“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.
Here are three 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.
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.