Machine Learning: More Common Than You Think

There’s a lot of buzz lately about machine learning. In many ways, it’s transforming the consumer experience and improving the products and operations of many companies. Plus, it’s not just for data analysts — machine learning has real benefits in the lives of the average consumer.

The Role and Implications of AI and Machine Learning within the Marketing Tech Stack

[Today, Sue is hosting Sanjay Sidhwani, SVP of Advanced Analytics for Synchrony Financial, as a guest blogger for The Consumer Connection.]

There’s a lot of buzz lately about machine learning. In many ways, it’s transforming the consumer experience and improving the products and operations of many companies. Plus, it’s not just for data analysts — machine learning has real benefits in the lives of the average consumer.

Ever wonder how Netflix serves up recommendations for the next movie or how your smartphone knows that you will be driving to work on Monday morning? Those are both examples of machine learning.

How is machine learning different from ordinary analytics? With traditional methods, an analyst defines the objective and looks for correlations between the objective and a defined set of data inputs. If new data comes in, the analyst needs to rerun the analysis and create new correlations and a new algorithm. This can take a while.

Machine learning is more efficient because it automatically takes new data inputs and adjusts, or “learns,” without manual intervention. So, the impact is immediate. How is it learning? The behavior drives the operation, not the programmers. Netflix recommendations are a good example. Once you watch a program or a movie, the next set of recommendations are created automatically without adjustments from an analyst.

Let’s take another example. Say you are considering buying a used car. What’s a fair price? Many factors determine this, such as age of car, miles driven, model and make. With enough data, we can infer the relationship between these factors and the price. This relationship can be linear, where the attributes have an additive effect (e.g., miles driven). But often the relationship is not linear. A car’s age, for instance, has a geometric effect on price (15 percent lower each year). In machine learning, the nature of these relationships doesn’t have to be a total guess. The programs automatically adjust these inputs and give us a fair price.

Machine learning can also help companies market offers more efficiently. One way is pattern recognition. There are patterns in customer buying behavior, for instance. Machine learning algorithms can predict the next likely item to be bought, helping a brand decide which customer should be targeted with what offer, better addressing their needs and wants and eliminating wasteful and costly marketing.

The challenge for companies is how to implement their learnings. What to do with the prediction — offer a discount? Display on the website? Send an email? The key to making the data impactful is “closing the loop” and refreshing the learnings so the data leads to actual behavior.

There is a budding community of data scientists and analysts who are exploring machine learning techniques. I recently attended a hackathon on Artificial Intelligence in our Innovation Station, a technology hub in our Chicago office. Most of the teams’ ideas used machine learning techniques combined with new types of data, such as facial recognition of an applicant’s LinkedIn picture to authenticate digital credit card applications or building a neural network chatbot that provides personalized service and account analytics.

The possibilities for marketers are exciting and endless. As we learn more about the technology, the real-world applications are likely to grow and provide even more value to brands and consumers alike.

Note: The views expressed in this blog are those of the blogger and not necessarily of Synchrony Financial.

Author: Sue Yasav

Sue Yasav is the VP of Thought Leadership at Synchrony Financial. She's responsible for developing strategic insights through surveys, social listening and academic studies on topics related to the financial services and retail industries. She authors white papers on consumer trends and articulates impactful strategies for marketers in the areas of digital transformation, customer experience and insights into specific growth segments of the U. S. population.  Sue has 20 years of experience in the credit card industry, encompassing 10 years at Citi Cards as VP in the Finance and Marketing organizations.  In the past 11 years at Synchrony Financial, Sue has been a Lean/Six Sigma Master Black Belt, a marketing leader for a high-end retail partner in NYC and the leader of Value Proposition Development.

One thought on “Machine Learning: More Common Than You Think”

  1. Sanjay and Sue, I just finished interviewing 23 AI executives. One of the people I spoke with referenced Netflix use of AI to personalize viewing recommendations, increasing customer satisfaction, and saving more than $1 billion in lost subscription revenue. More data = more machine learning = more revenue, or savings.

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