FRAMEWORK : Data Analytics

Give Credit Where Credit Is Due: Data Analytics and Accurate Attribution

In his Data Science Central post, “5 Insightful Ways Business Can Innovate Using Analytics and Data Presentation,” Richard Gartner admits that “performing proper business analytics accurately turns out to be more complicated than what companies imagine.”

Yet, “the future is bright”— for those businesses that can navigate the analytic complexity of effectively using data and insight to improve decision-making.

From quality enhancement to forecasting to perfecting automation, a common thread runs through Gartner’s article—the complex challenge and opportunity of cross-channel attribution to better judge decision-making and improve budgeting.

My relevant experience is to further the discussion of using cross-channel attribution for evaluating past decisions and guiding future spending decisions.

Forrester Research defines cross channel attribution as “using advanced analytics to allocate proportional credit to each marketing touch point across all online and off-line channels, which ultimately leads to the desired customer action.”

Look at this “typical” customer purchase journey:

typical customer purchase journey

[Illustration: published in Forbes, December 2014, designed by Theresa Wilcox.]

Of course, this journey gets even more complicated when multiple offers or multiple products are marketed to the same target audience or household. Even more—beyond contact channels—what else contributes to the desired consumer action?

Offer? Timing? Number of offers? Sequence of offers?

To pinpoint the prime “influencer” for the desired action, we create and write attribution logic, design attribution reporting, and perform attribution monitoring—all sophisticated analytical processes.

Attribution is all about understanding the impact of decisions you’ve made and how those decisions drive performance. Critical to understanding the effectiveness of your current marketing budget, accurate attribution shows you how to more effectively allocate future spending.

These days “innovation” is often associated with new disruptive digital tools, or services, or channels. Yet, you can “innovate” in the true sense of the word—“make new”—by using analytical tools and business processes for more exact attribution.

If you better understand the performance of today’s spending, your innovation—your “new, better idea”—will be to more effectively spend in the future to create more profitable growth.


FRAMEWORK : Data Analytics

Points and Miles and Rewards: Blockchain to the Rescue!

In how many loyalty programs does your household participate?

According to Kowalewski, McLaughlin and Hill’s HBR article “Blockchain Will Transform Customer Loyalty Programs,” the average U.S. household “participates in 29 different loyalty programs.”

If you’re a loyal customer, you may still say: “I don’t know when to use what. I have miles. I have points. I have cash. What is my best deal?”

Even worse, the “result is a maze of point systems and redemption options, with cumbersome processes for exchanging points among program partners.” In short, loyalty programs are “ripe for some kind of disruptive innovation that would make them easier to use.”

The authors believe blockchain “may just be the answer.”

It’s refreshing to finally see a clear explanation of a blockchain application closely related to consumers.

Here’s their explanation of how a blockchain-based loyalty program would work:

“Blockchain enables a ledger of transactions to be shared across a network of participants.

When a new digital transaction occurs (for example, a loyalty point is issued, redeemed, or exchanged), a unique algorithm-generated token is created and assigned to that transaction. Tokens are grouped into blocks (for example, every 10 minutes) and distributed across the network, updating every ledger at once.

New transaction blocks are validated and linked to older blocks, creating a strong, secure, and verifiable record of all transactions, without the need for intermediaries or centralized databases.”

Having been formerly disrupted by online travel agencies (OTAs), the authors believe that the travel industry is the “most at risk” for blockchain disruption.

The authors envision “development of four to six blockchain-based loyalty networks, each anchored by an airline, a hotel chain, or a group of smaller travel companies.”

What might this platform and blockchain network look like?

Visualize a bank or payment processor and airline as partner-anchors. Included in the network could be a major hotel company, car rentals, restaurants, sports and concert venues, etc.

And what would this mean for consumers?

The authors conclude that “for consumers juggling an array of loyalty programs, blockchain could provide instant redemption and exchange for multiple loyalty point currencies on a single platform. With only one wallet for points, customers would not have to hunt for each program’s options, limitations and redemption rules.”

Where are we now?

Just one example: IBM and startup Loyyal are now partnering “to develop a blockchain infrastructure for loyalty and reward programs.”

Look at this article for yourself—and you’ll see why I’m ready to join a blockchain-based travel loyalty program today.


FRAMEWORK : Data Analytics

What Will It Take to Digitally Transform Risk Management?

“What’s the risk in transforming risk management?” is not a trick question.

In their article, “Digital Risk: Transforming risk management for the 2020s,” Ganguly et al. explore the coming digital transformation in the most protective of business disciplines.

