CX Blog - Page 3 of 4 - Marketing Technology

February 17, 2017

3 Tips to Drive Business Value with CX – Fortified with REAL AI

With a first name of “Artificial,” AI has certainly entertained us with its virtual possibilities. Stories of wholesale disruption by robots and fully automated lives make for good movie material, but as of yet, AI hasn’t dominated the marketplace, consumer experiences, or business applications in a monumental way. AI has the potential to change our daily lives, yet for most, its impact so far has been nominal.

As a businessperson concerned with driving better customer engagement, you’re no doubt interested in this topic, yet probably carry some healthy skepticism about the potential for return from your AI investments, and the risk of them failing.

Congratulations! Your suspicion is not only natural, it’s warranted. Here are three tips for how to maximize value from your AI investments, and minimize any risk of disillusionment.

1. Provide predictions about Customer Intent

No doubt, you have scores of business intelligence systems that compile and codify data. They provide customer profiles, program dashboards, and other scorecard reporting of historical results. Although informative, these systems aren’t predicting anything. As such, they are rear view mirrors, providing a view of the past, but not anticipating and generating ideas regarding courses of action that may lead to more optimal outcomes.

Any investment in AI aimed at improving customer engagement must include capabilities to predict customer motivation. Why are they calling? Are they already upset? Are they highly likely to be shopping for another provider? What product or service best suits their true needs? How valuable is this customer over their projected lifetime?

Answers to these questions are always guesses, yet pragmatic AI systems today use proven statistical methods to minimize errors in predictions, calibrate themselves with feedback loops, and provide confidence intervals so users understand their range of applicability.

For example, it’s feasible today to have a portal providing your marketing employees with accurate predictions such as:

Customer value
Churn likelihood
Loyalty to brand

For service agents, predictions like:

Customer sentiment
Reason for calling
Nature of problem

For sales personnel:

Price sensitivity
Available budget
Perception of value

Effective AI has to improve your ability to understand what impels your customers to behave the way they do, or the way they may act in the near future. Work backward from these insights, and demand that your AI systems and vendors can prove they have experience extracting insights from available data, and in predicting and surfacing these items.

2. Make dynamic suggestions to better serve the Customer

Consumers do business with brands that provide repeatable value. That value comes from not just positive product use, but also from an enjoyable and smooth buying process, a friendly and efficient on-boarding experience, and stellar service.

As consumers experience a brand during those journeys, they rack up the score, keeping tally of the relevance and effectiveness of the systems and people they encounter along the way.

Any AI system worth its salt should provide ranked suggestions either directly to customers, or to customer facing employees such as:

Next Best Offer: The most relevant product needed, and an individualized incentive on it that will be both compelling, yet still economically affordable to the business.

Next Best Service Action: The best thing an agent can do next to maximize the chance of reaching an effective and efficient solution to the service problem at hand.

Next Best Sales Activity: The best action for a salesperson given available leads, accounts, contacts, and opportunities.

For the marketers responsible for providing next best offers, AI systems should help them recognize buying patterns, automatically perform tests, filter out offers that don’t apply, and statistically rank the best content & promotions for the right individuals. AI should even suggest the best timing for those recommendations.

For service workers, AI should deflect routine service requests to automated or self-service channels, guide agents on complex service cases, surface potential solutions to issues, and help gauge the sentiment of the customer during the process.

For salespeople, AI should predict the best contacts to engage with in an account, the activities most likely to move an opportunity to the next sales stage, and which accounts to spend energy on to maximize close rates and quota attainment.

3. Install a system that learns in Real-Time

Your world changes every day. As a professional, you wake up every day to news of competitive threats, new opportunities, and market conditions that vary the effectiveness of the strategies you employed yesterday.

If you were slow to react, or simply ignored these factors, you’d fully expect your overall business performance to degrade, so you listen carefully to these environmental conditions, and you adjust accordingly.

