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“Productivity means that balance between all factors of production that will give the greatest output for the smallest effort,” wrote renowned management expert Peter Drucker. This is precisely the type of productivity that machine learning (ML) can help with.
From sales and marketing departments to operations, legal, customer service, and finance, ML can be used to help people (and businesses) accomplish more with less effort.
Here are six ways ML can improve marketing productivity:
Every job includes at least a handful of repetitive, time-consuming tasks. The good news is: If a human can do it, a machine learning model can be trained to do it. And in the case of particularly mind-numbing tasks, ML often can do the job better, since it won’t get fatigued or distracted.
For example: Even with a tool like Google Alerts, it’s still incredibly labor-intensive to find and then read all of the potentially negative Tweets, Facebook posts, Yelp reviews, blog posts, and other mentions of your company online.
Fortunately, an ML model can be trained to read freeform text and recognize what’s negative—and what’s positive—in relation to your company, including its products, services, and people. ML can also increase the thoroughness with which this work gets done, saving the marketing team massive amounts of time and freeing them up to do more important tasks.
Imagine if marketers stopped contacting people who were never going to buy, and instead focused exclusively on people who were highly likely to make a purchase.
Using an ML model and training it to identify patterns in data about past purchasers can help. The model can then use these patterns to predict which potential customers will buy their products, while also identifying the patterns of those who haven’t purchased in the past. This enables the marketing team to focus its efforts on the most promising leads.
ML predictive models can also help optimize marketing spend—and reduce wasted time and effort—by identifying which trade shows and events deliver the desired outcome.
Shows that have never provided a good ROI can be removed from the calendar, and that budget surplus can then be used for more results-driven activities.
Most marketers do plenty of A/B testing. But there’s only so much bandwidth available for this kind of research. Using the pattern-spotting brilliance of ML can help marketers dramatically increase efficiency.
But how? A machine learning model can be trained on a combination of customer data that includes website analytics, chat-bot conversations, and purchase behavior in order to identify which combination of landing pages, product pages, videos, and downloads, for example, is most likely to lead to a sale.
This allows marketers to create campaigns that walk promising leads through an optimized path-to-purchase—without having to painstakingly test all of the possible combinations ahead of time.
The goal of marketing is to get the right message in front of the right person at the right time. But crafting and then disseminating personalized content to millions of people is an impossible task for any human—or team of humans—to accomplish.
That’s where automation comes in. Automated marketing powered by a machine learning model can take all of the guesswork out of who to target, what kind of content to serve them, and when to deliver it.
The result? A more consistent, cohesive customer experience that feels personal even though it’s being delivered at scale.
Plus, automated marketing also frees up marketers to focus on higher-level tasks, like strategy and planning.
It’s no secret that data is essential to effective marketing. But sifting through mountains of data points and then trying to figure out which ones actually matter can be a huge time-suck.
Fortunately, machine learning models can be employed to help automatically identify which data points are most predictive of success.
For example: If you’re trying to predict whether or not someone will open an email, the model might identify factors like subject line length, day of the week, and time of day as being most important.
With this information in hand, marketers can fine-tune their campaigns for maximum impact—without spending hours poring over data that doesn’t matter.
Machine learning isn’t just about making marketing more efficient in the here and now. It’s also about future-proofing your marketing efforts.
The reason? ML models can be trained to constantly monitor customer behavior and then automatically identify patterns and trends.
This information can be used to not only make near-term adjustments to ongoing campaigns, but also to inform long-term marketing strategies. In other words, machine learning can help you stay ahead of the curve, ensuring that your marketing efforts are always on the cutting edge.
Machine learning is helping marketing departments improve productivity by quickly identifying threats to the brand’s image and reputation, showing spikes in spending patterns, and highlighting areas in which there are gaps in click throughs and conversions. Armed with this valuable data, marketers can then tailor their campaigns and target audiences in order to improve campaign responses and achieve their desired results.