5 Marketing Automation Strategies to Increase Profits

While the concept of automation has been around since the beginning of the industrial age, its presence in today’s economy seems to be growing at record speeds. New technologies, combined with a better understanding of how they work, have allowed business of all types to grow in ways previously thought impossible.

marketing automation strategy business charts

One of the more recent developments in automation is its application in marketing. The emergence of Big Data, and the expansion of internet and smartphone use, means marketers now have their hands on a tremendous amount of information they can use to more effectively reach their target market.

However, as with any technological advance, just using it doesn’t automatically translate into results. You have to have the right strategy.

Marketing automation is all the rage right now, and here are five strategies to make it work for you.

Lead Identification

One of the most frequent debates that goes on between sales and marketing departments is what constitutes a quality lead. Differing opinions not only slow down the process, but they also can lead a salesperson to go after a lead that doesn’t really have much promise. One of the best marketing automation strategies you can pursue is one of lead identification. It uses data to help eliminate the guesswork ,giving sales team better leads to pursue.

The first step in this strategy is to automate data collection, as this will allow you to figure out exactly who is coming to your site. But more importantly, it will give you a chance to learn who is engaging with the content you produce. You can track clicks, time on page, downloads, etc. (using tools such as Google Analytics and UI parameters) and this will help you figure out which part of your target audience is responding most to your tactics.

Another great way to identify leads using automation is through subscriptions. Work with your web developer to have a subscription box pop up after people have spent a certain amount of time on the site. Signing up demonstrate affinity, meaning sales tactics will likely be more effective won these individuals. Implement these simple automation and use them to help make your sales efforts far more effective.

Lead Nurturing

The second strategy to pursue is lead nurturing. It’s one thing to identify good leads, but you need to manage them properly to be able to turn them into sales; even the best leads don’t all convert into sales.

The secret to this strategy is drip emails. These are emails that go out gradually (as if they were dripping) to confirmed leads. And they’re really easy to automate. You start by writing out the content of the emails, and then you set your system up to send different emails at different intervals. You could manage this manually, but you’d likely go insane trying to keep track of everything. By setting it up to work automatically, you can sit back and let the emails do the work for you.

And there’s quite a bit of evidence to suggest these types of campaigns work. In fact, one study found that every $1 invested in an email campaigns produces a return of $38. It’s tough to beat those kinds of results, and they’re really only attainable with an automated lead nurturing campaign.

There are a variety of different software options out there that can help you nurture leads, the most common and effective being Drip, ConvertKit, Vero and Mailchimp.

Brand Management and Engagement

Building and maintaining a brand is one of the key functions of a marketing department. And it requires a multifaceted approach across many different platforms. But it’s hard to beat the effectiveness of social media in building a brand. People use these platforms to stay in touch with those they care about, so being in on these conversations can only help your brand.

Actually doing this, though, can be a nightmare. Between all the different platforms, there’s far too much for any one person to do. The answer: automate. Platforms such as Hootsuite allow you to set up your social media posts in advance, meaning you can front load all the work and then only respond when needed. The software will alert you when someone has commented on a post or reached out to, and then you can respond directly to that comment, saving you from having to comb through all the content on your various accounts.

Furthermore, these services will help track engagement. They’ll bundle information about likes retweets, shares, etc., and this will let you know what’s working well and what’s not, allowing you to refine your campaigns and increase their effectiveness in helping you build your brand.

Customer Service

To stay competitive in today’s market, you must deliver top-notch customer service. While traditionally thought of as separate from marketing, nowadays both functions are closely connected. This bond has formed largely because of the information you can gather from your customer service efforts, and also because you can use customer service as another means of dealing with people.

The primary tool for automating customer service is a chatbot. These are a wonderful way to break the ice with people and make your site, company and brand seem more human. Offering to help people find something, or pointing them in the right direction when they land on you site, can go a long way towards helping to improve the customer experience and promote sales.

But chatbots can go one step further. Because they’re automated, they can collect and compile data that will help you understand your audience even more. They can identify frequent issues, and they can also help you learn more about what people come searching for when they land on your site. And all this means you can tailor your content to better meet the needs of your audience, increasing the chances you’ll connect with them and convert them into customers.

