Author: Ori

Customer experience is now at the center of modern business strategies. Such a customer-centric approach is growing in our times due to massive competition and options available to consumers. But how do brands create a competitive edge and offer an exceptional customer experience?

AI allows us to hit that sweet spot and deliver impeccable sales as well as customer service. It enables consumers to get a hyper-personalized experience that is quick and efficient.

When you consider it from a customer’s perspective, creating a better experience is all about simplification. Conversational AI can help simplify the entire consumer experience from product understanding to purchase and from post-sales services to customer claims.

But in order to simplify the process, we also need to look at the pre-existing data and make the best use of it.

Behind Every Great AI is a Lot of Data

The more data that’s available, the more training it will have and the better it will perform. There’s a clear correlation between the amount of data that is used to bootstrap an AI’s knowledge base and its accuracy in carrying on conversations with your customers correctly.

To be accurate, data is the building block behind Conversation AI. Through the help of available data and patterns, AI defines the understanding of a customer’s message and looks for the desired information.

Let’s take an example here: Imagine that you’re creating a virtual agent, and the virtual agent sits on a website or landing page.

The website is going to be connected to a tech stack such as a CRM, Marketing automation tool, etc. Systems that will give you insight into historical data, historical and real time actions of a customer.

To know more on how you can use messaging platforms to capture first party customer data – view our blog piece here.

Maybe the user is on the returns page. Or they have added a product to the cart, or have just logged in – at each instance, they would want to talk about a different problem and have a different conversation. Several common scenarios may include:

• I recently purchased a shirt but I received the wrong colour.
• When will I get my package? I ordered a product from your site a few days ago!
• I tried to purchase a subscription for your platform. The amount is deducted but I still don’t have access. Help!

Now, they may connect with your sales assistants, who in return can recognize their phone number, understand what their previous interaction with the company was, and whether this conversation is likely to be relevant to a previous conversation.

If you smartly use the data that you already have on your customers, their data from their conversations, data on their preferences, based on previous customer interactions, or contextual, based on the actions that the customer has taken, then you’re able to provide truly personalized, truly contextual AI assistance that is going to create a superior customer experience.

Why You Should Be Concerned With The Quality Of Data?

While the quantity and availability of data is a necessity to train a Machine Learning model, it is equally important to have accurate, precise, and useful data.

Data that is inaccurately collected or extracted would make Conversational AI inefficient. As such poorly collected data can actually defeat the purpose of quick, efficient, and automated sales as well as customer support.

Therefore any solution that we build with Machine Learning has to use a verified and accurate set of data in order to be useful and improvised over time.

Quality Data In Conversational AI Can Lead to Numerous Performance Advantages:

• Good quality data can allow Conversational AI to fulfill any customer requests or questions accurately, creating a better experience for them. In turn, it helps brands gain customer loyalty. A win-win situation, right?
• Accurate data can allow you to be safe against false promises and inaccurate customer expectations.
• Efficient Conversational AI solutions can provide you with an idea of consumer interest and frequent concerns. This can help businesses to gain feedback on where and how they can improvise. Lending results beyond the solution itself.
• Machine Learning models can improve over time if it is fed the right data. Much like a positive feedback loop, such a Conversational AI solution would get better over time.
• Only high-quality data can give expected outcomes. Without a verified Conversational AI solution, there’s constant room for error and such a Machine Learning system must be supervised throughout the implementation.

In the End, It’s All About the Right Dataset

Gen-AI carries great potential to transform your customer experience. However, it is essential to understand that such a solution can only be beneficial to you if it is built upon the right dataset.

Schedule a demo with our experts today to know how we can help you with effective deployment of Gen-AI into your business.

Search ads use keywords to find products that match the user’s needs. When customers search for something and find a match, they might have questions that reduce dissonance and move them toward conversion. Moreover, the data from all those could help your business or client in the future too.

Landing Pages:

As a digital marketer, you might have read and implemented thousands of tactics to get your clients the conversion they want. One specific part of advertising – search ads – is a lot trickier than any type of social media ad. When working on a search engine ad and its copy, you need to focus on the landing page structure, the content, as well as the users, search intent vis-à-vis your product offering, or your ad will never perform.

After tons of practical layouts and strategies to optimize the post-click experience on landing pages, there is still a loophole. A majority of search ads do not convert, and customers often click and bounce.

If you are using the age-old techniques of copy alternatives or graphical representation, you may be losing out on personalized experiences. Today’s customers are expecting personalized, immersive, and context-rich experiences.

Post-Click Automation:

96% of paid Google Ads don’t convert. While most marketers use automation services, a flaw focuses on the pre-click phase. To move with the personalization towards the post-click phase, a smarter way is post-click automation (PCA).

