Optimizing the e-commerce customer experience: the importance of data standardization
A recent study shows that 49% of consumers abandon a transaction if 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, 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 you optimize the customer experience? How can we build trust and make customers want to return 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 precision 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- Importance of the customer experience in e-commerce
The customer experience in e-commerce goes far beyond the simple transaction. It encompasses the entire customer journey, from product discovery to purchase and after-sales service.
A positive customer experience translates into increased sales, greater customer loyalty and improved 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 continue building customer loyalty.
This customer experience is not exclusive to BtoC sales.
Online sales aimed at professionals, notably those of industrial/specialized products, office equipment and IT, have seen a 10% increase in 2022 compared to 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.
So, the digitalization of purchasing is a phenomenon observed in both the BtoC and BtoB fields. 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 an in-depth understanding of customer needs and expectations, as well as the ability to respond quickly and effectively to those needs.
And this is where data quality comes into play, with data normalization being one of the key elements for e-commerce sites.
1. Problems linked 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 shoppers first conduct research online, 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/clothing 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 and 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 furnishings (for individuals or professionals) is the problem of converting measures and units.
This is another major challenge for data standardization in e-commerce.
In fact, e-commerce companies often manage products from different countries, each with its own system of measurement. 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 may simultaneously present:
-
in metric alongside measurements in feet or inches,
-
weights in kilograms may 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 estimates by supply chain players, 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 search engine rankings. 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 guarantee 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-enhancing process.
II) The steps involved in implementing data standardization
Step 1: auditby assessing and identifying data quality problems
The first step to successful data standardization is to audit the quality of existing data. This requires careful analysis of the data to identify 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.
What's more, the audit helps to understand the structure of the data, 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.
Step 2: Remediation, data normalization
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 may 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
The final 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 error-prone.
Opting for a QA 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 have 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.
Accuracy of product information: customers expect accurate and reliable details when searching for products online. Standardization eliminates errors and inconsistencies in descriptions, boosting customer confidence.
Ease of product research: well-organized data simplifies research, 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.
Enhanced SEO: accurate descriptions improve search engine rankings, increasing traffic and brand visibility online.
Conclusion: strengthen your customer experience through data normalization with Tale of Data
In conclusion, data normalization is an essential lever for improving the customer experience:
-
by building customer loyalty through precise, accurate and complete product descriptions,
-
effectively reducing avoidable product returns,
-
avoiding shopping cart abandonment due to lack of product information,
-
and improving search engine rankings.
To find out more about how Tale of Data can help your company standardize its data, see our "standardizing data" and "commerce and distribution" sections.
You can also watch our exclusive webinar on YouTube:
Data Mesh: a new approach to organizing and exploiting data
How do you design the ergonomics of a solution to make it accessible to all?
