Tony Plasencia

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Applied Creativity: Data,Tech and Ideas — The Consumer Viewpoint

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“ Young consumers born after 1995 are fully entangled in the world wide web, so much so that they probably couldn’t dissociate themselves from this way of living. For brands, ensuring your tech, data, insight, innovation (or whatever you want to call it) remains discrete, frictionless and useful will be the determining factor for future loyalty.”

I urge everybody to look through this

Cannes Lions International Festival of Creativity⁠

article that makes the relationship between Tech,Data & Ideas. In this article, it’s evident that AI is going to be huge for marketers to examine & organize the vast amounts of data at their disposal. “ Knowledge mining” seems to be on a resurgence thanks to the rise in machine learning that will allow for AI to “extract insights, patterns & scale relationships”. An example of this would be IBM’s new tool, Advertising Accelerator, that predicts which creative elements will drive engagement in specific campaigns.

I think that this is interesting in with the Millennial cohort for 3 reasons;

1) Millennials express ‘Experience’ as one of the biggest attributors to any relationship that they may identify with a Brand. This is thanks to the web, where 75% of Millennials do ‘extensive research’ before making a purchasing decision.

2) Millennials are ‘social’ in the sense that we are always attached to some sort of Media medium whether it be Snapchat, Instagram or Facebook. We are 81% more likely than our partner cohorts to post an online review about our experience & the product.

3) 31% of Millennials say the opportunity to give feedback and influence future offerings is important to them. In part, this is because Millennials are improvement-oriented and open to innovation: 64 percent report that their day-to-day behavior is driven by a desire to find new and better ways of doing things.

It’s extremely interesting to see the rise of AI being used to market more efficiently and intelligently to my cohort. I urge that this tech is used to engage ‘Artful Intelligence’ that promotes creativity but also keeps messaging ‘human’. In the age of Tech, Robots are unable to create and build relationships (yet?) but data can be leveraged to create stronger bonds with consumers that ultimately monetize not the business but the BRAND.

I wish to offer another small amount of bites to chew on, my latter point of monetizing the Brand vs the Business, may be a bit controversial but I will go ahead and offer my rational. With the above statistics in mind, it is evident that in order to garner the cash of Millennials a Brand must be able to become intimate. In order for that to happen, there is ONE rule I urge you follow & a rule that this new data can help with — your archetypes.

Getting creative & using Knowledge mining to utilize data, Brands are able to not only find their MVP but on a Macro-level find their base. These archetypes are as follows: fulfillment, identity, enhancement, ritual, nostalgia, and indulgence. We can identify these as the need states of our Customers, as soon as we know this, we know how to utilize data to market the Brand.

Conversational Artificial Intelligence

Check out the final here:

Conversational AI: What It Is and How It Works⁠

- Ada

Salesforce Chatbots: Live Chat, Integration, Automation

Summary:

* What is Conversational AI?

* How does Conversational AI work?

* Conversational AI vs. Chatbots

* Benefits of Conversational AI

* Conversational AI Use Cases

* Conversational AI by Industry

* Conversational AI Testimonials

* Implementing Conversational AI

What is Conversational AI?

Somehow you heard the buzzword 'conversational growth strategy,' or you're genuinely curious, so you've decided to figure out what this technology exactly is. You're probably familiar with some of the tools that use this technology, for example:

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Deloitte's⁠

Voice Content Analyser

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IBM's⁠

Watson

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Ada's⁠

Chatbot

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Cleverbot⁠

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Conversational AI uses both Natural Language Processing and Machine Learning components like chatbots, voice assistants, or an Interactive Voice Recognition System to interact with people in a human-like way.

Natural Language Processing - with the help of machine learning, this is used to analyze language through various data collection sources.

I.e., virtual assistants, Amazon's Alexa, and Siri

Machine Learning: Artificial Intelligence is made up of different algorithms and datasets, which through testing and increased input, will improve at making predictions and recognizing patterns.

How does Conversational AI work?

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*Image explains process of how chatbot understand user intent through machine learning

Conversational AI comprehends and engages in contextual dialogue using NLP + additional AI algorithms. As input grows, AI becomes better at recognizing patterns and making predictions.

According to

IBM⁠

, NLP is made up of these four steps that help us understand how conversational AI really works.

