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Navigating the Future of Marketing with MarTech, AdTech, and DeepTech

Arpit Gupta, Founder and On-demand CTO at Actionable Analytics, is a seasoned professional who has played a pivotal role in propelling numerous early and late-stage startups towards growth and successful exits. Arpit’s expertise extends to spearheading Transformation as a Service (TaaS) through a BOT (Build-Operate-Transfer) model, primarily focused on empowering hypergrowth companies within the FinTech and e-commerce sectors. He has collaborated with startups involved in search, discovery, and marketing technology (MarTech), leveraging data monetization platforms, customer profiling, customer lifetime value (LTV), and managing customer churn.

His accomplishments also include the development of Omnichannel capabilities within the e-commerce realm, achieved through a global data platform, and pioneering digital transformation initiatives for enterprises transitioning to cloud computing services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Arpit brings a wealth of experience from previous leadership roles at renowned companies, including Amazon, WalMart, Yahoo, Oracle, RAX, and AAG.

In a recent interview with The Interview World, Arpit offers his expert insights into how Advertising Technology (AdTech), Marketing Technology (MarTech), and Deep Technology (DeepTech) are revolutionizing the landscape of marketing automation. Here are the highlights from this discussion:

Q: What are the key trends and developments in MarTech and DeepTech that marketing professionals should watch for in the coming years, and how can they prepare for these changes?

A: Artificial Intelligence and Machine Learning (AI/ML) are already revolutionizing segmentation and targeting strategies. In the realm of advertising technology (AdTech), email marketing, SMS campaigns, and push notifications, AI/ML techniques are being harnessed to categorize the customer base into various socio-demographic groups. Subsequently, these customer segments are approached through the most suitable channels, at optimal frequencies and times, and in the language most likely to elicit a response.

With the introduction of GenAI, these targeted messages, emails, and content, in general, can be highly personalized for each of these customer segments. As a result, content assumes a pivotal role in conveying the value of the product or service you are offering to your customers. Harness the power of content generation from various sources such as video, audio, and text to create a more engaging and tailored connection with your audience.

Q: Could you share some successful case studies where AR or VR was used to enhance marketing campaigns, and what were the key takeaways?

A: Many e-commerce companies are leveraging Augmented Reality (AR) and Virtual Reality (VR) technologies to offer customers a tactile experience for products that have traditionally relied on touch and feel, such as lifestyle items including clothing, jewellery, shoes, and makeup. Virtual trials for these product categories are now being enabled by VR, while AR is being used to provide additional information and metadata about these items. This combination allows customers to have a lifelike, real-world experience before making their purchase decisions.

Furthermore, we’ve observed the use of AR and VR in training scenarios, such as driving simulations and medical procedures performed in a virtual environment using specialized headsets. While these applications are currently in their experimental stages, it is clear that in the future, these technologies will continue to blur the lines between the virtual and physical worlds.

These immersive experiences are predominantly focused on what customers encounter after they’ve entered the app, following the initial targeting phase. As a result, the emphasis is less on marketing and more on enhancing the post-click user experience.

Q: Data privacy and ethics are critical concerns in marketing technology. How can AI and data science help in maintaining ethical and compliant practices in MarTech?

A: GenAI occasionally generates content that approaches the boundaries of plagiarism since it is trained on a vast range of internet content. This means that the output it generates is rooted in existing information. This field is still evolving, and we must monitor its development to optimize its use.

In the meantime, it is essential to prioritize individuals’ privacy. If someone has not initially given their consent, we should avoid contacting them. Additionally, when evaluating campaigns, we should focus on aggregate levels rather than individual assessments. Any personal information, such as email addresses, phone numbers, or card details, should be appropriately safeguarded during analysis by using techniques like salting.

Q: Marketing automation tools are being infused with AI to streamline processes. What are the advantages and challenges of implementing AI in marketing automation?

A: Marketing Automation harnesses the power of AI to enhance campaign performance, increase content engagement, and boost open rates.

Like any AI implementation, it’s crucial to acknowledge that predictions may occasionally be inaccurate. Therefore, conducting AB tests prior to launching a major campaign is essential. Additionally, it’s important to continuously monitor and validate for model performance degradation over time, as strategies that were effective in the past may no longer yield the same results.

Q: How do you foresee the integration of AI and data science improving customer segmentation and targeting, allowing for more precise and effective marketing strategies?

A: Utilizing customer profiles allows us to transition from mere segmentation to the realm of personalization. This enables us to craft content tailored to each individual and deliver campaigns that are highly pertinent to their unique preferences. Thanks to the advanced MarTech tools available today, achieving this level of personalization is now achievable at scale.

Q: What are the potential risks and pitfalls associated with overreliance on AI and ML in MarTech, and how can businesses mitigate these risks?

A: Given the evolution of GenAI over the past year, there are potential challenges such as unanticipated obstacles and privacy concerns that teams might encounter. It’s crucial to bear this in mind before fully embracing GenAI. In light of constrained budgets, coupled with the impact of inflation and the global economic recession, people tend to gravitate towards established and proven techniques rather than embracing newer approaches like AI/ML.

One prudent approach could involve allocating a portion of the marketing budget, say 5%, for experimental campaigns, while directing the remainder towards traditional strategies. This strategy ensures that increased spending only occurs when the desired returns are achieved.

Q: Natural Language Processing (NLP) is essential for chatbots and sentiment analysis. How can businesses harness NLP to understand customer sentiment and feedback better?

A: The emergence of Gen AI, particularly ChatGPT, has ushered in a new era in the field. Virtually all AI bots now incorporate some form of ChatGPT, whether through OpenAI or similar large language models like Bard. These bots excel at various tasks such as answering frequently asked questions, engaging in conversational dialogues, and summarizing documents effectively.

However, it’s important to note that training GPT-based models requires a thoughtful approach. If you jump straight into asking questions without proper setup, there’s a risk of the AI generating incorrect or hallucinatory responses. To ensure accuracy, people are now employing techniques such as RAG (Retrieval-Augmented Generation), Transfer Learning, and chaining prompts, among others, to tailor these bots to specific domains and make them more relevant.

Q: The combination of AI and data analytics enables real-time marketing insights. What opportunities does this present for businesses in terms of agility and adaptability?

A: Analytics offers valuable business intelligence in several forms. It unveils historical insights, referred to as descriptive analytics, paints a picture of the future with forecasts, such as projected revenue and sales (known as predictive analytics), and ultimately, delivers its true value through prescriptive analytics. The true essence of prescriptive analytics lies in its ability to harness past and future data to craft solutions for the present. It transforms insights derived from analytics into actionable plans. For instance, if you anticipate a forthcoming drop in sales during the next quarter, prescriptive analytics can provide guidance on the necessary adjustments to evade this decline. This approach is akin to prescribing the right remedy for a business challenge.

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