Learn why marketing analytics often fails marketers and how data scientists can fix the problem.Industry insiders have always claimed that the Great Recession was a good thing for marketing analytics.
Industry insiders have always claimed that the Great Recession was a good thing for marketing analytics. They believed that marketers would invest more in data to prove their value to their clients at a time when most companies are cutting their marketing budgets.
I think many of us assumed the COVID-19 downturn would do the same thing for marketing analytics. However, that may not be the case this time around.
I’ve noticed that many peers at different companies have lost their jobs in the past few weeks because of the COVID-19 downturn. It might be that the crisis forced marketers to evaluate whether the costly analytics practices were really worth the money and work involved. Or it might be that we (the data professionals) never delivered as much value as we thought.
In reality, both sides probably share the blame. Neither the analysts nor the marketers have ever really approached marketing analytics the right way.
For the past ten years, the marketing industry invested heavily in building data warehouses, implementing advanced tracking, and hiring data professionals to analyze and report this data.
But along the way, marketing analytics began turning into snake oil.
The benefits were widely overstated for the amount of money invested. The solutions built were flimsy on quality. And the goals were often improbable (if not impossible).
I don’t think the analysts or the marketers intentionally did something dishonest. I think they simply did what marketing people always do — sell the benefits of a product.
The main problem was that marketers may not have been the right people to use this particular product.
Other industries have used data for much longer than marketing. Financial services, manufacturing, logistics, and tech companies have built highly complex data solutions to support and improve their organizations.
But a key thing about these industries that separate them from marketing is that they depend heavily on operational efficiency.
A few seconds makes a big difference in financial service transactions. Manufacturing and technology companies rely on operations for improving quality. And logistics requires advanced organization and efficiency to deliver goods consistently on time.
Like these industries, high quality data solutions also require operational efficiency. Because these industries have prioritized that efficiency for so long, they have an easier time building these solutions. The data they produce is more accurate and the various stakeholders actually use it.
Marketing agencies, though, have never relied on operational efficiency. At least not to the same extent.
In most situations, this is a good thing for marketers. It helps them win clients and adapt to the ever-changing needs of the consumers.
But in this type of environment, operational efficiency is simply a hard thing to prioritize, which leads to widespread data quality issues that undermines the data solution goals.
For the same reason you want your tax accountant to be good at math, stakeholders want their data to be accurate. Every time they find errors in reporting, and every time an analyst has to come back and make clarifications, the marketing analytics team loses credibility.
It’s hard to fight that credibility issue once it becomes widespread in the organization. Even though analytics team members may still get paid for producing what they believe is good work, stakeholders within the company will start going elsewhere for their data.
They’ll completely ignore your dashboard and instead go directly to the data source itself. It’s a pain for them to do this, but they’ll suffer through it when they believe it gets them more accurate data.
The irony is that these stakeholders often contribute to quality problems as well. Data collection is a partnership between the analytics team and stakeholders, and a lack of discipline from stakeholders contributes to the quality issues they complain about.
Marketers are comfortable adapting quickly to meet the needs of their clients. And marketing executives expect their own internal departments to adapt quickly to meet their needs.
This leads to constantly changing goals for marketing data solutions. The purpose of a dashboard or data warehouse is in constant flux and projects get stuck in development hell because of it.
The data professionals building these solutions find themselves making “one more adjustment” for the same project. These constant adjustments, without any clear end goal, only degrades data quality further.
These quality issues then get amplified by individual contributors who work outside the analytics department. A common example is when marketers move so quickly to launch a campaign that they may not remember to add URL parameter tracking until after the campaign launches.
It’s not unusual for marketing agencies to have departments dedicated to media buying, social media management, campaign planning, and account management.
All of these departments naturally produce data through their efforts. And the data is usually solid within the individual departments. Since a few people implement the social media campaigns, it’s very easy for them to establish consistent practices within their own teams.
Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue.
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