Wednesday, October 19, 2022

The Next Generation of Data-Driven Marketing

We already know that the marketing industry is in constant flux. What works today may not work tomorrow. By leveraging data, marketers can stay ahead of the curve, boosting customer acquisition and retention.

The History of Data-Driven Marketing

While phrases like “big data” and “machine learning” are all the rage today, data-driven marketing has been around for a long time. For instance, a 1984 newspaper features a job listing from a company that claimed to be “the leader in data-driven marketing.”

In the ‘90s, with the explosion of websites and “large data sets,” it became clear that the potential of data-driven marketing was enormous, “both for retrospective analyses as well as data-driven forecasting.”

In the ensuing decades, marketers witnessed an explosion in both data and computing power. With the realization that “data is the new oil” in 2006, data-driven marketing became a “must-have” initiative for many organizations, rather than a “nice-to-have.”

Data-Driven Marketing Today

Now, data scientists are the new rockstars. Analytics skills are in hot demand, fueling a shortage of tens of thousands of analytics professionals in the US. Even amidst economic turmoil, over 100 companies are hiring data scientists.

There’s more to this than hype, as the world’s biggest companies, from Amazon to McDonald’s to Walmart, are all data-driven.

Read More: Digital Marketing and Data-driven Business Decisions Will Take Over Traditional Practices

For example, Walmart uses data to anticipate store demand, so they can place the right number of associates. McDonald’s acquired an analytics firm, Dynamic Yield, for over a quarter-billion dollars, to project demand levels and reduce waste. Amazon owes much of its revenue to its data-driven “Frequently Bought Together” section.

The Challenges of Data-Driven Marketing

Despite the tremendous potential of data-driven marketing, many organizations aren’t taking advantage of data, often due to low technical expertise or a lack of financial resources.

Large companies, like the FAANGs, build teams of AI engineers and data scientists that use complex tools like Python to analyze and predict data. However, this isn’t cheap, with the average AI engineer clocking in a salary of over $160,000.

SMEs can’t easily build AI teams and infrastructure to take advantage of data, so they miss out on the benefits of data-driven marketing.

Democratizing Analytics: The Future of Data-Driven Marketing

However, this is finally changing with the democratization of analytics and AI. The next generation of data-driven marketing is when companies of all sizes and skill levels are using data to drive the bottom line.

The biggest tech trend that’s enabling this democratization is the “no-code” movement. No-code is all about increasing the accessibility of technologies so that products and services can be created more efficiently.

Read More: Is Being Data-driven Sufficient to Bring About Digital Transformation?

Giving every marketer the power of advanced techniques like predictive analytics is going to drastically change the field, making small businesses far more competitive. Further, users will have better experiences when marketers can deeply understand and predict metrics like churn and retention.

With greater insight into marketing data and the attributes that drive KPIs, we’ll see even more startup success stories, and hopefully fewer failures.

If we look back at grand business failures, like Blockbuster, we can imagine that if they’d better applied data-driven marketing, particularly things like churn analysis, they might still be booming today.

In the meantime, companies on the leading edge of data-driven marketing, like Netflix or Robinhood, will continue to put incumbents out of business.

Shanif Dhanani
Shanif Dhanani is the CEO and Co-Founder of Apteo, a no code analytics platform. Before his days as a CEO, Shanif was a software engineer and data scientist at Twitter, TapCommerce, and other high-growth organizations.

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