Tailored Content

Successful marketing campaigns speak directly to your target audience. They start by identifying the needs and expectations of a certain group, and they end by connecting with these people and convincing them to take an action, such as making a purchase or signing up for your newsletter. And the secret to doing this is to segment your audiences and deliver content that is directly relevant to them.

tailored content marketing automation strategy

For example, let’s say you’re a B2B business offering employment services or management consulting. Your goal as a company is to help people streamline their HR operations. As such, your audience is going to be people working in HR, as well as divisional managers responsible for making decisions about the business, as these are the people who will ultimately decide whether or not to hire you. And although these two groups are part of the same audience, the content you send to them must be different for it to be effective.

The secret to this is using an automated CRM software. It will help compile information about your many different audience segments, allowing you to create a distribute content tailored specifically for each group. For example, HR generalists may receive information about how to streamline the on-boarding process, whereas HR managers will receive content about how a (PEO) can save the company time, money and resources. It makes sense this kind of segmentation would work, and studies help back up this logic, indicating a 62 percent improvement in email campaign effectiveness with this type of tailored content.

Use Automation the Right Way

Automating marketing tasks presents an incredible opportunity to improve the efficiency and effectiveness of your marketing operation. However, for it to work, you need to have a good strategy. The five discussed here are some of the most effective known to date, so give them a try and watch as your marketing efforts produce better leads and drive higher sales figures now and into the future.

 

About the Author 

Jock works with investors and business owners to help people maximize the value of their business. Professionally, he is an expert in high-growth internet companies, having bought, built and sold three different businesses throughout his career. He is currently the CEO of Digitalexits.com.

Customer Experience, Artificial Intelligence and Machine Learning – Thoughts from Oovvuu, Canva & The Minerva Collective

Artificial Intelligence (AI) and machine learning (ML) is all the buzz right now, and rightfully so with the significant contributions it has made to redefining many aspects of business. However, many people are still skeptical about the application of AI and ML to enhancing customer experience.

Some would argue that machines cannot possibly take over customer service, something that has a heavy focus on human interaction. Machines lack the empathy and emotional intelligence core to providing a great customer experience. On the other hand, many also see the benefit of applying AI and ML to automate repetitive tasks, allowing humans to dedicate more time to, well, being human.

We reached out to some experts from Oovvuu, Canva and The Minerva Collective to pick their brains about the issue.

customer-experience-automation

What is the current state of customer experience, and how do you see it evolve with AI & ML technology?

Present customer experience is “all over the place, with wildly varying results. Two customers using the same service can have completely different impressions of their experience, and in many cases the service is clunky and poorly structured” says Anthony Tockar, Data Scientist and Co-founder of The Minerva Collective. The unfortunate reality is that 78% of consumers have bailed on a transaction or not made an intended purchase because of poor service experience. In fact, companies only hear from 4% of its dissatisfied customers. With so much choice available to consumers, it’s much easier to find another company with similar offerings than spending time complaining or calling about a problem. Which is why there is a very real need to focus on customer experience, a factor that is becoming increasingly important to retain the modern customer.

Paul Tune, Machine Learning Engineer at Canva, believes “there are two trends in improving customer experience:

  • A trend towards tailoring for the individual, as more data is gathered about each customer at a large scale, and;
  • A trend towards providing a smooth experience for customers across multiple touchpoints by anticipating their needs. “

To demonstrate how customer experience has evolved, Paul continues with an example. “Early recommendation systems, such as the recommendation engines developed at Amazon and NetFlix in the early 2000s, provided recommendations at a much coarser level, chiefly for specific groups of customers. The granularity of recommendations in the near future is going to be much finer. For instance, an engineer from NetFlix I spoke to recently, mentioned that a subscriber’s favourite character for a TV series would appear in the menu when the TV series is selected. This means having to learn more about each customer and predicting their habits. We also see this in the form of smart personal assistants, such as Alexa and Siri” he says.

Ricky Sutton, Founder and CEO of Oovvuu, adds on that whilst AI and ML “certainly has an element to play [in customer experience], it also lacks a key element…empathy. So my thought is that it will evolve. The more AI is used, the more it learns and the better it gets, but human-level empathy remains a pipe dream for now.”

human machine touch

What is the biggest lesson you have learned from applying smart technology to customer experience?

For Anthony, the lesson has been the need for people using smart technology to properly understand it – “My experience is that people often don’t trust what they don’t understand. The latest technologies have been great for grabbing headlines, but only the most forward-thinking businesses are serious about applying them to derive value. This isn’t necessarily a bad thing – domain knowledge is essential for good data science, and blindly relying on new approaches has many inherent risks. There is a lot that has been learned about customer experience over time and there is a need to explain smart technology to business people using the right language to allow them to fully realise its value.”