What is Post Click Automation?

Marketing funnels have a post-click stage where automation is done through marketing technology, which is known as post-click automation. It is a technology through which marketers leverage advertisement conversions by providing one-to-one personalized experiences. It fills the gap between the pre-click segmentation and post-click reaction phases by combining elements like AI, Ad Mapping, Machine Learning, etc.

How Does It Impact the Post-Click Experience?

PCA combines all elements of post-click expertise like A/B testing, page creation, etc., to provide a comprehensive solution in a single place. Ad mapping, scalable creation, personalization, and optimization techniques are combined to create experiences for every stage of the advertising funnel for finding the search ad performance.

But even with post-click automation that claims to resolve the issues of bounce rates and low conversion via search campaigns and PPC, a complete solution is yet to be found.

What are the Customers Expecting?

If a customer searches for something online, they are looking ahead to find multiple options that fit their consideration set. With thousands of similar products and services, brands cannot rely simply on creating a landing page per ad. Customers have a wide range of options, which further lengthens their decision-making process, leading to their indecisiveness.

If you are relying on basic search and tactics, your CAC will take a toll. So, what is the way out?

Improve Post-Click Experiences Through Personalization Using AI & Conversations

Every customer wishes to engage with the brand they are likely to purchase from. While there are certain informative points mentioned, the customer might still be skeptical. The best way to approach the increasing bounce rates is to aid them with a technological product that converses with them at their time and convenience.

1. According to Salesforce, 69% of consumers prefer using chat interfaces as they deliver real-time answers and personalization. 
2. According to Business 2 Community, 82% of consumers claim that instant responses to their questions play a significant role in the buying process when contacting brands.

Engagement:

Considering how chatbots are impacting businesses, Adster aids your search ad with a conversational layer on the ad. Users can directly click on the Search Ad, and they will be redirected to the chat section where they can have a conversation.

The virtual sales agent answers their questions, thereby directly sharing an option of Buy Now and Payment thereafter. In this way, within a short period, you build brand value, recognition, aid product discovery, and drive revenue.

All that you have heard about the Zero Moment of Truth is finally here.

Forget building landing pages with content, graphics, and videos with CTA’s. With Conversational Search Ads – Adster, there is no need to collect data manually.

The customer engages with the brand directly without multiple touchpoints. It builds a virtual environment with immersive and personalized experiences without having to click on the landing page – in turn reducing landing page bounce rates.

The technology of Adster is patented, which allows it to dive into the user’s search intent and drive conversation accordingly. With this AI-driven mechanism, users can have a rich post-click experience without going through the hassle of reading landing page content.

Moreover, Conversational Search Ads help advertisers connect with customers at their time and capture the zero moments of truth. Finally, it also collects the PII, shares rich media for conversation, and collects payment on their behalf with end-to-end encryption and data privacy.

Re-Engagement in a Cookie-less World

Adster’s conversational Search Ads offer marketers and brand owners the ability to re-engage and win back customers via cookieless device-based re-engagement.

Through a well-crafted, context-relevant drip campaign, curated and personalized nudges can re-engage the user back into the buyer journey.

For brands looking to improve lifetime value, these re-engagement nudges can create seamless and memorable post-purchase experiences, accelerating top-of-mind and loyalty.

Video Synthesis Tech + Conversational AI = A Winning Formula

Post-click experiences and personalization can further be amplified using Video Synthesis technology. Using Video Synthesis technologies, brands can communicate with their customers, using videos offering an immersive and personalized experience. 

With Conversational AI and Video Synthesis, the buyers’ post-click experience can transform into a customized post-click journey.

Imagine this: A user searches for a product – clicks on a search ad – interacts with a Virtual Sales Agent (Adster) and is delivered a personalized brand video using Video Synthesis technology.

Through combining AdSter’s conversational search ads and re-engagement and Video synthesis – your brand re-engagement and retention can skyrocket to a whole new level.

Depending on the user’s stage in the sales funnel, a personalized video delivered via a cookieless re-engagement can effectively fast-track a user along the funnel to closure, directly impacting your brand’s bottom funnel and conversion metrics.

A recent example of this is the recently conducted #notjustacadbury ad campaign by Mondelez with Bollywood star Shahrukh Khan.

Using Video Synthesis tech, Mondelez is enabling customers to create advertisements for their local stores. Using AI, Machine learning, and Video Synthesis, users can create a personalized video with SRK by giving details about their store and category and SRK recreates the personalized ad by repurposing this information.

