing up to 25 percent of merchant and category specialist’s time so they can focus on higher engagement work that supports more strategic initiatives. Furthermore, automating these processes unlocks savings in selling, general and administrative expenses (SG&A).

In this new world, no longer will merchants and category specialists have to work through hundreds of columns in spreadsheets, normalize the input from the vendor community, tap into dozens of different systems, and conduct multiple cycles of reviews and approvals to complete an item record. Rather than trying to juggle it all, highly specialized teams will manage the process and execute more complex, consumer-relevant attributions, vignettes and item groupings.

Free up merchants to add greater value

With technology advancements and data availability multiplying at exponential rates, retailers can adopt a model that automates repetitive, transactional tasks in an execution layer where specialized teams oversee data management. Such automation will free up time for merchants and category specialists to focus on being creative strategists and giving consumers the exact products and services they want.

In addition to significant efficiency benefits, retailers that work in this new way will have better foundational data with cleaner, more accurate and more specific attribution—which can lead to better business outcomes.

Consumer expectations are rapidly changing—it is more important for merchants to spend time on strategic, high-value add activities.

The process

Imagine an intuitive automatic item onboarding and intelligent attribution process where the customer ultimately defines the item master.

Step 1. Build a seamless connection between item management specialist and vendor
This first step is digitizing the data transfer from vendor to retailer. There are a few ways to do that – vendor portal, smart templates and robotic process automation (RPA). The vendor sends accurate item data in a consistent format that adheres to data standards. If the vendor’s data is incomplete, they are automatically prompted to correct it. Alternatively, a third-party solution can ingest the vendor catalog or spec sheets, aggregate vendor data and normalize it for retailer. This approach significantly reduces workload on both the vendor and the retailer. A portion of item onboarding fields will be auto-populated (e.g. hierarchy data). RPA tracks item onboarding requests and updates them into the master data/ERP system. Alternatively, a third-party solution can ingest the vendor catalog or spec sheets, aggregate vendor data and normalize it for retailer.

Step 2. Complete missing information
A set of predetermined business rules or trends will populate additional fields, (e.g. ticketing requirements). These processes can be automated up to 80 to 90 percent, freeing humans to focus on evaluating exceptions. Lifestyle, customer type and other attributes unique to a retailer will still require human intervention.

Step 3. Enable dynamic data enrichment
Machine learning and image recognition is used to populate product attribution, create product titles and copy, and present competitive information to drive better navigation to certain items and engage customers. Product image requirements (type and quantity) are defined to reflect customer preferences, interaction and conversion.

Step 4. Learn and adjust based on customer habits
The system continuously enriches the data, getting smarter the more customers interact with the product. Continual learning could reprioritize which attributes are important to customers’ shopping missions and potentially create new ones that did not exist previously. Item master data can be automatically enhanced consistently with additional product attributes (e.g. lifestyle use) based on what is relevant to the customer at that time. Cognitive computing allows the merchant and category specialist to extract shopper attributes, occasion perception and preferences. The system identifies consumption patterns that enrich behavioral attributes. This data will continually update based on the way customers are speaking about the product.



Realize the potential of automation

Retailers worldwide are seizing the advantages of automation. Some are collaborating with startups that have advanced technology capabilities, and others are partnering with larger, more established firms that can design and deliver future processes that are tailored to meet the retailer’s specific needs.

The future is evolving fast in retail. Retailers can be leaders in paving the way forward by exploring the power and potential of automated and intelligent processes—all while running an efficient business.

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