Data accuracy is a critical factor in meeting the challenge of personalization. Data entry errors, incorrect product classification or invalid email addresses will sooner or later result in a frustrating customer experience and a gradual decline in sales performance.
In this article, Data Enso explores three case studies that illustrate how data quality issues can affect the customer experience.
#1 Annoying customization malfunctions
Problem:
A clothing store with several physical outlets and an online boutique relies heavily on personalization to build customer loyalty. However, in the absence of a rigorous Data Quality Management policy, it is faced with a recurring problem: its customers sometimes receive promotional emails with typos in their first name, or even with a first name that is not their own. This confuses customers, making them feel less valued. As a result, customers are less likely to open communications and, ultimately, their brand image is degraded, with a direct impact on sales performance.
Possible causes:
Data entry errors when creating a customer account or subscribing to a newsletter, and the absence of real-time checks to detect and correct these errors before they are recorded in the database.
This situation can also arise in the event of poor data management when merging customer databases, for example when acquiring another company or consolidating several customer data management systems.
Solutions:
Implement data validation mechanisms at every stage of the customer information collection process, from customer account creation to newsletter registration. To deal with the existing database, the company can launch a database clean-up and schedule regular updates to eliminate errors, inconsistencies and duplicates.
#2 Product recommendations: the art of shooting yourself in the foot
Problem:
A sports equipment retailer, which also operates omnichannel, uses customer data to personalize its product recommendations. However, customers receive recommendations that don't match their preferences and/or don't make sense with their purchase history. For example, a customer who has just bought hiking boots receives recommendations... for golf accessories! This confuses customers and leads to a drop in engagement, reducing sales opportunities.
Note: 54% of e-tailers explain that product recommendations are "the main driver" of average order value (or average ticket).
Possible causes:
Incorrect classification of products in the database, incorrect association of products with customer profiles, incorrect, incomplete or out-of-date data concerning customer preferences or purchasing history, duplicates in the database.
Solutions :
Deploy a 360° data quality management strategy.
- Categorize each product using clear, precise attributes such as size, color, style, etc.
- Record and store customer preferences and purchase history in a structured and (above all) usable format
- Identify and remove duplicates, inconsistencies and errors in the database
- Use machine learning algorithms to analyze customer data and generate personalized product recommendations
- Perform regular database checks to verify the integrity and accuracy of stored information
- Implement a data validation process when collecting customer and product information to detect and correct errors in real time.
#3 A loyalty program... that becomes a frustration program
Problem:
A national restaurant chain has set up an elaborate loyalty program that awards points to customers for each purchase. These points can be exchanged for discounts and special offers transmitted to customers via automated electronic communications.
However, the company was experiencing problems with non-receipt of these communications due to invalid email addresses in its database. An analysis of the data revealed that a significant percentage of email addresses contained input errors that rendered them non-operational. As a result, some loyal customers, such as Mr Martin, who had accumulated enough points for a free meal and some goodies, did not receive an email notification with the discount voucher. This lack of communication has a negative impact on customer satisfaction in an industry where word-of-mouth is very important.
Possible causes:
Data entry errors when registering for the loyalty program, lack of validation mechanisms to identify and correct data errors at the time of entry, poor data management when updating customer information, etc.
Solutions:
- Integrate contact data validation functions into the registration process and update customer information to detect and correct input errors in real time.
- Carry out regular data quality audits of the loyalty program database
- Encourage customers to check and update their contact information by offering incentives, such as bonus points in the loyalty program.
- In the event of a malfunction, offer compensation to maintain customer satisfaction and trust in the loyalty program.
Data Enso: so that Data can play its role as a catalyst for the customer experience
Are your e-mails not reaching their intended recipients? Has your database accumulated errors and duplicates? Or perhaps you're looking to enrich your customer and prospect databases with reliable, relevant information?
Data Enso has developed powerful, RGPD-compliant Data Quality Management solutions. Our tools cover the entire spectrum of data reliability, from cleansing and deduplication to verification and enrichment. Our solutions can operate as contact form output or as curative processing (batch) of your existing database. Give us a try!