To Paul, what matters most, is the customer’s end-to-end experience. Meaning that all the touchpoints with the customer should be seamless. For him, “the challenge with integrating smart technology to improve user experience is similar to managing any other complex system: with more moving parts, there is a higher chance of failure in the system. Naively applying machine learning to improve customer experience is misguided. Machine learning works best if it is complementary to the customer experience, serving to enhance the experience of a great product.”

“At Canva, our goal is simple: we want to give the customer the best experience in empowering them to create and design. To that end, there are two aspects that we focus on. Firstly, how do we make the content that they need for their designs easily accessible. Secondly, how do we anticipate what resources might be helpful for them in the future. We achieve these goals by improving our search and recommendation services to enhance customer experience.”

The biggest lesson for Ricky is that “AI turns humans into super-humans, but only for certain tasks.” – “When we started Oovvuu, we hired editors to read articles and find relevant videos, and they were able to read one publication each and find 40 relevant videos per day. That same person using the AI tools that we created, can now read 100,000 publishers, and 300,000 stories a day, covering 26 million topics and find relevant videos from more than 40 global broadcasters. AI is mind-blowingly powerful for automating manual human tasks, but humans remain better at all the things that, well, make us human.”

ai-ml-customer-experience

What are some challenges for businesses who try to integrate AI & ML technology and customer experience?

Anthony, Paul and Ricky all agreed that a huge challenge for businesses is not having a solid data infrastructure, or a deep understanding of what exactly should be measured to achieve business goals and customer satisfaction.

“Many companies approach us seeking to use AI as a ready-made silver bullet for a business problem. Others come to ask to play with AI, so they can find a business opportunity. Neither really works.” Ricky said. “For us, the solution was to know what business problem we were trying to solve: namely, to put a relevant video into every article being published worldwide. We then used AI to solve it, but what we started with was very basic and not up to the job. We have had a team nurturing the teaching for almost 1,000 days to get it where it is.”

Anthony went on to add that “there is no silver bullet – good data scientists are required to translate these algorithms into business value. Having a solid data science strategy is essential, and through good leadership, increased data literacy and an understanding of how to build a high-performance data science team, businesses can harness these technologies to forge a competitive advantage.”

Paul concludes with another common challenge many businesses face when adopting AI & ML into their processes – the volume of data. “Present machine learning techniques rely on a relatively large amount of data to provide good predictions” he says. “While there is fundamental research being carried out presently to (hopefully) reduce the amount of data required to train these machine learning models, the current main technological limitation of requiring a huge amount of data is here to stay for the foreseeable future.” But “fortunately, this effect can be mitigated if the data collected is of sufficiently high quality.”

Are you implementing AI and ML technology in your business? Share your story with us in the comments below!

Customer Connection Web Diagram

About the Contributors

Anthony Tockar The Minerva Collective, AI, Machine Learning, Customer Experience, Woveon-476307-edited

Anthony Tockar

Anthony is a leader in the data science space, and has worked on problems across insurance, loyalty, technology, telecommunications, the social sector and even neuroscience. A formally-trained actuary, Anthony completed an MS in Analytics at the prestigious Northwestern University. After hitting the headlines with his posts on data privacy at Neustar, he returned to Sydney to practice as a data scientist while co-founding the Minerva Collective and the Data Science Breakfast Meetup. He also helps organise several other meetups and programs for data scientists, in line with his mission to extend the reach and impact of data to help people.

Paul Tune, Canva, AI, Machine Learning, Customer Experience, Woveon

Paul Tune

Paul Tune is a Machine Learning Engineer at Canva, responsible for developing solutions for tailoring and personalising content for Canva’s customers. He has several publications in prestigious computer science conferences and journals, including the ACM SIGCOMM conference in 2015. His interests include deep learning, statistics and information theory.

 

Ricky Sutton, Oovvuu, AI, Machine Learning, Customer Experience, Woveon

Ricky Sutton

Ricky is founder and CEO of Oovvuu, an IBM and Amazon-backed start up that uses artificial intelligence to match videos from global broadcasters with publishers worldwide. It’s mission is to use AI to insert a relevant short form and long form video in every article. In doing so, it aims to tell the news in a new and more compelling way, end fake news, and in doing so, repatriate billions from Facebook and Google back to the journalists and broadcasters who make the content.

