Marketing analytics is a strong area of enterprise investment. But, according to Gartner, marketers are currently only utilizing just over 60 percent of the functions available in their MarTech portfolio, which includes digital asset management, lead management, and new technologies like predictive analytics. Another study found that 47 percent of companies struggle to improve marketing efficiency and ROI after implementing an analytics program, and 40 percent see some improvement but still are not capturing the ROI they anticipated.

While there are numerous variables that can undermine the success of an analytics program, there are a few common mistakes companies should be mindful of as they evaluate their marketing analytics strategies and attempt to derive further value.

Below are four red flags you really shouldn’t ignore:

  • Lack of clearly defined use cases: A data-driven marketing organization sounds great, and what marketer wouldn’t want to apply technologies like predictive analytics to be more effective? But this desire to invest in technology can actually backfire if companies don’t take the first crucial step of defining exactly what it is they hope to achieve. To quote a recent opinion piece in Econsultancy, “A poorly defined use case paired with a cutting-edge solution will equal low-to-no returns.” As part of determining whether the use case is right for an analytics investment, consider the quality and accessibility of the data, whether new human resources will be required to support the initiative, and if value can be easily measured post-adoption.
  • Lack of skilled staff: As mentioned above, determining whether you have the right people to drive your analytics strategy forward is crucial. If the answer is no, however, companies often face an uphill battle in trying to recruit and retain the talent needed. In fact, the Econsultancy piece noted BDO’s UK Martech Report, which found that 83 percent of agencies and 68 percent of advertisers need more people with data skills.
  • A laser focus on data: The typical organization has a wealth of data that can be mined for valuable analysis, but it’s a mistake to think that you need to use every single piece of existing data to gain accurate results. According to McKinsey, this “all or nothing” mentality can waste as much as 70 percent of a company’s data cleansing efforts and result in data lakes that are not fit for the original purpose much of the time.
  • Not including the context: The data alone is not enough to derive marketing analytics success. Context is also important. As such, marketers must ensure their analysts have an understanding of the market at hand, are briefed on the desired business results, and can determine how factors such as seasonality might impact outcomes.

There are other industry and company-specific criteria companies must consider as they tweak their analytics programs in an effort to derive further benefits. But being mindful of the missteps outlined above is essential in order to avoid wasting resources and efforts on investments that will ultimately fail to deliver the desired results.

Want to learn more? Click here to learn how predictive analytics helps marketers better target sales opportunities and improve results.