AI in CX: Real or Superficial Intelligence?

Artificial Intelligence

By all accounts, 2017 has ushered in the dawn of the newest Artificial Intelligence (AI) era. Most technology hype cycles follow typical paths, quickly shooting up, often followed predictably thereafter by a meteoric reentry to reality.  Typically, the entire flight takes place over a decade or so, as the fuel of inflated hype burns out, and the gravity of commercial application pulls down on its excitement to test its true value.

AI, however, seems different. It has appeared, drew much fanfare, and then disappeared several times already – more akin to a comet, flaring a tail of excitement with each new orbit.  As it reemerges, nearing the heat of expectation once again, it lights up with a spectacular plume, flung into space for another long dark hiatus.

AI history suggests five such orbits already – so is it destined for cold dark space soon?

Superficial AI

Regardless of the metaphor du jour, what we must inspect is the true value returned today, not the imagined expectations of tomorrow. The best test of commercial viability is not an intelligence test; it’s whether consumers are getting more value, and if the business offering the products & services are using AI technology as leverage, providing those things with higher margins.

For example, my mobile device is now my phone, my Garmin, my camera, my alarm clock, my digital assistant, my video recorder, my dictation device, my virtual reality device, and so forth.  20 years ago, it might have cost me $5,000 for these services.  Today, I get it all for $500 – $700.

We’re all under pressure to do more in the same amount of time.  To that end, these devices have become indispensable – they are essential to modern day survival – adapt to them, use them efficiently, or you’re passed by.

Therefore, by some measures and definitions, AI has delivered this time around.  Personally, I don’t care when a big company announces their sixth AI acquisition, or what their advertisements or creative animations say.  In my view, the proof is if customers are buying, are satisfied with those purchases, and are reporting their lives are easier, more productive, and more enjoyable.

Businesspeople must apply the same tests.  Can they deliver better customer experience with AI?  Are their product & services measurably smarter and more efficient?

If they aren’t passing those tests, then it’s just superficial AI.

Real AI Value in CX

AI – Automated Intelligence

As we all admire the latest bright tail of inflated expectation, let’s study what AI has really contributed to delivering better customer experience (CX) this time around.

For starters, look again at that magical device, the smartphone.  It streams location data, activity levels, browsing preferences, timing behavior, and the like.  Businesses consume this contextual data, and use decision hubs infused with AI algorithms that in less than a second calculate a next best action or insight.  That’s real!   Big banks, telecommunication / technology firms, and retailers are doing this today to improve acquisition, on-boarding, cross selling, and retention rates.

For consumers, the insights automatically delivered include recommended products, drive time estimates, calendar reminders, and service alarms. Alerts & notifications remind when bills are due, when fraud occurs, or when more exercise is required to meet goals.  Cars drive & park themselves, thermostats learn, and media services understand consumer preferences.   Customers can interact with machines by simply speaking to them.

For the marketers responsible for engagement strategies, AI now recognizes buying patterns, automatically performs A/B and multi-variate tests, which ranks the best content & promotions for the right individuals, and even suggests the best timing for those recommendations.  For salespeople, AI predicts the best contacts, opportunities, and accounts to spend energy on to maximize close rates.  For service workers, AI deflects simple service requests, and guides agents on complex service processes to improve time to resolution, ultimately improving customer satisfaction.

Simply put, there can be little argument that AI has delivered value during this orbit, much of it in the form of automation as opposed to higher-level intelligence.  Fewer marketers deliver more relevant and better-timed tactics.  AI assisted sales means higher quality pipeline with sharper close rates. Contact center managers relish shorter handle times and more efficient call resolution with less staff, and consumers enjoy shorter wait times and voice / bot-assisted service. For those using AI, NPS and customer satisfaction scores are on the rise.

All of these outcomes are commercially feasible.  Every business (not just the avant-garde) must rapidly incorporate these proven technological capabilities.  Hesitate, and the likely result will be eventual irrelevance.

What’s next – In my lifetime?

With all this said it’s back to our question.  Can AI keep delivering, or is it bound to let us down soon?

As humans, we love to dream.  That’s important.  In fact, regardless of how fast machines move forward, it’s still something that separates us from them.   We envision a fanciful future, and plot our course toward it.  Along the way, we stumble, get humbled, get up, and plot again.  This is our nature.  Each step along this evolutionary path, we create and refine machines that help us achieve our dreams.

Our vision seems unchanged.  We long to make life easier and more enjoyable for more of us.  To do this, we must continue to refine our existing tools, and invent new ones that assist us, and make up for our physical and human limitations.  No different from our first instruments, modern day smart tools take over tasks we were never very good at, or simply couldn’t do. They help feed us, optimize our resource consumption, and make our very survival possible.  We are already dependent on them, and there is no turning back.

