The Implications of Big Data, Machine Learning, and AI on Marketing Automation
Any organization 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.
Automation and personalization are now key elements to engaging and communicating with consumers. AI marketing efforts rely in some part on the ability to personalize the consumer journey and create incredible and memorable experiences that keep customers coming back again and again.
AI Marketing, Big Data and Machine Learning
Today, it’s big data, machine learning, and artificial intelligence (AI) that have taken the spotlight as the new tools of highly effective marketing teams. The results are highly personalized, real-time consumer “experiences” that are significantly lower in cost than traditional high-expenditure campaigns.
With these tools, 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 optimization. Machine learning algorithms can collect this data in real time, and immediately personalize experiences unique to each visitor, eliminating the need for static profiles based on outdated or grouped data sets.
With this newfound wealth of data, and efficient processes in place, marketing teams can focus on identifying strategies to effectively use this technology to optimize operations and 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. This is not the time or place to jump into processes without considering goals, so marketers need to 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 personalized 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 personalized 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. It’s important for marketers to remember that personalized email marketing is now expected by individual consumers and B2B audiences alike. Leveraging this data to work for a brand in the smartest way possible can help increase engagement rates and win business.
Higher Retention Rates
In terms of what marketers have to consider, using advanced technology to win new business is only one side of the coin. They can also use these newfound metrics and consumer insights to increase current retention rates. If a consumer purchased something or engaged with a product offering, they still warrant communication, and often they will provide the richest data a marketer can collect. If current customers feel more relaxed and comfortable with a certain e-commerce 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 personalized experiences that make a product so valuable that it becomes fiscally irresponsible for a current customer to leave.
While some brands are incredibly experimental, trying new methods of personalization such as Bluetooth, advanced profiling algorithms, and even machine learning and AI, most are just starting to nail down the fundamentals. In order to stay competitive and relevant to consumers, however, organizations must continue innovating to improve the journey for those that interact with their brand—even if just starting to test personalization strategies.
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