 

customer service charter template download cta woveon

Navigating Your Way Past The “Trough Of Disillusionment” For Artificial Intelligence In Customer Experience

Guest blog by Steve Nuttall

The hype around Artificial Intelligence technologies is at its peak. According to the 2017 Gartner Hype Cycle, emerging technologies such as deep learning, machine learning and virtual assistants are at the “peak of inflated expectation”. Cognitive expert advisors have passed this peak and are now descending towards the “trough of disillusionment”. This occurs when interest wanes as experiments and implementations fail to deliver.

emerging technology hype cycle gartner 2017

The benefits of AI for customer experience management are potentially game changing. AI has the capability to analyse vast amounts of data in real time from various sources, including human behaviours and emotions. Expectations are high because this capability can then be used to create seamless and personalised customer experiences that are optimised to the device and channel of choice.

Pragmatists and battle hardened cynics will recall that when automation was first introduced into customer service channels, the results were often spectacularly underwhelming. So, is the application of AI to customer experiences destined to fall into the trough of disillusionment before climbing the slope of enlightenment? Or is there a path to follow to avoid the pitfalls of unmet expectations?

Intelligently using Artificial Intelligence for Customer Experience

In order to find out whether the application of AI to your business’ customer experience will take a downturn, it is necessary to first ask yourself: What is driving your organisation’s AI strategy? Is it because:

  • AI is all the rage in your industry and your organisation is fearful of being left behind?
  • If you take the lead in implementing AI, it will make you look smarter/cooler than your colleagues?
  • It sounds like a cool and fun toy to experiment with?
  • Your organisation needs to catch up with your competitors who have been early adopters of AI?
  • AI is a great opportunity to reduce the cost to serve our customers?

If the answer to any of the above is Yes, then the trough of disillusionment beckons.

Alternatively, if you are deploying or considering AI because…

AI can enable your people and optimise your processes to operate more intelligently and efficiently, in order to provide individualised and predictive experiences for your customers at scale

…..then a brighter future awaits.

For these technologies to have any chance of success you should have a clear sense of purpose of how to you intend to deploy AI to drive CX in your business. Here are three ways you can use AI in a purposeful way to create meaningful customer experiences.

1. Use AI to enhance your knowledge of the customer

Customer Connection Web Diagram

An example would be using data analytics to anticipate the needs of individual customers at each moment of truth and key stage of their journey. Some specific examples oh how businesses are using AI to enhance customer knowledge:

2. Use AI to create stronger emotional connections with your customers

Using AI to recognise a customer’s emotional state helps agents better respond to the customer during an interaction, thereby creating stronger emotional connections.

3. Use AI to empower your service agents

artificial-intelligence-robot

Not only can AI empower agents with emotional intelligence to reply appropriately to customers, it can be used as a tool to connect service agents with the right information in the organisation’s knowledge base in real time. Examples of why this can be powerful to a business: 

A recent study Fifth Quadrant CX undertook for Oracle showed that CX leaders acknowledge the potential of AI and are more advanced in trialling and implementing these emerging technologies to enable better customer experiences. AI is being used to combine data from multiple sources to create individual profiles for each customer, enabling agents to take immediate action on what customers want. Consequently, CX Leaders are outperforming their counterparts by creating emotional connections with their customers through more predictive and personalised customer experiences.

As a result, nearly two thirds of CX leaders say their organisation’s revenue growth outperforms their industry counterparts, compared with only a quarter of CX laggards. The proof is therefore clearly in the pudding: when applied in a purposeful and meaningful way, AI technology can enable organisations to increase agility and overcome competitive threats and leverage this advantage to drive acquisition.

 

Steve Nuttal fifth quadrant customer experience head of researchWritten by Dr Steve Nuttall – Head Of CX Research, Fifth QuadrantSteve has worked in various leadership roles as a market research insights professional for over twenty years in Europe, Asia and Australia. He leads Fifth Quadrant’s program of CX strategy research and is an international speaker and presenter on best practice customer experience. Steve assists organisations to deliver their customer-centric strategies and business performance goals including designing and implementing programs to help optimise the customer experience.

