Customer Engagement – From BI Guesswork to Prescriptive AI

Customer Engagement approaches, and the technology used to enable them, have evolved immensely over the last 25 years.  Two distinct eras define this period, as well as a major technological shift to real-time systems with AI feedback loops.

Prescriptive AI

The BI Guesswork Era

During the advent of the Business Intelligence (BI), Marketing Technology and Campaign Management era (circa 1990), marketers had limited predictive powers.  In many cases, when it came to what individuals really needed, they resorted to guesswork.  They channeled their energy to perfect efficiencies in targeting and automation.  Their main emphasis was finding an approximate audience for products so they designed promotions for large segments of the population. They fixated on finding segments that fit into certain “likelihood to respond” buckets, and then repeatedly tested timing, messages, and creative content by peppering those segments with treatments.  In other words, they identified massive groups, matched offers to these groups, and then used technology to systematize their marketing.

Although some of those marketers drew on basic models (such as RFM – Recency, Frequency, Monetary), which provided rough guidance on how deep to mail into a file, most didn’t even do this.  Typical response rates were 0.5% at best.  During this period, the average adult was receiving about 50 pounds of junk mail a year – coined junk mail because the promotions were irrelevant 99.5% of the time.  Thus, the majority viewed this activity as frivolous, mocking it with nicknames and jokes.  Regardless, marketers were unrelenting as they continually carpet-bombed until consumers either responded or learned how to opt-out.

Their tools of choice were crude in nature.  They were slow, not fine-grained, and certainly not customer-centric.  Usually, the campaign flowcharts they devised utilized basic analytics where deterministic queries ran against databases returning huge customer lists called segments.  If there was any further segment refinement, they relied on business intelligence technologies like OLAP (Online Analytical Processing) and dashboards to support their intuition.  Even as some of the more sophisticated marketers attempted predictions, providing those models with feedback was nearly impossible due to the batch processing nature of the flows and platforms they employed.  As shown in Figure 1, although some crept up the analytics value chain toward being predictive and answering the question “What will happen?” most fell short.

Figure 1:

business intelligence


Using a backward approach, engineers pre-developed the product, and marketers wrangled the packaging, promotions, and messaging to the audience – again using more guesswork than analytics.  It was difficult to react contextually, at scale, to actual individual needs, so instead they focused on groups of customers.

And so they executed bulk outbound communications at scale. With promotional ammunition in hand, readily available data afforded them reasonable targeting coordinates, and computers and devices served as the delivery mechanisms. The marketplace and emerging technology supported a numbers game and rewarded short-term economic gains.  Longer-term loyalty and longitudinal effects took a back seat.

By the turn of the century, direct marketers were plodding ahead using ever-richer consumer profiles that enabled them to focus promotions on increasingly smaller segments.  And even though in 1995, Peppers & Rogers had coined the term “1:1 marketing,” enterprise marketers were no where near direct conversations with individual consumers.  Still constrained by scale, they were stuck communicating to segments, albeit smaller and smaller ones.  What they didn’t realize was they were about to hit a wall (Figure 2)

Figure 2:

Real-Time Evolution

By 2005, marketers had the tools to perform hyper-targeting.  They aggressively tested different incentives, creative elements, and fine tuned things based on response metrics.   Scoring models were refined, though the expense was large, and the iterations long.  The results didn’t so much alter someone’s behavior, but more provided alternatives to consider, often ones that still had borderline relevance to a current need.

Often the goal, instead of steadfast loyalty, was simply to increase immediate purchases with minimal marketing waste.  In theory, if targets responded and steadily purchased, no matter the purchase, more purchases should follow.  Supposedly then, over the long haul, the business accomplished its goal of capturing more share of wallet.

Around 2010, some leading edge marketers who realized the value of a real-time approach, began hitting that wall.  The foundation of the system they had spent 15 years building was the wrong foundation.  It was a platform built for segmentation, and it supported the wrong approach. They needed a “Real-time 1:1” platform, customer-centric prescriptions, and a more dynamic feedback loop.

