Exploring Marketing Technology

Author: Jeff Larche

  • Apollo was the god of prophecy. His namesake is a digital insights time machine

    Apollo was the god of prophecy. His namesake is a digital insights time machine

    Adam Greco has been doing digital analytics implementations since before Adobe bought Omniture (now Adobe Analytics). I go back nearly that far, and have religiously studied his blog posts on the topic. He’s saved me hours of work. So I was thrilled to talk to him the other day about a new product category invented by his latest employer Search Discovery: Apollo, an Analytics Management System. I’m impressed, but I’ll be calling it a Digital Insights Time Machine.

    Here’s why:

    Excellent Digital Governance

    If you’ve been in my shoes, and Adam’s, you know the trouble an organization can get into if it doesn’t have buttoned-up digital governance. Like when an enterprise lacks a clear insights generation strategy.

    You see, that strategy describes business goals and objectives. and from them, predicts the reports that will be needed. This measurement strategy answers the question, What user behaviors are needed to achieve our business goals? Many organizations skip this step, and go right to implementation … Often trusting analytics team members with little understanding of business (!). This leaves these implementation pros having to make a best guess at what reports the enterprise will need down the road.

    Imagine you’re someone responsible for an implementation of Adobe Analytics, and the chat window lights up with a request from your boss’s boss. Or boss’s boss’s boss. “Can we get a report on XYZ?”

    When there is excellent digital governance in place. the answer you give is almost certainly Yes. But if you don’t, you’re faced with telling someone in control of your career that the report requires metrics that are not currently measured. Worse, the implementation will take weeks or even months because a change to the digital analytics data layer will be needed, and IT has many hotter priorities.

    If you’ve faced this moment of white hot panic, you probably have wished you could climb into a time machine and make sure those metrics get implemented out of the gate.

    Apollo is that time machine.

    The first thing you’ll see, if you get a demo like the one Adam showed me, is Apollo’s best practice library of business requirements, for reporting and insight generation.

    The list is vast, literally hundreds of requirements.

    Search Discovery — relying in part on Adam’s deep experience with clients in every industry — have provided building blocks for any type or hybrid of online business . From these business requirements flows all of the metrics and dimensions that will be needed to address them in reporting.

    Then things get really interesting

    This shows part of the flow that is followed as you set up your Apollo instance:

    Everything following out of the Business Goals and Objectives of this Measurement Strategy value tree is prompted from you by Apollo. Keep in mind that I’ve avoided arrows in the graphic, that would typically connect boxes from one column to the next, but as you likely have guessed, there is a one-to-many relationship flowing from left to right, with all the relationships being contained and documented within the Solution Design Resource (SDR).

    If you state a requirement such as “I want to report how many orders are placed each day, week, month, etc.,” you select that requirement as needed and Apollo automatically adds all of the variables, data layer objects, tagging, etc., that you’ll need to track within the SDR.

    More about that SDR: Unlike all of the SDRs I’ve ever encountered, the one in Apollo is part of its relational database. That means it can be exported to Excel if you feel inclined, but lives as a dynamic document that revises itself every time you make a change to requirements, metrics or reports.

    So when you join an organization using Apollo, you never have to encounter all of the lapses and omissions that come from an SDR that is only half-heartedly updated as an Excel file (often in several versions, causing you to wonder which contains the most “honest” implementation snapshot!).

    Leveraging APIs to Adobe Analytics, Launch, and even Workspace

    How does Apollo populate your instance of Launch? It’s connected via API to your instance of of both Adobe Analytics and Launch. Since it adds Launch tags, all that’s left is for you to do is refine its work and begin the testing.

    And because most implementations require IT to install or update the data layer, Apollo auto-generates the JSON code for that data layer. This makes the work of IT easier, improving the odds of speedy deployment.

    Finally, Apollo helps you at least two ways to debug the implementation once it is deployed. It uses those API connections to identify errors, and pushes to Adobe Workspace the reports that can make visual review of the data easier.

    Speaking of Workspace, all of the reports that are specified in this digital analytics “time machine” are pushed there, so all you’ll have to do is review and refine them.

