data quality

Scale Your Retail Insights Through Data Quality Excellence

Everyone in the retail industry is talking about data. From identifying what product sold the most to mapping customer journeys, data can be used to derive countless insights. That said, simply holding on to a lot of data doesn’t do much. Brands that don’t pay attention to data quality could end up being data-rich but insight-poor. Meeting high data quality standards is vital for retail analysts as well as data scientists working on algorithms for personalized product recommendations and more. Let’s take a closer look.

How does data quality impact retailers

Retail data takes many different forms. There’s customer data collected through web forms and point of sales data, inventory and operations data, market trends, web analytics and so on. Given the multiple collection points, data isn’t always structured according in the same format. It may also be siloed leading to issues with access and duplication. Errors in data entry could lead to capturing incorrect or incomplete customer details. Rather than help, using this poor-quality data to derive insights could hold your business back. Here are a few examples of the impact data quality can have on retail operations:

Supply chain optimization

Analyzing customer addresses can help retailers optimize their supply chain. Accurate data can provide valuable insights that streamline processes and maximize revenue.

To begin with, customer addresses play a vital role in approximating delivery timelines. Pinpointing the exact handover location allows last-mile agents to plan their route in the most time and fuel-efficient way. With the right address, there’s a lower chance of returns.

Further, customer location data can help retailers identify locations for new outlets and warehouses. For example, an additional warehouse may be set up near areas that see a high volume of online orders. When setting up a new store, the products stocked are not always the same across branches. To maximize sales, the inventory for a new store may be planned keeping in mind the type and price-point of products ordered by customers in the area.

However, incorrect or incomplete addresses can make delivery difficult and skew cost-benefit analytics on setting up new stores.

Inventory management

Retailers must continually balance having enough product on their shelves without holding too much inventory. Empty shelves could make customers turn to competitors. 34% of businesses responded to a survey saying they had delayed shipping an order because of accidentally selling a product that was not in stock.

On the other hand, if retailers hold too much stock, there’s a risk of it going out of fashion or getting damaged over time. Some items may also be season-specific. Forecasting demand in retail accurately has helped brands like Walgreens increase sales by 4% in just 2 months.

Of course, the accuracy with which inventory managers can determine order quantities for their inventory depends on the quality of past sales data. If you have incomplete sales data, the inferences drawn could be incorrect too leading to having too much or too little inventory.

Marketing Campaign ROI

For any marketing campaign to be effective, the messaging must be relevant to the recipients. Rather than put out a generalized message, campaigns must be tailored to respond to customer needs. Customer data can provide rich insights into customer behavior, demographics, their spending patterns, preferred product sizes, colors, etc. This allows retailers to make relevant product recommendations for a campaign and thereby increases the chances of conversion.

That said, if the data you’re using for product recommendation algorithms is unverified, the recommendations offered may be irrelevant to the customer. This can not only drop sales numbers but also frustrate customers and spoil your relationship with them.

Price optimization

Prices move up and down in response to changes in demand, season, market trends, etc. To attract more customers, retailers may offer special prices to different demographic groups. For example, retailers may offer discounts for bulk deals in stores located in low-income areas. To be able to make such decisions, they need access to information on customer demographics and their purchasing power.

If the information is good, the model can be successful. A study found that data can help retailers increase their operating margins by more than 60%. However, if the data is invalid or outdated, the inferences drawn will also be inaccurate. This could lead to a product being priced too high and thus reducing sales and revenue.

Customer experiences

Today is the age of the omnichannel sales experience. Customers expect retailers to offer them a seamless experience irrespective of whether they are shopping in a brick-and-mortar store, the website or an app. To deliver such an experience, retailers must be able to merge data collected through different interfaces and manage the same centrally. It is important not only for the data to be correct but also to ensure that no duplicates exist.

For example, let’s say a customer placed an order by the name John Smith on the website but only used the name John in the store, he may be mistaken as being two different people. This affects the retailer’s ability to understand who the customer is and how best to serve them.

Improving data quality to deliver better insights

As illustrated, data quality plays a critical role in determining how useful customer data is to the retailers holding it. To be considered good quality, data must be correct, complete, relevant, up-to-date and unique amongst other things. Hence the need for data verification and regular validation.

This is a task that can be automated to be most effective. Retailers can easily integrate data verification tools with customer onboarding forms and other points of data collection. These tools compare the data being entered against reliable third-party databases to verify the same. In addition, they can be used to validate data existing in a database at regular intervals to fight against data decay.

This is a game-changer for retailers! Imagine having access to crystal-clear data insights that pave the way for smarter decisions and unbeatable business success. Ready to elevate your retail game? Get in touch for a free trial and let Melissa Data Quality be your trusted partner on the path to data excellence and soaring achievements!

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