Enter the Prescriptive AI Era

Good marketers have always been similar to psychologists in that they study consumer behavior. With today’s data and technology, it’s possible to take engagements one-step further – diagnosing, and treating those customers to alter their behavior methodically over time.  Stealing a page from the broadcast advertisers’ playbook – who use “subliminal seduction” – many marketers are marching toward implementing systems that use incremental and proactive drip therapy to persuade inner minds toward brand myopia.

The only piece missing from the puzzle is a real-time platform.  Traces of this began appearing in 2010, as big data systems, parallel computing, solid-state storage, and other technology advances drove computing costs radically down, and speeds up.

Today the pieces are in place, and more are climbing aboard, as real-time platforms have fully emerged and are cheaper and more reliable.  It’s now feasible to use customer-centric prescriptive tactics at scale and get huge lift over baseline approaches.  Models can predict behavior to an amazing degree of accuracy.  The artificial intelligence (AI) models both diagnose and – using Decision Management – proactively prescribe next-best-action engagement treatments.

Figure 3:


Everyone knows engagement professionals today have more channels.  They’re no longer constrained to broadcast media delivery systems (that lack dynamic feedback loops), and can now use digital response media and even physical surveillance.  And with this plethora of channels, they can administer and perfect personalized, contiguous, and hypersonic stimuli-response strategies.  Essentially, they can employ an always-on brain, powered by rich consumer data, advanced machine learning algorithms, and a 24 x 7 continuous learning loop.

What’s more, these machine learning technologies and embedded predictive algorithms can work in a very deliberate and intelligent way, dynamically creating conditional content and promotions, each time consumers reengage on a digital channel.  Incremental repeated responses (or lack thereof) allow these models to learn, tune themselves, and in essence direct and alter the future – programming individual behavior.  Customers are enticed to reveal ever-increasing amounts of personal information, in exchange for points or some privilege, trusting the exchange is amenable, and the information use one-dimensional.

All of this behavioral activity – social, purchase, demographic, and so forth – is recorded, with the aim of feeding it back into those same algorithms that iterate to find new patterns, refine predictions, and subsequently inform Decision Strategies that recommend the next series of treatments.  In some cases, these systems can even run autonomously, using advanced data science techniques such as genetic algorithms, game theory, and reinforcement learning.  System designers seed the rules of the game, configure the objective function and constraints, and then push “Go.”  The designers and their business counterparts peer in on occasion to monitor whether goals, such as higher loyalty and profit, are trending in the right direction.

Figure 4:

AI Learning Loop

Although this suggests overt manipulation, it’s not necessarily malevolent.  Provided customers have choice (and are well informed and discriminate), and businesses operate ethically (on a level playing field), the economic scales can still balance, and brands that provide products and experiences with the best value can still prevail, and consumers get a fair exchange of value.  You may have noticed, however, a few important “ifs” in this last statement.

Whether we like it or not, we now live in the Prescriptive Era, where the mission of brands is to get to know us, maybe even better than we actually know ourselves. That might sound crazy, but consider this statement from a recent article, “The Rise of the Weaponized AI Propaganda Machine” [i] where an analytics firm compiled data on Facebook likes and built millions of consumer behavior profiles, subsequently fed into an AI political campaigning machine:

“With 300 likes, Kosinski’s machine could predict a subject’s behavior better than their partner. With even more likes it could exceed what a person thinks they know about themselves.”

Whether you buy this or not, the fact remains that consumer profiles are becoming richer and consumer behavior predictions more accurate.  Data are exploding, as are the algorithms voraciously feeding on them.

Brands compiling this data and wielding their algorithms do it because they say they want to know us better.  Presumably, this enables them to continuously add value, deliver insights, help automate our lives, and make attractive recommendations.

Ostensibly then, for consumers, it comes down to a few simple questions:

  • How much is our data worth to us?
  • What’s the value of the insights that brands provide when they use our data?
  • Are we getting an equitable exchange?
  • Can we trust brands to honor their commitments regarding the use of our data?
  • Do we understand the fine print in those agreements?

