In 1985, Coca Cola launched with great fanfare an improved version of its emblematic drink, soberly christened "New Coke". Everything pointed to stratospheric success: the taste was validated by a consumer panel, the quantitative studies were dithyrambic and the marketing budget colossal. " I've never been as confident as I am today," said Coca Cola CEO Roberto Goizueta on the eve of the new product's launch.
Today, the launch of "New Coke" is considered one of the greatest commercial failures of all time. As BigCommerce explains, this failure is first and foremost " one of poor data quality ". A series of errors and approximations skewed the decision-making process.
Thirty years on, in a world where the amount of data produced on a daily basis has undoubtedly multiplied by billions, the quality of Data is becoming a critical element of the business, all the more so when the latter is carried out predominantly or exclusively online. And the momentum is set to continue: according to Gartner, 65% of companies should have completed their transition from an intuition-based model to a Data-Driven paradigm by 2026. Data Quality and e-commerce: what's at stake? What best practices can be implemented to harness the full potential of Data for sales performance?
The 6 key criteria of Data Quality
Data Quality refers to the tools, processes and techniques used to measure the accuracy and usefulness of a data set in relation to the business. Although the definition of "quality" differs according to business sector, Data Quality analyzes data sets on the basis of six major criteria:
- Accuracy : is the data correct in its entirety?
- Uniqueness : is each entry unique? Are there duplicate entries?
- Completeness : are all the data required to make a decision available?
- Usefulness : is the data actionable? Is it useful for decision-making?
- Reliability : are the data trustworthy? How do they compare with data from authoritative sources?
- Timeliness(or freshness): is the data up to date?
To find out more, take a look at our Data Quality glossary. By definition, e-commerce involves managing a higher volume of Data than in a physical store (or Brick & Mortar). Indeed, each transaction is associated with an email address, a telephone number, a surname, a first name, a postal address, a Facebook or Gmail account, and so on. This traceability makes it easier to track consumers' purchasing behavior, so as to rationalize marketing decisions and boost sales performance, whether for first-time purchases, cross- or upselling or loyalty-building purposes.
E-commerce: the 5 major challenges of Data Quality
From product recommendation to inventory management, from the performance of promotional campaigns to the correct delivery of parcels and after-sales service, data quality is involved throughout the e-commerce value chain. The stakes are therefore high.
#1 Data Quality to recommend the right products
According to Invespcro, 54% of e-tailers believe that product recommendations are "the key factor" in improving the average customer basket. Many use specific tools to recommend the right product to the right customer at the right time.Â
Here's the thing: these tools use raw data, and any error can lead to systemic biases that can derail the entire recommendation strategy. A customer who is recommended masculine products (or vice versa), recommendations for products already purchased or out of season, etc., are just a few examples.
Reliable data on profile, purchase history and location will enable machine learning algorithms to become even more relevant, turning product recommendation into a powerful sales catalyst.
#2 The importance of quality data for better inventory management
With precise data on your customers' buying behavior, you can better anticipate orders, whether by estimating the time needed for a "refueling" purchase for consumables, or by identifying a seasonal factor.
You'll be able to adjust your inventory accordingly to avoid stock-outs or the accumulation of stocked products at a significant extra cost.
#3 Reliable data to put an end to delivery errors
Data quality has a direct impact on the logistics performance of online stores (and physical stores). All it takes is an error in the postal address or a field in the wrong format to trigger a cascade of problems: undelivered parcel (or parcel delivered to the wrong address), return, manual processing of the database to identify the error, solicited customer service, negative opinion on specialized platforms and social networks, loss of a customer, tarnished brand image, and so on.
In fact, 87% of French people consult customer reviews before making a purchasing decision (Ifop). Inevitably, reviews about packages that don't arrive safely will scare off your potential customers. Which brings us to the next point...
#4 Reliable data to authenticate customer reviews
Has this unflattering but highly visible review been written by a "real" customer who has actually bought from you? If your database is shaky, verification will be laborious, and you won't have the arguments to respond to this review to reassure your potential customers.
With reliable data, you'll be able to reconcile the various elements of the review with the customer's purchasing history, the characteristics of the product purchased, its origin (supplier), any delivery incidents, the customer's exchanges with customer service or after-sales service, etc.
#5 Quality data = high ROI marketing campaigns
In your Data capital, email addresses and telephone numbers are both precious and sensitive variables. All it takes is one poorly-calibrated emailing (or SMS) campaign to trigger a series of opt-outs, resulting in the loss of leads hard-won through colossal LeadGen efforts. Data cleaning is both an indispensable prerequisite for marketing campaigns (curative on existing data through deduplication, correction and enrichment) and an ongoing action via real-time validation of first-hand data as it is entered. It also means capitalizing on every exchange with customers and prospects to qualify, enrich and update the database.
Data Enso expertise at the service of e-tailers
Whether in a phygital configuration (physical point of sale and e-commerce) or Pure Player (exclusively digital activity), Data Enso mobilizes its technology and expertise to turn your Data capital into a major competitive advantage.
We support e-commerce professionals throughout the entire process, from auditing their systems to implementing appropriate solutions for :
- Reliable data collection: data entry assistance, automatic correction in real time
- Ensure the veracity of collected data: tool for verifying emails, phone numbers, companies in real time
- Enrich databases: retrieve company information sheets with few initial fields to complete
- Optimize collection systems by reducing the number of steps to be completed
- Clean up and correct existing data with our batch solution.
Discover our solutions for e-tailers or try us out!
