More businesses are taking advantage of machine learning’s potential and the results are far from disappointing. Some business leaders are hesitant to utilize machine learning, though. In the past, it’s received a poor reputation for being heavily reliant on data science and advanced algorithm building – a skill not every business owner may possess. Similarly, hiring an outside expert isn’t always an option due to budget.
The mindset is slowly changing as progressive retailers see the benefit of utilizing this new technology. Besides, nowadays there are so many platforms readily available like OptiMove or IBM Watson. These options are ideal because they provide a simpler way to integrate the practice into long-term business goals. Now is the precise time that marketing teams should embrace machine learning to better compete with others within their industry. Any delay means risking getting left behind.
According to Katie Sweet of Business to Community, machine learning has been “described as the science of getting computers to act without being specifically programmed to act.”
The key to marketing is a balancing act between art and science, where data provides the science and marketers provide the art. In other words, machine learning fills this gap. It solves data problems by using advanced algorithms (the science), while marketers, who are solely dependent on the human brain, can be creative (the art). Essentially, this computer intelligence provides value by using advanced algorithms to solve data problems that challenge marketers because they are solely dependent on the human brain.
The benefit of machine learning is it identifies patterns, makes inferences based on those patterns, and then acts on those inferences. All this happens without the supervision or actions of a human marketer. The potential for how this could impact marketing is truly limitless. Whether it helps free up a marketing team’s time or provides solutions to problems before the brand even knows they exist, the possibilities are endless.
Continue reading to find out specific ways we foresee it impacting the field of marketing in the future.
A good deal of automation software programs use machine learning to assist marketing teams in improving the effectiveness of their email campaigns. Machine learning is touted for its ability to analyze complex customer behavior, making it the perfect channel to replace existing automation programs.
In order to launch and maintain a profitable email campaign, you have to understand how to engage with your audience. This includes how often you should send emails, what subject lines to use, what to list in the form field, and more. All these minor details influence whether a consumer will open your email or sent it straight to their junk folder.
The computer’s ability to learn helps automate decisions for marketers, making it possible for teams to best determine which campaigns will draw engagement and conversions.
Targeted outreach data is essential for overall marketing success, which includes email marketing. Marketing teams rely on data to understand when consumers are more likely to make a conversion. That data is used to determine how often sales and giveaways should run, when social media posts should occur, and who should be the focal point of the advertisements and recommendations.
Machine intelligence can analyze past customer behavior and optimize delivery down to a compact window. Marketers can further refine future campaigns based on the results. This helps tailor those campaigns to the specific needs of their customers.
Below is an example of an email I received less than three hours ago that may have used machine learning. How easy would it have been if they did?
Forbes believes machine learning will aid in lexical analysis, a relatively new process that helps entrepreneurs analyze what words, phrases, and sentences will best engage consumers. This data can create content that is meaningful to companies and will likely increase traffic and conversions.
It’s common for marketers to test out a variety of words and phrases when constructing any type of content from text ads, web content, and beyond. The end goal is to determine what phrase(s) will provide quantifiable metrics for success.
Computer intelligence takes the guesswork out of test by using an algorithm to determine which words are more appealing to your consumer demographics. The intelligence of the algorithm is more accurate and requires less time and effort from the marketing team.
It’s not surprising that building intelligent marketing algorithms can potentially benefit both consumers and marketers. The capabilities for this type of intelligence can influence the customer experience. For example, business owners can personalize the customer’s experience through their brand and website, as opposed to targeting small groups of individuals.
There are related algorithms that can be utilized to tailor product recommendations for individual customers. It can also suggest categories and brands that might interest people. In addition, algorithms can help tailor site navigation and search results so they’re unique to each customer. This is ultimately more effective than typical A/B testing and results yield a dynamic profile of human behavior that far surpasses traditional methods.
Machine learning also assists retail marketers by prioritizing campaigns from most to least imperative. Brands who may feel overwhelmed by large amounts of data can find it challenging to understand which problem to start solving first. Machine learning simplifies this process. It can quickly sort through data to locate patterns or problems and alert you to areas which need your attention most. Not only is this a more accurate way to solve problems, but it saves time, as well.
Oftentimes, marketing teams are so overloaded with analyzing data that there isn’t any time left over for creativity. This results in cookie-cutter campaigns that yield incremental ROI, rather than moonshots that pay big dividends.
Since these algorithms free up time that teams may have otherwise spent crunching numbers, they’re happy to report more moments left over to dream, create, and be otherwise inspired.
Pattern recognition and machine learning have come a long way since they were first introduced in the 1960s. With the quick advancements in technology over only a few decades, we can expect everything to evolve even quicker. Smart retail marketers know now is the time to take the plunge and integrate machine learning into the MarTech stack.
This all comes with a warning, though. With everything being automated, there will be room for error, which is completely expected. Don’t forget to balance your reliance on algorithms with human insight and experience. Only then will your campaigns thrive.
Keep in mind that machine learning is not the same as using artificial intelligence (AI). Stay tuned for Part II next week!
How do you foresee machine learning impacting marketing in the future? Do you have any experience incorporating machine learning into your business? Comment in the section below!
(Image credits: sailthru.com, optimove.com, screenshot taken by author April 2017)