6 Practical Ways AI Can Improve Your Digital Marketing Strategy

by Michael Finn

Artificial intelligence is increasingly becoming table stakes for today’s marketing teams. Marketers’ use of AI has increased at a rate of 44% since 2017, according to Salesforce. Not only is adoption of AI increasing, but the technology is having a positive impact. Salesforce also found that high performing marketing teams are 2.7x more likely to be using artificial intelligence than underperformers.

Looking to dip your toes in the AI waters? Here are six practical ways to put artificial intelligence to work in your digital marketing strategy.

1. Identity Resolution

Analyzing behavior is a great starting point to understand your customers’ purchasing behavior and the effectiveness of individual webpages and marketing campaigns. But with most customers moving across multiple mobile, work, and personal devices through the day, reconciling each person’s behavior across all instances is a formidable challenge. AI helps marketers reconcile all of a customer’s unique activity across devices and throughout time with a comprehensive customer ID graph. Teams can use this information to personalize offers and experiences, which in turn will increase conversion rates.

2) Multi-Channel Attribution

In today’s highly fragmented environment, it is increasingly difficult to attribute credit for conversions to a single customer interaction or marketing campaign. Marketers can use AI to develop algorithmic attribution models that map each individual’s journey through your offline and online assets. Multi-channel attribution helps your team understand which channels work best for which products, which channels are most effective for loyal customers, which aspects of your strategy are underperforming, and more.

3) Personalization

With AI, marketing teams can uncover not only which path each customer has already taken through your online and offline assets, but also predict possible future paths. This opens up countless opportunities for marketers to meet customers where they are and engage with them on a highly personalized level. For example, telecommunications company Sky is currently using Syntasa’s AI platform to integrate digital and customer data from their website, apps, and even digital cable boxes in order to deliver highly effective cross-channel experiences for each individual customer.

4) Product Recommendations

Many companies rely heavily on personalized product recommendations at their brick-and-mortar retail stores. If you’re buying a smartphone, for example, an associate might recommend you also get a protective case. AI allows marketers to use (and improve on) this model for online shoppers, by providing personalized data-driven recommendations to each customer. When consumer electrical and mobile retailer and services company Dixons began using Syntasa’s AI platform to provide personalized product recommendations, for example, they tripled their add-to-basket rates when compared to manual recommendations.

5) Churn Reduction

According to the Harvard Business Review, it costs the typical organization 5 to 25 times as much to acquire a new customer as it does to retain an existing one. AI can help you reduce customer churn by identifying exactly where the customer experience is breaking down and enabling you to deliver personalized offers to keep users engaged. With AI, you can forecast churn before it happens and develop best practices to retain customers for the long term.

6) Ad Fraud

A recent study by cybersecurity firm Cheq found that 18% of online ad traffic in a four-month period (October 2018 – February 2019) was fraudulent. With U.S. marketers poised to pour more than $129 billion into digital ad spending in 2019, according to marketing research firm eMarketer, ad fraud should be a pressing concern for all organizations. AI can help combat ad fraud by learning what constitutes suspicious behavior (e.g., navigation patterns, time on page, number of visits) and flagging it accordingly. AI models have a vast advantage over rules-based models in that they can evolve over time and analyze far more dimensions to learn to combat the most highly sophisticated fraud attempts.

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Michael Finn

VP of Product Marketing at Syntasa

Mike is VP of Product Marketing at Syntasa (a Marketing AI Platform loved by Marketers, Data Scientists, and Data Engineers for its ability to unlock real value from their enterprise and clickstream data).

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