3 Ways to Differentiate Your Retail Business with Data Quality

What’s the most important aspect of your business as a retailer? What you sell is important, as is customer service. However, data quality is an often-overlooked aspect of running a retail business.

Retail data quality can actually be a competitive differentiator. Read on to explore three ways retail data quality delivers value to retailers. 

What Does Data Quality Mean?

There are six dimensions of data quality: 

• Accuracy – Does this information match what’s really happening?
• Completeness – Is this information comprehensive?
• Consistency – Is one piece of data stored in more than one place the same in every instance?
• Timeliness – Is your information available when you need it?
• Validity – Does the information have to be in a specific format?
• Uniqueness – Is this the only time this information is in a database?

# 1: Product recommendations

One of the biggest online retailers on the planet – Amazon – is a great example of a business driven by data quality processing. One of the ways in which Amazon uses data is to recommend products to shoppers. The online retailer takes the information it’s gathered about a shopper and utilizes machine learning and artificial intelligence (AI) to determine what that person will most likely buy next. 

In this situation, the quality of data feeding the machine learning models and recommendation engines  is crucial. This includes having an accurate single view of the customer – to capture the demographics and behaviors of a unique shopper for analysis. In addition, information about what was viewed on their site, abandoned in their cart, and ultimately purchased must be accurate, complete and in a consistent format for analytics. Only then can AI make good recommendations. The alternative is recommending  products that aren’t relevant, which alienates customers, damages reputation, and decreases sales.  

# 2: Shipping and deliveries

Another important need for high data quality in retail is the area of shipping and deliveries. A 2019 report shows that 65 percent of retailers said that failed or late deliveries are a significant cost to their business. A primary cause of delivery mistakes is bad address data. 

Bad address data quality can have multiple sources. An address can be mis-typed by a customer on a web form, entered incorrectly by a customer service or sales person, or be in the incorrect format for that particular country. 

From intelligent type-ahead, to standardization and validation, ensuring data accuracy is critical. Better data means faster, more efficient and less-costly shipping and happier customers. 

# 3: Inventory

Retailers have long ago realized the need to change inventory based on seasonality. Grocery stores stock up on ice pops for summer, baking supplies in the fall. Clothing retailers start marketing bathing suits at the end of winter, anticipating the warmer weather to come.

However, the most successful retailers go way beyond – keeping up with current consumer trends, and what’s on the horizon. Having too much, too little, or just the wrong inventory is a costly mistake – resulting in missed sales, higher costs, and steep discounts (or liquidation) of unsold goods. 

Data analytics plays a critical role in optimizing inventory – and data quality is essential to ensuring that the insights resulting from that analysis can be trusted. With data that’s accurate, complete, valid and timely, you make better decisions and save money.  

Precisely Trillium: Ensuring retail data quality 

Precisely Trillium software can play a critical role in your retail data quality efforts. With Trillium Discovery, business users can evaluate data sources in three simple steps to quickly gain insight, and profile it for accuracy and completeness.  Trillium Quality delivers enterprise data quality built for business.  It’s a versatile, powerful data quality solution that supports your rapidly changing business needs, data sources and enterprise infrastructures – including big data and cloud.  Retailers use its data cleansing and standardization features to understand global customer, product and financial data, in any context – making pre-formatting and pre-processing unnecessary.

Improve your retail data quality to save money, make better business decisions, and build deep, long-lasting relationships with customers. To learn more, download our eBook “Exceeding Expectations: Four Ways Data Quality Promotes Customer Loyalty.”