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.