Think about your AI systems the same way. They must include adaptive mechanisms, where recommendations made are monitored, in real-time, and dispositions are fed back into the machine, so it can learn from its success and mistakes. Marketing, service, and sales systems receive feedback constantly in the form of customers either ignoring your treatments, or responding to them, so ensure your AI system uses them. Your AI system should rapidly improve its performance, as it’s fed more data, and as it tunes itself. If it’s not, after a short trial period, start asking some hard questions to your provider.

Make sure your results (even if delayed), are monitored, measured, and understood. An accurate measurement of the real business value from AI comes when you understand the baseline, and can measure the lift you get when you employ the insights and recommendations delivered by AI.

Track response rates, conversion rates, incremental revenue, return on investment, and compare to what your vendor promised, what you expected, and what you need to achieve.

AI is a broad topic, yet to improve customer engagement and your outcomes, boil it down to these 3 things; understand customer intent, make relevant suggestions, and learn in real-time so your performance improves over time. If you do these, you will realize REAL value from AI.

January 27, 2017July 15, 2018

AI in CX: Real or Superficial 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

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.

January 15, 2017January 15, 2017

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.

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 2^nd 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

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.

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

January 7, 2017February 6, 2017

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.

December 24, 2016February 6, 2017

Machine Learning Ecosystem for Marketing – Episode #6

In this 6th short video in my Machine Marketing Series, I explain the Machine Learning Ecosystem for Marketing and review about 50 major players.

I cover the 6 categories of Machine Learning vendors in the landscape I created with Tier 1 & Tier 2 players:

Category 1 – Paid Media / Digital Recommendation Vendors
Category 2 – Big Data Cloud Platforms with Machine Learning Services
Category 3 – Open Source Machine Learning Tools
Category 4 – Business Intelligence Vendors with Predictive Analytics
Category 5 – B2B Marketing / Account-Based-Marketing (ABM) / Lead Management
Category 6 – Real-Time (Multi-Channel) Interaction Management (RTIM)

In each category I discuss the Tier 1 (largest players) and the Tier 2 (contenders / firms to watch).

December 16, 2016December 30, 2016

Artificial Intelligence 2017 -5 things NOT to underestimate

Here are 5 things you will undoubtedly underestimate about Artificial Intelligence (AI) in 2017

“We are Now Controlling the Transmission”

If you aren’t familiar with the 60’s TV series “The Outer Limits” you need to watch this intro (its 58 seconds long).

Artificial Intelligence is controlling more than you realize, and in 2017, it’s going to accelerate. AI algorithms are already affecting which products & services you see when you perform a search – regardless of the device or interface. AI is pushing messages, offers, & advice to you that you may never have asked for. Artificial Intelligence is deciding what shows up in your news feeds, on the sites you frequent, and in the apps you use.

Sometimes those results will be relevant and useful. Yet often, they won’t be that relevant and will have bias. So beware, be careful, and try to understand better what is showing up and why. Be an activist. Consumerism is empowering. Use that right to force companies into making AI better, in essence taking back some control.

“We know who you are…and where you are…Well kind of”

A little over 2 years ago, I wrote a blog post entitled “Customer Data & Decisions – Reflections of Me.”

The forces I cover in this piece related to the collection of consumer data will continue to accelerate in 2017.

My advice is work to get your own data house in order next year. Go beyond the obvious, like pulling your credit report, to being prudent about how you safeguard and when you share any of your data. Its valuable, but its also vulnerable.

When you install apps, be thoughtful about which ones you grant permission to track your location. Did you know that Uber now tracks your location from the time you open the app to 5 minutes after you arrive at your destination? Uber values that data, and can leverage it in a variety of ways – but do you want them doing that? What have you received in return? How will they share that data?

Think about these factors when you decide how much data to share, and spend time to understand how firms are using and compiling YOUR data. In some cases, you may not have direct control, but nonetheless it’s important to understand what’s happening since after all, it is your data.

I’m not opposed to companies collecting relevant data to feed into Artificial Intelligence systems to use in a responsible way to add value to my consumer experience. Yet I expect value in return, and I expect a firm to respect my privacy.