Transforming Sales Funnel

Conversational Search Ads bring a dynamic change in the sales funnel as instead of multiple touchpoints, you reduce it to a bare minimum of one or two. The entire length of your sales funnel shortens, which results in quicker action and faster revenue generation. Sourcing emails and sharing multiple emailers to convert? The Conversational AI bot will do all of that and more – through seamless API integrations with your MarTech stack.

To Conclude:

Landing pages are beneficial for search ads, but if you are looking to deliver real-time personalized and immersive experiences, adding conversational search ads to your marketing arsenal will accelerate your conversion metrics and fast-track your sales funnel.

Reduced bounce rates, lower customer acquisition costs, and cookieless personalized re-engagement are what you get with Adster Conversational Search Ads. Book a free demo with our experts to experience it yourself.

We are living in the golden age of Artificial Intelligence and Machine Learning and are seeing a multitude of applications in our everyday lives that have been touched by AI.

This invariably gets us thinking – how does AI work? How can I understand the nuances of AI?

Well, we put together a list of the top 10 books that you could read to improve your understanding of AI.

Many works have been published in the last two years that provide in-depth knowledge of the underlying concepts, technological methods, and applications of artificial intelligence. This list features books written by well-known computer scientists and practitioners who work in the AI field.

Whether you’re an AI/ML researcher, engineer, or business professional, you’re sure to discover a few fascinating books to add to your reading list this year.

1. Driven: The Race to Create the Autonomous Car – by Alex Davies

According to the “great man” theory, history is mostly shaped by heroes—big, brawny, brainy males (always dudes) who use brute power and intellect to create the future. Alex Davies, a WIRED graduate, has written a new book that debunks that old assumption. Davies delves into the history of self-driving cars and the zany, energetic ensemble of characters (still mainly males) who are attempting to bring the technology to fruition in Driven. As Davies explains, the dream can only be realized with the help of others. Until it doesn’t, that is. Then the lawsuits—and, in the instance of one engineer, handcuffs—fly.

Robot cars may one day transform how we live in the modern world. By 2050, autonomous vehicles might be worth $7 trillion, and multibillion-dollar corporations like Alphabet, GM, Ford, and Tesla are racing to iron out the wrinkles. AVs, on the other hand, were a scholarly curiosity around the turn of the century.

Then, in 2001, an obscure language in a financing bill directed government funds to the development of robot technology.

(A tip: keep an eye out for the native tortoises, who will pee on you if you try to relocate them when racing a robot across the desert.) This is a book for anyone who is tired with hero stories and wants to discover how the business of producing world-shaking robots really works.

2. If Then: How the Simulmatics Corporation Invented the Future – by Jill Lepore

Quick Quiz: Who was the first presidential contender to win a contested election by using computational modeling of the American electorate? You’re 56 years late if you said, Donald Trump.

The year was 1960, and John F. Kennedy had hired a little-known company named the Simulmatics Corporation to survey American voters, forecast their behavior, and provide campaign counsel using its groundbreaking “people machine.” Jill Lepore examines the early days of algorithmic behavior modeling and its eventual influence on politics and society in her history of Simulmatics (“the A-bomb of the social sciences,” a “Cold War Cambridge Analytica”).

Her portrayal of the 1960s acts as a mirror for 2020: America is in the midst of a racial justice revolt, a technical weapons race with its geopolitical adversary, and an election molded by technological influence. If one replaced “Simulmatics” with “Facebook” on any given page, the storyline would almost make sense. If Then tells the narrative of technology’s forefathers, whose work gave rise to the “people predictors” that shaped contemporary democracy and paved the way for today’s Silicon Valley behemoths.

3. Rebooting AI (2019) – by Gary Marcus and Ernest Davis

New York University professors Gary Marcus and Ernest Davis illustrate the technological and theoretical differences between successful AI that is confined by a set of rules (or a defined environment) and successful AI that can effectively engage with the complexity and nuances of an open world. This book is for researchers and businesspeople who wish to make concrete forecasts about AI’s near-term future. Ernest Davis is a Professor of Computer Science and Gary Marcus is a Professor of Psychology and Neural Science and the CEO of Robust.AI. This book, according to Noam Chomsky, is “lucid and highly knowledgeable, from a critical but sympathetic standpoint.

4. Applied Artificial Intelligence: A Handbook for Business Leaders – by Mariya Yao, Adelyn Zhou, Marlene Jia

For many, the commercial application of AI remains a baffling concept; however, Mariya, Adelyn, and Marlene lay down both the technical and business aspects of AI for individuals working in the field as well as those with a passing interest. This book is not an overabundance of technical material that many people will struggle to comprehend, but rather a step-by-step guide to identifying opportunities, assembling a diverse team, and conducting strategic experiments in the field, with all the information you’ll need.

