Data might be cool, but decisions are sexy!

Decisions live in the domain of courage and enduring values
by Robin Way on April 12, 2017

At the gym last week, I saw someone wearing a company-branded t-shirt proclaiming “Data is cool”.

I had a lot of reactions to that statement, not the least of which was the subject-verb disagreement (since every Latin major knows “data” is plural, but let’s get past that). This might sound strange coming from an analytics professional, but no, in my opinion, data are not cool.

To many people entering the domain of data science, data might be novel and mysterious, but really, by itself, data are the measurement of a process. Measurement is only academic unless it’s informing a decision, so by itself, data is not actually cool.

What is cool? Our ability to capture data at ever-greater frequency, latency, and accuracy, and how it allows us to make decisions faster, more efficiently, and at a more fine-grained level—which produces stronger outcomes. When those decisions allocate resources towards strategies that maximize our chances to produce wins… that’s pretty cool.

It’s fundamentally the role of leaders to make decisions that allocate resources towards winning strategies. That role as the decision-maker is hard, risky, requires persistence and faith, and is the domain of courage and enduring values. When people make decisions about allocating resources towards those outcomes producing the best results, for as many people as possible, and reducing the pain of loss as much as possible, while still being efficient and utility-maximizing… now that’s more than cool, it’s sexy.

So there really ought to be a t-shirt that proclaims the core truth: “Data might be cool. But decisions are sexy!”

Financial services CDO feedback on open source analytics deployment

Are open source analytics ready for production?
by Robin Way on February 28, 2017

When I attend industry conferences or speak with Chief Data Officers (CDOs) and Chief Analytics Officers (CAOs) of large financial institutions, one popular question that arises is, “What do you hear about open source analytics in other large banks? Is it ready for production?”

While I’ve encountered substantial growth of exploration and analytics development occurring in private and public clouds using open source analytics, I’ve also been a little surprised (in two ways) at the findings of these CDOs and CAOs when it comes to actually deploying analytics assets using open source.

First source of surprise: large financial institutions with whom I’ve spoken are getting nasty wake-up calls about failures of their selected open source platforms to provide robust, reliable results. The initial attraction was the price of open source tools; the subsequent feedback is a broader appreciation for total cost of ownership, which isn’t as attractive as they first anticipated.

Second source of surprise: that collectively, we didn’t see this coming.

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Corios Harmony next-best offers, delivered in the real world

Omnichannel customer monitoring and offer delivery via SAS and Salesforce
by Robin Way on December 21, 2016

These days, customer acquisition costs are through the roof – and it’s become more important than ever to understand buyer behavior and target promotions effectively. To be truly effective, information about your next best offer campaigns should come from more than just traditional product and market data.

In order to maximize the effectiveness of contact strategies, we think our clients should deploy a data-driven approach, leverage predictive analytics, and attack the problem from an omni-channel perspective – allowing them to present the right offer to the right buyer at the right time.

The Corios Harmony solution delivers our Marketing Analytics Platform, or MAP. MAP is a closed loop system, recording interactions with customers across all touchpoints, for marketing and sales, to deliver the appropriate message, and to monitor the customer’s own interactions with us. In short, it’s a learning and predictive platform to help you present the right offer to the right customer at the right time through the right channel.

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Three implications of mathematics on the World Series

Sabermetrics provides useful advice for modern data science
by Robin Way on October 31, 2016

Many of my friends who live in the American Midwest are captivated by the Major League Baseball World Series, not only for the drama of two teams with a rich history and many years of waiting for this opportunity, but also because it provides a welcome respite from news about our presidential election. While the Oakland Athletics, the baseball team for whom I root, will be waiting for a while before our next World Series appearance, I’ll share my enthusiasm for the Indians and the Cubs fans, and one of my favorite sports.

I remember, as a college student, being enthralled with reading the box scores every morning in the San Francisco Chronicle, watching the ERA of the pitching squad, and monitoring the amazing batting average of Carney Lansford, the A’s third baseman at the time. This experience of reading the box scores, and how they changed day to day, influenced the way I watched the game. I was thrilled when Michael Lewis published his book “Moneyball” in 2003, focused on the emerging role of analytics and how it shaped the strategy for the very same Oakland A’s that I loved and rooted for.

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Three reasons why email marketing isn’t as free as you think

Contact strategy optimization for digital channels
by Robin Way on October 25, 2016
I’ve recently interviewed retailers active in digital marketing to learn more about their analytics practices. In three distinct interviews this week, I learned that there is a consistent pattern of treating digital outbound communications as an essentially cost-free channel.
  • Specifically, these retailers act as though expected sales revenue will increase as the number of outbound promotional customer emails increases, without any boundary or penalty for over-contacting their customers.
  • These retailers also treat customers as largely fungible; in short, every customer gets the same contact policy, measured by the number of direct emails they are eligible to receive each week. I didn’t get the impression that these retailers actually believe that customers are fungible, only that in practice, there is no differentiation across customers given their historical behavior or any measurable performance data.
  • Finally, there are broadly-held and deeply-rooted perceptions amongst these retailers’ brand and product managers that any change in business as usual will dilute sales revenue and can’t be tolerated.
In contrast, we’ve found that retail customers are not fungible, that there are real penalties for over-contacting customers (some short-run and some long-run), and that practical solutions exist for tailoring the number and mix of promotional contacts per customer without sacrificing sales revenue. In fact, it’s likely that optimizing the mix of communications will grow sales revenue.
If your organization isn’t in the retail sector, there are plenty of analogous examples in other vertical industries. Consider the number of times that a brokerage financial advisor contacts their clients every month with portfolio rebalancing opportunities or active trading alerts, which in our experience, varies from daily, to once or multiple times weekly, to once monthly, and sometimes not at all.

Here is the way we’ve seen sales revenue dynamics actually work when augmented by measurable evidence from customer behavior. The first expectation (which is not usually supported by actual performance) is that sales revenue will increase as you continue to increase the number of contacts per customer per week. Some retailers place an upper bound on the number of contacts per customer per week, and others also place a floor on the minimum number of contacts for some classes of high-value customers. Nonetheless, the widely-held practice is that since the variable cost of an email is virtually zero, you may as well send as many emails as you can, to maximize total contacts and to reduce the average fixed costs of email communications.

cso-fig1

Expected sales given varying number of contacts per week

 

Working with one of the country’s largest apparel retailers, we found that there are very real costs to over-contacting customers. Short-run revenue losses can result from contact fatigue, by reducing the customer’s interest in opening or clicking through the email to a landing or promotional page. Long-run revenue losses can result from opt-out activity. Some enterprising retailers have introduced “opt-down”, which actively asks the customer to receive at least one email per week, but not to leave the retailer entirely. (Why don’t they ask this of every customer?)

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