Tag: Big Data

In depth information on customer big data, including blogs, infographics, and a mini-cast series of instructional videos

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Machines Won’t Take Over…But A Few AI Titans Might

Lately, if you’re like me and enjoy following the AI narrative (even if just for grins & giggles), you’re inevitably sucked into philosophical wormholes that always seem to pop you out at the same place – a world where machines rule all.

AI titans
Tech Titans

Strangely, though, we rarely encounter future scenarios that follow a path we’re already on, where machines are but tools used to assist us. If we project this scene forward, some interesting questions to ask are, “What does that world look like, and who are its haves and have-nots?  Are AI titans forming?”

AI, for all its hype and promise, is still very much in its infancy.  Far from being able to get up, put on its clothes, and take your job, AI today is less of a super scary robot, and more like a smart washing machine (funny you should ask, as there is one of those).  It can help us conserve resources and do specialized tasks more efficiently, like getting clothes clean using fewer resources, but it really can’t do higher order thinking we take for granted like abstract judgement and reasoning. However, that super smart washing machine (and all its other specialized variants) has an owner, and together they can wield tremendous influence.  And anti-trust laws (put in place over 100 years ago to prevent corporate behemoths from controlling entire markets) may be full of loop holes in the digital age.

Using a singularity argument where machines alone rule provides a convenient escape from a more complex debate about a future where various human and machine forces collide and collapse together.  In this scenario, a select set of firms use walled garden data to feed their AI, and as such, seize unprecedented levels of control, influence, and power.

Here’s an example.  We’re already seeing a massive rationalization of power and influence collapsing into AI titans like Google, Facebook, Apple, Microsoft, and Amazon (controlled by surprisingly few individuals); not pure machines, but formidable entities nonetheless, fueled by AI, and directed by small pools of mighty people already circling their wagons around a plethora of data.

In the short run, we (the consumers) seem to benefit, getting innovative little features and conveniences such as travel guidance and digital yellow pages, but unbeknownst to most, to get these we sacrifice gobs of data and hence privacy.  Each time we travel with GPS on, our whereabouts are tracked and stored.  Each time we search, we provide preference footprints.  Meanwhile, the behemoths rack the data up, building behavior and preference repositories on each of us.

So what’s the rub?

First, it’s our data.  Thus, it would be nice to be able to view it, and if it’s wrong, correct it.  The European Union passed a law recently that goes into effect in May 2018 called GDPR – General Data Protection Regulation.  Its intent is to give consumers more rights and transparency with their digital data.  Other consumers outside the EU could use similar privacy protection laws.

Second, to some extent, without being cognizant of it, our choices are already being limited.  For example, when you search in digital maps, perform online comparison-shopping, or ask a voice pod for restaurant recommendations, the top options returned may not be calculated objectively.  Ranking algorithms already place higher emphasis on businesses that pay more to play, and search conglomerates, like Google, rank their interests (including businesses they have a stake in) higher.

Each time we purchase something, we’re casting a vote.  When we go through a buying cycle, we are creating implied demand, and when we purchase we’re reinforcing that the supply is meeting the demand we created. When this cycle is cornered, choice becomes an illusion.  To illustrate, on June 27, 2017 the EU slapped Google with a record-breaking $2.7 billion fine, charging the tech titan with doctoring search results giving an “illegal advantage” to its interests while harming its rivals.

Third, firms can and will use your data for their benefit, and not necessarily yours.  Prior to the digital age, people stereotyped others by their physical choices such as their house, car, job, shopping habits, and clothes.  Although today those choices still factor in, we also project digital personas: where we surf, what we share and like on Facebook and Instagram, what devices and channels we use, how we interact online, and so forth.  When these behaviors are crunched and codified, they become rich fuel for algorithms that can manipulate, discriminate, or even do harm, without the algorithm’s owners having any concerns for side or after effects.  Show preference for fast cars and thrill-seeking vacations, and not only will you receive more of those offers, but you might also receive higher insurance premiums.  Share enough medical history, and an insurer’s algorithm may score you at high risk for a chronic disease, even when there’s no medical diagnosis, and there’s no certainty you’ll ever develop that condition.  That might make it very hard to get medical coverage.

