Tag: AI and ML

  • Trends in Consumer Behavior Analysis

    This article has been contributed by Anubhav Pandey, Chief Strategy Officer, Consortium Gifts

    Consumer behavior is all about the steps people take when deciding to buy and use a product or service. Thus, with the development of society, emergence of different cultures, economies grow, the nature of these decisions also transforms. Today consumers are driven by factors such as better lives, improved technology and a connected world. Understand and analyse these trends and shifts are very crucial for an effective marketing strategy.  Let’s take a look at some current trends that are impacting how consumers make decisions:

    Artificial Intelligence and Machine Learning

    AI and ML are slowly revolutionizing how corporate entities within the market analyze and forecast consumers’ behavior. This lack of focus on the details of purchasing decisions causes a shortfall when compared with traditional survey techniques, which may give less clear, less accurate, pictures of consumer behaviour. Whereas, AI and ML can present a better and accurate picture of the direction consumers are heading. It is possible to forecast follow up purchases the customer is likely to make, the frequency of such purchases, and even when the customer is likely to leave your website’s shopping cart. For example, the case of Netflix. Its recommendation system is based on a machine learning algorithm that suggests what kind of show or movie you might like to watch next. 


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    Digital Convenience

    Today’s consumer expects a convenient shopping experience facilitated by the increased cases of e-commerce. Amazon and Zomato are some of the modern platforms that have changed the approach to presenting products for sale and availability of products, where options like one-click purchase and ‘fifteen minutes delivery’ exist. These innovations have set standards of operations for consumers in different industries hence becoming the benchmark for businesses to follow.

    Social Media Analytics

    If we take India into consideration where there are over 800 million internet users, social media plays a strategic role for analysis of consumers. Social media analytics can be used to monitor customers’ attitude towards a particular brand, the general trends in the market, and even the sentiment of the public. Social media pioneers like Instagram and YouTube have tremendous influence over the buying behavior particularly among the youths. Research found that 63 per cent of Gen Z believe in Influencer marketing more than the typical brand commercials. It has further led to increase in influencer marketing since brands have allocated huge budgets for collaboration with social media influencers. Furthermore, SMM facilitates the analysis of social media sites and help business to market their products in the right way to the right audience.

    Personalisation

    Although personalisation was once an experimental concept, it has become mandatory in today’s marketing environment. The audience now wants specific experiences provided by brands that are unique to the individual. Often, through considering the user’s preferences, the companies that apply personalisation keep the attention of users longer, for instance, Netflix offers shows advisors, as well as Spotify offers users’ special compilations of tracks.

    Machine learning based applications observe people’s behavior, thereby defining unique and passionate experiences. Such specificity of the approach is not only designed to improve the customer experience but also to compel him or her to return for the next consumption occasion. One can conclude that the more a brand focuses on personalization, the better it is for the brand’s relationship with its audience.

    Data-driven Insights

    Consumer behavior analysis has been revolutionised through data proliferation. A McKinsey study revealed that companies leveraging advanced analytics have seen a 20 per cent increase in customer satisfaction and a 15 per cent boost in revenue. Through text mining method unstructured data such as social media posts and customer reviews can be analysed to extract insights. It helps addressing the common pain points and has a scope of improving customer satisfaction by 25 per cent. There is also natural language processing or NLP which enables computers to understand and interpret human language, facilitating sentiment analysis and customer feedback analysis. Examining data points collected over a series of time is a great way to identify patterns and trends.

    Social Proof

    Word of Mouth influence through social media is rising to be a significant power that drives the consumers. Endorsements from fellow consumers especially within the social media platforms play a big role towards the sell of a product. On the other hand, negative comment may discourage the potential customers.

    Data Visualisation and Storytelling

    Data visualisation is an essential tool for understanding consumer behaviour. Interactive dashboards and infographics make complex data easy to digest, allowing businesses to spot trends and adjust strategies quickly. Real-time insights help companies stay agile, responding to market shifts as they happen. By presenting data in a visually appealing and accessible way, businesses can make more informed decisions. Data visualisation also enables companies to communicate insights across teams, driving collaboration and encouraging data-driven decision-making at all levels.

    Neuromarketing and Biometrics

    These are one of the most advanced form of tools. These methods include electroencephalography to measure brain activity to understand emotional responses and cognitive processes to identify most effective advertising stimuli and assess brand perception. The eye-movements can also be tracked and analyse to decode the areas of interest and attention. It is also helpful in revealing which elements of a website or ad are most engaging and inform design decisions. Marketers also use galvanic skin response as a strategy to measure physiological changes, such as sweating, to assess emotional arousal. It gives insights to emotional impact of marketing campaigns and identify products that evoke strong emotional responses.


