In the ever-evolving digital world, visual content became the kingpin of communication. In fact, people are sharing millions of images and videos every day across various social media platforms. Brands recognize this trend and constantly explore ways to leverage visual content to better understand their customers’ preferences and behaviors. One such technological boon they’re harnessing is visual image analysis. But what is it? And how does it help companies better understand how their products are being consumed? Let’s discover the effectiveness of visual analysis.
Visual image analysis unveils several types of information that are relevant for understanding users
What is visual image analysis?
Visual analysis with machine learning (ML) is a technique that involves the processing and interpretation of images and videos to understand patterns, objects, and behaviors within them. It can detect different kinds of information within an image. In a consumer context, this involves several steps:
- Image classification: identifying categories with ML algorithms, associating images with certain lifestyles, activities, etc.
- Object detection: recognizing specific objects within an image.
- Sentiment analysis: recognizing emotional states from images.
- Scene recognition: recognizing the context of an image (place, time, etc.).
- Image-based recommendation systems: suggesting products based on visually similar items.
Consumers today often share their experiences with products through images on social media platforms, review websites, and blogs. These images can provide a wealth of information to companies, going beyond what traditional text-based feedback can offer. Machine learning has significantly transformed social media platforms, allowing them to offer enhanced user experiences. By analyzing vast amounts of data, social media apps can provide personalized content, targeted advertising, and deeper insights into user behaviors. For example, Facebook and Instagram use ML to analyze sentiment, trending videos, or reach more people. LinkedIn uses ML to optimize user experience by analyzing user connections, skills, and reactions to posts. AI also transforms social media marketing, making campaigns more personalized and effective. Besides communications, ML helps brands enhance their marketing strategies by both suggesting specific visual content to the users and assisting brands to analyze their customers’ content.
Image Analysis, the future of e-commerce and retail businesses
Through image analysis, marketers can identify a myriad of information on users, such as:
- Sex, gender (through visual cues, clothing, hairstyle).
- Age (through facial features).
- Frequency (by tracking when images are posted/viewed).
- Satisfaction (interpreting visual and textual reviews).
- Type of use/occasion (when and where products are used).
- Lifestyle/habits (interpreting images consumers share).
- Socio-cultural factors (through cultural symbols or indicators).
- Design/packaging (analyzing the effectiveness of the brand’s strategy).
- Pricing positioning (through users responding to the pricing strategy).
- Conversation style (text within visual content).
- Mood (facial recognition and emoticon use).
- And the list goes on!
With visual analysis, brands can understand how and why users engage with (their) content and their preferences regarding online content. All these indicators help companies gain precious insights on their customers, creating individual 360° profiles to target them best during marketing campaigns. Visual analysis leads the way to enhanced personalization, accurate customer profiles, efficient quality control, competitor analysis, and, in the future, augmented reality shopping!
But in terms of performance, what does visual analysis really mean?
We now understand the concept of visual image analysis and how it works, but what does it bring to the table? Here are 2 NextUser’s use cases to enlighten you on that part:
Diageo – Listening with AI
Diageo is a British multinational alcoholic beverage company including brands such as Smirnoff (vodka), one of the best-selling distilled spirits in the world. Smirnoff wanted to better understand why and how consumers think, act, and feel the way they do, and to use this information to provide personalized recommendations. To address this challenge, Diageo utilized the NextUser Social Enrichment 2.0 platform. This platform enabled a comprehensive understanding of the Smirnoff brand’s following, social media exposure, and consumption patterns. Additionally, the platform provided context by analyzing the brand space, brand competitors, and brand influencers within a unified view. Using the full potential of A.I., NextUser analyzed over 300 variables per user, including images, texts, and personality traits, to build communities of Smirnoff users. This data was then gathered and made available in a single platform, allowing Diageo to visualize the size of these communities, understand consumers, identify brand influencers, and compare these insights with their competitors. This resulted in 11 subspaces analyzed and 30k Instagram influencers identified.
Analysis of the context of consumption for the brand Smirnoff
Nestlé Recetas – Using visual image analysis for better targeting
Beyond knowing who their clients were, the brand Nestlé Recetas wanted to unveil information on who they were, how they operated, and where was their time and attention. To get this type of insights (interests, personality, uses, brand website behavior, influence & trust levers, etc.), Nestlé trusted NextUser and their data gathering and visual analysis platform. As a result, several clusters were identified for Nestlé Recetas, based on their interests and lifestyle. Furthermore, these analysis helped Nestlé build 360° user profiles, and to get a comprehensive and precise understanding of their targets in order to deliver a personalized storytelling. This boosted customers’ engagement towards the brand with 74.4k registrations (between May 2020 and October 2022) and +21k persona profiler’s answers gathered.
Analysis of the context of consumption for the brand Nestlé Recetas
Through this analysis, we realized that images on social media not only captivated audiences and amplified messages but that they could also ignite higher engagement rates than text-only content. By visually narrating stories, companies enhance memory retention and supercharge your brand’s resonance, matching with consumers’ doings on online platforms. Strategically utilized, images boost your branding, carve out recognition in the digital landscape, and even turbocharge your SEO. Plus, they increase content readability, creating a compelling, user-friendly narrative. Understanding how consumers communicate their thoughts through images is a way to better address them with a similar medium.
Final thoughts
Visual image analysis has opened new avenues for companies to understand their customers and how their products are consumed. In a world where images speak louder than words, it provides an in-depth view of the consumer-product relationship. It helps tailor marketing strategies, improve product design, and ultimately enhance customer satisfaction.
As technology advances, visual analysis capabilities will only expand, providing even more valuable insights for companies. Embracing this technology today can give businesses a competitive edge, helping them stay ahead in the digital landscape. Companies can uncover hidden treasures by analyzing images, driving their business forward.
To stay at the forefront of trends and innovation, leverage the power of visual analysis with NextUser!
