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Data is the new oil. Its return on investment is high, and having more is always better — if you know how to use it.
In recent years, marketing has pivoted to rely more heavily on data. Whether you’re dealing with content strategy or email funnels, the data can elucidate if your message resonates with your audience. But as this vast amount of information grows, we need more effective means to make sense of it.
By finding patterns and self-optimizing for better results, machine learning (ML) applications are helping marketing teams around the world connect to their customers better.
Missing Puzzle Pieces
Shyam Venugopal is VP of global media and consumer data strategy at PepsiCo. He says, “For a long time, none of us would have thought that selling food and beverage over e-commerce would have been such a large business. We are operating in an environment that is constantly changing.”
Customers no longer visit stores and purchase items in the same visit. They take a preliminary glance and check out competitors on their phone, place the order on a desktop computer, then track the delivery from their phone. But the customer acquisition journey? That’s another big, complex monster.
A customer may have seen an ad on three different websites before clicking on it. After clicking and browsing, the customer may not have returned for another month. And this time, maybe they’re not interested in what they originally were browsing for. Then, they might place their order on their work computer, leaving the customer acquisition unattributed.
Ad conversions go unattributed this way all the time, and it leaves out valuable information for many marketing teams. Without this insightful data, it can be difficult for marketers to predict a customer’s next move or tailor a strategy to their needs.
Connecting the Dots
A recent survey by Boston-based MIT Technology Review Insights showed that data is quickly becoming the backbone of personalized marketing experiences. 1,419 marketing executives from various verticals responded, and based on their company’s market performance, the executives were classified as “leaders” or “laggards”. Two-thirds of leaders said data will be a major key in their company’s ability to thrive.
Leaders are 60% more likely to believe that a marketing team should own customer acquisition from prospecting to post-sales strategy. With this approach, marketing can affect every part of an organization and give their data more breadth by adding additional metrics.
Using ML, marketers can make sense of the large amount, variety, and sourcing of data. ML can also automate tasks that would take humans hours to complete manually, reducing error rates and improving efficiency.
The Role of Marketing Is Changing
These days, it’s all about the experience — for users, customers, and everyone in between. A user’s experience can be negatively impacted by any small mismanagement in a company, so companies need to shift their priorities from profits to perfecting the experience.
Allison Hartsoe founded data analytics consulting firm Ambition Data. She says, “In each part of the organization, the definition of what a customer is may be different. The organization needs to create a unified view of the customer—a larger, broader definition that everybody benefits from.”
This may include restructuring a company, hiring C-level analytics executives, or rethinking the company’s mission and goals. As Hartsoe points out, “It becomes necessary because the customer data is spread across so many departments; so to be comprehensive you need the organization to align. It’s about changing the way the entire company thinks.”
By sharing data between departments, employees can work on continuous improvements inter-departmentally, instead of within discrete roles. Hartsoe says, “Using metrics changes the process of decision-making.” Experts believe ML will change marketers’ roles slightly; 73% of leaders from the MIT survey say their duties have already shifted more than 10% from manual activation to strategizing.
A Better Perspective on Customers
But what good are data and ML if they can’t tell us more about the future? Well, that’s exactly what many marketing departments are using ML for.
Predictive analytics help to paint a more accurate picture of what return customers look like, what they’re interested in, and how much they’re likely to spend during each return visit. Some advanced models can even flag certain first-time customers as potential return customers. The MIT survey found that leaders are 53% more likely than laggards to believe ML helps them better detect purchase intent.
Of course, ML is very flexible. It can also be used to measure how effectively the marketing budget is being spent. If needed, the algorithm can adjust campaign budgets to bring in better return on spend. Using ML in digital advertising can improve the lowest-performing campaigns by 10% or more.
Machine Learning = More Human Marketing
Companies often have all the information they need to create better experiences for their customers. It’s just hiding away in the untapped data across every department. Customers are much more likely to return when companies understand them. And machine learning is proving to be key in deciphering the data to gain this fresh perspective.
How is your company utilizing machine learning? Is it already an essential tool for your marketing team? Let us know how machine learning is transforming how you work!Tags: advertising, AI, AI and ML, app development Boston, artificial intelligence, artificial intelligence app development, artificial intelligence app development boston, Boston artificial intelligence app development, Boston machine learning apps, machine learning, machine learning apps, machine learning apps boston, marketing, ML, ML in advertising, ML in marketing