Intelligent Marketers Are Using AI To Better Customer Experience Offerings
Artificial Intelligence or AI has become a widely popular term, especially since the onset of the pandemic last year which propelled the digital medium to become a mainstay.
AI has grown by leaps and bounds since its inception in the 1950s. It is an elementary system or algorithm that can perform complex tasks that normally requires human intelligence such as problem-solving, recognizing emotions, and even diagnosing diseases or forecasting changes in our environments.
Almost all facets of life or otherwise has been touched by AI in some way or the other. So why should the field of marketing be left behind? Now, in 2021, AI has the potential to help everyone involved in the marketing process perform their jobs exponentially well than they could ten years ago. Marketing through AI is a framework of leveraging technology to improve both brand and customer journeys and is used to boost return on investment (ROI) on marketing campaigns. This is done by using data analytics, machine learning, and other processes to gain insight into the target audience.
With the insights gained, brands can establish effective customer touch points and create space for more one-on-one interactions and exchanges with audiences. Now with the onslaught of the pandemic situation, it has become even more imperative for marketers to shift their focus towards creating enhanced consumer experiences online as we adapt to a digital life.
With the ability to collect data, analyze it, apply it, and then learn from it, AI is transforming digital strategies. Marketers can identify micro trends and even predict trends in some cases and take strategic decisions about where they can allocate their budgets and who can they target. As a result, brands can reduce advertising wastage and ensure that their spend delivers the best possible results.
AI has the potential to help marketers map out end-to-end content strategy. Recommendation algorithms and semantic searches use AI and machine learning to learn user behaviors and sentiments. While omnichannel marketing platforms facilitate lead capture across channels to help marketers run targeted campaigns, cross-sell, and up-sell; chat bots, voice bots, and conversational AI provide 24x7 real-time query resolution and chat records for subsequent analysis. This can provide brands with deep insights into their customers. Consequently, brands can then curate content and strategies that make customer interactions more viable, efficient, relevant, and enjoyable.
Additionally, customers' experience with the brand results in feedback. The feedback collected from customer scan also be analyzed with the help of AI. Brands can create a word cloud with words they would expect from their customers. For instance, brands look for words such as reliable, innovation, quality & precision. When these words are included in the customer feedback, then the brand is creating the required impact. But if the feedback comprises of words other than the desired words, the brand will have to rethink & re-work its value proposition and communication strategy.
Recommendation algorithms and semantic searches use AI and machine learning to learn user behaviors and sentiments
But every rose has its thorns! AI can be utilized to a certain point where data is structured, and the industry is organized. The biggest challenge anyone faces is the mammoth of unstructured data which leads to difficulty in data cleaning.
Before data mining and analyses became prevalent, data was collected as per the problem statement. The problem statement was first defined, research was conducted, data collected and then a solution was drawn.
Today, data is readily available. Various government websites publish extensive amount of data in the public and multiple companies are built on that government database.
In the present times, the real challenge faced by marketers and brands is, asking the right question for the already available data. The objective now is how to understand what the problem statements are, how does one frame the problem statement, how will they be solved and how does one get relevant insights.
Another problem faced by brands is proving the accuracy of the data provided by AI. Theoretically, this is also called the `Black Box Problem'. Computing systems programmed using machine learning are increasingly capable of solving complex problems in AI. Unfortunately, these systems remain characteristically opaque: it is difficult to "look inside" to understand why they do what they do or how they work.
Thus, manual intervention and AI need to work hand in hand because domain knowledge along with gut feel are prerequisites of building a strong problem statement.
To conclude, marketing decisions come from human beings and AI can only supplement marketers and improve the communication strategy basis the customers response. Some key considerations for marketers to gain pertinent insights with the help of AI are
• Ensure the data is sufficient for the AI algorithms to learn from and
• Never leave it all for tools when it comes to personalization.
The content needs to be natural to users
• Define where AI can make the biggest impact and bring on the highest ROI
for the business but beyond a point decide where human intervention is
required and why.