Can machine learning really change digital marketing?
BUSINESSES turned to digital marketing when they realized that their customers are online and no longer reading newspapers, magazines, and other periodicals.
And although moving a part of their marketing budget to search and display ads and social media made a big difference, many are still struggling to optimize costs and make the most of their digital marketing dollars.
What’s most interesting is that many companies have access to data about actual customers, in real-time, and yet, are consistently failing to create synergies and transform their digital marketing efforts.
The solution? Machine learning. The technology is available ready, and marketers who want to get more out of their ads and reach the right audience at the right time and on the right platforms must explore the capabilities that machine learning provides.
To help get you started, here are the top three ways machine learning can (and will) transform digital marketing over the next couple of months:
# 1 | Makes the transition from real-time to real-life
Marketers today have access to all sorts of data. Be it from past campaigns or from webpage analytics, companies can track exactly who is visiting their page, how long they stay, what elements they engage with, and what causes them to leave — and also products/services are most interesting, among other things.
Right now, marketers use this data to change how future campaigns and landing pages will look like.
However, if they build smarter platforms, data that companies collect can not only help make big decisions about future changes but also paint a better picture of the customer and make micro-changes in real-time to help nudge them along the sales funnel.
Obviously, this is most useful to e-commerce sellers, but all kinds of companies can benefit from such capabilities.
# 2 | Simplifies forecasting and optimizes budgets
Elaborating on the previous example, when organizations understand what their customers really want and like and how they engage with the company, they’re likely to adjust their ad spends to those preferences.
However, in many cases, there’s a lag between collecting data and optimizing ad budgets (or even creatives). Using machine learning, marketers could speed up their A/B tests, and allocate budgets to campaigns that customers actually respond to in a positive way — all in real-time.
Further, machine learning could also help simplify forecasting for marketers by using data from various historical campaigns and factoring in data from new sources to create smarter, better, and more robust schedules, budgets, and insights.
# 3 | Crunches the data to help create better content
To be honest, content is an important part of marketing. And although marketers might hire the best copy-writers and designers, the content they create might not always resonate with the audience.
Further, content that is put out by the company might appeal to some audiences some of the time but not all audiences all of the times — and that’s only natural.
However, with the help of data, marketers can find out not only what content appeals to the right customers and make sure it reaches them at the time and on the platform that is most convenient to them but also help marketers understand what content works best (and therefore guide writers and designers appropriately).
- Moving to the cloud is critical but is it secure? McAfee weighs in
- How RPA helps when one software doesn’t talk to the other
- Everyone is investing in technology but what are they buying?
- China’s social media censorship is effective but disrupts advertisers
- How technology impacts small businesses across the APAC