This is also true for customer experience tools.  Our expectations are high and climbing.  We expect to interact with brands that listen, understand our preferences, react accordingly, and when something goes wrong, can turn on a dime and make things right instantly.

When I enter a website, I expect the search to be intelligent, the user experience to be delightful, and the checkout process to be flawless.  If I chose to do all this while mobile, I expect the same experience on my smartphone.  If I need help, my first reaction is, “why did things go wrong in the first place…how could this have been prevented,” and then I test if resolution comes fast with low effort – and does the business learn from the mishap.

This is the new normal.  Unfortunately, many brands today are not delivering on this type of customer experience.  The bar is high, but the elevation of game is not so much a demand from technology as from organizational re-tooling and reorganization to accommodate for technologies already commercially available.

Technological advancements will continue to accelerate.  Smarts will show up in more devices. We will demand our machines become more human, especially in delivering customer service and better experiences.  As humans, we love a personal touch, a social exchange, a sense of community and belonging.  So far, machines have not been able to deliver on any of these aspects.  That’s changing.

Presently, there is very interesting research going on to bring more human-like aspects to machine interactions. Google’s DeepMind research lab has made impressive gains in speech synthesis (text-to-speech) in a project known as “WaveNet” where robotic voices are becoming a lot less robotic.  Similar advances in Chabot research is leading to smarter bots able to remember details, learn right from wrong answers, and hold basic conversations.  You can try one of the better ones at http://www.mitsuku.com/

These developments are exciting.  The possibilities are enormous.  Yet until these become commercially viable and noticeably better with true customer engagements, you should train your eyes on what is real in AI today.  For now, focus your investments and efforts on delivering real CRM value from AI tech today in the form of things like simple service request deflections, intelligent routing to the right agent, relevant product recommendations & next best offers (based on individual behavior profiles), and guiding salespeople with next best activities.

Meanwhile, keep close tabs on these other AI CX innovations as they progress, take some calculated risks on a few promising areas, and prepare for the next revolution of AI.  The AI comet will be back shortly.

Rise of Machine Marketers – Transforming CX

Machine marketers are smarter marketers, always using machines for advantage.  But this isn’t new.

Direct marketing was born out of the ability to exploit addressable media as the way to garner feedback on whether their enticements were working.  Catalogs and snail mail with reply forms, evolved to email, telemarketing, and other mechanisms – smarter marketers understood guesswork would never win over using data, technology, and the scientific method.

Machine Marketers

Database Marketing

In the 90’s, the ability to more massively codify and share customer data, and use it to steer marketing campaigns drove a revolution. It sparked a major shift of media spending away from general advertising using TV & Radio, to addressable programs.  Database Marketers, the offspring of Catalog Marketers and ancestors of Machine Marketers, scraped for individualized customer information to power personalized treatments – where direct response open rates, response rates, and conversion rates kept score.

They loved data because when they used it to drive targeting in their programs, the patient responded. Realizing their treatments were working, they wanted more data, wanted it fast, and wanted it in pure forms. Native sources worked well, but they sought alternate supplies in the forms of public, compiled, and modeled data – anything to test for a slight edge.

A new market formed with a vast array of players, arising to meet the growing demand for customer data.

The 2nd Coming of Big Data

Then, a number of things happened. Even more individualized data poured onto the market.  Consumers shopped and bought online.  Consumers went mobile.  Consumer devices of all kinds started streaming behavior data.  Consumers readily traded personal information for points and promises.

Hardware continued to plummet in price and better software meant cleaner and more accessible data.  Data compilers flourished, with data as their raw material, and database & data science technology their assembly line, and the internet their logistics network.

Database marketers had struck oil again, but this time it was BIG – and IoT data was the source of their new bubbling crude.  Data refineries appeared everywhere.

Internal IT had competition – their 90’s data warehouses rendered obsolete by a Big Data revival.  Open source databases like Hadoop, were faster and ran on commodity hardware. SaaS providers offered a variety of big data subscription services, and agencies used bigger and faster hosted databases.

There was but one small problem. Insights weren’t leaping out of these primordial big data reserves.

Data Science and Data Mining Come of Age

Meanwhile, mad “Data Scientist” marketers continued to manipulate and tune their statistical models to improve lift. Early on, they realized that algorithms devised hundreds of years prior could now be fine-tuned and fully unleashed to predict which customers were more likely to respond and buy their products.

Less sampling with faster machines and more data meant better results.  Suddenly, more people became interested in what they were doing.  People were peering over their shoulders. The press told stories of firms predicting a pregnancy before grandparents even knew.  Adding fuel, the biggest brands on the planet (Google, Facebook, Amazon, et al) got into the game, doing big reveals, seemingly weekly, on the methods to their data science madness.

It was time to give this a makeover, market it, and commercialize it.  “Geez,” said the creative marketer. “We can do that!”

