黑料社入口

The marketing landscape is undergoing nonstop disruption. Rapid innovation and the rise and spread of automation have created an 鈥渁lways-on鈥� culture that鈥檚 鈥� and 鈥�.鈥� As a result, the marketing function itself is transforming in order to keep up with an entire generation of mobile-first, digitally and socially native consumers 鈥� with an unprecedented demand for instant gratification and high-quality, personalized experiences across all channels. Empowered and informed, today鈥檚 consumers are evolved, and so are their expectations.

At the same time, marketing and advertising have been experiencing other internal and external pressures, igniting a growing need for transparency, accountability and ethics 鈥� such as the changing privacy regulation landscape and  to ban all political advertising. Rising standards bring a new level of complexity to the industry, and it鈥檚 now more important than ever that marketers verify the quality of their data.

To effectively meet consumer needs, it鈥檚 imperative that organizations are able to harness relevant and high-quality data in order to understand and engage their customers. But poor data quality persists for marketers, impeding insights and leaving a wreckage of wasted resources and marketing spend in its path.

A  highlighted the biggest challenges that marketers face around data and the pressure of keeping up with the latest industry changes. Almost half of the marketers surveyed say the abundance of data channels and sources makes it harder to plan a long-term strategy, and 鈥�83% of marketers at large enterprise organizations admit the rise of new technologies and techniques means it鈥檚 now more difficult to stay on top of everything.鈥�

Despite these pain points, data remains the backbone of business decision making 鈥� the percentage of time that companies use marketing analytics to make decisions has  over the last six years, according to a recent report by The CMO Survey. Yet, many organizations still have trouble optimizing the quality of their data and, in turn, the quality of insights they generate.

Across industries, data is fundamental for businesses today. But if it鈥檚 not reliable and accurate data, then it鈥檚 meaningless. The reality is that successful marketing campaigns depend on high-quality data, but marketers often struggle to achieve it. This was among the findings from a commissioned study conducted by Forrester Consulting on behalf of my company, 黑料社入口. The July 2019 report, which surveyed 409 professionals who make marketing/media performance and measurement decisions, also revealed 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 from poor data quality, such as inaccurate targeting, lost customers and wasted media spend. The findings also uncovered several barriers marketers face, including the need to manage a wide range of data sources, soaring data volumes and integration issues, as well as privacy concerns and regulatory complications.

We know there鈥檚 a disconnect when it comes to leveraging the power of data. According to Clicktale鈥檚 鈥淒efining the Digital Experience鈥� report, which surveyed 200 marketing and customer experience (CX) professionals, almost a third of marketers don鈥檛 feel they鈥檙e effective at utilizing their web and mobile data. Beyond that, over half of respondents (54%) said they 鈥渄on鈥檛 believe they have a strong understanding of their customers鈥� behavior across digital channels,鈥� and 20% reported feeling like 鈥渢hey will never truly understand why their customers buy.鈥�

Equipped with higher quality data, marketing leaders are able to gain a better understanding of their customers 鈥� their needs, wants, expectations and purchasing behavior 鈥� which allows marketers to more successfully satisfy and engage customers, elevate brand awareness and drive sales conversions. But this is the conundrum that we, as marketers, face: Poor data quality is marketing鈥檚 affliction, and high-quality data is marketing鈥檚 holy grail, yet achieving high-quality data is marketers鈥� ongoing battle.

Below are three best practices for managing data quality and ensuring data quality alignment:

鈥� Prioritize data quality, and make it a strategic initiative within your organization. Bad data is more than just marketing鈥檚 problem. Given that reliable information is foundational for business decisions and ultimately a business鈥檚 success, companies need to make data quality a priority. As a best practice, organizations should establish a comprehensive data quality initiative that encompasses people, processes and technology. Delegating the right people is essential, as is creating positions (e.g., data manager or data governance manager) with well-defined roles who will be responsible for data verification and developing policies for data collection and cleansing.

鈥� Define and verify high-quality data. As part of data quality management, one of the first steps for marketers to improve the quality of their data is to define high-quality data 鈥� this includes defining what it means to the marketing team and outlining goals. Note: Defining data quality is an ongoing effort with a multitude of moving parts. In our report, we identified seven quality dimensions that marketers should verify and align their data across for best results: timeliness, completeness, consistency, relevance, transparency, accuracy and representativeness.

鈥� Organize disparate data sources with unified marketing measurement. Copious data sources and overwhelming volumes of data have posed a challenge for marketers. But it is possible to manage disparate data sources. With strategies and tools like unified marketing measurement, marketers can aggregate and analyze varied datasets and form actionable insights. Unified marketing measurement breaks down data silos and provides a holistic view of the customer across all data sources and channels.

The bottom line is low-quality data delivers low-quality insights, and when it comes to marketing analytics, delivering trusted data is vital. Looking ahead, those who do not prioritize data quality risk being left behind.