Making Returns on the Conversational Economy

Article by Adam Rawot, CEO Woveon

I remember reading an article almost ten years ago talking about how teens were sending over 40 texts a day on average. The tone of the article was incredulous, but the statistic pales in comparison to how we exist online now. Speaking personally, it’s not implausible I send off 40 messages before 10 AM in my morning inbox check in. Sarah Guo, a partner of Greylock, expressed it succinctly when she took to Medium: “More than a decade ago, academics such as Thurlow described a “communication imperative”—human beings are driven to maximize their communication volume and satisfaction. More recently, researchers have described it as compulsion.”

While constant connectedness is old news, technology has finally achieved a point it can leverage this behavior. As with all big shifts, there will be survivors and those who don’t adapt fast enough. Companies will need to change to a conversational mode of thought to maintain the experiences users expect and deliver the individuality anticipated.

People Always Talk

multichannel communication

Nearly 25 years ago, Harvard Business Review wrote “today if you’re not on the phone or talking with colleagues and customers, chances are you’ll hear, “Start talking and get to work!” In the new economy, conversations are the most important form of work.” Conversations are how we track knowledge flows. Conversation flows are how people create value, share information, and illustrate how companies operate.

A cited example is McKinsey. McKinsey prides itself heavily on the intelligence of its members, and by an extension the true value of McKinsey over other firms is its extensive knowledge base. That knowledge is curated and developed through internal conversation and shared through internal conversation. In short, McKinsey is conversation.  

We are entering a new age for product development – one dictated by the conversational economy. Broadly, the conversational economy is the catchall phrase for companies, products, and ideas built on, alongside, or relying heavily on a conversational interface. More simply, they are services that leverage conversation.

This definition is board, and intentionally so. While some apps like iMessage, Snapchat and email obviously fit into this definition, conversation works as a backbone in services like Facebook, customer service complaints, and online advertising as well. Finding a common backbone helps derive a working model for these services.

Between the myriad of mobile apps used every day, access to the internet, and the seemingly innate human need to feel connected, conversation based platforms are dominating our lives. We have effectively destroyed the asynchronous quality of day to day life. We persist online, and, consequently, our conversations with one another never really begin or end. This data stream is a jackpot for product creation.

Smarter Everyday

Artificial intelligence, in the eyes of the public, has snuck past an important threshold. Presentations by titans like Facebook and Google have assured that we are moving away from the robotic idea of natural language processing in a rigid sense to natural language understanding. In other words, instead of responding to a keyword or a phrase, computers are beginning to be able to understand sentence, paragraphs, and intent.

There are a variety of causes for this – improvement of machine learning and deep learning, Moore’s Law, and rate of mobile and app data collection, to name a few. Algorithms and software are taking on their own intelligence. Just the idea that failed outcomes can make systems better is an astounding twist compared to five years prior.

Additionally, we’re in the middle of the boom of ambient computing, the idea that our environments and surroundings are responsive. We don’t have to open our phone or flip open a laptop to be connected. On the way to work I may pass a few smart cameras, a plethora of listening iPhones and Galaxy phones, an Alexa, Chromecasts, and more. Despite this, I would characterize myself as one of the less connected people in my demographic. At every step of my day my voice can be heard, position tracked, and activity monitored. Being connected no longer has much to do with if our phone is on our person or if we’re behind a keyboard.

Although passive collection has subtly pushed past our natural aversion to share information with technologies we don’t understand and people we don’t know, this one-sided trade has come with the expectation of usability. When software doesn’t work or apps crash, we no longer blame ourselves, we blame companies. We are inundated with choices, but that means that we have little tolerance of poor experiences. Users are more empowered than ever in that they don’t have to subject themselves to experiences they don’t want or content they’d rather avoid. We so demand these freedoms that events like net neutrality rapidly cause public outcry and social faux pas by companies like EA tank sales.

Computing, connectedness, and data almost completely undermine how product managers need to think about designing products. The need to leverage conversation to deliver value has emerged as one of the most critical company problems. IDEO acquiring a data analytics company, giants like Apple acqui-hiring boutique companies with human-centric software, and Salesforce pushing Einstein all serve as mine canaries that even the most established companies are racing and struggling to adapt.

Buying In and Cashing Out

As George Box famously cited – all models are wrong, but some are useful. Where is the utility of viewing products as ongoing conversations?