You have a vote in Artificial Intelligence evolution. Don’t let it run amuck

You vote with your wallet & purse everyday. Intelligent devices are popping up everywhere and dropping in price, and firms are dropping Artificial Intelligence into almost everything we buy. From voice activated appliances, to connected cars, we now live in a world where having a conversation with a computer is commonplace.

Again, what you buy helps decide which AI infused products thrive and which ones die. Buy wisely. Perform some reviews. Render your opinion. Your opinion on a frying pan may mean a little less to society then your review of how an interaction went with a Chat Bot.

More money than you can imagine will be spent on it

How much will be spent on AI? No one really knows. This post has some interesting estimates. Let’s just say that all of us will underestimate the amount of energy that will be put into AI related research & technologies in 2017. It’s going to be HUGE – and it’s not just in 2017. This is a revolution happening.

AI will have bias because of its creators & the data it feeds on

Artificial Intelligence actions & outcomes must be monitored & governed by its carbon-based creators. Yet ironically, its creators are the ones that introduce bias into the AI brain.

From biased data (think about how a hiring bot might make decisions about which candidates are most likely to succeed in a job….it will use past employee performance data…which is biased by the hiring practices of the past), to the bias rules & code from the inventors.

Its incumbent on us all to be mindful of these tendencies, and advocate aggressively against bias in the machine.

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

December 3, 2016February 6, 2017

Machine Learning Measurement: Episode #5

In this 5th short video in my Machine Marketing Series, I explain Machine Learning Measurement and monitoring techniques.

I cover the concept of a Model Factory and Dashboard where you can:

Measure Machine Learning Lift employed in Marketing and CRM programs
Monitor Model Features and Model Data use
Pit models built for Customer Experience (CX) against each other (Champion / Challenger)
View KPIs (Key Performance Indicators) on a business Dashboard

Use these tips & best practices to benchmark against your efforts, and compare how your platform stacks up in using machine learning in your marketing and Customer Experience programs.

November 28, 2016February 6, 2017

Marketing Results: Machine Learning Episode #4

Getting positive marketing results for your customer engagement efforts is what it’s all about.

In this episode, I explore what to expect in terms of outcomes when you effectively employ machine learning (ML) and artificial intelligence (AI) in your marketing efforts.

I cover performance ranges I’ve seen actual companies achieve including:

Churn & Attrition reduction
Response Rates for your marketing execution & tactics
Sales Lift increases which means revenue to the bottom line
ROMI – Return on Marketing Investment
NPS – Net Promoter Score improvements
AHT – Average Handle Time reduction

You can use these figures to benchmark against your efforts, or even help you build a business case for embarking on using machine learning in your marketing and Customer Experience programs.

November 20, 2016January 9, 2025

Martech Ecosystem – Major Players – Updated Jan 8, 2025

I created this graphic so you can map out your own partner & vendor Martech Ecosystem. The “Major Players” shown are examples and are not intended to be comprehensive.

It’s a qualitative assessment. I’ve used my many years of experience with the worlds largest companies – plotting which vendors have emerged as the dominate players in their respective categories.

If you are not already familiar with the extensive work done in this area by Scott Brinker, please check out his website https://chiefmartec.com. Scott has done a comprehensive job cataloging thousands of MarTech (and other tech) companies in a variety of different areas of specialization. His latest super graphic stuffs 11,038 companies onto one page (has grown from 3,874 when I made this initial post in 2016). You can view it here: https://chiefmartec.com/2024/05/2024-marketing-technology-landscape-supergraphic-14106-martech-products-27-8-growth-yoy/

By contrast, my picture is intended to filter that list down to some of the major players in key areas, and is especially useful to larger corporations, or software vendors that seek to operate & partner for selling to enterprise class organizations.

If you would like a copy of this .ppt, please comment in this post that you would like a copy, include your email, and I’ll send it to you.

November 12, 2016February 6, 2017

Marketing Timing & Content: Machine Learning – Episode #3

Great timing & content lead to great marketing tactics and performance.

