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This article was contributed by Ajay Mangilal Jain, Senior Partner of AI & Automation Practice at Wipro Limited
Ecommerce has long been growing in popularity with private consumers and enterprises alike, but the pandemic drove an unprecedented flurry of activity even from segments that hadn’t previously embraced online shopping. With this rapid growth, and with customers’ evolved expectations for timing and delivery, there is a growing need for direct-to-consumer brands to accelerate their marketing capabilities. At the center of this trend is the need for content, which must now be scaled across different platforms and segments quickly and intelligently. However, this process is very demanding, and effective content creation for multiple platforms — including ecommerce — is almost impossible without appropriate artificial intelligence (AI) and machine learning (ML) infrastructure.
When AI succeeds, so do content and content creation
To influence people, companies need to say something smart and relevant to the customer. Great content resonates, creates relevance, and influences behavior. Creating this kind of content requires analyzing data across multiple platforms, evaluating response rates to different materials, and diving into customer sentiment and engagement. Unfortunately, all of this takes time, lots of time.
AI and ML have the potential to speed up this process. AI has the ability to analyze large quantities of data and make recommendations about the content most likely to elicit the intended response. This automated analysis helps companies generate meaningful content and scale-up content development so that it is ideally suited for different platforms and market segments.
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Historically, direct-to-consumer brands have relied on AI and ML primarily for social listening and insights. While some social platforms have introduced in-app shopping, the majority of consumers still make purchases through traditional channels, and their social-media use is focused on product research. This makes social media a great place to influence consumer behavior and capture data. AI and ML consolidate data from these platforms — analyzing context, relevance, sentiment, and feedback to determine what motivates the consumer and predict the best performing content for each scenario.
Using AI/ML to extend ecommerce
AI and ML can play a key role in the development of ecommerce content as well. With more purchases taking place online, new ways have emerged to meet demand. This has introduced new complexities for content marketers as direct-to-consumer companies look to extend their presence to other platforms and commerce channels. By leveraging AI and ML, companies can overcome those complexities while increasing their visibility across platforms and gaining insights that ultimately drive growth.
Consider the case of an international chocolate brand. At the start of 2019, the company had a sales presence both on its own website and a prominent ecommerce retail website, where it hosted a number of product pages to address various segments and test different keywords and images. The marketing team used the platform to analyze the most successful pages and determine which elements consumers found most relevant. In addition, the team had to determine what search data was also most relevant.
The brand wanted to extend its online sales presence to additional retail websites and social platforms. This expansion, while promising, would essentially “trap” each outlet’s consumer behavior and sentiment data inside the respective platform. The challenge would then become how best to efficiently analyze what resonated with each platform’s audience and continue creating effective “feel-good” content that sets the company apart from its competition.
By leveraging AI and ML, the chocolate brand was able to capture and combine data from its ecommerce channels, its own product site(s), and all the new platforms. The AI-enabled ability to gather and analyze content for each product, segment, and platform allowed the company to rapidly scale up and create the most relevant content for each digital property. In addition, the increased efficiency accelerated the content creation that resonated with target consumers, while also resulting in higher page visits and increased sales.
While AI and ML are often viewed as a technology with limited applications outside of dry data analysis, they can in fact be used to fuel creativity. These tools enable companies to analyze branded content from multiple systems, create bridges between platforms, enhance content creation, as well as to empower their marketing teams to create and scale the most relevant content across multiple platforms. Infusing AI into a marketing strategy helps direct-to-consumer brands quickly identify content that resonates, creates relevance, and influences behavior. All of these functions provide companies the ability to quickly scale and react to sentiment changes in real time.
Ajay Mangilal Jain is Senior Partner of AI & Automation Practice at Wipro Limited
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