Admittedly, not all of the use cases lead to undesirable outcomes.  In late 2016, American Banker ran an article on next-gen biometrics detailing how banks use consumer digital behavior signatures to detect fraud and protect consumers from its effects.  And although consumers initially do benefit from such a service, what’s interesting (and concerning) is the nature of the behavior data fed to the fraud detection algorithm:  the angle at which the operator typically holds the smartphone, pressure levels on the touch screen, and cadence of keystrokes.

Unquestionably, the bank’s primary goal is predicting whether an imposter is behind the device in question.  Nonetheless, what’s stopping this same bank from using that data to predict a consumer’s likely mental state, such as likelihood of inebriation, legal or otherwise?  Moreover, whether that prediction is ultimately accurate is irrelevant to the immediate recommended action and the subsequent consequences.  We have little protection from the effects of algorithmic false positives, and today, except for credit scores, few brands have any accountability for model scoring accuracy.

Here’s a scenario.  An algorithm thinks you’ve been drinking based on your smartphone behavior and flags you as too drunk to drive and disables your car, forcing you to find another way home.  That’s one thing, but think about this – that same data might also be available to prospective employers, who use it to forecast your job performance, scoring you lower than other candidates based on its dubious drug use prediction.

Who owns and manages your digital behavior data?  Are they subject to use restrictions? The answer is (although the data is about your profile and your behavior) – you don’t own it and your rights are limited. And although some of the more inconsequential data is scattered about (such as name, address, date of birth, and so on), the deeper behavioral insights are amassed, stored, and crunched by the AI titans, with seemingly no limits or full transparency, and with little insight into where its shipped, and who else might eventually use it.  They suggest we simply trust them.

Those that ignore history are doomed to repeat it

History is always an amazing teacher.  In the 19th century, railroads consolidated into monopolies that controlled the fate of other expanding industries, such as iron, steel, and oil.  They dominated the distribution infrastructure – just as today’s AI titans, in many respects, control the lifeblood of modern day companies – their prospect and customer traffic.  And those other expanding industries (iron, steel, oil) were no different.  They too controlled the fate of other expanding industries, which all needed their materials.

Soon after their start, Google’s founders adopted a mantra, “Don’t be evil.”  In October 2015, under the new parent company Alphabet, that changed to “Do the right thing.”  Although the revised phrase still rings with the implication of justice, it raises the question of who benefits from that justice, and if there’s a disguised internal trust forming.

Everyone knows that business, by its very nature, is profit driven.  There’s nothing wrong with that, yet history teaches us that we need checks and balances to promote a level playing field for other competitors or potential entrants, and for consumers.

History Lesson

In his 1998 book “The Meaning of it All,” Richard Feynman, a famous scientist, tells a story of entering a Buddhist temple and encountering a man giving sage advice.  He said, “To every man is given the key to the gates of heaven. The same key opens the gates of hell.”  Unpacked and applied to AI today:

  1. The term “every man” can imply an individual, or organization made of people, or humankind as a whole.
  2. Science, technology, data, and artificial intelligence are but tools. As history shows, humans use them for good and evil purposes.
  3. AI’s impact on the future isn’t pre-determined. Each of us can play a role in shaping how it turns out.

Let’s ensure we live in a world where many (not a select few) benefit from AI’s capacity and ability to improve lives, and that those responsible for its development, evolution, and application are held to fair and ethical standards.

Can AI be the rising tide that lifts all boats?

The power and potential of artificial intelligence technologies is clear, yet our ability to control it, and deploy it sustainably is not.  Who should regulate and control it (and its fuel- our data) is an evolving and ongoing debate.

Used responsibly and applied democratically, we all stand to benefit from AI.  Paradoxically, while it renders some of our old jobs obsolete, it retrains us for a new world where it and we play new and more rewarding roles – where living standards rise and mortality rates fall.

What’s our guarantee we’re marching toward that future?

Honestly, there are no guarantees – our world is devoid of certainty.  However, we can influence likely outcomes by advocating for practical checks and balances.  Call me a dreamer, but I envision a world where our privacy is valued and respected.  Where we better understand the value of our data and get a reasonable exchange in return when we share it. Where we appreciate what happens when we release it, and can hold those accountable that illegally mangle or pawn it; and a world where we have assurance that when we share data, others uphold their end of the agreement, and we have recourse if they don’t.