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    Consumer Journey Mapping and Analytics

    Identifying all interactions between a customer and a brand is crucial. A previous Bain & Company study established that companies that mapped their customer journeys have witessed a 25 per cent increase in customer satisfaction as it can identify pain points, optimize experiences, and increase loyalty. Businesses now also perform a behavior flow analysis by studying  the sequence of customer actions and decisions to reveal the hidden patterns, identify bottlenecks, and inform marketing strategies. Another very useful tool in mapping consumer journey are heatmaps, which basically visualise customer engagement with websites and apps. The role of heatmaps in optimizing website design, improving user experience, and increasing conversions is crucial.

    Ethical Considerations and Data Privacy

    The post-cookie era is encouraging a new age of privacy-first marketing. While collecting consumer data is essential to analyse consumer behavior, it is also important to implement policies and procedures to ensure data quality, security, and compliance. We are at a stage where every business need to prioritise transparency, consent and ethical data practices to succeed and grow. Hence, consent in the king here. To increase transparency and trust, it is crucial to obtain customers’ clear consent for gathering and using their data. Data anonymisation and pseudonymisation are useful methods to avoid data leakage and safeguard customers’ private data. Privacy-preserving technologies like differential privacy and privacy-preserving measurement can be utilised to enable brands to measure campaign effectiveness and analyse user data without compromising individual privacy, ensuring that marketing efforts remain both effective and ethical.

    Conclusion: The Human Touch

    While technology plays a pivotal role in analysing consumer behaviour, maintaining a personal connection remains essential. Brands that combine data-driven strategies with genuine empathy and meaningful interactions can stand out in today’s crowded marketplace. Balancing innovation with a human touch is the key to building strong, lasting relationships with consumers. As technology continues to evolve, businesses must adapt while keeping the customer at the heart of everything they do.

  • AI Firms Say Analysing Use Cases and Robust Data Strategies Key for AI

    One phenomenon that knocked the wind out of everyone’s lungs in 2023 was artificial intelligence! Right from chatbots to AI tools for video and content creation, it has caught everyone’s fancy.

    India wasn’t far behind, as several companies and apps mushroomed, reflecting the global scenario. A NASSCOM report shows that more than 60 Indian startups started shop in April and June 2023. The optimism surrounding AI has rubbed off on investors, too.

    NASSCOM’s India Data Science & AI Skills Report shows spending on AI in India topped $3 billion in 2022 and is expected to jump to $4.2 billion by 2024.

    In fact, Goldman Sachs sees investments in the AI sector topping $160 billion globally by 2025, with India having an advantage of “resilient growth and strong demographics,” which could attract investments from global investors and corporations.

    “The intricate dance of AI and ML capabilities, coupled with multi-channel integration, has propelled businesses toward a future where agility and scalability are paramount,” said Abhijit Dutta, Chief Strategy Officer of Hostbook, a cloud-based accounting services firm that also offers automated business solutions.

    Despite the rapid growth of AI companies, challenges remain when it comes to integrating AI into traditional corporate processes.

    In this article, StartupTalky speaks to a few AI consulting companies that shed light on AI integration in India.

    AI Awareness
    Data Mining Strategy
    Identifying Use Cases
    AI Training

    AI Awareness

    The top challenge faced by AI companies is to dispel myths surrounding AI integration, said Agam Chaudhary, founder and CEO of Two99, a collective of agencies with a focus on advanced e-commerce, technology, and marketing.

    Explaining how they tackle the problem, Chaudhry said, “We show them it’s not a sci-fi flick but a practical tool that can make their lives easier. We bring out the success stories custom-made for their industry. We lay it all out on the table—the good, the bad, and the ethical considerations. Building trust is crucial. We’re like AI consultants, working hand-in-hand with them, understanding their worries, and customizing solutions that fit like a glove,” Chaudhary said.

    Based on client experiences, PwC had listed some myths that clients seemed to express about AI: ‘ businesses don’t need AI, and they are ‘too risky, to name a few.’

    Over time, there seems to be a gradual attitudinal shift towards AI. A survey of 54,000 workers conducted by PwC in September showed that a third of respondents believe AI will help increase productivity and efficiency. More than a quarter said it would help them learn valuable new skills.

    An AI survey by Uplekha found that 61% of Indian companies feel AI will make work more efficient.