Consider the mission statement for Datacoup, a data company based in New York, who have gone one step further and are trying to make a marketplace where consumer’s have a more direct exchange of value for their data:

“Our mission is to help people unlock the value of their personal data. Almost every link in the economic chain has their hand in our collective data pocket. Data brokers in the US alone account for a $15bn industry, yet they have zero relationship with the consumers whose data they harvest and sell. They offer no discernible benefit back to the producers of this great data asset – you.”[ii]

So are you getting value for the data you’re giving up?  Are the “Prescriptions” you get in return an equitable exchange?  Are you aware of what happens to your data after you release it?

A Day in the Life of Your Data

We all joke about the eye-glazing 56 page “Terms and Conditions” from Apple that we always accept and never read.  We want the free software, and don’t worry about the consequences. However, if you use that approach for everything you do online, that mindset is dangerous.

Consider this for a moment.  Most firms have language that allows them to send your data to affiliates, which is a fancy word for other companies. Once floating in the ecosystem, it’s grinded, distilled, and appended to other copies, until records of your preferences, habits, and behavioral are expressed in 5,000 or more different ways.  If it’s wrong, it doesn’t matter, because you don’t own it, don’t have access to it, and can’t change it.  In many ways, it’s another version of you, right or wrong.

Is Prescriptive AI Working?

So back to the question of whether it’s helping.   It’s fair to say there are cases where it adds value.  Here are some examples:

  • You decide you aren’t satisfied with your telecommunication services. You’ve made it obvious (with various signals) you’re considering other alternatives.  Your current provider prescribes an attractive bundle that satisfies your needs. You get a better bundle of services, and your provider retains you.  The bundle is custom tailored for you, using AI.
  • You have investments with a firm. You provide additional data on your financial goals, risk tolerance, and other investments, and they provide advice (prescriptions) on how to achieve your goals over time, within the parameters you set.  They provide various alternatives and education that prove useful to your financial planning.   Presumably, some of those alternatives include additional investments with them, and turn out to be good choices.
  • Your health plan suggests meaningful diet, exercise, and other tips that promote a healthy lifestyle. They are custom tailored to you, based on your family history, age, and other personal data you provide.   They reward you with lower premiums or credits.

These are just a few examples, and many more exist across industries such as travel and leisure, automotive, insurance, and retail.  And while good exchanges do exist, there are plenty of examples where the prescription doesn’t justify the information surrendered because the value exchange is unbalanced, or the prescriptions are ineffective.

Final Thoughts

In her book, “Weapons of Math Destruction[iii],” Cathy O’Neil writes:

“Many of these models, like some of the WMDs we’ve discussed, will arrive with the best intentions.  But they must also deliver transparency, disclosing the input data they’re using as well as the results of their targeting. And they must be open to audits. These are powerful engines, after all.  We must keep our eyes on them.”

She highlights important considerations we must heed.  I’m not convinced we’re spiraling toward a dystopian society regarding the use of prescriptive AI for customer engagement, but I do believe a balance is necessary between efficacy of these systems and fairness.  As responsible marketers, we should be mindful of the ramifications of the models we use for prescriptive purposes, and as consumers, it’s our job to demand transparency, choice, and a level playing field.

[i] Anderson And Horvath,, January 2017

[ii] Datacoup,, February 2017

[iii] Cathy O’Neil, 1st edition, Weapons of Math Destruction (New York: Crown), 2016.

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


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.

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.


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.

Martech Ecosystem – Major Players

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.

Its a qualitative assessment anyway, but I have used my experience in dealing with some of the worlds largest companies and thus which vendors have emerged as the 800 lb gorillas in their respective categories.

martech ecosystem

If you are not already familiar with the extensive work done in this are by Scott Brinker, please check out his website  Scott has done a fantastic job cataloging the thousands of MarTech companies in a variety of different areas of specialization.  His latest super graphic stuffs an amazing 3,874 companies onto one page.  You can view it here:

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, click here.

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.

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.

AI for Smarter CRM
AI for Smarter CRM

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:

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

  1. Preference
  2. Personalization
  3. Performance
  4. Pervasive & Persuasive


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.


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.


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.”


