Improving personalization with machine learning

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One thing that is usually uppermost in your mind as a marketer is how to ensure that you not only survive the competition but also become one of the market leaders.

And in order to become a market leader you are expected to work seriously on personalization but doing this at scale because you must focus on the global market, must require automation and that is where machine learning comes in.

You must create a digital presence that will help in better customer engagement, raise brand awareness, and reinforce business objectives. It’s expected that you must have been working on your web content and building out your CRM capabilities, you must also have it behind your mind that there is the absolute need to have various efforts underway to automate key marketing activities.

With the global market as your target, getting personal maybe a little difficult task to achieve but you can enhance this with a personalization engine. Your ultimate aim will be to target the content you deliver to your customers and prospects based on what you know about them and what you believe they might need.

Personalization or customization

Before embarking on machine learning integration, it’s essential that you refrain from mixing up personalization with customization. While personalization is carried out for the customer’s benefit, customization, on the other hand, is initiated by the customer in an effort to drill down to the desired content.

In the research by PWC titled ‘Financial Services Technology 2020 and Beyond: Embracing disruption’ it was observed that customer intelligence will be the most important predictor of revenue growth and profitability. 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 are able to 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 a consumer expectation. All these big tech companies are able to accomplish this onerous task by integrating machine learning, which is quickly turning out to becoming an essential and must-have tool in content personalization.

Interestingly, there are quite a number of personalization engine vendors. Evergage, Monetate, Certona, and Dynamic Yield, are some of the vendors out there in the market that offer this service. Gartner’s “Magic Quadrant for Personalization Engines” 2019 report shows that personalization engine adoption is up 28% since 2016.

You must locate the essential points in your customer journey that are optimal for adding a personal touch. Context has always been the source of the differences between customers that usually trigger a need for specific content.

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.

1. Making use of secured demographic data

The basis of demographic data is to have access to your customers’ distinctive behaviors 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 are eager to lay their hands on it.

A comprehensive demographic data can often reveal an entire socioeconomic profile for customers — their distance from retail locations, average income, average age, ethnic ratios, youth or college student populations and sometimes even married versus single statistics.

While your competitors will make use of this data to train and improve their predictive model as well as simplifying the ultimate personalization data crunch just the same way you will, cybercriminals will use the information to launch attacks at your customers or even cripple your business.

It’s true that as a new startup founder, you may be considering the financial implications of having to secure your data but this will go a long way to save you from very bad experiences. Where you don’t have the funds for a paid VPN, nothing stops you from subscribing to the services of a free VPN.

What you end up achieving is the ability to mask your I.P. address and encrypt all traffic which will help with geo-blocks and contribute to your secured demographic data and ultimate online privacy.

2. Who makes up your social media audience?

Cross-channel personalization is a very beneficial source of information because a customer’s social media 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.

3. Catching in on your consumer’s online behaviors

Besides demographic data and who belongs to your social media audience, another source of information that enables your workable insight into the individual consumer in personalization is applying machine learning for a comprehensive knowledge of your consumer’s online behavior. The navigation path of your potential consumer can reveal a great deal about the person.

You will have very useful insight into your consumer’s preferences, the amount of time a consumer spends browsing pages on your site is a revealing clue to the degree of priority and a source of valuable data. While you may not be able to garner all this valuable information manually, machine learning can easily make sense of this somehow “erratic” behavior.

Machine learning is able to articulate the repeated site visits and come up with an in-depth and knowledgeable profile of the customer and what they care for.

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 endeavor to personalize content across all channels. 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.

John Ejiofor is the founder and editor in chief at Nature Torch. He can be found on Twitter @John02Ejiofor.

The post Improving personalization with machine learning appeared first on Search Engine Watch.

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