If you would like to continue contemplating some of the top ethical implications of AI’s evolving story, click on this link:

https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/

Here’s my favorite quote from it:

“If we succeed with the transition, one day we might look back and think that it was barbaric that human beings were required to sell the majority of their waking time just to be able to live.”

Peace

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Event-based marketing (EBM) & CEP use cases for CRM

Updated: February 3, 2020

Introduction – The Key Event Types

First, check out my latest article on this topic recently published: Shush – Listen for customer signals with event-based marketing & service —- Out of that article surfaced these distinct event categories:
Event Category Example
Account status Average bank account balance trending down (by X standard deviations)
Behavior-Account Roaming charges incurred or within x% of the limit
Behavior-Device Failure of device/machine
Behavior-Person Digital browsing – showing purchase interest/intent
Calendar Major shopping holiday approaching
Contract-Account Changes in the account/contract terms & conditions
Environmental Severe weather alert – hurricane warning
Forecast Model score updated – Churn/attrition score rises above a threshold
Inactivity No activity (of a certain type) in the last 30 days – e.g., no deposits
Law/regulation Change in the overall privacy policy
Milestone Birthday – Age changes (milestones such as 18, 55, 65, etc.)
Product/Service Replenish – consumable products, such as printer ink
Product-wide/Service-wide The interest rate on all accounts of type X increases by x%
Profile-Person Investable assets increase (or decrease) by x% (or crosses a threshold)
Transaction status Order status change (disruption in availability, timing)
Below you’ll find an inventory of event-based marketing (EBM) and complex event processing (CEP) use cases for customer experience management.  In each, the system senses behavior and alerts a user or another system to the unusual activities or conditions that warrant further investigation or action.

Vertical Complex Event Processing Use Cases

Fiserv: Consumer Banking and Credit Cards

  • Unusual account activity (e.g., large deposits/withdraws)
  • Unusual account activity trend (e.g., average daily balance down by two standard deviations)
  • Inactivity pattern (e.g., no transactions in last week)
  • Missed transaction (e.g., missed direct deposit)
  • Credit card spend activity use pattern (by spending category)
  • Insufficient funds pattern
  • Web or mobile click activity indicating an interest in a product

Insurance

  • Fraudulent claims activity

Operations

  • Predictive maintenance systems

Media and Communications

  • Dropped call pattern or degradation of signal/service
  • A customer has increased roaming (or other unusual account usages) behavior
  • Customer in route to a foreign country pattern
  • Popular programming based on a set-top box and social media insights
  • Prepaid consumption detection and stimulation
  • Churn detection

Healthcare

  • Claims fraud
  • Care interruption pattern
  • Fitness monitoring
  • Hygiene procedures pattern
  • Healthcare patient monitoring

Horizontal Complex Event Processing Use Cases

Customer Service Center / Retention Department / Loyalty

  • Customer struggling to get help pattern
  • Payment due
  • Strange returns activity
  • Customer likely wants to cancel service
  • Customer’s birthday
  • Customer’s service anniversary (e.g., been a customer for X years)

Marketing / Cross-sell & Up-sell

  • Customer online interest in a product or service
  • Customer in store interest in a product or service
  • Customer in the proximity of a store
  • Customer usage stimulation – Drop off in use of a product/service
  • Increase in use of a product/service
  • Loyalty Program – Monitoring points activity
  • Loyalty Program – Monitoring points expiration date
  • Monitoring social sentiment
  • Monitoring social influencer

Non-CX use cases

Here are some examples that are not for CX, but instead to improve business and operations efficiency:
  • Algorithmic stock trading such as if Stock A rises by X% and Stock B doesn’t automatically buy Stock B
  • Transportation security and fraud detection such as an id card used twice in a short time frame (e.g., piggybacking) or high volume transactions on a new account – and then automatically alerting the right parties
  • Detecting transportation congestion and incidents, and proactive notification of alternative routes
  • Inferred detections suggesting that a vehicle has crashed (and severity of crash), such as when an airbag has deployed
  • Communications security such as false alarms going off in a certain time window, not followed by other alarms that would be expected (false positive alarming)
  • Communications security such as network monitoring for detecting denial of service attacks, and alerting the right parties of this situation
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Rise of Machine Marketers – Transforming CX