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

  1. Quality
  2. Qualification
  3. Quantification
  4. Quicker


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.


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.


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.


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.

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:

  1. 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.
  2. 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.
  3. 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.
  4. The death of critical thinking trumped by speed over quality.
  5. 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. 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.

5 traits of Super Marketing & Content – All year round


As a lifelong marketer, I love this time of year.  Super Bowl season is when everyone is talking about one of its main attractions – The Super Bowl Commercials and Content.   Which are the most creative, funny, and memorable?  Can you actually remember the brand that ran them?   Do you remember that ad from 20 years ago?

What dawned on me was this.  As marketers, if we could only harness this level of excitement, interest, engagement and positive reactions toward marketing and selling, year round, then we would have the right recipe for massive success.   But like the concept of Growth Hacking, we also need to find a way to harness key elements, but then execute without shelling out inordinate amounts of money.

If you have noticed, there has also been an expanding halo effect around game day commercials.   Supporting digital programs in the weeks leading up to the big day, social media contests, Top 10 greatest ad videos, and chatter for days afterwards.

So what makes Super Bowl advertising so compelling (besides the inordinate amounts of money shelled out)?   I think it boils down to these key things.

  • Great ads are extraordinarily funny, surprising – sometimes even a bit shocking – and certainly unique and innovative, in a way that relates to a wide audience.
  • They tease out emotions we can relate to (universal appeal), and are amplified in a way that we cheer.
  • Everyone gets to be a critic, in the moment, and our emersion in current events is often tested. So since we feel our opinion matters, we pay attention and weigh in.  In other words, we are engaged.
  • There is a sense of anticipation and a high bar. We have seen the stage and set before, yet the actors and props are unknown.   Nonetheless, we know those that take the stage have to be pretty good.
  • Products aren’t being sold by the company. Instead, it’s either an experience or feeling, or if it’s a product it’s being sold by a celebrity, an ordinary child, or even an animal.  All things we love and relate to more than companies and brands.

Easier said than done?  Not necessarily.  Although your budget, creative powers, and your casting may be more limited than Apple or Coke, you can still follow these rules of thumb, asking yourself this question each time you create content, “Is this going to capture the imagination of my audience, and build upon a great story and reputation.”

Take an example.   Suppose you are selling business software – conceivably not the most glamorous product in the world to try to sell.   Is your content creation approach really just like everyone else in your market?  Describe your features; show your return on investment, blah blah blah.   Is that catching and unique?   Will you pull some emotion out of me?   Will I champ at the bit to share my opinion with others?  Will I wait with bated breath for your next piece of content?

Rhetorical questions with obvious answers.   You need to take risks to win.  Yet you can do this in a way where the risks won’t really be that great, and the rewards are likely.   Stand out.  Use others to tell your stories, be funny and visually stimulating, and articulate the extraordinary experiences I will have when I sign up to take my journey with you.

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

Improve Customer Experience with 5 (Almost) No-Brainers

As a consumer, I’m often disappointed when companies don’t get the little things in Customer Experience (CX) right.  For example, I frequently stay at hotels, and I always ask for a room away from the elevators, but they never remember it, and I can’t note my preference.

customer experience duh

CX professionals and Marketers can benefit by reaching the first level of being “Customer Friendly”, and doing this with consistency and meaning.  Small things matter, such as knowing someone has children, what products they already own, and promoting what is relevant to them.

Sounds really simple?  Yet to do that – if you aren’t a small business – you need coordinated mechanisms to record customer preferences, common history, a standard set of rules, and then a way to surface key information and recommended actions to all customer facing staff and systems.  Functionally it’s not that intricate, still today many companies don’t do this well.

Why is that?  I think it’s because they aren’t seeing the cumulative value, and as such can’t envision or measure the overall benefits derived by connecting these dots.  Simple improvements to customer experience – applied one step at a time – build upon each other.

Everyone agrees that having institutional client memory (a 360 view) is valuable – and great entrepreneurs relish it, but large organizations struggle to build and provide it, for a variety of reasons. Constant acquisitions, distributed staff, legacy technologies, and silo systems – or worse, simply losing sight of customer centricity.  As the excuses build up, competitors swoop in with new technology and agility, and fill the customer experience vacuum.  Although these players may be disruptive, usually they aren’t employing earth shattering methods or technology.