In this episode, I explore how you can use machine learning (ML) and artificial intelligence (AI) to improve your message timing and the content you employ – further improving experiences for your customers.

I explore “2 Cool Areas” of Machine Learning & Artificial Intelligence applications – to make your marketing smarter:

Timing Optimizing for your Marketing Execution & Tactics
Automated Content Generation and Predictive Content Recommendations

My tips are aimed at improving your marketing efficiency & effectiveness.

November 9, 2016May 6, 2020

Machine Learning & AI Use Cases for CRM

Updated: May 6, 2020

Below you’ll find a good organization of classes of AI use cases for CRM (Customer Relationship Management) & CX (Customer Experience). Presently, there are countless examples of applying Artificial Intelligence (AI) and Machine Learning (ML) to solve a broad class of problems beyond CX, from cancer diagnosis, robots in manufacturing operations, streamlining product development, detecting anti-money laundering, spam filtering, improving cybersecurity, to self-driving cars.

Great marketers and CX experts have always been change agents. As new-age change agents, they focus on AI use cases that enhance CRM, and have either already been proven to lead to successful outcomes, or that show significant commercial promise.

But what is AI and how can it be used to take customer engagement to new levels? To make it simple, think of AI as the application of software technologies using standard hardware for:

CRM AI purposes –

Task automation:
- Doing something in less time with fewer steps
- Accomplishing something with less total effort
- Doing something with less or even zero human intervention
Detecting, classification, and alerting:
- Sensing and understanding a current problem (customer is dissatisfied or struggling)
- Sensing current customer intent (wants help; wants more information; trying to buy)
- Placing similar things together (customers, products, content)
- Informing someone or some other system once a certain confidence threshold is reached
Predicting something is likely to happen:
- Customer likely to buy more (or less; or never buy again)
- Customer is likely to call for service
- Contract is likely to close
Suggesting (in reaction or pro-action) a course of action leading to more optimal outcomes:
- Doing X will resolve the problem
- Recommending Z will satisfy the requirement
- Offering Y will increase customer lifetime value and/or maximize profit

Machines are capable of doing these in a faster, more accurate, and more cost-effective way versus humans doing them manually. When that happens, the result is a superior customer experience and better business results.

CRM is a technology that improves customer experience (either unassisted or assisted by an employee) as it relates to customer’s interactions with a brand’s marketing, sales, and service, and the fulfillment of commitments by those three functions.

Artificial Intelligence (AI) technologies –

Regarding different sub-areas of Artificial Intelligence and its class of technologies, consider this organization:

(Note: these categories are not mutually exclusive in that a given application may benefit from one or more of these technologies)

Machine learning – Algorithms using statistical methods designed to predict or forecast something. The learning is either supervised (i.e., assisted) because we either know the outcome trying to be achieved (or we have a teacher to inform us) and can determine if the model is right or wrong – or unsupervised (i.e., unassisted) because we have no idea of the patterns or outcomes that may be best). The system “learns” if over time its predictions and performance improve.

Text, Video, Audio, and Image Analytics – this code scans unstructured data, and finds entities/objects, and classifies them (and/or extracts them). Deep learning is an advanced form of image analytics – using a technique with layers of brain-like neurons (Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN)).

Complex Event (Monitoring) Processing (CEP) – an engine fed streaming data from one or more source, which detects certain patterns and then initiates follow on actions or processes.

Deep Q&A – using an AI system that has access to previously compiled knowledge sources; it scans, filters, and presents the best likely answers to questions.

Natural Language Processing – this code translates spoken voice to text, or to some other form useful as input to another system. It can also do the reverse, translating some other system’s output to spoken voice. It also can translate from one language into another, or just detect the language.

Natural Language Generation – this code takes input (images (still or moving), text, etc..), and generates text descriptions of what its seen or found.

Numeric Analytics – this code uses commonplace mathematics (simple formulas) to surface insights, focusing the receipting’s attention to aid their future decisions.

