{"id":1989,"date":"2015-08-04T11:56:35","date_gmt":"2015-08-04T11:56:35","guid":{"rendered":"https:\/\/email.uplers.com\/blogs\/?p=1989"},"modified":"2020-04-07T11:55:10","modified_gmt":"2020-04-07T06:25:10","slug":"interview-sandy-hathway-co-founder-cmo-avari","status":"publish","type":"post","link":"https:\/\/email.uplers.com\/blog\/interview-sandy-hathway-co-founder-cmo-avari\/","title":{"rendered":"Interview with Sandy Hathaway – Co-Founder \/ CMO, AVARI"},"content":{"rendered":"

Email marketing is becoming extremely personalized with every growing day. Well, thanks in no small part to the availability of information and data. But, a heap of data is of no use. You actually need a sophisticated personalization software and automation tool that helps you achieve your personalization goals easily.<\/p>\n

To talk more about personalization and predictive dynamic content, we interviewed Ms. Sandy Hathaway<\/a>, Co-Founder \/ CMO, AVARI<\/a><\/p>\n

Below is the download of our discussion:<\/p>\n

What according to you is a good way to personalize automated email series in this era of email marketing?<\/b><\/p>\n

Personalization is critical in order for an email campaign to be successful. The reason why it\u2019s so important is because it\u2019s what today\u2019s consumers want. They demand customization because they don\u2019t have a lot of time to waste with irrelevant content.<\/p>\n

And when consumers get the experience they desire, they are more responsive to what you have to offer. However, it typically takes quite a lot of work to execute a personalized experience in an automated email series.<\/p>\n

You have to define the objective, plan the flow and content, set up the automation and then measure results to see if it’s performing as expected. Many marketers are happy to get that far, let alone think about optimizing content to be more personalized.<\/p>\n

A common way to personalize is use first names in the greeting \u2013 it’s easy to do and has positive impact, but it’s not enough. A more sophisticated way to personalize is to use certain criteria like gender or zip code to refine an audience into a slightly smaller more targeted group. That also works well, but when you start combining segmentation with automation schemes that need to run indefinitely, the complexity increases dramatically.<\/p>\n

That’s why I believe that new methods involving predictive analytics and machine learning are the best underlying technologies for scalable personalization, especially in an automated series.<\/p>\n

How do you think predictive content will change the face of email moving further?<\/b><\/p>\n

Predictive content<\/a> in email reduces complexity by using a machine to figure out and display the next best content for each person on an individual level, rather than requiring a human to try and think up and program rules for all the relevant scenarios. Not only is it a huge time saver, it’s the most intelligent way to deliver personalization to thousands or millions of people all at the same time.<\/p>\n

Furthermore, machine learning is key \u2013 a predictive system alone is not enough \u2013 it needs to learn from engagement in order to show better content each subsequent time. The reason why marketers should care about machine learning is because it automatically drives content optimization down to the individual level, at-scale. With machine learning, data analysis which used to take days can now be done in just a few seconds in the cloud.<\/p>\n

To put it in relative terms, think of AB testing and the drain on resources that can turn into. Jonas Dahl, a business analyst at Adobe, made a great point about this his article \u201cFour Reasons to Lose a Little Control and Trust Automated Personalization\u201d:<\/p>\n

\u201cAutomated personalization offers \u2018always on\u2019 optimization of personalization. It continuously collects data about visitors and the performance of each offer and adjusts the machine-learning models to reflect the most current visitor behavior. This allows it to serve winning offers at any point in time rather than based on data collected over a limited, past testing period as A\/B testing does.\u201d<\/p>\n

In short, predictive content with machine learning will strengthen the position of email as the number one channel by which consumers wish to receive relevant communications from brands.<\/p>\n

Which are some good characteristics of a content recommendation system that uses predictive analytics and machine learning to propel an email marketing program?<\/b><\/p>\n

Great question. All personalization systems are not created equal. There are some very specific characteristics that one should look for, including:<\/p>\n