We’ve all heard about “cost of bad data” – that is erroneous, murky information affecting decision-making and ultimately the bottom line. As they say, "Garbage in, garbage out," or “GIGO.” In fact, last year, IBM estimated that bad data cost the U.S. a stunning $3.1 trillion. This giant figure is the cost for all businesses, across all business functions. For example, all sales professionals deal with bad lead data; delivery people get bad customer information; IT grapples with poor estimates of customer usage; HR receives inaccurate recruitment estimates.
A recent study by Forbes Insights and KPMG revealed “84 percent of CEOs are concerned about the quality of the data they’re basing their decisions on. The role data plays in enabling future technologies such as artificial intelligence and the Internet of Things is critical—but one that will be undermined if businesses do not make data quality a priority.”
Now, if we shine a spotlight only on the cost of bad marketing data, it’s jaw-dropping to consider the expense of relying on flawed information, which affect marketing decisions and predict future campaign success. Forbes Insights and KPMG call out just a few ramifications of bad marketing data, which are further developed with my own thoughts here:
- Reputational damage: This can range from small, everyday damage to large public relations disasters. For example, in 2015, using incorrect data, Pinterest accidentally emailed congratulatory emails to some of its users on their upcoming weddings with special offers. However, many of the recipients weren’t getting married and were actually single. Many took to Twitter to share the invalid email.
- Inefficiency: Having disparate data, multiple separate teams and organizational siloes can lead to overreaching in a company’s media/marketing supply chain. By taking into account the many data sources and internal processes involved when gathering information, marketers can identify inefficiencies and develop best business practices.
- Lost and suppressed revenue: Poor data can lead to lost revenue in many ways—communications that fail to convert to sales because the underlying customer data is incorrect, for one example. Other common consequences of bad data may suggest spending more or fewer media dollars is actually required to succeed. Or that a particular customer segment is more or less relevant than it actually is, leading to over- or under-spend. This bad information has a substantial opportunity cost with tangible financial impact.
Marketing data comes from paid, owned and earned sources as well as demographic and attribution info in addition to competitive intelligence and other sources. That’s a lot to take in, not to mention who is touching each piece of information at all points - an ad agency analyst or a consumer insights team. All these hands touching the data create “dirty data” and affect the input, conclusions and decisions.
As a brand manager, data output affects your decision-making and your reputation. Brand managers steer the ship, and as such, must consider not only the sources of information but also how that information is parsed, consolidated, cleaned and analyzed. Without this guidance, strategic brand decisions are made with bad data, and a key competitive edge is working with the cleanest possible data.
Want to know more about "bad data" and how affects your marketing? Check out the rest of the series: