Ways To Use Machine Learning in Marketing

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Ways To Use Machine Learning in Marketing

Optimization, automation, analytics, and personalization: these are the typical tent-pole goals for any digital marketing campaign. The pillars you one day hope your campaign will stand upon.

Wouldn’t it be marvellous if a single set of technologies could add to, or even multiply, the effectiveness of all of these pillars? Well, I think that’s enough false confusion; let’s talk about the marketing powerhouse of machine learning (ML).

ML has become fairly ubiquitous in the past few years, with many companies already having adopted it. With that being the case, it seems foolish for me to expound the virtues of ML and how it can greatly impact your digital marketing, but that’s what I’m going to do anyway, as I’ve no doubt that at least someone out there has adopted ML with very little, if any, consideration paid to how it is they’re actually going to use this new-fangled doohickey.

So, whether you’re looking into it for the first time, or you’ve adopted it and have so far found that all it has done is burn a considerable hole in your balance sheet, how exactly does ML help?

Stress-free customer service

It is a truth held as self-evident that the vast majority of customers prefer live chat to being kept on hold the majority of their day. While that’s easy to say, it also happens to be true, with 79% of people preferring live chat to having to build the nerve and patience to call in.

Such chatbots are invaluable, as they are always active (thus allowing for zero customer wait-time), they are a part of an ever-expanding knowledge database and also never have to sheepishly say: “Um, yeah, hang on a sec.” when scrambling for an answer. Not to berate anyone who has had to suffer the tangible hell of on the phone customer service (from either end, as it’s inexplicably universal), but it’s far easier to sort out your issues with the autonomous system that effectively knows the answer to everything you could ever ask it right out of the gate.

Top-class personalization

Brands selling similar products can lean on little in terms of differentiating themselves from the competition, but one way in which this can be done is through their customers’ experiences.

The most clear-cut implementation of this is Netflix. Netflix uses ML in order to suggest and filter content based on what the user has previously watched, rated, or ignored. This system is not without its hitches, as I’m sure anyone who’s used Netflix can attest, but it is otherwise efficient, and often helpful.

Many marketers have adopted this strategy of “Hey, glad you liked it, how ‘bout this?” into their marketing strategy, and have utilized it to great effect in their personalized email marketing campaigns.

Smart content creation

AI is obviously set to automate any job with a seemingly predictable and repeatable set of tasks, but it’s obviously difficult to go about automating any task which involves any modicum of creative thinking, or is it?

While this may seem ironic coming from someone who is currently doing something which will soon be performed by an automated system, I, for one, welcome our new AI overlords.

Conversational AI is one of the tent-pole systems in this eradication of humanity. Frase.io, for example, is a tool which showcases how ML is knocking on the door content creation as we speak. Conversational AI allows computers to communicate with people in a way that doesn’t make them think “Oh god, not this automation crap again.”

Another smart-automation tool is Natural Language Generation (NGL), which is able to automatically create concise descriptions from any data set in order to create digestible insights for your stakeholders.

AI-Powered Copywriting is yet another automation tool, one which simplifies the hell that can be email marketing. A good example of this is Phrasee, a tool which allows you to write better email subject lines as well as ad copy and any number of push messages.

Web design and user experience (UX)

Even the term ‘web-design’ can send a cold shiver up the spine of any marketer. It is a considerably important headache that is often plagued by minute, or even wholly subjective, problems.

ML throws yet another life raft into the tumultuous ocean of marketing though as it allows for the collection and translation of an inconceivable amount of user data which can speak to their individual preferences, as well as which sections of the website they most frequent.

This allows for the designer to put down their Ouija board and instead begin building from a state of statistical accuracy. A common web-design tool is Wix ADI, which collects user data from millions of users in order to create stunning websites (including their own.)

Marketing automation

Let’s rattle of some quick marketing automation stats:

  • 49% of businesses are using marketing tools for email automation.
  • 79% of renowned brands have used tools for marketing automation for over 3 years.
  • Brands who manage their leads using tools for marketing automation experience a 10%+ revenue bump after 6-9 months.
  • Brands who manage their user experience through marketing automation achieve 451% higher qualified leads rate.

Optimizing your advertisements

Advertising is a considerable cost sink for digital marketers, one which is predominantly based on manual decision-making, with considerations such as: which advertising channel should we use? How much ad space should we buy? When’s the optimal time to begin advertising? How long should the campaign last, and how much can we even afford?

As with all manual processes, advertising is constrained by human limitations such as stress and fatigue. ML can help optimize this process.

For example, the Lookalike Audience feature on Facebook allows marketers to reach out to potential customers who have seemingly similar attributes to your existing customers, similarities which may be too labour-intensive or minute for you to pinpoint manually.

Social media management

Social media marketing has essentially become the epicentre of digital marketing, with several crucial marketing functions having been moved over to Facebook, Twitter, Instagram, and Youtube.

ML can help marketers optimize their social media resources; however, ML is not some superficial plug-in that can only help incrementally. Here are some ways in which ML can be a true powerhouse for your brand:

  • Social Listening:
    ML can analyse an incalculable amount of data in order to understand how audiences engage with specific content themes and types. This not only tracks brand mentions, keywords, and hashtags, but also draws insights that can help you to create content that resonates with whatever specific audience you are seeking to attract.
  • Perfect Timing:
    ML not only helps you with gauging your audience so you know what to post, it also informs you when you should post. Tools such as Cortex help with this by analysing the usage patterns of hundreds of thousands of profiles.
  • Reputation Management:
    ML can help marketers identify what users really think of their platform, outside if the high-pressure realm of a review or formal questionnaire. Yext, for instance, is using ML in order to help brands identify what is being said about them online, allowing marketers to respond more quickly and with more of a focus on any potential issues being addressed as opposed to casting the widest net possible in the hopes that the users will be happy with the few fixes or adjustments they DID happen to make.

From all of this it may seem that ML is the potential lynchpin to any successful digital marketing strategy, but that’s because it really is. ML can help with practically any conceivable marketing issue you could think of, and the automation is only going to get easier and more extensive as more companies continue to adopt it.

Comments (02)

user

Jane Ronan

3 months ago

Reply

Python is easy to use compared to some of thr other languages.

review

Jennifer Andrew

3 months ago

Reply

yes and python engineers are becoming more plentiful.

review

Thom

3 weeks ago

Reply

We hired a team from Orcawise AI services which turned out to be a good investment.

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