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

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

Machine Marketers

Database Marketing

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

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

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

The 2nd Coming of Big Data

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

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

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

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

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

Data Science and Data Mining Come of Age

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

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

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

AI and Machine Learning – The re-launch

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

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

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

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

Michelangelo meets Newton – When Content met Context

CX AI

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

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

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

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

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

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

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

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

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

artificial intelligence evolution

Machine Marketers Rise Up

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

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

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

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

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

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

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

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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.
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5 tips to avoid becoming “A Distracted Marketer”

DistractedCustomer

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

TooManyChoices

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.

ContentClutter

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.

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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 http://www.dmnews.com/marketing-strategy/the-2015-marketing-wish-list/article/387692/

Comments and alternative views are always welcomed.

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

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The CSI Guy – Customer Success Investigator

sherlockRecently I read a fascinating article on how crime investigators are using machine learning to find patterns and uncover insights that otherwise wouldn’t be detectable.   Investigations ranged from criminal to cybersecurity, competitive counterintelligence, and corporate litigation.  In one example, a firm showcased how – if this technology had been available during the Enron era – they might have proactively detected the scandal, potentially averting the crisis.  Their demo traversed 500,000 Enron emails, and as it did it learned how to flag suspicious ones.

It’s not news that pattern detection software works for fraud detection.   For years, banks have employed systems that scan structured credit card transaction data, and flag unusual activity.  Yet what occurred to me is this same approach and its specific techniques are extremely well suited to the art and science of developing customer intelligence against unstructured big data.   Customer experience pros could then unlock new mysteries, and take appropriate actions leading to great customer success outcomes.

The first thing criminal investigators do is they gather all the facts they can, from any available source – Emails, phone records, texts, web activity.  The adage being “Leave no stone unturned.”   It’s never clear at the start of any investigation which clues might matter, and may link to others – so all are important regardless of their form.    As the investigation unfolds, machine learning techniques, such as neural networks that use self-organizing cluster maps (known as SOMs) can help find patterns, and eventually help the investigators form a hypothesis.  Available evidence is used to test whether the facts fit the theory.

Shifting the frame of reference, gaining customer intelligence and using it to solve for marketing and customer experience challenges can benefit from this same methodology and technology:

  • Consumers leave clues about their preferences and behavior in many places; sometimes in unstructured forums, like social media, product reviews, and blogs.
  • It’s virtually impossible to sift through this data without the aid of technology and automation.
  • Machine learning can be used to find patterns in customer activity, such as what product they are most interested in buying, or that their sentiment is trending toward total dissatisfaction.
  • Once patterns are detected, predictions can be made and actions triggered in efforts to anticipate needs or alleviate matters.

As a consumer, my natural reaction might be to say, “That’s creepy and spooky”.   Ironically though, most firms simply want to use this to improve your experience with their brands since they know it’s critical to their health.  Repeatedly, surveys show above price and product, people leave because of dissatisfaction with the way they are treated.

But the level of dissatisfaction is qualitative and differs by customer.  One customer who experiences a single network issue may become enraged, while another may be more tolerant.   Knowing this and the value of each customer helps the company treat each situation with a custom tailored response.

That all sounds like common sense and easy, right?  Try doing it on millions of customers, with billions of bytes of unstructured data in their direct conversations and behaviors, and their indirect musings on social media, in blogs, and elsewhere.   Moreover, try to learn when each customer reaches various stages of interest or displeasure, and overtime improve your ability to predict these and take timely action.

Since the dawn of time, we learned that to survive we needed help from machines.   Use this newest breed of machines along with time tested investigation techniques to crack the enigma of your customers, gauge their state of mind, and delight them with personalized experiences.

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

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Customer Data & Decisions – “Big Data – Big Value”

In my previous blog (“Big Data – Big Waste?”), I advocated about the importance of an upfront blueprint to help focus big data efforts in areas that lead to valuable insights.  Taking actions on these insights is ultimately how you glean value from your big data.

bigdata_value1

The 5th “V”

Strangely, many who espouse the virtues of big data rarely start by describing the value that can come from it, but instead pontificate about its attributes – Volume, Velocity, Variety, and sometimes a 4th V – Veracity.   Let’s talk about the 5th “V” – Value.

bigdata_value2

What is “Value” really?

