Next-Gen Brand Commerce

The Impact of Dirty Product Data on Total Revenue

The impact of incorrect product data on customers and your brand

It’s OK to be dirty if you’re a martini. When it comes to data, though, dirty is never good. The impact of incorrect product data today is especially distressing. When a brand manufacturer thinks about dirty product data, it calls to mind hundreds of hours of manual clean-up in spreadsheets.

Unfortunately, that’s the only way most brands know to clean that data up.

It’s understandable that the horror of manual clean-up is what brands think of first, because that’s the immediate requirement when faced with dirty product data. To launch new products or send product information to a new seller, clean-up has to happen fast.

There are other consequences of dirty product data, though, and they’re even more chilling. The impact of incorrect product data is even bigger for a brand’s total revenue. That impact grows exponentially the bigger the product catalog, too.

How does product data get dirty?

Product data encompasses all the descriptive information, images, videos, and other content tied to each item in a catalog. Those products are expressed to the world in many attributes: product titles, product categories, hero images, instruction manuals, and dozens more.

Both essential and enriched product data must be organized and optimized for different templates. This means there are different versions of product data floating around. A manufacturer who works with a dozen sellers, for example, will have a dozen versions of product data to manage.

That’s a lot of spreadsheets and manual work — and just as much opportunity for error.

Each department that touches product data complicates the matter more. The marketing department has a different spreadsheet than the customer service team, and they both organize different attributes. Text is entered in one format on one spreadsheet, and another format on the next. Add these issues to the dozen versions of product data floating around for all those sellers, and this becomes a data minefield.

In this environment, product data ends up dirty — filthy — with incomplete product information, inconsistent formatting, varying taxonomies of attributes, and data that’s just plain wrong.

Most brand manufacturers find out about dirty data when trying to launch products or when customers return products because they “aren’t what they expected.” Dirty product data costs businesses revenue in more ways than one.

What does dirty product data lead to?

The impact of incorrect product data can be seen in business productivity and in total revenue. Because of the impact of other factors on these metrics, dirty product data can float under the radar. It gets dismissed as a “necessary evil” with no better way to manage product data in sight.

There are, however, better ways to manage product data. With streamlined product information management, a brand can eradicate these revenue-busing consequences:

• Wasting hundreds of hours on manual product information management. Usually, key thought leaders in an organization are stuck doing this task. These hours spent affect revenue with an enormous opportunity cost. No brand’s best thinkers and strategists should spend time on manual data clean-up.
• Unimpressive marketing. Today’s best manufacturer marketing comes from accessible product data. Keeping data accessible (i.e., clean and in the cloud) allows marketing teams to connect classes of products and utilize all the audio-visual-rich product content a brand has. Marketing teams can also personalize the customer journey with specific product classes and recommendations that feel unique for each user. This level of personalization is what consumers today expect.
• Missing data (and missed opportunities). Any brand working with multiple sellers or channels has to scrub product data squeaky clean for multiple product information templates, which is a huge task. Any template left incomplete (e.g., any special attribute fields unique to that template) will result in fewer purchases and less revenue because those products won’t stack up against the competition on the same channel.
• Inaccurate data. This is a big one. It’s a shame to leave attribute fields blank on a seller’s template, but it’s practically a crime to load incorrect product data. Customers take this one personally because their expectations are set based on the product information you provide. If a product isn’t what they were expecting, they can almost hate you for it. This results in returns, nasty reviews (which stop others from buying), and a low customer lifetime value as you constantly scramble to get new customers to buy.

Protect your bottom line with centralized data

Too many brand manufacturers store product data in static silos (i.e., spreadsheets) throughout their organization. Data sprawl like this leads to dirty product data — and fundamental confusion about which versions of product data are the single source of truth.

The answer for brands is in the cloud.

Today’s brands can pool product data together into one cloud-based repository where attributes can be merged and managed. This special repository is a product information (PIM) software. In a PIM, product data can be quickly organized and cleaned up, and then later be exported with precision (with the exact products and attributes needed for any seller, channel, or marketing campaign).

PIM technology solves key organizational problems but it’s still on each brand to use the PIM well. When implementing a PIM, brands can start by creating a flowchart to clarify how data is updated and how it will flow into the PIM for every department in the organization.

Simplify and streamline data-cleaning activities

Even implementing a PIM with the best intentions will leave the lingering impact of incorrect product data if adoption rates in an organization are low. Artificial intelligence in product information management is helping see greater ROI on PIM technology, but the right processes need to be in place also.

Without making regular improvements to catalog data, it will decay in value over time. Brands today can focus their energy first on the implementation of a solution to get the maximum value from a PIM.

Consider the following practices to make buy-in, implementation, and onboarding of a PIM easier:

• Designate one person to be in charge of data (delegating tasks to others is fine, but this person will be the steward of product data for the brand)
• Use the PIM’s built-in logic to automate as many product data updates as possible
• Utilize both static and dynamic lists to organize product data
• Load all product content (images, videos, etc.) to the PIM, too, to link associated products and use product collateral smarter

Keep your martinis dirty and scrub product data clean to protect and grow total revenue. Get even greater value from a PIM solution by getting early buy-in and ensuring easy adoption. Greater revenue (and fewer headaches) is all brands have to gain.