Marketers today are navigating in an era of ongoing technological disruption. Digital transformation and the influx of smart technologies have evolved customer expectations and led to an unprecedented demand for instant gratification and high-quality, personalised experiences across all channels.
At the same time, there is a growing need for transparency, accountability and ethics in marketing, especially as additional states continue to introduce new data privacy laws. These rising standards bring a new level of complexity to the marketing landscape and it’s now more important than ever for marketers to verify the quality of their data.
In the age of empowered consumers, competitive advantage hinges on an organisation’s ability to harness the data needed to improve customer understanding and engagement. But poor data quality remains marketers’ Achilles heel, hindering insight while draining valuable resources.
A new report from Domo and Censuswide highlights the biggest challenges that marketing leaders face around data and the pressure of keeping up with the latest industry changes. Almost half of marketers say the abundance of data channels and sources makes it harder to plan for their long-term strategy, and 83% of marketers at large enterprise organisations admit the rise of new technologies and techniques means it’s now more difficult to stay on top of everything.
Despite these pain points, data remains the backbone of decision-making in business — according to a recent CMO survey, reliance on marketing analytics to make decisions has increased from 30% to 42% in the past five years. Yet, many organisations still struggle to optimise the quality of their data and, in turn, the quality of insights they generate. So, what’s the disconnect?
To put it simply: data is only as valuable as the insights it produces, the actions it influences, and the results it fosters. Without the ability to ensure data integrity, marketers are relinquishing their most powerful tool — and a massive competitive advantage.
The reality is marketers rely on high-quality data for fueling successful marketing initiatives, yet they struggle to achieve it. In fact, Forrester Consulting polled 409 US-based midsize and enterprise organisations on behalf of Marketing Evolution for its report, “Why Marketers Can’t Ignore Data Quality,” and found that while 82% of companies place a high priority on refining data quality, more than a quarter of all marketing campaigns were hurt by substandard data in the last 12 months.
Marketers cannot afford to make data quality considerations an afterthought. Survey respondents reported a number of negative consequences due to poor data quality, such as inaccurate targeting, lost customers and wasted media spend (21 cents of every media dollar spent by organisations in the last year was wasted as a result of poor data quality). The findings uncovered several barriers that marketers face, including the need to manage a wide range of data sources, soaring data volumes, integration issues, as well as privacy concerns and regulatory complications.
While poor data quality remains marketers’ Achilles heel, high-quality data is undoubtedly marketers’ holy grail. Equipped with higher quality data, marketing leaders are able to more successfully satisfy and engage with their customers, elevate brand awareness, and drive sales conversions. In order to improve data quality and maximise its insight potential, the Forrester report identifies seven quality dimensions that marketers should align their data across for best results:
- Timeliness: Timely data comes from sources that are up to date. Access to faster data enables relevant insights that meet business objectives
- Completeness: Complete data records are ones where all expected attributes are provided. A complete customer and marketing data set ensures that all behaviors, intentions, permissions, and sentiments are captured for robust analysis, such as understanding channel halo effects or how customers feel about your brand
- Consistency: Consistent data references a common taxonomy across platforms, channels, and campaigns. Having consistent data for things like campaign codes and customer identifiers can help marketers speed up the data collection process and analyse trends over time, without worrying about data being labeled correctly
- Relevance: Relevant data directly relates to the analysis being performed. Adding a slew of data into the system won’t help solve the business problem if it’s not relevant. Relevant data helps answer marketing business problems, address customer behavior questions, and make day-to-day decisions
- Transparency: Transparent data refers to data whose sources are easy to trace and identify. Marketers who understand the data nuances from first-party and media sources, such as ad servers, will be able to determine if specific streams of data are necessary for their marketing performance analysis
- Accuracy: The adage “garbage in, garbage out” has never been more relevant in today’s data-rich world. Only accurate data can reflect true actions
- Representativeness: An important part of targeting, representativeness ensures data collected and leveraged for insights accurately reflects the marketplace or an advertiser’s target audience
Tackling data quality improvements and boosting marketers' trust in their data proves a worthwhile effort — as the key to achieving business success hinges on an organisation's ability to harness high-quality, reliable data. Firms with above-average confidence across these seven quality dimensions report improved understanding of campaign performance, faster decision-making, reduced waste in media spend, better customer experiences and customer targeting, and improved trust from both employees and customers as a result of greater data reliability.
When it comes to marketing analytics, delivering trusted data is vital. High-quality data is the foundation of any high-quality brand — empowering marketers to effectively understand and influence shoppers at the right moment, while also enabling marketers to demonstrate accountability for consumer spend. Looking ahead, those who do not prioritise data quality, run the risk of being left behind.