Lasting value is created when there is a positive exchange between you and your customer. Yet if you sell something that doesn’t meet their needs, you create the illusion of value, only to see it wiped out later.   Conversely, discovering and acting on activity patterns can lead to explicit and latent needs being met, resulting in customer satisfaction and lasting value.  Tracking big data effectively can lead to these discoveries.

To illustrate, if I observe a customer’s repetitious buying patterns, and then offer to sell products in bundles, bulk ship them for less, or proactively send them, value is created for them – and you ensure a future purchase stream.

Let’s look at some examples of what firms are doing to unearth insights, take actions, and create value.

Real life examples

A leading automotive firm installs numerous sensors in high end vehicles that gather driver data –   predictive analytics warn drivers when fatigue might be setting in.   Farmers use wearables on cows, improving fertility and birthing success rates.

A major online retailer uses purchase history to predict what products you are likely to buy in the future, and stages those closer to reduce shipping time.  A travel site can monitor real-time flight activity, anticipate delays, and notify travelers – often before the airline does.

bigdata_value2b

These are but a few examples of the way firms are already using sensors, streaming big data, finding actionable insights, and creating value for their customers and them.

What should I do?

Ask yourself two simple questions:

  1. Is my company using modern data collection, streaming big data in real-time and using predictive intelligence to understand the patterns?
  2. Are we taking immediate action on these insights to enhance the customer experience?

If you answered yes to the first, but not to the second, you have the infrastructure, but without action will get no benefits.  If neither is true, you are falling behind by the day.  But it’s not too late.  Act now for value.

You’ll need a unified system that can ingest structured and streaming unstructured data, perform real-time analytics that monitor for patterns, decision strategies that arbitrate and trigger the right actions when unusual opportunity or risk is detected – and a system that can also automatically kick off processes to alert personnel, open a case, or notify customers.   Make sure to find a system that can give you this in one platform or otherwise you will waste valuable time implementing, integrating, and adjusting various pieces when you could have been creating value.

Comments and alternative views are always welcomed.

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

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Customer Data & Decisions – “Big Data – Big Waste?”

Part 1 was about what firms really do know about me as a customer. Part 2 covers the ever popular topic of Big Data and why it needs a sponsor, action plan, and a solid analytic platform.

What did you say?

Did I get your attention with my somewhat controversial headline? Maybe it’s actually not that contentious because simply wiring to and capturing lots of data (e.g., Big Data) does nothing for you except add cost if you don’t effectively glean insight from it, and take action on it.   It’s no different than any other asset, in that if it’s idle, it’s sucking energy and not providing any return.   Like a big data black hole, where data enters but no insight can escape. How do you combat that?

blackhole

Have an action plan based on the kinds of customer decisions you want to improve, investigate data required, and constantly test, monitor, and refine that plan.   This plan will dictate what data you should be seeking and exactly how you will leverage it. In other words, work backward from your desired outcomes.

You might also be asking, what is big data? Good question.   As a participant in the business intelligence revolution, I’ve seen massive databases used for years for decision purposes. So what is new and different?   Actually, there are a few things.

First, customer data has been historically captured, scrubbed, matched and restored into on premise structured databases.   This led to the enterprise data warehouse with the so called “360 degree view” of the customer.   These systems required data expert intervention to add new data elements, were usually on premise, and latency rendered the view stale for today’s standards. Consumer and market expectations have evolved to expect on-demand and streaming data reflecting the latest and greatest view of the customer.

Second, since it ultimately required a target structured store, unstructured data, which is massive, became difficult to assimilate into one structured data warehouse.

And third, the variety of structured and unstructured data sources have grown, so much that again using an approach of trying to codify and blend all of that data into one mart did not meet flexibility, agility, and timing requirements of business people trying to make better decisions.

Ok, I need a plan. What next?

What if you could identify and sway vocal and influential customers? What if you could proactively identify customers at risk, and take actions to not only save the relationship, but turn them into ardent supporters?

