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The Implications of Big Data, Machine Learning, and AI on Marketing Automation

Lindsay Tjepkema
Lindsay Tjepkema , Marketing Director, Americas , Emarsys

Any organisation looking to remain competitive in today’s high-tech digital world must constantly innovate and pilot new technologies and, most importantly, listen to consumers and the market for indicators of change. The basic commercial landscape is rapidly shifting towards automated processes and data-backed decision-making, and marketing is no different. In the United Kingdom alone, the number of AI companies doubled from 2014 to 2016. A new AI company was founded in the UK on almost a weekly basis during that period.

That growth rate shows no signs of slowing down, either. Automation and personalisation are now key elements to engaging and communicating with each and every consumer. All marketing efforts, in some form, rely on the ability to personalise the consumer journey and create incredible and memorable experiences that keep customers coming back again and again.

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AI Marketing, Big Data and Machine Learning

Today, big data, machine learning, and artificial intelligence (AI) have taken center stage as the new tools of highly effective marketing teams. The results are highly personalised, real-time consumer “experiences” that are significantly lower in cost than traditional high-expenditure campaigns.

With these tools it’s possible that every single interaction a prospect or consumer has with a product, whether through a website, email, or social interaction, is tracked and recorded for future optimisation. Machine learning algorithms can collect this data in real time, and immediately personalise experiences to create unique journeys for each visitor, eliminating the need for static profiles based on outdated or grouped data sets.

With efficient processes and a newfound wealth of data in place, marketing teams can focus on identifying strategies that effectively use this technology to optimise operations as well as marketing output. Without a well-planned strategy, machine learning can simply become a cog inside a big machine, and AI can become just another wasted expenditure instead of a highly advanced resource. Marketers shouldn’t jump into processes without considering goals, so they must take the time to contemplate the ideal outcomes and plan accordingly.

Below are three main implications of big data, machine learning, and AI marketing:

Detailed Consumer Profiles

The highly personalised data available from machine learning and AI can help feed consumer profiles. Better knowledge of customer and prospect audiences means marketers can deliver the right message, to the right person, at the right time. The key is for marketers to capture this data automatically, during every single possible consumer interaction, including CRM, and even offline, data, in order to build a completely comprehensive profile. Marketing teams can then take this a step further with scoring and analytics, which prompt refined strategies for highly personalised and relevant content.

Increased Engagement Rates

Big data, machine learning, and AI can also influence consumer engagement when it comes to marketing automation. With deeper insight into consumer demographics, socio-economic data, and geographical patterns, marketers can make proactive changes to their digital marketing strategies. The only way to begin influencing online behavior and email interactions is to truly understand the numbers behind the actions. Marketers must keep in mind that personalised email marketing has now become expected by both individual consumers and B2B audiences. Leveraging this data to work for a brand in the smartest way possible can help increase engagement rates and win business.

Higher Retention Rates

Using advanced technology to win new business is only one small aspect of marketing. Marketers can also use these newfound metrics and consumer insights to increase current retention rates, which is just as important. If a consumer purchased something or engaged with a product offering, they should still receive personalised communications, and in turn, will often provide the richest data a marketer can collect. If current customers feel more relaxed and comfortable with a certain ecommerce brand, they will likely offer up more information about themselves in exchange for promotions or deals. Marketers can then retool this new data and create extremely personalised experiences that can make a product so valuable that it becomes fiscally irresponsible for a current customer to leave without purchasing.

Final Thoughts

While some brands are incredibly experimental and aren’t hesitant to try new methods of personalisation including Bluetooth, advanced profiling algorithms, and even machine learning and AI, most are just dipping their toes into these new strategies. In order for brands to remain competitive and relevant to consumers, they must continue innovating to improve the journey for those that interact with their brand—even if just starting to test personalisation strategies.

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