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Why B2C Marketers Need AI to Get Ahead of the Competition

As we approach the end of the 2010s, it has become standard for every company to have a web and social presence. However, the amount of traffic and sales generated from these channels can sometimes be disappointing given the effort and expense required to maintain them.

So why is it that some companies are able to drive traffic to their products in a way that seems to scale effortlessly? Chances are the successful companies are using artificial intelligence and machine learning. And they’re not just using these technologies to find new leads; they’re using them to successfully convert them into customers.

In the last article of this series, we examined the different ways Artificial Intelligence (AI) and Machine Learning (ML) can impact the entire customer journey. In this series, we’ll dive deeper and look at some real-world use cases for how AI and ML can be used in marketing.

Marketing personalization: multiply your engagement at scale

Marketing ads are often generalized to be able to target as large an audience as possible. The more users you can reach with a single ad, the better. However, this generalization usually comes at the expense of personalization, which is labor-intensive and requires a human sales force.

With AI and ML, organizations have the opportunity to analyze customer and user data to bring personalization-at-scale to even a bootstrapped startup’s marketing. Gaming website, GameSpot has experienced the benefits of email personalization at scale. By using AI and ML platforms, they were able to analyze the behavior and interests of each of their subscribers to create and send over 10 million custom tailored emails. This kind of personalization would not even be possible without AI and ML analysis and automation. The results were a boost in overall email engagement by 2.8x, a 32% lift in CTR, and a 180% lift in CTOR.

Intelligent recommendations: 1 billion streams in 10 weeks

Traditional eCommerce and streaming service recommendations are based on simple connections found in your shopping history. If you bought a fantasy novel last month, a traditional recommendation engine would recommend another best-seller fantasy book. While these have proven effective for many digital businesses, recommendations don’t take into account many of the complexities of a user, let alone the amount of available data created by the millions of other internet users.

Intelligent, AI-enabled recommendations help companies create tangible advantages in their verticals by offering users recommendations that have a high likelihood of converting into a sale. Take Spotify for example, who use AI and ML to create a unique Discover Weekly playlist for every user on the platform. The recommendations were so successful that 1 billion songs were streamed by the AI and ML-powered feature in the first 10 weeks of release and generated roughly $7M in royalties for artists. Even more impressive is that 71% of Discovery Weekly users saved at least one recommended song for later consumption, and 60% of users stream at least 5 more recommended songs.

Predicting customer behavior: capitalize on retention

Being able to predict your customer behavior can give you a lead on your competitors. Customer churn (when a customer stops doing business with your company) is an actively monitored statistic for most organizations, especially since we all know that acquiring new customers is 5 to 25 times more expensive that retaining existing customers.

Until recently, it was difficult to predict when a customer would churn and what could be done about it. AI and ML create a whole new segment for your marketing efforts: customers predicted to churn. Online children’s fashion store, Dinda, leveraged AI and ML to analyze mobile user behavior and segment at-risk users with targeted campaigns. This segmentation allowed them to target and personalize their mobile app notifications at scale. New & active users received an increased number of relevant notifications while re-engagement campaigns were created to minimize users the algorithm predicted would churn. This led to a 60% increase in revenue generated from their mobile app purchases in only a single year.

Wrap up

Next week, we’ll cover how AI and ML can be used to increase engagement and drive marketing to businesses. Get in touch with the Hanu Rock Stars to discuss the possibilities of using artificial intelligence and machine learning in the cloud to create marketing machines that consistently outperform the competition.