Robotic Process Automation – this code repeats tasks that it’s instructed to repeat.

Deterministic rules – if/then/else statements that don’t change unless a programmer changes the rule.

Click here for an AI diagram of how I’ve organized these into building blocks so you better understand where each belongs in terms of its contribution to automation or true machine intelligence

CRM AI Vehicles –

Regarding implementing AI use cases in CRM for improving customer engagement (e.g., using any code and a combination of the above algorithms), consider these main vehicles:

Virtual Assistants – a module designed to aid and assist a human with decision making, problem resolution or task completion including:
- Chatbots (such as Facebook Messenger)
- Conversational interfaces (such as Alexa, Google Home)
- Office assistants built into desktop software or mobile apps (e.g., automatically schedule meetings)

Digital Recommendation Engines – ranks and bundles content recommendations, product recommendations, offer recommendations, and service recommendations and uses containers/spots on web, mobile, agent desktops, email, kiosk, and other digital devices to serve these to:
- An employee or agent to sell a product or solve a service case
- Engage directly with a consumer through any digital device

CRM AI Use Case Categories –

Given these areas, there are hundreds of potential valuable AI use cases for CRM. Here are some examples:

Marketing

Profiling and Tactic Execution:

Predicting missing or outdated customer data values

~Forecast data value using time series, or simple inflation adjustments
~Predict data value from an image

Segmentation, clustering, targeting
Next best product / service recommendation
Next best content recommendation
Next best promotional recommendation
Next best channel to engage on
Next best time to send
Next likely transaction (and timing of it)
Next best search keyword

Predicting Customer Behavior and Value:

Models to predict customer churn
Models to predict customer preferences (including a preference for a particular product/service)
Models to predict customer lifetime value
Models to predict current customer value
Models to predict customer wallet share
Models to predict customer re-activation

Dynamic Price Optimization

Optimize retail price of products/services
Optimize incentive levels (discounts) of products/services
Optimize price paid for ad buys

Marketing Operations / MRM / Marketing Planning:

Tagging/filtering content – Recognize an object in image or video
Tagging/filtering content – Match an audio track to known copyright material
Tagging/filtering content – Classify image content type
Content generation
View trending topics
Forecast market size, marketing revenue/costs
Budget allocation planning/media mix optimization

Influencer Marketing:

Content identification

Sales Automation

Next best lead
Next best sales activity
Next best sale rep
Sales contracts
Sales and demand forecasting
Competitive intelligence

Customer Service

Customer Service / Support

Next best service, support, training action to resolve a complaint/problem
Intelligent routing: Find the next best expert/agent to resolve a complaint/problem
Text and images uploaded to support resolving cases
Tag and classify support tickets
Support process automation
Estimating service volumes, wait times
Optimize scheduling and support resource utilization
Contact center volume forecasting
Estimate wait time
Optimize scheduling and support utilization optimization

Predicting Customer Service Behavior:

Models to predict levels of customer frustration or satisfaction
Sentiment analysis with all forms of data.
Reasons behind high churn likelihood
Models to predict intent (likelihood of a customer calling and why)
View trending support topics

November 4, 2016February 6, 2017

Smarter CRM: Machine Learning Episode #2

You want Smarter CRM, don’t you?

In this “Mini-Cast” on machine learning (ML) and artificial intelligence (AI) for marketing & CRM, I outline the numerous applications you can use to improve customer experience.

In this second episode, I delve into the various areas of AI & ML applied to Marketing, Sales Automation, and Customer Service where ML & AI play huge roles in taking those functions to new levels of insight and intelligence, enhancing productivity, effectiveness, and delivering better customer engagement.

Episode #2 on YouTube:

October 29, 2016February 6, 2017

Machine Learning Marketing Series – Episode #1

Introduction & History of Machine Learning:

In this inaugural episode, I explore some useful history on the various areas and aspects of machine learning and artificial intelligence, and its evolution leading up to today.