As pressure builds, large entrenched players are forced to take stock.  They reflect on expensive efforts that didn’t meet ROI promises, such as failed Data Warehouse and CRM efforts, and it taints them with an overly jaded mindset.   If this describes your corporate culture, pull an alarm bell, ringing for an attitude adjustment.  The message is clear-cut.  We have to fix this – pronto, and it shouldn’t take millions and years.  Find the CX “No-Brainers” and execute on them.


All the rage these days is about Personalization.  And personally, I love it.  And it’s so easy.  Just make sure to remember all the little things about customers, particularly the things they share, and make them feel special.   And that, my friends, in a nut shell, is Personalization.

Here are 5 examples of No-Brainers and likely outcomes.  Literally hundreds more can spring from these:

  • Getting little things like names right is really important. My name is Vince Jeffs – not Jeff Vincent (in fact, where I work at Pegasystems, there is a Jeff Vincent, and you can only imagine the fun that creates).  If you call me Jeff repeatedly, my sense is you don’t know me and don’t really care.  Record children and/or pet information also.  If they tell you something about them (like their names), record that.   Make sure you use it wisely, when it counts and with class.  If you do, it matters in a massive way.  For instance, if you sell Pet Supplies, you think this might be important?
  • Document product interest and purchase activity, whether based on search, surfing behavior, or traditional conversations and transactions. Incorporate that knowledge into your near term marketing efforts.  Customers will love when you send relevant offers.
  • A customer shares when it’s their birthday or anniversary. You remember it, and on that special day, wish them well.  I don’t think I’ve ever heard anyone complain about being wished a Happy Birthday.
  • Demonstrate you remember their business, and show appreciation for it. Keep RFM statistics (How Long/Recent, How Frequent, How Much), and use it astutely.  A staff member saying, “For 10 years we have had the pleasure of having you as a customer.  Thanks so much!”  Again, you think that might make a difference?
  • Remember something really unique about each customer, use it judiciously, and make them smile. Don’t try to infer this, by appending 3rd party data.   You aren’t clever trying to deduce I’m a cat lover because I subscribe to Modern Cat.   However, if I mention I love Blues Guitar, and you cater to that later by recommending shows in the area, there is no way that won’t tickle me, even if the shows aren’t that interesting.   At times, you will have no choice but to put prospective customers into segments, because you otherwise can’t reach that audience.  Yet once I’m a proclaimed customer, ditch the mass segmenting and assumptions, and go instead with my stated preferences.

Your goal is to do this at scale, while executing like the corner bakery.  There is no reason why you can’t put an initial Customer Decision Hub – An Always on Customer Brain into place in 90 to 180 days; regardless of the size of your firm. This is why I entitled this post “… (Almost) No-Brainers” because you need this brain, but what it initially thinks about isn’t that sophisticated.

customer decision hub

True, it will take time to connect to more data sources and customer channels, but in less than 6 months, your foundation will be in place.  In mega firms, there will be 100’s of people telling you it can’t be done.   In spite of this, I’ve seen it done.   Start with one or two channels, a single business purpose, and build decision strategies that are driven by a customer profile.   Assemble a library of actions associated with the business purpose.   Then rank these actions and execute on them.

And here is the really amazing thing.  Implementing subsequent no-brainers entails very little incremental cost.   I’m not a fan of fuzzy math, but this is a case where 1+1 does equal 3.

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

Top 10 Things for Marketers to Deliver on Great Customer Experience


A slight re-vamp of my last post taking out the holiday twist…

10.  Really get to know your customers. Make sure every customer facing person and system has access to your knowledge.

9.  When you have the privilege of interacting with your customers, listen to them (that’s context) and adjust your message, actions and offers accordingly. Provide them value, and get to the point fast.

8.  When they need help with their buying decisions, make it easy and convenient for them. Use your knowledge of them wisely (e.g., location, which buying stage they are in) and they will love you for it.