Step 1: Input Generation - Users provide inputs either through voice or text

Step 2: Input Analysis - If the input is text-based, NLU (natural language understanding or also comprehending what is being said) is used to break down the meaning and intention of the input, but if the input is speech-based, ASR (automatic speech recognition) + NLU are combined to analyze the data.

Step 3: Dialogue Management - NLG (natural language generation or also giving a response in a human-like format), a component of NLP, makes the response to the user.

Step 4: Reinforcement Learning - Machine Learning (determining the right response) can refine replies over time to ensure their responses are correct and accurate.

Conversational AI vs. Traditional Chatbots

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Traditional Chatbots - claim to have conversational capabilities, but humans will have to write scripts and dialogues behind the scenes; the chatbot is told what to say in response to exact keywords and train the bot for every foreseeable scenario.

A real AI chatbot conversation requires conversational AI, which does not need a script but rather progressively teaches itself through reinforced training based on it's set algorithm.

Conversational AI

Key Outcomes

Key Components

Scalability/Omni-channel Presence

Machine Learning/NLP

Smart and Dynamic System

Algorithm Training

Deep Understanding of Customer

Sentiment Analysis + Data Insights

There are no rows in this table

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Chatbots

Key Outcomes

Key Components

Not Scalable, Predictable Answers

Scripted Code

Not intelligent, lackluster experience

No Algorithm

Unable to meet customers where needed

Integrated Presence

There are no rows in this table

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Benefits of Conversational AI

How does conversational AI help?

Everything from a small-to-medium business to a large enterprise can find great benefit from building out an AI platform. The benefits break-down into two categories

a) Customer-Centric

b) Organizational-Centric

Customer-Centric

*Scalable solution that is easy to add to infrastructures which improve customer acquisition due to bottom/top-down application

* Increased engagement and sales throughout customer's journey with personalization and recommendations, i.e., on messaging apps, social media interfaces, and live chat, i.e., generating data insights

*Reduces cost to serve customer during interactions based on engagement channels (15-70% cost reduction) while improving quality of service

Organizational

* Improved employee satisfaction due to decrease in bottlenecks that are streamlined by A. I platform and increased Revenue per customer

* Cost-efficient solutions streamlines customer experiences with customers since staffing is expensive, limited within hours, and conversations are repetitive.

* Reduces Churn in with customers and increases Organizational NPS

Conversational AI Use Cases

Customer Success Automation

Online chatbots are quickly replacing live agents. Bots answer common FAQs like shipping, and they offer personalized advice or cross-sell products through messaging bots on eCommerce sites with virtual agents, messaging apps, or completing tasks.

Bots are useful for reducing time, improving cost efficiency, and freeing up resources. They also replicate human-like experiences, which lead to a high rate of customer satisfaction.

Marketing Automation

Conversational AI changes digital marketing; it allows for automation in all such processes heavily dependent on humans, like content generation and pay-per-click ads.

Personalized Marketing creates and maintains personal connections with customers, making an emotional connection with your company, and they are more likely to purchase your products. By creating this "human" way for brands to engage with customers, the chatbots gather all the information they can to provide a more personalized experience.

Artificial intelligence is the way to grow your brand or product, exceed your customers' expectations, and get started; companies should get in touch with a good agency that offers customized digital marketing services.

Conversational AI by Industry

Insurance Industry

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Image⁠

explains possible entry paints for Insurance companies to utilize this technology and insert it into their ecosystem

According to a

Juniper Study⁠

study

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the use of 'Conversational AI' or 'AI that you talk to' will lead grow to $556b by 2025 with a 125% growth YoY across the Insurance Industry. They are currently used to add value to the user, their experience, and the Organization.

Enhance Customer Support and Speed to Claim resolution

Improving Workflows

Fraud Detection

By automating workflows for repetitive tasks that staff have to undertake with Conversational AI, both organizations and employees can focus on customer claims. With a major focus around the customer, data privacy and security focus on many insurance companies. Conversational bots using NLP are currently being utilized to monitor/detect ambiguities and rate risks in claims and applications. Using chatbots, insurance companies have found that they can better customer experience and transform their customer journey by meeting their customers at the channels and times where help is needed.

