The post is sponsored by Validity.
Data is the new oxygen.
As the use of artificial intelligence (AI) evolves, email marketing finds new applications. While AI has driven spam filtering and fraud prevention solutions for a while, we’re now seeing it increasingly used in content selection, engagement prediction, and journey mapping – to name just a few.
Marketers are benefiting as a direct result. AI helps achieve true one-to-one personalisation and relevance – at scale. This drives greater engagement and builds stronger long-term relationships.
It’s a big reason why email is the consumers’ most popular channel for receiving marketing, scoring highly for relevance and trust (according to the Data and Marketing Association’s research), and programmes are delivering strong return on investment (ROI), especially with AI also enabling more accurate attribution.
But there’s a big catch. Around four-fifths of businesses running AI and machine learning projects encounter problems with data quality, according to Alegion.
Primary issues include not enough data, data not in a usable form, and bias or errors in the data. This creates major costs, with a recent Validity research report estimating almost half of CRM users forecasting losses in annual revenue between 5% and 20% due to poor quality data.
So, when we talk about data quality, we’re not just talking about good email addresses. Rather, we mean a complete view of your customers that adds real value to your relationships with them.
The coronavirus pandemic has required marketers to adapt their promotions, content, and tone of voice; having the data to achieve this credibly and authentically has never been more important.
This is reflected in a new report published by Validity and the Digital Marketing Association called the “Email Data Quality: Compliant, Correct and Complete”. The report highlights the importance most marketers place in data quality and its three core tenets:
• Compliant: Collected, stored and used in line with relevant legislation and standards.
• Correct: As accurate, error-free and true as possible.
• Complete: Inclusive of all the information required to fully understand the customer.
The report reveals the majority of organisations monitor data quality (77%) and agree that it’s a very important part of their email marketing programme (62%) – or at least moderately important (33%), with just 6% saying it is unimportant.
However, just two-thirds of marketers (67%) rate their organisation’s level of data quality as at least good compared to industry best practice.
In addition, the use of KPIs to measure the quality of data are common (61%), but they’re not yet universal. The most popular metrics show a tendency towards choosing “good news” measures – engagement rates (40%) and sales/revenue (37%) being the two most common.
A standout theme from this report is the rise of the customer data platform (CDP). This creates a unified customer database – cleaning and combining data from multiple sources into a structured format for use by other marketing systems – meaning richer data.
CDP users are far more likely to use preference centres as part of their acquisition strategy, and to segment/personalise based on subscriber interests.
Customer engagement is seen as a direct function of data quality, and the ability to calculate cost-per-acquisition, ROI and customer lifetime value is significantly higher for these businesses.
All too often, data quality represents a classic case of not knowing what you don’t know, which is why the findings in this report are so important.
The results surface what’s important when it comes to the “3Cs” of data quality: compliance, correctness, and completeness, and provides a benchmark of where you and your organisation are on your data quality journey, as well as flagging opportunities for improvement.
Download your complimentary copy of Email Data Quality: Compliant, Correct and Complete.