The writers define “digital risk” as a “term encompassing all digital enablement that improves risk effectiveness and efficiency—especially process automation, decision automation, and digitized monitoring and early warning.”

They elaborate: “Essentially digital risk implies a concerted adjustment of process, data, analytics and IT, and the overall organizational setup, including talent and culture.”

As such, the magnitude of change is considerable.

The authors see three areas of change: processes, data, and organization, and they detail approaches for each. As a result, the potential “benefits of digital risk initiatives include efficiency and productivity gains, enhanced risk effectiveness, and revenue gains.”

The report identifies three specific areas that are “optimal for near-term efforts: credit risk, stress testing, and operational risk and compliance.”

Using capabilities and processes you’ve built in your digital environment to execute these risk management areas doesn’t mean that you are changing the discipline of risk management itself. 

Risk management has always relied on time to validate processes. For example, in digital development, moving from testing to production will still demand validation of risk performance and regulatory requirements.

As complex and critical as risk management is, the discipline certainly needs automation at least and enterprise wide transformation at best.

Yet digital transformation will be a “multiyear journey.” As the authors explain:

“Because of its highly sensitive environment, risk is digitized end to end over a longer timeline than is seen in customer-service areas. Specific capabilities are developed to completing and release discretely, so that risk management across the enterprise is built incrementally, with short-term benefits.”


FRAMEWORK : Data Analytics

Add a Human Touch to Banking’s Information Business

“More than ever, banking is as much an information business as it is a financial business,” claims Bryan Yurcan in his American Banker article “Why Small Banks Need Big Data.”

Further, he warns: “With the vast amount of data banks have on customers and transactions and spending habits, those that aren’t effectively mining this data risk falling into irrelevance.”

Although compliance, risk management and fraud prevention are popular areas of analytical focus, solidifying the foundations of business processes is where data analytics is critical. I always think about Atul Gawande’s The Checklist Manifesto – as we implement new strategies using our data, do we have true checklists along the way and audits at critical points?

We must ask ourselves: Are we using our data to ensure we are in compliance with the law AND are we providing our customers access to the most relevant products and services for them?

We work at the intersection of art and science. Today, banks have unprecedented access to an array of advanced analytical tools and technologies – the science. We need to blend the application of these tools with the human touch – the art.

As I wrote in a blog series “Why Analytical Modeling Will Never Be The Same,” three human roles are necessary for effectively using predictive analytics and machine learning to achieve financial impact:

  • Data Experts
  • Computer Scientists
  • Business Experts

When I spoke with Henry Zelikovsky, CTO, Director of Engineering at Starpoint Solutions, he succinctly captured the human touch:

“We use technology and machine learning in order to process an enormous amount of data in a predictable manner to give to a person better qualified information within our decision support tool…but the decision is from the person.”

So, when you and your fellow experts unfold your Swiss Army Knife of data, analytics and technology to solve banking challenges and build business solutions, remember that these are tools – and “don’t forget the human touch.”


FRAMEWORK : Data Analytics

Join a Deep Dive into the Intricate Murky Regularity in Data

In reading Gideon Lewis-Kraus’ New York Times Magazine article  “The Great A.I. Awakening, we join an exploration of “how machine learning is poised to reinvent computing itself.”

Lewis-Kraus introduces us to this awakening by relating how a Google Brain team used neural networks in an A.I. machine-learning system to exponentially improve Google Translate—literally overnight. Converted to the A.I. system from a traditional programming platform, Translate “had demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime.”

Google Brain, founded five years ago on “the principle that ‘neural networks’ that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility.”

As Lewis-Kraus reports, Google’s CEO Sundar Pichai differentiates between current applications of A.I. and the ultimate goal of artificial general intelligence – “Artificial general intelligence will not involve dutiful adherence to explicit instructions, but instead will demonstrate a facility with the implicit, the interpretive.”

Here’s the leap in learning: “If an intelligent machine were able to discern some intricate murky regularity in data about what we have done in the past, it might be able to extrapolate about our subsequent desires, even if we don’t entirely know them ourselves.”

This method of machine learning with pattern recognition is transferable to almost every property and domain. As the article concludes: “Once you’ve built a robust pattern-matching apparatus for one purpose, it can be tweaked in the service of others.”