7.  Work closely with your product and engineers to ensure your product is indeed awesome. Bring them your innovative ideas and ideas from your customers.

6.  Tell them stories about your fantastic products, but more importantly have your best customers or someone they trust tell these stories.  That’s right – like it or not – they trust them more.

5.  Make sure you entertain them with these stories. Nothing is better than some humor and excitement to get attention.

4.  Treat each and every interaction with your customer as a “Moment of Truth.” It may be your best and it may be your last.

3.  When they get to the point where they are really interested, don’t sell to them – help them buy.

2.  Always respect them. Respect their privacy.  Listen to their concerns.  Try to understand their needs.

Microwave at 10 sec

1.  Evaluate every action you want to take as a marketer, and ask yourself one simple question. Will this improve Customer Experience?


Marketing Wish List – “All I want for the holidays”

In my series on Customer Data & Decisions, I’ve explored how marketers covet that prized asset a “360 Customer View.”  In this blog, I turn things around, viewing from the consumer’s eyes what’s expected from marketing today, in the form of a marketing wish list.

Marketing Wish List

The Final Countdown – A consumer’s holiday wish list for marketing

Hello Marketing, I’ve been thinking about my wish list for the holidays.   Like a kid in a candy shop, here it is:

  1. First and foremost, please make sure your company builds great product and services. I don’t care how well you wrap the present, or how much money you spend on the wrapping paper.  I care about the present inside, and whether it’s useful to me.
  2. Once you’re sure you have a great present inside, go ahead and tell me all about it. I don’t mind hearing your passionate stories about how great it is, if it’s really great.  But don’t take too long to do it because I’m busy and you won’t have my attention for long.  While you have my attention, feel free to entertain me.  I like that aspect of marketing.
  3. Oh, and by the way, before you decide on this great present for me, make a reasonable effort to figure out why I’m possibly interested in it. Once you do that, then and only then, direct your clever marketing at me.
  4. Maybe I don’t know I need your present. Fine, so tell me quickly and concisely why I need it, and what unique value it will bring to my life.
  5. When you decide to tell me you have a present for me, don’t expect me to work miracles finding out about it. Make a reasonable effort to figure out where I spend my time.  Marketing, I know you are like a modern Santa, with access to all kinds of big data on my every move that gives you real-time information on whether I’m sleeping or awake; my exact location at all times, etc. – so use it wisely.
  6. Take notice to when I pay attention to your marketing – what’s working for me and what isn’t, and learn from that. Like a comedian knows, timing is everything.  If I’m not clicking or I’m not laughing, then ditch the joke.
  7. FinalCountdownOnce you get my attention, treat that moment like we were potential friends meeting for the first time. If you’re interesting, friendly, and we have things in common, we will probably want to meet again.   So treat our relationship accordingly.   Make an effort to get to know me before you try to close me.   Marketing gets a bad name from “ABC – Always-Be-Closing” selling.
  8. Basically, don’t sell to me, help me buy. Point me to helpful information that demonstrates your present has value, will last, is fairly priced, and that others I’ll trust love it also.  If you are offering me something special, make sure there is a good reason for it being “special for me.”
  9. If I’ve told you or given you obvious clues that it’s not the “Greatest Gift Ever”, respect that. In other words, don’t sell beyond the “Really I’m Not Interested Now”. If I didn’t tell you why I’m not interested – you can ask me – but whatever answer I give honor it.  Don’t push any further.  After all, just as bad as me not taking your gift, is me accepting it and later telling others how useless it was and re-gifting it because I really didn’t like or need it.
  10. Microwave at 10 secAnd finally, Santa, can you please take 15 minutes or less and talk with that Gecko Company and ask them to spend – I don’t know maybe 15% less on mass advertising next year? I just can’t get that Song “The Final Countdown” out of my head.  On second thought, never mind, I know what they will say: “If you are a Mass Marketer, you spend big bucks to get inside heads….it’s what you do.”

For interesting further related reading, and reflection on the year 2015, read “The 2015 Marketing Wish List” at

Comments and alternative views are always welcomed.

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