Specifically, I delve into unsupervised machine learning, deep learning, and software robotics as applied to customer relationship management.

Watch my entire “Mini-Cast” series of short 5 to 10 minute videos on machine learning (ML) and artificial intelligence (AI) used in marketing & CRM to improve customer experience (CX). The entire series is available from my website linked to my YouTube Channel:

Episode #1: Machine Learning Introduction & History (this one above)

Episode #2: Smarter CRM – Machine Learning Applications for Customer Experience

Episode #3: Marketing Timing & Content – 2 Cool Use Cases for Machine Learning

Episode #4: Marketing Results – Amazing Outcomes using Machine Learning

Episode #5: Marketing Learning Measurement & Monitoring

Episode #6: Major Players in the Machine Marketing Ecosystem

Episode #7: Adtech & Martech Machine Learning Convergence

Episode #8: Machine Marketing – Hot Trends (coming soon)

June 27, 2016June 28, 2016

Mind your P’s & Q’s for Balanced Marketing

Let’s not pretend the four P’s of marketing are dead, useless, or outdated. They aren’t. You still have to price your product or service and promote it in the right places.

These age-old marketing corner stones are as important today as they ever were to your operations. What they don’t do, however, is provide you a compass to help guide strategy and insure your success. For this, you need new age P’s and Q’s that will orient you, and serve to infuse both a predisposition toward “customer centricity,” and help you take pragmatic actions with customers for balanced marketing.

In truth, customer centricity is simple to achieve. Give customer what they want. What’s difficult is to do this and run a growing and profitable business. To thrive in the digital age of the consumer you need a measured approach to a variety of factors.

So think about the below P’s & Q’s, and practice them to achieve balance when you plan and perform your marketing:

Four P’s

Preference
Personalization
Performance
Pervasive & Persuasive

Preference

Instead of centering your marketing on your products and simply spouting off about all its attributes, concentrate on understanding customer preferences first. Regardless of your business, this is a better approach. Sure, there are times when customers can’t easily articulate a need. Consumers weren’t breaking down Apple’s doors demanding an iPod, yet they clearly wanted portable music. So even in these cases, if you understand their behavior and fundamental need, you’re more likely to fit an offering to an expressed or latent requirement.

If you understand the market you are in, and what the customers really want in that market, and you hear them out, then you are on the right path. If you can’t pinpoint them, and you rarely do research and gather data, you probably aren’t actually listening to your target audience. Get to know your customers. Listen to them. Live in their shoes. Learn from them. When you do this, you will uncover huge opportunities to serve them better.

Interface with customers on many levels. If you sell jets, listen to and understand more than just the airline buyer, but also the infrequent & frequent passengers, pilots, flight attendants, and maintenance crews.

Personalization

Make personalization self-service. Figure out how to allow your customers to self select the marketing they receive. Make it a dialog and give plenty of options. Then, use that data to customize your marketing activities and the product and services you promote. Often, the same offering will fit different customers, yet make it so because they selected it as opposed to they had no choice.

Performance

You can listen, and you can configure, but unless you can deliver, your marketing is the noise before your defeat. Put time & effort working with your product teams, your operations, your fulfillment, your service staff, and your training folks. Once you get a sale, make sure the customer is not subject to old school mentality where the post-sale devil appears and declares, “Oh all those promises were for when you were a prospect, now you are my customer.”

Pervasive

Get your message out – everywhere you can where your target audience is. Nowadays, there is a lot of marketing clutter, and you are going to need to work hard to get reactions to your impressions. You don’t always have to spend traditional marketing dollars or use classic methods to do this, because you can growth hack. For example, consider using “Contextual Incremental Marketing” as a methodology to sustain your message in digestible chunks more likely to sink in over time. Leverage your customers as a marketing channel – when they have success, they are your most credible spokespersons. Spend a portion of your marketing efforts teasing out their accolades and distributing their stories.

In addition, leverage social influencers, blogs, crowd sourcing, partners, affiliates, analyst, review sites, and local community efforts to spread your word. In other words, use the digital network effect to your advantage.

