4 Ways That Machine Learning Is Impacting Digital MarketingNahla Davies
Machine learning has been changing how marketers reach new audiences and continue to draw existing customers back to a product. Understanding how to implement Machine Learning in your marketing strategies and the benefits it will reap are important considerations in this digital age.
Although many people use the term “A.I.” - there is yet to be such a thing. Artificial intelligence is something that would live, think, and processes similar to how a human might. Part of the reason why no A.I. exists in digital marketing is due to the fact there is no set definition of intelligence and consciousness. Machine learning is based on computer algorithms that have been given a data set and told to identify some sort of pattern from that data set.
This article will cover four ways that machine learning impacts digital marketing so you can focus more on generating revenue than mass marketing.
1. Better targeting
When it comes to better targeting, you may instantly think of Facebook Ads or Google Ads. These companies use machine learning algorithms to offer better targeting for your ads in order to maximize your advertising goals.
These algorithms are much more complex than tracking simple likes on a post. They follow statistical correlations between user behavior in order to help best profile and predict your interests and behaviors. For example, predictive targeting and testing uses machine learning to analyze past behavior patterns to learn and predict future ones. The behaviors might include clicking certain ads, visiting certain sites and buying certain products. This data allows companies and ad providers to better target customer segments and deliver improved results.
With better targeting data, companies can offer product recommendations to consumer audiences that are most likely to take action on the ad. This data continues to get more advanced as it predicts a person’s future buying habits and needs based on historical information. For example, data can reveal the future buying habits for people who buy a bicycle or ski jacket, and provide recommendations before the customer knows they want a certain product.
2. Reducing losses through SEO
More advanced uses of machine learning include reducing losses based on customer feedback recorded in social reviews. There are a few US companies that will integrate a machine learning program into your Google, Yelp, or Trip Advisor restaurant listing to identify and collect reviews related to negative feedback using sentiment analysis.
Sentiment analysis is a subsection of natural language processing used to identify the state of communicated information. The reviews will then be analyzed by a machine learning program and sorted to identify the common themes. The data presented can be used to remove products that customers did not like, clean bathrooms or improve facilities, and reduce spending on assets that don’t add value to the customer’s experience.
If there are enough reviews about your business, it’s possible to clean up the data so that it can display detailed information about which square foot is bringing in the most money and which is losing the most. Trimming the metaphorical fat of your restaurant or other business can help improve the reviews left on various social platforms and lead to higher SEO rankings.
Cleaning up reviews is also beneficial to the company’s bottom line that a marketer represents. Small gains in online reviews can lead to 5-9% increases in revenue. Those numbers can make or break a restaurant!
3. Better communication
Machine learning can also be used to improve the most basic pattern in our lives - communication. It can improve your communication in a few different ways that help move consumers down your sales pipeline and toward making more purchases.
Email design optimizations are one area where machine learning has made a number of improvements. With the power of machine learning, and the ability to recognize patterns, you can train a model to write better subject lines and email bodies and better deliver the right email to specific user groups.
There are now AI tools that apply machine learning to write marketing content from blogs to social posts to just about any form of content produced by marketers. Tools, such as Jasper, have read more than 10% of the internet and are able to produce high-quality content with natural language. The tool can even be adjusted to write in a number of different tones.
Additionally, machine learning can improve communication through personalized drip campaigns for each potential lead. Not everyone is on the same schedule when buying a company’s products. There are also predictive time algorithms that determine the best time frame when people are most likely to open an email to boost the overall performance and success rate.
A successful drip campaign can lead to a 119% higher click-through rate than standard emails. That is a huge difference in the conversion rate and potential profits that can boost your marketing status.
4. Understanding customer movements
Once you’ve attracted people to your website, it is important to find out how visitors navigate it. Implementing machine learning software into your company’s website that can track users’ mouse movements, how long they spend browsing a page, and the link they used to get to a specific page can help identify patterns with the highest conversion rates, thus improving revenue.
Some software will also recommend improvements to the design of a website to lead to the highest conversion rate and reduce the number of clicks it takes to get a consumer to purchase a product.
There are APIs that can be implemented into your website as well that track users across other sites and will help recommend products they might be interested in. Have you noticed those Instagram ads for a product you just Googled two minutes ago? Well, that’s machine learning in real-time using pattern recognitions to show you deals for things you may be interested in.
Get ahead of the curve
With technology continuing to advance at a breakneck speed, marketers need to adopt audience intelligence tools to collect as much data as possible and identify the most efficient ways to convert leads.
Machine learning can help companies sort through data and identify patterns and correlations. Whether it’s purchase habits, churn rates or providing LTV forecasting - anything that involves data can be used to train a model. Customer data can help companies better segment audiences, predict customer behavior and offer targeted recommendations to individual customers.
It may be in the best interest of your company to hire a software developer specializing in machine learning and data management so that you can make your own tools to help your company grow. Although the expense is high, good software is an asset these days. And if the custom platform that was made for your company works, it may provide another revenue stream for your business.