Financial Services

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Chatbots are easily accessible and applicable to financial institutions, but they also provide a ton of benefits for the ever-growing sector. As financial services look to make their services more engaging, personalized, educational, and connected, they turn to these technologies to reshape customers' omnichannel experience. The benefits that companies have seen are as followed

Enables Self-Serve

Personalized Banking

Data Insights leading to better CX

By utilizing Conversational AI at the correct parts of the customer journey, financial services find they are better enabled to redefine the customer experience. As customers fill-out an application for an account or sign-up for a loan, they can be fully guided and serviced by this AI that provides necessary resources, content, and help. Similarly, by utilizing the self-serve function and bots, the opportunity to offer personalized offerings presents itself. With bots' help, banks are finding that they can meet their customers where they are needed and can tailor marketing around this. Yet, this wouldn't be possible with the biggest benefit- insight from data. With a conversational AI, you can collect insights on your customers' questions, feedback, or general concerns about your product. These insights don't end there but can be extended to engagement metrics, conversion metrics, and conversational analytics, leading to overall better decision-making and customer experience.

Conversational AI in Telecommunications:

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*IoT, a white hot category of AI which is set to redefine how communication and people are connected

The

IDC⁠

, a research institution focused on Artificial Intelligence, has indicated that around 63% of Tele-com companies invest in these technologies to optimize and improve their overall infrastructure. Conversational bots serve an interesting role in this because they can serve the customer quickly and efficiently. Below are some ways that companies are reaping these benefits

Enhanced productivity and workflows

Increased Customer Support

Increase cross-selling and up-selling

The telecom industry utilizes conversational technology to eliminate repetitive tasks such as tending to support or storing the copy of the conversation. In this sector, having correct copies of conversations with customers allows companies to build around feedback to create enhanced brand experiences. As telecom companies identify new ways to support their customers, they are utilizing these chatbots. Chatbots allow for a personalized support experience - when something complex arises, it's delegated to a human; if it's simple, the chatbot will quickly handle it. These enhancements have provided higher retention and improved CSAT. More happy and loyal customers usually mean more spending. AI algorithms are dynamically intelligent thanks to the data that is inputted. Telecoms have been able to utilize this to upsell and cross-sell their services. Along with recommending services along the customer journey, this algorithm can create recommendations based on the user's profile and purchase history.

Conversational AI in Airlines:

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The travel industry suffered the brunt of the pandemic. A decrease in travel and a decrease in both margins and profit has led companies to rethink how they approach their customers. In the wake of the pandemic, many travelers had their plans upended.

AirAsia's AVA team ⁠

said they were handling 10x the normal number of queries: up to 500,000 per day in April. Their bot dealt with a 1,671% increase in support tickets coming through Facebook Messenger and WhatsApp, resolving 87% of cases. Companies have found these technologies beneficial and prove more and more; check them out below

Reduce costs and Increase Revenue across the Organization

Automate repetitive tasks and create a streamlined process to free up Agents

Improve customer service to enhance customer loyalty and experience

Like the other industries in the list, conversational AI allows Agents to be customer-focused by automating repetitive tasks such as refunds, changing flights, or ancillary actions. Unlike others, though, customers experience an omnichannel purchasing journey in the airline industry and expect to be met everywhere. On average, a support call costs around $2.20, that add's up. Airlines are currently up to 60% cost reduction per call using these technologies. By utilizing these smart technologies, companies can increase

their customers value by building their loyalty with a positive experience each time they 'travel'.

Conversational AI in Ecommerce:

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* Depiction of how Artificial Intelligence is revitalizing the ecommerce space to create a more sticky experience

According to

DigitalCommerce360,⁠

E-commerce sales have risen 44% from 2019 to the end of 2020. The stats don't stop there; surprisingly, 60% of customers will leave a site if they can't find what they're looking for or find help. Meaning that the need to revolutionize the way online shopping is experienced and how customers interact with your site is - now.

Juniper⁠

predicts that by 2023, 70% of all chatbots accessed will be retail-based. This means that not only will this be part of the buyer's journey and retail ecosystem, but that business is currently reaping benefits as such:

Serve as a lead generation channel

Educate customers, creating a better shopping experience

Collect insights on customer's and visitors to the site

Generally, three things determine when a person will buy from your e-commerce site - design/ease of your site, buying experience, and your product. When customers leave- companies are utilizing proactive messaging to run their experiences like a funnel.