Take these types of questions, and work backwards to formulate your plan.   Call it your big data blueprint.

worldascpu

Do you already know who the most influential customers are?   If not, start there.   How would you define this?   Conventional wisdom may first suggest it’s those with the biggest network of followers or highest NPS score. But upon further review, what might be more important is customers that actually frequently refer versus ones who say they will.   Working back, you would need data like mentions and referral codes. So determine the particular outcome, and then concentrate on connecting to the data you need to monitor and track those actions – viral actions such as re-tweets, re-posts, forwarded links, reference events, and such.   Then, rate your customers on that basis – building a Clout Score – the higher the score, the more clout they have with others and the more they refer you.   This score is then connected with actual behavior instead of formulations, surveys, or postulates.

Likewise, figuring out which customers are at risk, you might hypothesize that a major service interruption would put them at severe risk, and thus simply being able to run a query to find all customers impacted by key service disruption events would suffice.   Yet often, customer retention risk is much more complicated than that, and it’s likely that in this case you need a behavioral model that considers various risk factors, such as service disruption patterns, social sentiment, clout, customer loyalty, competitive options, and switching costs – and then test that model against real churn outcomes to calibrate its effectiveness.

Having sponsors is vital because invariably some aspects of your big data plan will involve capturing and leveraging data not readily available, and thus sustaining funding and resources to see your project through will require champions – people who believe strongly in the cause, and can help.

What technologies can help me get to my happy place?

We don’t live in a simple world.   We accept that, or get lost, frustrated, and fall behind – but we do expect technology will continue to help us navigate the intricate world.   So we seek the simplest and fastest solutions to complex problems.

Your answer is you will need many technologies. Accept that and do business on that basis. Select your stack based on requirements that your vendors are open, constantly invest in innovating their underlying technology, have exceptional integration both with their own sister products and with the outside world. Consider firms with a robust ecosystem and strong reputation for training, partners, and professional services.

Big data systems involve storage and retrieval of unstructured information, which is data that has not been highly codified from its raw form.   For example, data entered into free form text such as comments to blog posts or data collected from digital activity such as granular website click activity.   Big data is also real-time streaming data coming from various sensors that are always on, and stream data (often 24×7) such as devices that report precise location of objects (e.g., mobile phones).

Partner with a vendor that has solid, modern, and open technology, and has it in one platform. Beware of companies that get the marketing right and have compelling messages, slides, and even case studies, but under the covers have 2 or more actual platforms stitched together, and requires more custom coding to meet your requirements. How ironic that I’m warning marketers to be wary of the marketing!

Comments and alternative views are always welcomed.

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

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Customer Data & Decisions – “Reflections of Me”

In this post, I explore what firms really do know about me as a customer.

reflection2

What do they know, and how did they get that?

Wow! Where do I start?   Many companies today are doing everything in their power to amass as much data on me as possible so presumably they “know me” and make more relevant marketing offers…or as they say, “to provide me with an exceptional experience through an ongoing conversation.”

As empowered consumers, we are the judge of whether they are doing this well. Are they really capturing the right information and in a way that is respectful, well-timed, and used appropriately? This subject quickly stretches into ethical and political implications, but to avoid that, I will just lay out some facts about what companies are doing:

  • First and foremost, they save everything I do with them. All interactions, all transactions, all orders, all clicks on their website – basically any activity on their physical and digital properties they capture and keep – in some cases for 4 years or more.
  • They can often freely share this information with subsidiaries and affiliates (which means a lot of other companies) unless I explicitly ask them not to.
  • They append 3rd party data – lots of it. The sharing of data about me is ubiquitous. Appending means other companies are capturing data and sell it and it’s often indicative of my affinity to like, want or to buy something. This can pretty easily be matched to me with a presumption, for example, I also like to golf because I subscribe to a golf magazine.
  • They are looking at my patterns of activity.
  • They progressively profile through very short but repeated data collecting. For instance, I sign up on a website and provide basic information, then I agree to a news letter, and they capture some preferences, I download their mobile app, and so forth. Eventually, they may know whether I own or rent, have children, or are planning a kitchen remodeling.
  • And they try to predict their next best move. In other words, they are trying to figure out what I really need and want. Called “Next Best Action” technology, and usually found in larger companies, there are very large teams tasked with calculating lifetime value, building rules, testing propensity models – and ultimately a hub that makes promotional, product and service recommendations.

