What Artificial Intelligence Can and Can’t Do for Marketing

For years now, Artificial Intelligence has been a hype, yet a vague notion until recently. 2016 was the year developers put AI into practical use. AI has been widely adopted by various high-tech software and hardware commodities like chatbots, newsfeeds, search engines, recommendation engines, predictive analytics and more. Most tech companies started to invest in next-gen marketing technologies incorporating AI solutions. According to the marketing intelligence platform Tractica, revenue for enterprise AI applications will increase from $358 million in 2016 to $31.2 billion by 2025, growing almost 10 times bigger.

AI is not solely used in automation tasks, a lot of the precious work done by humans right now is estimated to be taken over by AI in the near future. By 2018, Gartner expects that 20% of business content such as shareholder reports, legal documents, and press releases will be authored by machines.

Let’s have a closer look at how the concept of artificial intelligence evolved throughout the ages.

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What is the key value of AI for marketing?

Discovering how AI can be implemented to martech is crucial for marketers, as AI is predicted to improve the quality, agility and efficiency of their operations. AI is predicted to transform the way marketers work. Innovative companies have already put AI backed solutions into use to enhance customer experiences through advanced segmentation and predictive modelling technologies and many more are in the process of adopting AI. Results of a survey conducted by Demandbase, in conjunction with Wakefield Research, revealed that 80% of marketing leaders believe AI will revolutionize marketing by the year 2020.

Big Data into Meaningful Insights to Better Understand Customers

AI’s primary value for marketers though, lies in its capability to turn data into actionable insights. With loads of data generated from multiple channels today, AI is of great assistance for data mining and turning it into meaningful insights that marketers can leverage. These insights help marketers recognize patterns in visitor behaviour and better understand customer needs, demands and complaints, enabling them to deliver more relevant and timely messages. These insights also lead marketers to determine when is the best time to reach out to their customers and through which channels.

Data Analysis for Increased Engagement

One of the core features of AI is that it is not a stable model, but it learns from data in time. Dynamics of a website can change over time especially during seasonal periods. AI adapts to these kinds of fluctuations to generate more accurate data. Therefore, AI allows marketers to deliver personalizations, optimized individually based on visitor behaviour and also bigger data, increasing the chances of engagement. These capabilities of AI, like learning and adapting systems, power marketing automation. It is almost impossible for marketers to determine the best audience segments and CPCs to optimize budgets, manually. Rather a complex model should follow changes and update itself accordingly. The AI technology provides marketers to focus on results and take actions accordingly, showing marketers which part of the business to invest more.

How Insider Leverages AI

Although AI sounds like magic, it is not. To discover ways AI can help improve your business, it needs to be customized and developed to the right models that serve the purpose. Some AI-powered products you can leverage within Insider’s platform are recommendation engine and predictive segmentation. Both engines run on a machine learning algorithm that tracks visitor behaviour and learns from it to turn the data into actionable segments, helping marketers make data-backed decisions.

Businesses engage visitors via smart recommendations whether the ultimate goal is to maximize AOV, increase pageviews or simply boost engagement. Insider’s data-backed recommendation engine runs on an advanced machine learning algorithm, enabling marketers to deliver hyper-relevant product and content recommendations.

The predictive segmentation technology on the other hand helps marketers predict the future behaviour of customers based on their past behavioral patterns. While this segmentation capability can be used in a variety of personalization scenarios, it is widely used to optimize ad spend, bringing the disjointed worlds of martech and adtech together. Visitor behaviour on web, mobile web and apps are tracked and a machine-learning algorithm places visitors into ready-to-use segments, in real-time. Marketers can use these segments to deliver personalized experiences, campaigns and ads. For instance, a visitor’s likelihood to make a purchase online is predicted based on their online behaviour and knowing which segments are more or less inclined to buy an item on a given e-commerce site, marketers can easily prioritize who they would like to target with discount messages or advertisements. Leveraging likelihood to purchase algorithm saves marketers effort, time and money, also reducing customers’ churn risk.

Insider empowers global brands to deliver highly personalized experiences and predict the future behaviour of customers thus saving time and budgets. Please see some of our success stories with clients, Evmanya and LC Waikiki, who have leveraged AI for enhanced customer experiences