Stage1- Acquisition- dive into customer insights to retarget customers who drifted off and rewind them into their site to purchase or redirect to sales.

Stage 2- Activation - utilizing chatbots' intelligent and dynamic environment to provide a great experience by providing personalized shopping advice, qualifying customers for purchase, and purchase recommendations.

Stage 3- Retention- collecting insights on users purchase history to retarget for future purchases and refine the experience, which increases brand loyalty and creates a positive feedback loop

Companies have utilized talking AI to redefine how they interact with customers through digital channels while re-creating purchasing and brand experiences.

Conversational AI in SaaS:

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Software-as-a-Service has been growing as an industry over the last few years. According to

Gartner⁠

, the industry has dominated growth on a large scale expecting to grow to $117.7B by 2021 while growing 24% YoY. Examples of SaaS platforms include but are not limited to Salesforce, DocuSign, Slack, and Zoom. Though not all workplace centric, there is a ton of benefits for businesses on this software that connect to the cloud,' such as:

A. Collect insights on conversation, gain the ability to highlight sentiment and buying preferences.

B. Increase in retention

C. Automate the customer experience & process

As SaaS grows and becomes integrated with our day-to-day, this forces companies to create products that fit our needs. To do this at a high and scalable level, there needs to be a collection of data. These highly complex solutions can dissect and analyze how we talk to an AI, overcome our objections, and analyze how we purchase. This leads to an increase in retention as customers can have both an effective and valuable interaction that helps them find the right solution for their needs. By utilizing the chatbot essentially automating customer experiences, businesses can rest easy that their customers are being served on-time and in a valuable manner.

Conversational AI Testimonials

"We've surfaced Ada to our customers so customers' simple questions can be answered immediately, and more complex ones are seamlessly escalated to a live agent. We've seen fewer tickets, faster resolution times, less stress for live agents, and most importantly, happier customers." - Ada (Mailchimp - David Clark - Senior Director of Operations & Analytics)

"Replika is a better friend than your human friends. It's the only interaction that you can have that isn't judging you…a unique experience in the history of the universe." - Replika -Phil Libin, Founder of Evernote

"Digitization and technology innovation play an increasingly big part in the banking sector, but as we all move faster than ever, we realize the need for meaningful human interaction - even with our bank. As we add new customers every day, we are scaling our operations quickly. We are achieving incredible customer satisfaction rates with the help of Liveperson, exceeding anything ever seen before." - Liveperson- Claudia Vasenna and Marco Puccioini- Head of Buddybank & Head of Concierge at Buddybank.

Deciding if conversational AI is right for you.

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Find out what you can save - Chatbot ROI Calculator | Ada⁠

You'll find a super helpful calculator put together by the Ada team, which helps decide how to move forward with your Conversation AI strategy. Calculating ROI is fairly easy and can be done in five steps:

Identify Eligible Queries

Calculate % of the type of query traffic (complex vs. simple)

Calculate agent time spent on Eligible chats

Estimate the annual costs of handling Eliminate chats

Compare costs of Agents vs. Chatbots

Forrester AI has identified that ROI is the single most important characteristic for organizations to decide on how to approach these technologies. Yet, the tech is failing due to a lack of strategy, lack of organizational readiness, and overall preparedness. If you're getting ready to play the long game and lead your transformation, the Ada team can lend a hand by partnering with you at all stages:

1. Training and launching your conversational AI to optimize its efficiency

2. Provide recurring feedback and insights to your team to highlights what's working/not

3. Help you strategically plan and execute your Conversational AI strategy

Ada connects worldwide, coming in as one of the most efficient conversational AI redefining the customer experience; companies have seen increases of +61 on NPS with a 30-day launch rate for these technologies. Find out more

here⁠

Implementing Conversational AI

How to Build a Conversational AI: How to set up Ada?

Ada is the new front line of your customer experience, as it allows businesses to provide a personalized experience in an instant! Ada's AI chatbot is no-code, reduces costs, and drives up Revenue, all while freeing up live agents to have a stronger impact on your business.