Really very little of this is new my friends, it’s just massively accelerating.   In 1992, one of my favorite non-fiction authors, Erik Larson, wrote a book called “The Naked Consumer: How Our Private Lives Become Public Commodities.”[i] Back then, his impetus for writing the book was based on a pretty simple event driven mailing he received for his child’s first birthday. Intrigued, he chronicled a world he saw as already borderline out of control with consumer data sharing. Imagine his sentiments now with the growth of the internet, digital channels, social, mobile, and big data.   I think he might change the title to “The MRI of the Consumer.”  This month, Scott Brinker posted a blog entry estimating that nearly $22 billion USD of venture capital funding has been poured into the marketing technology companies he pastes onto his marketing technology landscape and admits it’s probably underestimated.

As a long time marketer, I’m not that paranoid or really that appalled at what is going on. I still believe we live in a world that has checks, balances, methods and free choices.   Often, as consumers, we decide how much information to give up in return for something.  In most cases it’s a conscious choice. And there are ways to combat and prevent abuse. My biggest concern is security, as information is repeatedly hacked and then used for purposes it was never intended for. Better security and education are needed, but in general I don’t think it’s as surprising today to the average person as it was for Larson 20 years ago.

But rest assured, this picture of you is getting clearer – and there is a substantial amount of corporate energy being poured into filling in the blanks. The popular term is “The customer journey”, and now also being called “The customer movie”, with the intent to define every frame.

Yet motives and reality are two very different things.   I might want to be rich, but simply wanting doesn’t make it so.   And really, who I am versus what specific habits or preferences I have in relation to a certain product or service is generally where the line is drawn. For instance, a home improvement business would love to know what kinds of building skills I have, what tools I have, and what projects I’m considering, yet I don’t think they really care about what music I like.

What do they intend to do with my information?

I believe at the heart, companies just want to sell more of what they have and do it at the lowest possible cost to them.   It’s that simple.   But they know the world is competitive, there are choices, switching barriers have eroded, and if what they are offering (or failing to) isn’t a match, or at the right price – I will go somewhere else.

So they collect data, study events and patterns of activity, test timing, try to get preferences right, personalize content, and hope I’m impressed when they take actions.event_detection

Are they becoming specialists majoring in knowing one aspect of me, and knowing it well?   Perhaps, but make no mistake their not your best interests are in mind, and if information is useful to another party, and a business transaction makes sense, it will happen. Ironically, businesses are better at sharing customer information then the healthcare industry is at sharing patient information, although finally we are seeing some improvement there.

How did they do that?

It’s really not rocket science, yet amusingly marketers are applying technology that is also used to help launch and guide rockets.

Space_Shuttle

When a rocket launches, there are sensors monitoring all its complex systems. As a consumer, your systems – what you say, what you do, where you go – are being monitored.   Hotels are now placing beacons at key locations such as the front door, to detect when you arrive.   Stores are using similar technology to gauge your potential interest in a product sitting on the shelf you are next to.

There is already software and technology, and the cost is dropping, to gather this data and allow the marketers to access it and build rules on it (e.g., if customer arrives, alert front-desk personnel and pop-up appropriate offers).

Rocket scientists make heavy use of statistics and probability theory to understand the amount of redundancy necessary in systems, the likelihood of something failing, or predictions of weather to gauge best launch and landing windows. Marketers use all these techniques to tune their systems for response time versus cost, whether a new promotion will succeed, or timing a communication.

Also, the cost of storing, aggregating, distilling, modeling, and using this information is dropping rapidly.   The internal discussion has shifted from how much data should be saved, to how more data can be synthesized and insights gleaned from it.

A confluence of freely shared institutionalized best practices, application speed and simplicity, cloud computing, automation, and scientific testing procedures has led to more companies with access to better marketing technology – and a better, albeit still incomplete picture of you the customer.

Comments and alternative views are always welcomed.

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

[i] Larson, Erik. The Naked Consumer: How Our Private Lives Become Public Commodities. New York: Penguin Books, 1992.