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Optimizing the e-commerce customer experience: the importance of data standardization

Optimizing the e-commerce customer experience

A recent study shows that 49% of consumers abandon a transaction if the product information is of poor quality.

How do you ensure that every visitor finds exactly what they're looking for, when they're looking for it, and that they're satisfied enough to come back again and again?

In the world of e-commerce, the customer experience is more than ever the key to success. Every interaction, every click, every second spent on a site can make the difference between abandoning a shopping cart and completing a sale.

But how do we optimize the customer experience? How can we build trust and make visitors want to come back to our site?

The answer lies, among other things, in the quality and standardization of your data.

Definition of standardization

What is data standardization?

Data normalization is the process of transforming data to enable it to follow a common format, representing a certain standard, or meeting predefined norms. This is known as data normalization or standardization.

This step ensures consistency and accuracy in the information published on an e-commerce site.

This standardization is the key to unlocking an optimal customer experience and instilling confidence in the information consulted.

I) The importance of the customer experience in e-commerce

The e-commerce customer experience goes far beyond the simple transaction. It encompasses the entire customer journey, from product discovery to purchase and after-sales service.

E-commerce purchase path diagram
E-commerce purchase path diagram

A positive customer experience translates into increased sales, greater customer loyalty and enhanced brand reputation.

According to a study by Toluna Harris Interactive for Fevad*, dated November 2022, "55% of online shoppers are loyal to one or more websites". It is therefore essential to maintain a high level of user experience over time in order to maintain customer loyalty.

This customer experience is not exclusive to BtoC sales.

Online sales aimed at professionals, particularly those of industrial/specialized products, office equipment and IT, rose by 10% in 2022 compared with 2021, according to a Fevad study dated 2023. In fact, these sales have accelerated sharply since the Covid crisis, with a significant +41% increase in 2022 compared with 2019.

The digitalization of purchasing is a phenomenon observed in both the BtoC and BtoB sectors. As a result, data standardization is becoming a crucial issue for all e-commerce sites, whether they target individuals or professionals.

However, delivering an exceptional customer experience is no easy task. It requires a deep understanding of customer needs and expectations, as well as the ability to respond quickly and effectively to those needs.

And that's where data quality comes into play, with data normalization being one of the key factors for e-commerce sites.

1. Problems related to errors in product data sheets

Errors in product descriptions pose a major challenge for e-commerce businesses. Not only do they affect the customer's perception of product quality, they can also impact search engine rankings and customer confidence.

Data quality is crucial, as many buyers do their research online first, even for purchases that will later be made in-store. The web remains the main source of information for consumers; it must be reliable and accurate. In France, after price, the two most sought-after types of information are product description (28%) and technical specifications (23%), according to a study by Opinion Way published in February 2021.

To illustrate the importance of data standardization, let's take the example of the cosmetics sector, one of the three most important sectors for online purchases, after fashion/apparel and footwear.

Information on ingredients, allergens and certifications - such as the organic label - is of vital importance to consumers. However, this information can vary between brands or countries, leading to inconsistencies and confusion for consumers looking for specific characteristics. Consequently, if this information is not standardized, it can hinder research and thus the finalization of the sale.

*FEVAD: Federation of e-commerce and distance selling

2. Measurement and unit conversion problems

Another case, very common in certain sectors such as clothing, footwear, but also furniture (for individuals or professionals) is the problem of converting measures and units.

This is another major challenge for data standardization in e-commerce.

E-commerce companies often manage products from different countries, each with its own measurement system. Suppliers do not always convert product measurements into all existing systems. It is therefore up to the seller to make the conversion.

In the absence of standardization, the site can simultaneously present :

  • in the metric system alongside measurements in feet or inches,

  • kilogram weights can coexist with pounds (for products from the UK)

The potential source of confusion and misunderstanding for customers is great. Non-standardization of measurement units can easily lead to ordering errors, which in turn can result in product returns.

According to supply chain estimates, the cost of a product return is usually between 15 and 30 euros. This includes shipping costs, round-trip transportation, processing by an employee, as well as reshipment in the event of product exchange. Reducing returns due to quality problems and data standardization is a simple step for organizations equipped with the right solutions. This can have a significant impact on returns management, customer satisfaction and company margins.

3. Negative impact on website search engine ranking

Finally, data errors and inconsistencies can have a negative impact on a website's search engine ranking. Google, for example, takes various factors into account when evaluating a website's position, including data quality and relevance. All professionals aiming to appear at the top of search results know that this objective can only be achieved under certain conditions, and the quality of product referencing is one of the crucial elements.

So data standardization is not an option, but a necessity to ensure a quality user experience. Before exploring how standardization ensures an optimal customer experience, let's take a look at the main steps involved in this quality assurance process.

II) Steps to data standardization

Step 1: auditing by assessing and identifying data quality issues

The first step to successful data standardization is to audit the quality of existing data. This requires careful analysis of the data for errors, inconsistencies and gaps.

To illustrate this step, the use of a Data Quality solution is the most appropriate and effective.

To avoid time-consuming manual analysis that cannot cover all potential problems, data auditing becomes an automated, simplified and efficient process, guaranteeing exhaustive detection of anomalies.

In addition, the audit helps to understand data structure, identify problematic fields and recommend cleansing and normalization rules to be applied. These rules are suggested automatically by the tool, or can be customized by the user to suit his or her specific context.

2nd step: remediation, data standardization

Once the problems have been identified, the next step is to use the solution to make the corrections. Thanks to its "fuzzy logic" algorithms, Tale of Data is able to provide a list of words with an approximate spelling.

Consider an online store offering a wide range of electronic devices.

In product descriptions, there can be specification errors, such as "15" LCD screen" instead of "15" LED screen". Thanks to the Tale of Data solution, which recognizes the strong similarity between the two words, these errors are automatically corrected on an ongoing basis. This ensures that product information remains consistent and accurate for customers, while enabling site searches to deliver relevant results.

Step 3: Maintain data quality over time

Finally, the last step in implementing data standardization is continuous integration. This involves the daily monitoring and regular updating of data.

Many companies perform this task manually, which is tedious, costly and prone to error.

Opting for a quality assurance solution enables standardization processes to be automated and planned, in line with the rules defined by the organization. This saves significant time and ensures that all data is processed exhaustively.

III) How does data standardization improve the customer experience?

As we've just seen, data standardization is an essential process that has a direct impact on the customer experience. By guaranteeing the precision, accuracy, consistency and relevance of information, it improves the customer's interaction with the e-commerce site on several levels.

Accurate product information: Customers expect accurate, reliable details when searching for products online. Standardization eliminates errors and inconsistencies in descriptions, boosting customer confidence.

Ease of product search: well-organized data simplifies searching, reducing frustration. Each product is correctly categorized, improving navigation and reducing bounce rates.

Personalized experience: clean data helps to understand customer preferences, promoting relevant product recommendations. This boosts engagement and satisfaction.

Improved SEO: Accurate descriptions improve search engine rankings, increasing traffic and brand visibility online.

Conclusion: enhance your customer experience through data normalization with Tale of Data

In conclusion, data standardization is an essential lever for improving the customer experience:

  • Building customer loyalty through precise, accurate and complete product descriptions,

  • by effectively reducing avoidable product returns,

  • avoiding shopping cart abandonment due to lack of product information,

  • and improve search engine rankings.

To find out more about how Tale of Data can help your company standardize its data, see our sections "standardizing data and "commerce and distribution" sections.

You can also watch our exclusive webinar dedicated to the subject on YouTube :


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