Imagine having a conversation with your financial advisor. He can tell by your tone of voice or facial expression if something makes sense or if you disagree with something, and he can adjust accordingly. But wouldn’t it be odd if your advisor started making suggestions that were completely irrelevant to your financial situation? It would also be troublesome if he offered suggestions that you’d already discussed in a previous conversation or, worse, had already acted on.
You would be beyond frustrated to have such an experience when speaking face-to-face with an advisor -- or anyone trying to help you -- yet for years, we have accepted such lack of personalization as par for the course online.
How many times have you encountered such an irrelevant experience? I can think of many examples. Business-to-business (B2B) technology sites have suggested that I download a whitepaper I just downloaded. Retail sites have sent me emails recommending women’s shoes when I have never shown any interest in women’s apparel. I’ve seen retargeted ads across the internet that show me products I viewed at some point but quickly decided weren’t right for me. Once you start noticing these unpersonalized experiences, you spot them everywhere.
In the last couple years, though, a few forward-thinking companies have changed our digital expectations. The mass adoption of services like Netflix and Spotify have created a new expectation in the mind of the consumer -- one-to-one personalization. The technology that Netflix and Spotify are using to create truly personalized experiences is a type of artificial intelligence (AI) called machine learning.
The term “machine learning” sounds highly technical -- and it certainly can be -- but its applications can be highly valuable to marketers. While you don’t need to know all of its technical intricacies, it does help to have a general understanding of the concept. To explain what machine learning is about as it relates to one-to-one personalization, let’s start with an approach that preceded it.
Rule-based experiences work in marketing -- but on a very limited basis.
It might help to more firmly establish how machine learning differs from other marketing strategies first.
The way that most marketers have delivered personalized experiences in the past is through rules and segmentation. Segments are created manually when a marketer decides to group customers, site visitors or app users by some predetermined criteria.
For example, a B2B marketer can segment site visitors by industry. Then, the marketer can manually set up a rule to display some kind of experience to those different segments. The marketer could select a relevant piece of content such as a whitepaper or a case study to promote to each person depending on which industry segment he or she falls into. This creates a personalized experience, because a person who falls into the financial services segment would see a different piece of content than someone who falls into the healthcare segment.
This approach can work, but it’s very limited.
The problem is that the rules are written by humans based on what they believe to be true. In the previous industry example, the marketer must decide which piece of content to promote to each segment. But each person is unique. Even within an industry, visitors could be at different points in their journey, or they could have different interests or content preferences. Their intent from visit to visit can also change. A few segments and rules cannot take all of this information into account.
There’s simply too much data for a human to sort through without the help of machine learning.
Posted By: Ayush Singh
Thursday, May 3rd 2018 at 4:31PM
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