Four Q’s

Quality
Qualification
Quantification
Quicker

Quality

A new adage I’ve heard that I totally disagree with is “Speed trumps Quality.” It’s a sad commentary on society in general, but specifically for Marketing and Products, it’s a problematic long-term strategy. You will erode your customer trust if you don’t have quality marketing, sales, and products. Strive for high quality, and take the extra time to get it right – the alternative of sloppy or mistake-ridden output is unacceptable to your customers. Find me a brand that has stood the test of time, and I will show you a brand with quality at its core. So measure twice and cut once.

Qualification

Don’t be afraid to qualify with whom you do business. You have a business to run, and if you did a strategic business plan (I hope you did – if not – go directly to jail and do it – do not pass Go – do not collect $200) – you have a specific market and customers you are focusing on. If you stray outside that, using the same plan, it’s a recipe for failure.

Consider customers that aren’t properly qualified for your existing products as a potential future opportunity for another adjacent market with another product. Then, study it and make a deliberate decision of whether to enter that market segment.

Quantification

If you aren’t measuring it, you aren’t managing it. Moreover, if you aren’t managing it, you can’t improve it. You need to commit to sustained measurements so that you can adjust dynamically. Consider this – if a police officer pulls you over and asks you why you were doing 60 mph in a 35 zone, and your answer is that you didn’t think you were speeding because when you checked a week ago, you were going 35 mph – that argument won’t hold weight, and it doesn’t hold weight in your business.

Quicker

You need quality, but you also have to be quicker than you were before, and certainly faster than your competition. This means speed in detecting changing market conditions and agility in adjusting your messages and product to that new dynamic. You must be real-time in your ability to use customer context, and factor it into your decision-making, using a next best action approach.

May 16, 2016

5 tips to avoid becoming “A Distracted Marketer”

In June of 2015, I wrote about the Distracted Consumer with their heads buried in smartphones and attention spans decreasing from 12 to 8 seconds in just 15 years. It occurred to me that marketers, subject to the same rush of options, information and environmental forces, are highly distracted also, and may not be focusing enough to get real value out of the solutions they buy and attempt to implement.

As a consultant for years, my DNA is wired to help solve problems and find ways for clients to streamline and improve their marketing efforts, ultimately improving customer experience for their clients.

Stick with me (proving you can outlast the attention span of a goldfish) as we explore 5 areas of distraction that can derail you from sustained improvement, and consider ways to combat those:

The explosion of options and the sea of information regarding technologies & practical applications, much of it contradictory, hyped up, biased, and confusing, makes it difficult to find and focus on the right technology platform.
The amount of data available can be overwhelming and lead you down countless dead ends. Big data, little data, slow data, fast data. Its data galore and finding the right data and putting it to use in a timely fashion before it decays into worthless bits can be challenging, costly, and elusive.
The disruptive organizational environment. Matrix management and average tenure in jobs less than 3 years means instability in teams, long term planning, and accountability. Disruptive isn’t always an adjective to be proud of.
The death of critical thinking trumped by speed over quality.
Agile as a crutch to why people drop everything to work on the new thing when often the newest thing isn’t always the most important thing, or the thing leading to highest impact & value.

So many choices

Let’s face it. It’s great to have choices. It allows us to discriminate as consumers on features, quality and price, and push our suppliers to compete and innovate. Marketing technology is certainly no exception. Chiefmartec.com catalogs nearly 4000 companies supplying a variety of technology to the marketing function. Yet picking solutions is very different than walking down a cereal aisle and deciding on your next breakfast. More akin to a pharmacist that needs to understand the interactions when mixing medications, a marketer needs to understand how these technologies will (or won’t) interact to produce a productive overall solution.

Before you pick a technology stack, explore examples where other firms have successfully deployed a solution with various vendors “stacked” together. Ask yourself if they received the value and within the budget and timing they expected. Once you pick one, be patient (within reason) and give your user community a chance to adjust to it, and adopt its full potential before you jettison it for shiny new parts. Find a core linchpin vendor, and build around that firm and its technology, making sure that company is stable, innovative, and invests reasonably in its products versus its exit strategy or next acquisition.

