5 Incredibly Costly Big Data Marketing Mistakes

Low-quality big data assets can lead to incredibly costly marketing mistakes. Research by Experian indicates that low data quality has a direct impact on revenue for 88% of modern organizations. Average losses are approximately 12% of revenue. For organizations who are shifting towards data-driven marketing and customer experiences, low-quality data can lead to costly mistakes.

How Bad is the Average Marketing Big Data?
Per eConsultancy, 22% of information on contacts, leads, and customers contains inaccuracies. Perhaps most concerning, the average organization’s quality index is headed in the wrong direction. Twelve months ago, the average inaccuracy rate was just 17%. Incorrect data can have a real impact on your team’s ability to build segments, understand behavioral triggers and preferences.

In contrast, organizations with a high degree of data accuracy are more likely to appreciate:
● Efficiency
● Cost-Savings
● Customer Satisfaction
● Informed Decision-Making
● Protection of Brand Reputation

Poor-quality or old customer data can lead to a series of costly marketing mistakes. Join us as we review some devastating errors that can be directly attributed to inaccurate customer data.

1. Low Advertising Conversions
Low conversion rates on programmatic advertising is a symptom, not an issue. Poor click-throughs and conversions can be attributed to a lack of mobile advertising, poor segmentation, irrelevant data, or other factors. However, far too many marketing teams fail to take appropriate action in response to low advertising conversions. Instead of working to improve the breadth or quality of data, they continue generating ads. Before running more ad campaigns, marketing teams should take appropriate action to ensure they can achieve better returns.

2. Inconsistent Brand Experiences
Without accurate or up-to-date data, your brand communications could send the message that you don’t know your customers. You may generate programmatic advertising for products your customers already own. You could send an email blast for baby products as their children are approaching preschool age. Marketers need to actively combat a brand experience that’s inconsistent with a customer’s needs and activities. If you miss the mark repeatedly, you’ll struggle to build customer loyalty and sales.

3. Poor Email Deliverability
The average return on investment (ROI) for email marketing at mid-sized organizations is 246%. However, organizations have the potential to significantly exceed these benchmarks with appropriate timing, segmentation, and other big data-driven activities. Email communications to outdated contact lists have the potential for a high bounce rate, or percentage of emails that are undeliverable. Email segmentations that are vastly inaccurate could also increase your risk of being pinged as spam. In the mind of a consumer, spam is simply 'unsolicited bulk email.' If your messaging is irrelevant or feels too much like a mass communication, it’s likely unwelcome.

4. Mobile Neglect
Far too many big data marketing strategies are focused on desktop advertising, email receipt, and experiences. In reality, consumer behavior demands mobile marketing. As of 2015, adults now spend more time engaged with mobile devices than desktops, laptops, and other connected devices combined. There’s a good chance that, at least 50% of the time, your desktop-optimized advertising is consumed on mobile devices. This can lead to poor user experience (UX) and returns on investment.

5. Poor Verification Methodologies
All too often, major brands go viral for all the wrong reasons. Poor data verification can lead to mistakes that are embarrassing, insulting, or even hurtful to their loyal customers. OfficeMax sent coupons addressed to 'Mike Seay, daughter killed in car crash.' The addendum to the customer’s name was unfortunately true. The company ultimately issued a public apology to the customer. Manual data verification processes are rarely effective in the big data age. Fortunately, using a data management platform (DMP) or another tool to perform quality checking against 3rd party data can eliminate much of the risk of similar mistakes.

If your organization’s data quality is average or below average, you’re at risk for many of these expensive marketing mistakes. By taking the appropriate internal steps to improve your quality standards, you can improve the ROI and impact of your marketing efforts.