Simple steps setup or implement your AI chatbot are as followed:

Tip #1: Strategize Internally

Assigning someone who works closely with the customers is the first step to managing the bot.

The second step, figuring out which channels you will be using so your live agents know which they will be using (live chat, messaging channels) and how the bot integrates with your CRM platform.

Finally, start measuring results. Using analytics for customer behavior, resolution time, and frequent service issues informs the team and improves communication levels.

Tip #2: Personalize Interactions

Collecting contact details, account information, and personal information in the early stages of interactions allow the Chabot to support dialogue and solve bigger problems.

Personalizing interactions feel more human-like, and the chatbot can complete more complex tasks. The second a live agent needs to jump online; they are empowered with all relevant information and past conversations to connect with the customer.

Tip #3: Grow Revenue

The Chabot is a salesperson, by giving them all the information to share with their consumers at the beginning of the journey allows them to guide consumers to purchase.

Track which interactions have led to a sale or an upgrade, then find ways to up-sell and cross-sell to customers based on needs and responses.

Intro to AI

not published on Ada yet → link will be added once published.

What is AI?

Artificial Intelligence is usually used to cover any technology with 'human-like aspects or is close to what we perceive to be a robot. This is only half-correct—artificial Intelligence, both the science and engineering machines that are 'intelligent. The emphasis is on software that either mimics or exceeds human intelligence. Some definition below should be helpful to understand the complexity and applicability of AI -

Machine Learning: An AI application that provides the ability to learn and improve from training without being programmed manually. Machine learning focuses on developing programs that can access data from live or synthetic data and use it to learn for themselves. Being trained to look for patterns in data algorithms can make better decisions in the future. The goal of ML is to create systems that can operate and grow without human intervention.

Natural Language Processing: Artificial Intelligence machines process, dissect and understand human language in any context to perform tasks automatically. Such as answer-to-question, summarization, navigation, and machine translation. Analysis of both semantics and syntax is necessary to understand the structure and identify how words relate to each other in NLP.

Artificial Neural Networks - i.e.: 'neural networks' are systems inspired by the biological neural networks that make-up animal brains. Neural networks are connected units or nodes called artificial neurons, which loosely model the neurons in a physical brain.

What is AI Software?

Unlike AI, which in itself is an umbrella term. AI Software can be both defined and concretely related to some technological features. AI software like Artificial intelligence refers to a machine's abilities to resemble or surpass human intelligence with no intervention.

AI software features include Machine Learning, Speech & Voice Recognition, Virtual Assistant, Business Intelligence platforms, etc. Software with AI achieves intelligence levels by learning various data patterns and insights that are constantly being adjusted through algorithm training, creating more 'intelligent software.

Three Types of AI

Process automation

RPA or Robot Process Automation is the automation of digital and physical tasks - typically administrative tasks, transactions, financial activities, or addressing queries. RPA uses both Artificial Intelligence and Machine Learning to configure and automate tasks in 4 crucial phases - planning, development, deployment/testing, and support/maintenance.

- Planning- gathering process to be automated, test objects, finalizing implementation approach

-Development - the creation of automation workflows

-Deployment/Testing - uncovers unexpected outages and bug-free products

-Support/Maintenance - ensures that the product can constantly be deployed and iterated to create a positive experience.

Businesses that use RPA have seen improvements in Customer Satisfaction due to lower bug risk leading to a lowered operational risk for users. The automation within itself saves time for businesses and delivers consistent results making it a cost-efficient add to any organization's tool stack.

Cognitive Insight

Uses components of artificial intelligence - algorithms or 'deep learning' to detect patterns in vast volumes of data and interpret their meaning for organizations to analyze data and predict outcomes.

Cognitive insights allow analytics to be more flexible and valuable than traditional analytics. By aggregating information more clearly in datasets and training models with different aspects and amounts of data, companies can use data to create personalized strategies for customer profiles and segments.

This technology analyzes customer data droves and can be used to detect identity fraud, investigate claims, speed hiring, automate personalized advertising, more accurate data modeling, and predict trends or buying behavior.

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TechTech

Applied Creativity: Data,Tech and Ideas — The Consumer Viewpoint

Conversational Artificial Intelligence

Intro to AI

Tech
Tech