I’m drowning in a big data lake

Have you ever started searching the internet for something and 30 minutes later find yourself reading an article that has absolutely nothing to do with what your initial pursuit? Sales & marketing data trails are no different. With a remarkable amount of data, often linked together with drill downs, report hyperlinks, summary and details tabs – you can quickly get lost in a maze of data.

Marketers need new discipline and training to navigate data and discern bad data from good data. Before embarking on data exploration, it helps to have a hypothesis in mind, and then set out on a path to prove or disprove it. Use caution and judgement to verify data resources, tossing out dubious sources.

Stick with some core tools that enable you to navigate, slice & dice, fuse, and distill data. Learn some deep features and exercise your proficiently with them, just as you regularly exercise different muscles to stay in shape. Spreadsheets, like tools in a plumber’s toolbox, haven’t changed that much in 20 years and in many cases work just fine.

Disruption corruption

These days, we’ve been disrupted by much more than travel and leisure start-ups. The workplace culture is one that breeds and seems to reward attention deficit disorder and interruptions. There is actually a premium put on impulsive behavior, hatched by a technology crazed society that fears they may become outdated and obsolete thinkers because they have been offline for more than 15 minutes.

Interruptions abound in our lives. It’s what’s killing our attention span. But to be a happy and productive marketer, you need to find your quite place and time. I find it early in the morning. My brain is fresh, my associative thinking runs freely, and I have some time to plan ahead and some control over the interruptions, which later in the day become harder to ignore. I use this time to plot out longer term efforts, take stock in what I’ve accomplished and what’s in flight, and then factor that all together to adjust priorities in tune with my long term objectives.

Find your happy place and time, and use it to get deeper into a subject, a skill, or to perform some research that otherwise invariably falls victim to death by fragmentation. Avoid forcing something out prematurely if it hasn’t been subject to testing or vetting commensurate with the expectations of its audience. Use your practical judgement to tradeoff the benefits of a rush job with the consequences of shoddy work. Justify the time needed to question, fact check and sustain deep analysis. The world still needs a healthy dose of this from those willing to buck the new world disorder.

The need for speed – the death of the deliberator

I’m not suggesting speed is always bad. If you can crank something out with “good enough” quality, and you beat your competition to market, you gain a first mover advantage. That said, it’s easy to see the deterioration of product, service and content depth and quality, as massive quantities are pushed out at breakneck speeds by dubious producers and publishers – or simply regurgitated into the echo chamber of the digital world.

Yes, it’s the golden age of the individual, where anyone gets a platform to be an author and self-proclaimed subject matter expert. Yet alone, that doesn’t make the ideas worthwhile or the end product immune to scrutiny. Take the necessary time to question things, double check them, and dig deeper to find something unusual or interesting as you probe into that next layer of analysis that can lead to real insights.

Hand me my agile crutch please

Agility is critical during a performance, when things don’t go according to plan, or as a marketer when you are:

Responding to seismic market forces
Using it to quickly test and learn and refine new ideas.

Don’t let it, however, becomes a crutch for failing to plan, concentrate, and see tasks through. Form a hypothesis or formulate a project plan, plot a path, and stick with it unless you encounter an actual example of a major market force that should be factored in. I’m sorry, but these disruptions don’t happen every day, although rhetoric would suggest otherwise.

Agility works best when it’s backed up with a thoughtful game plan. The game plan forms the blueprint and guardrails you operate within. The tactics you employ to achieve your goals should be flexible as you tactically maneuver once the action begins. If you alter your course in ways that have no connection to your overall strategic plan, the clamor from these random adjustments will be the noise prior to failure.

Instead, use the agile approach as a methodology to execute on a grand plan & mission – with the difference being that you are simply releasing, communicating, and adding value to your constituents on a more frequent basis during the full length of your overall master effort.