Three sure-fire ways to use machine learning for improved personalization

Personalization or customization?

Personalization is the amazing outcome of your customer intelligence that will ensure you’re able to control over-messaging customers with blanket promotions, this will also translate into a huge reduction in media buys.

Personalization is a critical mission your Startup cannot afford to toy with in order to embark on effective marketing. Once you can personalize the journey of your potential customers you are on to increased customer engagement and long-term loyalty.

You can take a cue from the way Netflix does movie recommendations, music suggestions from Spotify and special promotions on Amazon to really comprehend the effect personalized content is having and that it is not only becoming the norm but consumer expectation.

As personalization is predictive, machine learning has started playing a central role.

The following are three ways you can utilize machine learning to improve personalization

Making use of secured demographic data

The basis of demographic data is to have access to your customers’ distinctive behaviours and preferences and this you can effect with machine learning. While it may be easy for you to lay your hands on this information, there is a cliche to it.

Your competitors, especially those who have access to large search engines can use these search engines to find out highly personal information about your customers, such as medical issues, employment status, financial information, political beliefs, and other private details. This data, of course, will be collected, stored, and linked to your data profile.

The only way to effectively “opt-out” of this, is to keep your data safe and out of the hands of data collectors. Cybercriminals also know that this information is a gold mine and is eager to lay their hands on it.

Cross Chanel personalization

is a very beneficial source of information because of a customer’s social media the channel of choice is an avenue to discovering how friendly the customer is to mobile contact.

It’s also a channel to accumulating demographic data for the mere fact that different age and social groups prefer different social media platforms.

For instance, Gen Z is known to have a preference for Instagram and Snapchat, while Gen X and millennials cling more to Facebook.

It’s very important for you to know that in order for you to succeed in integrating machine learning into your effort at improving personalization, you must endeavour to personalize content across all channels.

Applying machine learning for consumer’s online behaviour

This will ensure that your customers feel personally engaged in real-time and wherever they are.

Product pages on your Startup websites should be full of zest and tailored to each individual’s preferences. Deploy predictive advertising on the consumer’s social media platform of choice.

You just don’t stop at your efforts on your website, exploit the opportunity email offers as a dependable personalized content repository, the reason is that it’s easier to come up with optimized content in an email than it is to spiritedly work such wonders on a webpage.

However, the integration of machine learning as an application of AI affords you the opportunity of improved personalization at scale.

What do you think?