Citing example after example, we see this transferability:

  • A neural network built to judge artwork adapted to drive an autonomous radio-controlled car
  • A neural network built to recognize a cat was trained to read CT scans
  • Neural networks built for other purposes can now find tumors in medical images much earlier than radiologists

Remember, the machines are learning how we learn, how toddlers learn. As professionals, our aptitude, knowledge and skills acquired through personal and business experiences is also transferable to other and varied domains, areas of study, business verticals, and enterprises.

This is the key to growth – learning, teaching, learning – as business leaders, as a society, and of course, as children, and now, machines do.


FRAMEWORK : Data Analytics

Simple Can Be Hard—for Machines and Humans

Who doesn’t want to simplify?

Search Google for advice on simplifying and you’ll find thousands of words of wisdom from Lao Tzu (6th century BCE) to Henry David Thoreau, to Mahatma Gandhi to Steve Jobs.

Today, machine learning is here to help us simplify, as Jongbin Jung, et al. explain in “Creating Simple Rules for Complex Decisions.”

In my own experience, the “art” is to know when simplicity is appropriate and when more complexity is required – and we must keep this in mind as we consider how machine learning can help us simplify making complex decisions.

The authors have “developed a simple three-step procedure for creating rubrics that improve yes-or-no decisions.” Their approach offers “the performance of state-of-the-art machine learning while stripping away needless complexity.”

The example they use – like the authors we discussed in FRAMEWORK (April 7, 2017) “A Guide to Solving Social Problems with Machine Learning” – is pretrial release decisions. By creating a rubric made of two weighted variables – age and past court dates missed – this simple calculation “significantly outperforms expert human decision makers.” The team created both weights for age and missed court dates and a “risk threshold” for the yes/no decision of release or return to custody.

Of course, oftentimes the path to simplicity is not simple.

What we don’t know is what data these variables are based on, how the leading indicators were created, what historical outcomes were studied, etc. Evidently, a lot of complex work went into creating this rubric.

The goal is always to create simple rules. However, whether it’s done by humans or machines, the process by which you create simple rules is complicated. For rules to be truly effective and simple, you must go through complex processes – and the machines will need our up-front help.

“Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”
—Steve Jobs


FRAMEWORK : Data Analytics

What's So Smart About Artificial Intelligence?

Try asking Siri: “What does “AI” mean?”

Ian Bogost more successfully tackles that question in his Atlantic article, “‘Artificial Intelligence’ Has Become Meaningless.” Distinguishing what he calls “supposed-AI” from real artificial intelligence, Bogost searches for a meaningful definition.

The author, of course, doesn’t suggest that artificial intelligence itself is meaningless but that the term “AI” escapes clear meaning by imprecise usage and exponential overuse.

From Google to Facebook…from the Homeland Security to Coca-Cola…from earnings call transcripts to corporate strategy to press releases—examples of “supposed AI” abound. Bogost believes that most systems “making claims to artificial intelligence aren’t sentient, self-aware, volitional, or even surprising. They’re just software.”

To gain clarity, Georgia Tech artificial intelligence researcher Charles Isbell defines AI this way: “Making computers act like they do in the movies.”

Not to seem “too glib,” Isbell then identifies two features “necessary before a system deserves the name AI.”

First, the system “must learn over time in response to changes in its environment.”

Second, “what it learns to do must be interesting enough that it takes humans some effort to learn.”

Real AI is not just repeatable standard process; rather, it’s the learning of things as we go through the process of learning itself. This method includes interface, integration, conversation—all the things that human beings experience in learning and applying what they learn. It’s this learning that differentiates artificial intelligence from “mere computational automation.”

The same kind of imprecise terminology and clarity of definition affects our understanding of “analytics.” Often used as an umbrella term, “analytics” encompasses many skill sets, techniques, specializations, and tools along its continuum.

Think of the commonly defined analytical processes:

  • Descriptive – what happened
  • Diagnostic – why something happened
  • Discovery – learning and insight
  • Predictive – likely to happen
  • Prescriptive – recommended action

We use all relevant techniques, tools and technologies available to perform these analytical processes. In turn, the application for these analytic processes vary depending on the business need. Some examples of business needs which require different expertise and tools are:

  • Information solutions—data queries to advanced reporting
  • Business solutions—campaign tracking to predictive response modeling
  • Quantitative solutions—decision trees to game theory

Running the gamut from reporting to cognitive computing, analytics needs to be viewed and discussed in ways that appreciate the complexity, applications, skill sets, value, and the human element.

What do AI and analytics have in common? Look to the core of analytics to find a meaningful definition for “AI”—“machinery that learns and then acts on that learning.” Just like in the movies.

Photo Credit: Gordon Tarpley from PS-Hoth Set