Big Data & Machine Learning’s Impact on the Future of Marketing
Cygnis Media Editor

Big Data and Machine Learning’s Impact on the Future of Marketing

The marketing industry has always relied on data. But the difference in data we have today is the sheer magnitude of it. The fact that most big data is unstructured makes it difficult for marketers to gain actionable insights from it.

Lately, marketers are learning that artificial intelligence, specifically machine learning (ML), are perfectly suited for this task. By iteratively learning from data, machine learning algorithms allow computer programs to find hidden insights by detecting patterns in data without being programmed on where to look.

When applied to marketing, this can patterns that allow marketers to generate insights by identifying and digging deep into factors like consumer preferences, behavior and current market conditions.

How useful can machine learning and big data be for marketers? Consider these stats:

  • The market for global cognitive computing is expected to reach $12.5 billion by 2019 – Research and Markets
  • 30% of all companies will use artificial intelligence to augment at least one of their sales processes – Gartner

What do big data and machine learning have in store for marketing in the future? Let’s find out.

Optimizing Customer Journeys

Optimizing User Journeys

Before marketers turned to the web for insight into online consumer behavior, mapping customer journeys was pretty predictable. For example, you didn’t need to worry about factors like negative online reviews (on social media or online forums) that could hurt your brand’s image and influence purchasing behaviors.

With online accessibility, the buyer journey is different now. To illustrate, consider these stats:

  • Nearly two thirds of American adults are on social media 12 hours a week.
  • 55% of buyers do their research on social networks.
  • Customers expect brands to respond on Twitter within 2 hours.

Long story short, customer journey mapping isn’t linear anymore – it’s based on a circular pattern of touchpoints as consumers analyze, select and share experiences about products. The challenge for you this time, is to meet customers along these points in their journey and not only through one on one interactions.

Data science and machine learning can offer insight into consumer desires:

Optimal targeting:

With machine learning, marketers will be able to discover the conditions and contexts for precise targeting. This means that they won’t have to manually figure out which offers should be targeted to certain demographics, when and how.

Identifying pain points in user journeys:

As machine learning algorithms and software evolve, we can expect to gain more insights into what drives online user journeys. It has already been put into practice. Consider SessionCam which uses a machine learning algorithm to identify a visitor’s struggles on a web site that may negatively influence their experience, giving each session a Customer Struggle (CS) score.

Automated Customer Service will become the norm

Automated Customer Service

The world isn’t entirely new to automation. You can order your favorite Subway sandwich with a mobile application, book a doctor’s appointment, or even a plane ticket online, all without talking to a single person.

And now, thanks to machine learning, automation may be the new face of customer service. By predicting patterns in data, based on customer queries and consumer history, machine learning may be:

Recommending Services:

Experts predict that 85 percent of all human interaction will be handled without the need of a human agent. Consider Mastercard which plans to launch the Mastercard KAI, an AI powered bot, that will be able to identify spending habits of users based on their past spending habits and use this data to recommend financial services.

Selling services without prior training:

Think of Netflix, a customer centric service and a video recommendation system. It recommends videos by using machine learning algorithms to identify patterns in customer data – what they watched, when did they watch it, and the area on the Netflix screen where they found the video.

You can imagine what the future has in store if you apply the same ideas to a sales oriented business. Precise data like this can make a new salesperson into a sales veteran who will be able to cross sell and upsell certain products without any experience.

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Enhanced real time bidding

Enhanced real time bidding

In an age of mobile and display advertising, acquiring ad space on major publisher websites is worth its weight in gold.

Over the last few years, there has been an increase in programmatic ad buying or RTB (real-time bidding), an automated bidding system used by publishers and advertisers through ad exchanges. In mobile and display advertising, RTB means buying individual ad impressions (ad views) in real-time or while it is being generated from a user’s visit with the goal to:

  • Maximize conversion rates (app installs, sign ups, online purchases etc)
  • Minimize the cost per acquisition

To optimize and automate the ad buying process, there may be more demand for machine learning algorithms to improve bidding and gain more impression opportunities for advertisers. For example, they may be able to identify optimal bid amounts per impression based on data like time of day, the area where the advertisement is placed and the content of a web page.

Lookalike marketing will gain traction

Lookalike marketing

Lookalike audience are people who are likely to be interested in your business because they exhibit similar traits (age, location, etc) to the people who already are. Consider the Facebook pixel, a script powered by machine learning algorithms, that you can install on landing pages to track conversions, create similar audiences, design re-marketing strategies and gain insights about how visitors use your website.

Pixel allows marketers to reach out to users who share similarities with their audience. The whole process ultimately:

  • Automates audience targeting
  • Offers a more intuitive approach to targeting

For marketers, tools that can generate insight from vast amount of consumer data more precisely and faster than they can themselves, can be valuable in the long run for:

Interest based targeting:

Your Facebook campaign and pixel finds data on thousands of users. By analysing their interests, Facebook can find other users just like them. Facebook will then take this data and match top four interests and behavior that they have in common.

Let’s say that you are promoting a bakery and Pixel finds that people who clicked on your ad had common interests like jazz, green tea, cats, and football. Facebook provides you this information because it knows what other pages a user is interested in. You would never have known that but the algorithm behind Pixel does. Now you can optimize your marketing by basing it on this combination of interests.

Faster lead targeting and conversions:

Lookalike targeting means that your ads are shown to people who are likely to respond to them since they share traits similar to your best customers. Machine learning driven tools like Facebook Pixel have shown a lot of potential in harvesting insights from data to fuel conversions. If the technology takes off, it can cut down the time, money and resources marketers need to target potential customers manually.

Predictive analytics will take off

Predictive analytics

Marketers use predictive analytics algorithms to gain insight from data by determining patterns in it and making decisions based on it. Amazon recommending a book you may like and Pandora’s recommendations about books you may like are all examples of machine learning over data streams. These brands use the predictive power of machine learning algorithms to make these recommendations based on previous exhibited user behavior (like buyer history on Amazon).

Imagine the implications if the same principles were applied to ad campaigns before they are even created. If machine learning can predict a rainy day by analysing cloud formation and use it to make it rain less, it might be able to predict how certain ads may perform with associated data. Here is how:

Making ads smarter:

Predictive analytics data generated by machine learning tools might help marketers make advertising smarter. To illustrate, let’s say that you have to promote an airline. Tools like Google Adwords can help you find out that your target audience is researching low cost flights during the holiday season.

But what about data that you can’t see, like weather conditions? It doesn’t snow in every state which can affect ticket purchases amongst audiences scattered in different states.

If machine learning takes-off applying associated algorithms to analyse patterns in traveler data (such as tickets purchased on specific times of the day in certain states) may allow marketers to make more informed decisions on the the content and timing of advertising campaigns.

Making ads more profitable: Businesses spend millions on marketing campaigns. And not all of them bring in profits as expected. Machine learning can allow marketers to devise strategies that target audiences are likely to respond to and make ads generate more revenue.

Conclusion

Marketers can gain better insights into customer psychology by using big data to understand behavior patterns. And machine learning will help them get there.

Interested in leveraging Big Data & Machine Learning?

We can custom build & integrate Big Data and Machine Learning into your existing workflow to help you make more informed decisions.

To learn more about big data, find out how it’s helping artificial intelligence is transform healthcare.




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