AI and Machine Learning – The re-launch

Our story takes us to circa 2012.  The time was right.  Cars were beginning to drive themselves; IBM’s Watson had won Jeopardy; Google was predicting our search terms and winning at the game Go.  Our iPhone was conversing with us, and Amazon & Netflix were courting us with recommended products to buy and movies to watch.

Honestly, no new science unexpectedly sprang forth, but as happens old science around for decades (decision trees, neural nets, Bayesian learning), became an overnight – well let’s call it an over 5 year – sensation.

What happened was how technology revolutions occur.   Attention begot investment, huge investment bought more innovation, and marketable innovations caught more attention – and the virtuous loop was in motion – adequately fed by a rich venture capital environment.

Marketers assembled the pieces into cost effective working solutions. They collected and compiled consumer data sources, cleansed and filtered them, fed them into pattern recognition and self-learning systems, detected opportunities and alerted touchpoint systems, automated waved campaign schedules, and connected their outputs to fulfillment systems.  They did all this via an interconnected stack of private and public clouds, transferring data and insights in seconds.

Michelangelo meets Newton – When Content met Context

CX AI

By 2016, another phenomenon unfolded.  Creative & scientific minds more closely collaborated.

Deep Learning, the science of neural networks, commoditized language and image processing, changing how we interfaced and worked with machines. Clunky interface paradigms gave way to elegant ones that were responsive and rewarding.  Design thinkers (those artsy fartsy types) were no longer an afterthought. Au contraire, they were now a strategic advantage.  Consumers dictated the definition of great customer experience: Relevance, value, simplicity, and visual beauty.

Machine Marketers, ever the opportunistic breed, seized the moment, further refining their targeting and personalizing creative treatments across available channels. Machines further assisted their agency suppliers, assisting them in turning out better, faster, and cheaper creative.  Technology further assisted marketers, auto generating optimal SEO terms, email subject lines, and even catchy tweets.  Machines advised on the optimal time to execute campaigns.  Next best recommendation rankings used statistical probability to find relevant products & services for more refined targets.

Beautiful creative no longer took months to produce.  In many cases, consumers produced content for brands – and the content bottlenecks holding back visual personalization broke lose.

Science and technology glued yet another critical piece into place.  Touchpoint systems where customers interacted could now understand natural language, and instantly fed back contextual data (location, last behavior, weather conditions, intent, mood, and so forth) straight through to systems primed with algorithms that learned in real-time, recalculating next best actions in a conversational mode.

“Computer, find me the closest coffee shop.”…”Ok, I found one 2.5 miles away, do you want directions?”

“Computer, I need a highly rated case for my X phone for under $25.”…”Ok, I found four with 5 star reviews that fit your X phone for under $25, do you want to hear about them?”

Fronted by Natural Language Processing (NLP), personalization engines married conditional & appealing content with contextual recommendations – spawning audio & visual personal assistants.  The result: off the charts lift and conversions.

These were contextual, conversational, and relevant interactions.  This was transformational.

artificial intelligence evolution

Machine Marketers Rise Up

In the end, let’s face it.  Marketers want to do one thing more than anything – sell more stuff.  Yet the smart ones know that the best means to that end is relentless focus on the customer.  Ensure each is a happy camper via an individualized relationship, and satisfaction and profits increase.

Thus, today more than ever, ALL marketers had better face one important fact.  They can’t achieve customer centricity at scale using the tools, data, or organization of yesterday.

Like any profession, winners constantly seek a new competitive edge using the latest technological advances in equipment, repeatedly testing innovations, measuring for improvement, and fine-tuning.

Artificial intelligent interfaces are changing the ways consumers interact with their devices, provide data, and interact with brands.  Data is flowing freely, and although privacy laws seem to ebb and flow, the trend has been toward more data sharing and the ability for the crafty to gain a deeper understanding of consumer behavior.

Technology – cheaper, smarter, more portable, and easier to use, continues to translate into the potential to deliver more relevant and convenient customer experience.  Those that get this, and execute on it, will win.

Machine marketers are those who master using the latest data & technology to their advantage – rising to that challenge, they rise to the top of their craft.

Note:  These views are my own, and not that of my employer

Adtech Martech Convergence – Episode #7

In this 7th short video in my Machine Marketing Series, I give my views on the “Adtech Martech Convergence”  specifically as it relates to using machine learning.

 

I cover four main layers of technology to consider as this conversion takes place:

  • Customer Behavior Data – Why the Adtech Martech convergence may force a better coordination of this data as its compiled  along the customer decision life cycle.
  • Basic Analytics & Insights – I give some examples and why this area isn’t a huge concern or risk area.
  • Advanced Analytics (Machine Learning) – I explain why integration here is key, and give some marketing use case examples.
  • Programmatic Real-Time Automation – I outline key aspects of automation & workflow, and why these areas are essential to combine for a coherent Adtech Martech solution stack.