What Benefits Does AI Technology Bring to Retail? Three Ways It Helps Brand Marketing!

Using AI tools to develop digital marketing strategies has already become routine for many brands. This is especially true for retail and e-commerce businesses that face the general public directly. In a fiercely competitive market, if a brand wants to stand out and capture consumer attention, new AI marketing tools can help it achieve precise, real-time and statistically supported results in three key areas: collecting consumer behaviour, detecting consumer intent, and analysing the best strategy for message exposure.

In this article, we’ll talk about these AI technologies and how they help brands succeed in marketing.

Using Natural Language Processing (NLP) to uncover intent in questions and browsing

Marketing today emphasises intent-based targeting — understanding what consumers are subconsciously thinking while they wander around online. AI marketing tools developed with NLP language models help brands do exactly this. In addition to copy-generation functions similar to AIGC, the key strength of NLP bots is that, through deep learning, they can understand the sentences consumers actively type: the meaning they want to express, incomplete sentences, and even the emotional tone in their wording.

NLP’s probing ability is often used in customer-service chatbots, social-media listening, market research and consumer insight, and social copy generation. NLP makes it easier for machines to understand what human consumers mean, while also helping marketers instantly grasp the hot topics, keywords, public sentiment and discussion directions that consumers care about at the moment. When designing attention-grabbing content or planning personalised interactions for existing members, marketers can rely on objective market data instead of just their own ideas, and truly build communication that starts from the consumer’s perspective.

Example: Bangkok travel vs. special-offer air tickets

For example, consumer A interacts on social media with two types of posts: travel-spot introductions for “Tokyo travel” and for “Bangkok travel”. Normally, data analysis can only judge that “A wants to travel abroad soon and is comparing where to go”, then starts pushing various air-ticket and package offers.

But if we bring in an NLP AI tool, we can quickly see, from a large amount of data, the differences in how consumers interact with different topics. Maybe A only gives “Likes” to Tokyo café posts, but both likes and saves Bangkok café posts; in comments under Bangkok posts A asks the author about business hours, while comments under Tokyo posts are mostly “tag friends for giveaways”.

From this, we can tell that A is clearly more interested in going to Bangkok than to Tokyo. At this point, the messages marketers should push to A are “exclusive guides and deals for Bangkok”, not promotions for every country, which would only spread A’s attention thin.

AI smart analysis of the best times to advertise and post

Marketers in charge of publishing content and running ads inside a company will notice that social-media business dashboards often provide “recommended posting times”. These help ensure that every piece of brand content — whether an ad, a social post, or an SEO article — can achieve the highest possible exposure and reach.

As data analysis continues to advance, connecting social platforms with AI MarTech tools allows us to analyse social-behaviour data in more detail. Based on recent or historical interaction records, the system can suggest suitable target audiences for the current content, appropriate channels, best posting times, and even recommended ad cycles and budget amounts (you can refer to the AI tool Linker AI as an example).

When every time the brand exposes a message — whether it is a promotion, a social post or an SEO article — it can accurately take into account “time”, “channel” and “audience”, we can be sure of two things:
1. The message will gain a high level of exposure.
2. The promotional content pushed out each time will match what the audience wants to see, or at least will not be something they strongly reject.

Short-term and long-term benefits of AI-driven precision delivery

In the short term, when marketers use AI to filter before exposing brand messages, they can lower the cost of ineffective impressions and, with a smaller budget, gain marketing results with high conversion rates and high returns — this is what we call precision delivery. Especially for paid advertising, brands need to pursue the goal that “every bit of ad spend is used on audiences, channels and timings with high potential to convert”.

In the long run, if the audience for each exposure is precise enough, the brand’s favourability in consumers’ minds will gradually increase. The reason is simple: the brand always provides “what people who need it actually need”, instead of randomly blasting out too much information and making people annoyed.

Precision delivery helps brands build the image of “a brand that understands consumers”. A group of consumers who are treated as such an audience are more willing to actively learn about the brand’s core values and unique traits. When consumers feel that “this brand is not just pushing products on me, but is standing on my side and sharing similar values and personality”, they are more likely to choose the brand’s products and to keep following and interacting with the brand.

AI smart segmentation and prediction of high-potential audiences

Earlier we mentioned consumers “being listed as the brand’s audience”. This means that based on market positioning, product features and price, the brand looks for consumers whose needs may match. At the same time, the brand also studies all reachable consumers’ personas , preferences and values, behaviour when browsing brand websites, and search intent, then sets audience tags to let AI dig out and bracket: “these consumers have a high probability of becoming customers”.

In the past, to find a brand’s audience, companies would first narrow things down through brand positioning and market positioning, and then ask data analysts to cross-compare results from many advertising channels, marketing channels and social platforms to identify target consumers.

Today, there are already many AI MarTech predictive segmentation tools on the market that can analyse consumer behaviour and identify customer groups with a high probability of purchase (for example, Crescendo Lab’s “Smart Segmentation – Purchase Propensity Prediction” ). With easy-to-use, user-friendly AI tools, brand owners can strengthen their ability to learn how to segment and how to find highly potential consumer groups on their own, without having to rely on data analysts to manually pull and analyse data for every strategy.

AI prediction is faster than manual analysis and can keep learning to continually improve accuracy. For audiences that have already been bracketed, AI tools can monitor 24/7 whether they respond positively to brand messages or newly launched products. Each round of optimisation improves the precision of predicting potential audiences, making brand marketing more and more accurate so that every strategy uses its effort efficiently and hits the right consumers.

For example, a fashion brand can use AI to analyse consumers’ browsing history on its website, pre-checkout behaviour and online purchase records to successfully identify a target group B who especially likes “Style-B” clothing. The brand can then design targeted marketing strategies for this B-group, increasing conversion rates and customer loyalty. This is the marketing effect created by “smart segmentation”: not only higher performance, but also brand campaigns that better match consumer expectations.

Conclusion

The sections above show how AI technology helps marketing in three ways: collecting consumer behaviour, detecting consumer intent, and analysing the best strategy for exposure. Through predictive analytics, precision delivery and deep learning, brands can approach consumers more strategically and also develop unknown markets. Embracing AI tools as part of brand-marketing strategy is not only how brands keep up with the times, but also a key factor for achieving brand success.

Digit Spark makes active use of data science combined with business and marketing logic to help companies create digital content and service-process experiences that are closer to real market needs. At the same time, it leverages AI to revitalise brand strategies and helps enterprises comprehensively enhance digitalisation, data usage and brand-performance operations.