5. Prediction Machines: The Simple Economics of Artificial Intelligence – by Ajay Agrawal, Joshua Gans & Avi Goldfarb

This 250-page primer on AI and Machines tackles the most common questions a newcomer to the business could have. Prediction Machines is an easy-to-understand book that checks all the boxes when it comes to demystifying the nature of AI and how it affects the economy today. Prediction Machines’ applicability and adoption at many levels of the economy will only improve as they become more common and inexpensive. If you want to understand how the employment market will change in the coming months and what management skills you should have to deal with the disruptions, this AI book could be a must-read for C-suite executives. The assessment is based on the present AI owners’ and users’ decision-making abilities.

6. The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity- by Byron Reese

The three key discoveries that eternally transformed civilization are fire, agriculture, and the wheel. Setting this theory in action, author Byron Reese compares AI to the Industrial Revolution, citing numerous examples of AI’s involvement in changing the curve. Byron is the founder and CEO of Gigaom.

In this fascinating, essential, and accessible exploration of the coming advances in robotics, computing, and related technologies from the publisher, of one of the world’s most popular technology news websites, Gigaom.com, learn about the next stage of humanity’s evolution—the age of artificial intelligence.

Byron Reese, says we’re developing robots that can perform human-like tasks. We’re working on creating computers that might be able to think for themselves. We’re probably on the verge of developing a new life form: a conscious computer to which we could delegate our thinking minds, together with a companion robot to execute our bodily functions. Reese tackles the impending technological revolution and its species-changing repercussions in enthralling and accessible language.

He aids us in comprehending both the practical and existential implications of this brave new world, resulting in a full description of what it means to be human.

7. Life 3.0 Being Human in the Age of Artificial Intelligence – by Max Tegmark

We are at the start of a new age, as Max predicted three years ago. Science Fiction, which was formerly just considered fiction, is rapidly becoming a reality. Over nearly 400 pages of information, the MIT professor, whose work has aided mainstream study on how to keep AI positive, separates myths from facts, utopias from dystopias, to investigate the next chapter of our lives. Are artificial intelligence systems safe? How can we expand it without robbing people of their sense of purpose? Should we be concerned about an autonomous weapons arms race? All of this and more is covered by Max.

8.Machine Learning Yearning – by Andrew Ng

Andrew Ng’s free e-book will show you how to best structure your Machine Learning projects. Andrew covers everything from ML system issue diagnosis to mismatched training sets, end-to-end learning, and transfer learning. For years, it has been suggested that the best way to make strategic decisions in Machine Learning is to enroll in a program/course where you will have direct contact with a mentor; however, this e-book eliminates the middleman in assisting you on your journey to becoming an educated individual in all things Machine Learning.

9. AI Superpowers: China, Silicon Valley, and the New World Order – by Kai-Fu Lee

This AI book was written by Kaifu Lee. AI Superpowers, a best-seller in the American market, naturally brings up the Chinese and AI white-collar sectors.

“PricewaterhouseCoopers believes that AI implementation will boost global GDP by $15.7 trillion by 2030. China is expected to take home $7 trillion of the total, roughly double the $3.7 trillion gained by North America.”: Kai-Fu Lee

Kai-Fu Lee, a former Google China CEO, and AI entrepreneur, has extensive first-hand knowledge of the Chinese AI movement. The book on AI covers the four waves of AI and the ongoing struggle for AI supremacy as well as the future of workplace automation:

• Internet AI
• Enterprise/Business AI
• Perception AI
• Autonomous AI

10. Data Mining: Practical Machine Learning Tools and Techniques – by Ian Witten, Eibe Frank, Mark Hall, and Christopher Pal

This book meets every expectation that you may have from a book on AI.

Data Mining: Practical Machine Learning Tools and Techniques is a handy book on data mining and analytics whether you’re taking a course in Python, Big Data Analytics, or SAS Analytics. It describes how Machine Learning algorithms operate for data mining and performance enhancement workflow. This book is presently accessible in English, as well as Chinese, German, and Korean translations. Based on customer feedback, we believe the writers are true experts in the field of Data Mining and Machine Learning, and that they have successfully communicated their expertise to readers by including authoritative presentations of modern data mining techniques.

These books will be valuable to anyone who has developed a career around AI and ML for progressing through the field and grasping some of the most sophisticated, practical, and pressing concerns that the field faces. These books are excellent resources for anyone looking to improve their technical abilities, productivity, or knowledge of the subject. Every week, a slew of new books on comparable subjects are produced and released.