In this video, Intellimize CEO Guy Yalif explains the core differences between 3 methods of website optimization: A/B testing, rules-based personalization, and machine learning predictive personalization.

As you think about which method to implement to drive conversion rate lift, consider the following questions:

• Is my audience all the same?
• How many tests do I do in a week?
• Do I know when visitor behaviors change on my website?
• Am I able to deliver the right experience to each individual visitor at the right time?

To learn more about how Intellimize can help your marketing team with website optimization efforts, simply click on the “Request Demo” button on our website.

If you’re just getting started with personalization, you might be wondering what types of product and/or content recommendations might help you with your marketing team’s web optimization strategy.

To learn more about these approaches and practical ways to apply them to your digital marketing strategy, watch the video below.

There are several reasons why marketers want to share product and content recommendations. They might want to ensure that visitors are seeing more ads, moving further through the sales funnel, or giving marketers some information about them in return.

Marketers might do this through a number or ways, including:

• Frequency: Using the frequency of relevant activity to drive what you show a prospect. Examples include: visited a category more than X times, frequently visited categories, or viewed a product more than X times.
• Recency: You might also use recent behavior to help you determine what to show a prospect. Some common examples of this include: showing recently viewed items, showing recently purchased or downloaded items, or showing recently viewed categories.
• Popularity: Using recent purchases, votes, or positive signals to increase popularity. Older purchases, votes or positive signals count for less than recent ones.

For more an even deeper look into advanced product and content recommendations, check out techniques that delve into user-based, item-based, and content-based recommendations, as well as collaborative filtering, in the video below.

When you’re optimizing websites, it’s important for marketers to understand the different approaches you can take in an effort to drive more revenue and conversions. You can use data to continually improve the experiences that you’re showing on your website.

There are generally two ways you can do this: following a “winner take all” approach, or personalizing (either by segment or individual). Here’s a breakdown of each approach:

Fork in the road: “Winner take all” vs. personalization

Winner take all

This is essentially picking just one experience to show on your website. This is useful when it’s important to have only one experience. For example, you might have one onboarding flow with a specific type of technology vs. another on your website that you want to direct visitors to.

Personalization

• Personalizing by segment: You might want to treat your customer segments differently on your websites. For example, you might want to send enterprise prospects to a website experience optimized for a chat with your sales team compared to lower-value prospects that you want to direct to a self-serve website experience.
• Personalizing by individual: You take this approach a step further and tune the website experience for each individual website user — this is where machine learning personalization tools like Intellimize are quite helpful.

Because individuals behave differently on your website, we believe it’s important to not treat everyone the same. Perhaps even more importantly, people’s behaviors don’t stay the same on your website over time. Having the ability to tune your website to individual customers in the moment is a key advantage for marketers looking to grow revenue and conversions. Watch the video above to find out more about why we recommend personalization as a key driving force for website optimization.

To find out more about how you can apply website personalization to scale at your organization, contact us by clicking on the “Request Demo” button on our website.

Thanks to advanced algorithms and machine learning, there is a wide range of applications that digital marketers can use to grow their marketing strategies and drive more revenue for their organizations.

There are many branches in the machine learning tree, but we believe that these are the differences that matter most to marketing practitioners, like demand generation managers and growth marketers. By understanding which algorithms are appropriate for which business problems, marketers can get practical value out of machine learning.

In this video, Intellimize CEO Guy Yalif will walk through several common problems that marketers face, and how machine learning can help solve them.

What is machine learning good at in marketing?

• Lead scoring
• Finding the ideal price to sell your products and services
• Finding the ideal promotional amount to use for your products and services
• Determining whether or not a person will click on an ad
• Determining whether or not an email is spam
  Determining what product or content to show on your website
• Recognizing the content of an image
• Writing an email subject line
• Understanding speech
• Customer segmentation
• Finding business insights from data
• Fraud detection or outlier detection
• Conversion rate optimization
• Determining a sequence of emails to send to prospects

Each of these common marketing tasks falls under a specific type of branch in the machine learning tree. To find out more about machine learning and how it influences marketing, watch the video above.

Personalization is another area where machine learning is making a big impact. To understand how you can apply machine learning at scale to optimize your website for each individual customer with Intellimize, simply click on the Request Demo button on our website.