A helpful place to start is in how companies have historically fended off competition. These ‘moats’ include things like brand loyalty, unique data sources, and intellectual property. However, as technologies like AI are more readily available via open source projects, cloud hosting and computing are only a few clicks away, and systems of engagement continually emerge, the traditional ideas of tech defensibility are evaporating. In a Greylock article on Medium, they wrote “In all of these markets, the battle is moving from the old moats, the sources of the data, to the new moats, what you do with the data.”

In another words, companies are now finding defensibility through the experiences they create. To create these experiences for customers in the conversational era, companies will have to harness existing behavior, respond personally, and work faster.

Harnessing existing behavior is an exercise in invisibility. The real frontier for conversational companies to generate solutions for problems before the consumer is even aware. For example, Facebook realized that people asked for recommendations on their newsfeeds. Instead of creating a new service, they had posts automatically update with reviews and locations. They created a new card that changed automatically depending on what a user wrote. As expressed by a product designer at Facebook: “We didn’t try to invent a completely new behavior; rather, we found an existing one and made it way better.”

To cite an example within my own career, food industry companies often lose hours if not days within food recall investigations. Tracking a faulty shipment through several distributors can be tricky. We worked to create a product that reads the complaint before the owner may even be aware it exists and start and investigation. By the time an owner is even aware there is a problem, a report is ready. By approaching complaints, invoices, and shipments and messages between companies, value can be created seamlessly in a second layer.

As I’ve written about before, personalization is an increasingly critical element of producing customer lifetime value. Harvard Business Review started to notice this trend in their research on customer service: “Even as artificial intelligence becomes embedded in everyday interactions; human conversation remains the primary way people make complex purchases or emotional decisions.” The fatal error in a lot of software products is focusing on company efficiency over consumer experience. While these changes may boost bottom line in the short term, they encourage competitor entry and consumer drop off.

Conversational apps have an obvious outlet for personalization, and the power behind them allow easy switching between automation and human elements. More simply: “these intelligent agents will facilitate one-on-one conversations between consumers and sales or customer service representatives rather than simply replacing human interaction.” Imagine a case where someone sits on a delayed flight and sends out an angry tweet. A conversational built system could find the message, tag it, and route it to an agent. While the agent delivers a personal response with an update, the system has already sent an alleviating reward of extra miles to the customer. The captain may be alerted of sentiment on the plane and deliver an announcement. While an autoresponder may have been cheaper, the customer will now remember the exceptional level of immediate service and is more likely to return. As information and computing become free, the real commodity becomes the personality of the person on the other end of the line.

In the shorter term, there’s a simpler way to think about AI adoption – people don’t trust what they don’t understand. In the classic product management advice, it’s best to start with a problem and move to solution. Leveraging conversation is a means to building a better product, but that doesn’t change what the bottom lines should be. In other words, “Bots do not need to be human to be human centered.”

Outside of the shift in new product priorities, another major implication is how we use the technologies we use currently. In a blog post, Dan Rover (sp?) declared that bot won’t replace apps, but inboxes were the new home screen. Our email, text messages, and more were queues demanding our intention and driving our usage.

Companies leveraging platforms like WeChat have been able to effectively create micro services and apps for things like ordering that have integrated seamlessly with how we act now. Bot companies that are able to daisy-chain onto conversations to do scheduling and commuter planning have shone in venture capital funding. It’s not inconceivable the next unicorn will have nothing to do with creating a new platform but layering effortlessly onto the ways we talk with those platforms now.

Speak Now

We talk online all the time, but computing has finally let us create value from that. Companies need to invest in ways to leverage these conversations to deliver seamless and personal content. This means focusing on personnel and focusing on alleviating frictions than automation. Companies that don’t value the communication imperative and connectedness of customers will soon find themselves lagging in experience, and, later, sales.

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A prime example of this is Amazon Web Services’ fast climb to dominance. Legacy systems like Oracle required costly deployments and developers, and setting up cloud instances on AWS is only a few clicks away. IaaS records have shown Amazon’s sheer dominance. Oracle, trying to defend by housing data and curating an elite brand, couldn’t compete with Amazon’s engagement accessibility.

Perhaps the most obvious implication of smart conversational apps is efficiency. However, despite all the news and hype around an artificial intelligence singularity, businesses – and their customers – still revolve around the interactions person to person. This means that products needs to be resolved around facilitating conversation, collecting information, and iterating form that information. The AI boom has made it easier than ever to facilitate personal conversations no matter where customers are online.