Creating an Engaged and Loyal Customer Base with NLG, Optimization and Deep Learning

    Creating an Engaged and Loyal Customer Base with NLG, Optimization and Deep Learning

    The pandemic has transformed the way companies engage with customers. It has resulted in an increase in demand for high-quality content. Language is now at the center of most customer experience strategies. It’s a more difficult environment for marketers than it was in 2019, but it’s also one that’s full of possibility provided organizations have the necessary tools.

    To remain competitive in 2021 and beyond, every business should embrace the new opportunities that AI brings to marketing. However, just because AI-powered marketing platforms are becoming more common and easier to use doesn’t imply there aren’t any risks associated with using AI in marketing.

    CMOs have a vast range of technology at their disposal. Many CMOs considered AI-driven platforms and capabilities to be “must haves” when AI initially became prominent, despite the reality that they have rarely lived up to the hype.

    So, let’s first take a look at three of the biggest hurdles that AI faces in marketing.

    Customer profile data

    Almost anyone can construct seemingly indestructible algorithms or machine learning technologies for marketing automation. However, those algorithms are typically only as good as the data with which they are fed. Personalization, for instance, has long been a target use case for AI in marketing, whether it’s for behavioral targeting, product recommendations, or one-to-one personalization.

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    However, AI’s efficacy is limited by what it knows about a customer. It doesn’t matter how good the machine is if a company can’t supply high-quality, consistent customer profile data.

    AI marketing technologies

    When it comes to handing over control to a machine, marketers are understandably wary. And with good reason – maintaining brand integrity is just as vital as automating and scaling. Many AI marketing technologies take a “set it and forget it” approach, putting a brand’s reputation and ability to appropriately connect with customers at risk. Customers can become frustrated quickly if automation leads to cross-channel, cross-device stalking. Marketers are particularly concerned about the risk of “message overkill” that some automated strategies might cause.

    Investing in AI

    Many CMOs feel compelled to invest in AI simply because it is expected of them, rather than focusing on the business issue they are attempting to solve. There is no doubt that AI can assist in the resolution of real-world customer issues.

    CMOs should concentrate on these and choose a solution that can clearly demonstrate a solution and a return. It’s also vital to start small and gradually increase investment in these technologies once they have proven to be effective, rather than investing large quantities of money up front without clear and quantifiable success metrics.

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    NLG, Optimization and Deep Learning

    When it comes to language, it appears to be more emotive than scientific or data-driven – but this isn’t always the case. It is possible to use a data-driven approach to language to generate emotive messages that accentuate the brand’s identity. Marketers who get their messaging right can increase their bottom line while also building a loyal and engaged customer base.

    Natural Language Generation (NLG) is an AI field that has been around for decades, but when businesses combine it with optimization and deep learning techniques, they get a powerful tool that can solve a real-world marketing problem – how to get the right message in front of customers at the right time and across channels.

    The solution to this problem combines proprietary NLG, optimization and deep learning with appropriate measures to ensure that all language is consistent with the brand and tailored to the demands of each marketing campaign. This combination overcomes three challenges – it’s trained only on the brand voice, ensuring that every message it generates is accurate; it provides automation and scale, but with human oversight and approval workflows built in to give marketers the confidence they require; and it selects the appropriate message to send for each campaign and calculates the financial impact.

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