    Employee Attitudes on AI by PwC
    Employee Attitudes on AI by PwC

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    Data Mining Strategy

    Data mining is the cornerstone of AI and ML. AI heavily leans on vast sets of existing data and statistics to come up with a near-perfect AI model of content or analysis, which can then be applied to the problems in question.

    According to E&Y India Chairman and CEO Rajiv Memani, India is the second largest generator of data after China, which is an added advantage for training AI models.

    Yet, the availability of clean data sets has been an issue.

    “All three aspects, i.e., clean data, relevant data, and a sufficient amount of data, are important. The models need sufficient data, and for financial risk use cases, they should cover historical data from at least 1-2 economic cycles. In the absence of such data, these AI and ML models produce suboptimal results and end up losing user confidence in using these models,” said Abhinava Bajpai, Co-founder and Head, Acies TechWorks.

    The government is in the process of developing India’s comprehensive AI strategy, which involves building an India Dataset Platform and an AI Compute Platform. 

    Information and Technology Minister Rajeev Chandrasekhar recently elaborated on these, saying that the Indian dataset platform will be one of the largest and most diverse collections of anonymized datasets to train multi-parameter AI models. Meanwhile, the India Compute Platform will create a substantial graphic processing unit (GPU) capacity for enterprises to train AI models under a public-private partnership.


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    Identifying Use Cases

    In a bid to jump onto the AI bandwagon, companies are struggling to adopt specific use cases, AI experts said.

    “I would say that you know, if organizations are looking at adopting AI, look at some of those achievable use cases, which they can then take right and partner with companies to achieve those,” said Rohit Yadava, Chief Operating Officer, MSys Technologies, which offers digitalization services to companies.

    Echoing this view, Sairam Vedam, Chief Marketing Officer of Cigniti Technologies, says, “Our approach has always been to understand the existing data strategy of the company. Also, what is the existing automation strategy of the company because we are a born-testing quality engineering company? So, we look at AI applications through those two lenses. And then, as I said, educate, experiment, experiment in the sense of experiment on use cases.”

    Another report by Deloitte outlined the use cases of AI across six major industries: consumer, energy, resources, and industrial; financial services; government and public services; life sciences and health care; technology; media; and telecommunications.

    For instance, use cases within the consumer goods segment could include aiding content generation, trade promotions, creating new product prototypes, creating an immersive marketing experience, market intelligence through data access, on-demand customer support, and shopping assistants.

    “Performing cost-benefit analysis of AI and ML models before implementing them is important for the continuous and persistent use of such models… The size of the business and revenue and cost impacts need to be considered before implementing AI and ML models,” said Bajpai from Acies Consulting.

    Global Data Science and AI Installed Talent
    Global Data Science and AI Installed Talent

    AI Training

    Training staff with AI know-how has now become imperative. This is apparent from the rise in demand for AI training and AI-related upskilling courses.

    “It has become essential for executives to learn AI. Data science training is specifically helpful to train aspirants in AI, and by ensuring the holistic development of executives, these programs can become a game-changer in helping industries realise the true potential of AI,” said Piyush Arora, senior director of business strategy at AI-based learning platform Edvancer.

    Edvancer has seen a 4x rise in applications for AI courses and a 100% increase in interview opportunities for students with data science and AI qualifications.

    A NASSCOM report shows India is currently ranking 2nd in training and hiring AI talent in the world.

    “A major portion of the future talent demand will come from the existing tech workforce through upskilling; learning curves are becoming shorter, and skills are becoming redundant in 18 months,” NASSCOM said, adding that “design thinking” is a key skill to implement AI and not merely the ability to build and run complex algorithms.

    However, all this training comes at a heavy cost.

    A study conducted by the Boston Consulting Group and the Indian Institute of Management-Ahmedabad estimated that just the top 500 Indian companies would require “at least one million hours of training.”

    “Organizations must invest in significant upskilling of mid- and senior-level management on the business aspects of AI, digital transformation, ‘agile’ ways of working, and more. Companies have a choice to prioritize AI and adopt it or perish—and the nature of this technology is such that either scenario would come about very quickly,” the report said.

    The top 500 listed companies would need at least 25,000–30,000 advanced practitioners of AI and ML in the next 3–5 years, including AI professionals, data scientists, data engineers, and enterprise architects, the report said.

    Conclusion

    AI maturity has been a buzzword in 2023, given the boom in AI and its peripheries. According to AI watchers and experts, this maturity will accrue over a period of time with enough use cases to innovate, experiment, and smartly apply collated data. However, the large boom in AI within the country has laid bare the talent and skills gap. Hence, training and upskilling pertinent AI skills must become a priority for companies going forward.