    Apollo has impressed me so much that I can’t wait to get my hands on the working system, for my first client to use it. If you’re also intrigued, contact Adam for a demo. Like me, you’ll get a glimpse into the future of our industry, where we can spend more time on strategy and insight generation, and less on wrangling code and change requests.

  • Smart business decisions come from simplifying problems. Bars can help!

    Smart business decisions come from simplifying problems. Bars can help!

    Solving complex business problems “ain’t always rocket surgery,” to cite a colloquialism I just discovered. It often boils down to a little bar and a big bar.

    As an outsider, I have the luxury of both working with data scientists and in gentle opposition. The first part is easy. Data scientists work with my clients’ data daily, and have produced elaborate models to help make things more understandable.

    But I also work in gentle opposition. To quote best-selling author and distinguished professor of economics (and fellow “outsider”) Steve Levitt, it’s all in the incentives. He’s excerpted immediately below from one of his People I (Mostly) Admire podcast episodes. You can listen to this one-minute audio clip by clicking the player, or read on … the transcript immediately follows it.

    Little Bar / Big Bar

    When I work with firms that have data scientists, what I find almost uniformly is that they operate in an incredibly complex space. They’re very concerned with technicalities, with techniques, with things being hard. And I think the answers are often very simple. So I try to always do simple things, and try to relate them in very basic ways. Like, my favorite kind of graphs are Big Bar / Little Bars graphs.

    They’re graphs that have one really little bar … and one really big bar, and those are the kind of graphs that I show to CEOs if I’m trying to convince them of something. And the CEOs say to me, ‘Wow, that makes sense to me. I don’t understand how you take the same data that my data science team has and I never understand anything they’re saying.’

    So, you might say ‘The answer is to do things really simply,’ but I think it’s more complicated when you think of incentives. Because much of the power that comes to data scientists in firms and organizations is because they are completely and totally inscrutable. And the other people have no idea what they’re doing. And by having a set of skills that no one else has, you can wield power because no one understands why you’re doing it. You have a very special talent. And so, I have the luxury of being an outsider.

    Steve Levitt, “I’m Not as Childlike as I’d Like to Be” | People I Mostly Admire Bonus Episode

    Wow! Can I relate. I hear this frustration when I talk to the leadership within my clients. Which is why I have espoused simplicity in everything I report, and have for years.

    Of Oracles and Job Security

    At the end of a talk I gave in 2018 about experience optimization, I urged the audience to think of themselves as modern day oracles. But instead of the literal magic tricks that the Greek vestal virgins employed to keep their jobs, we only have our science to keep us regarded as useful and legitimate.

    Scrupulous science.

    Many data scientists stop there. They generate reports using the latest and greatest methods, but when the output is shared, business leaders too often have no clue what the data is telling them.

    That quote I shared with you from Steve Levitt was in response to a question about the future of data science as a profession. If you are a data scientist, consider this advice: Spend more time listening to your employer about the problems dogging them. And if you’re one of those employers, you cannot go wrong by hiring, when appropriate, someone outside of the data scientist industrial complex to whip up the bars!

  • Why isn’t the Disaffection Index (DI) more popular with campaign marketers?

    Why isn’t the Disaffection Index (DI) more popular with campaign marketers?

    Many years ago, when self-proclaimed Email Diva and my own personal career doppelganger Melinda Krueger conceived of the Disaffection Index, my only qualms were with its name and acronym. She wrote about this campaign KPI in one of her MediaPost pieces. If I remember correctly, she even sent me a pre-read. I told her I loved the metric but predicted it would become more commonplace with campaign marketers if she gave it a TLA (three letter acronym). I was only half joking.

    You be the judge. First, what is it?

    Rather than unsubscribe / delivered, the DI is calculated by dividing unsubscribes by the response rate …

    Calculated this way, the DI tells you how many people either a.) clicked on your e-mail for the sole purpose of getting off your list or b.) were so dissatisfied … they chose to unsubscribe.

    Excerpt from “The New Unsubscribe Rate”

    Today every subscriber and brand loyalist is worth too much to squander. So why isn’t this KPI on every campaign manager’s dashboard?

    Last Click Index (LCI) to the rescue!

    I humbly suggested at the time that LCI was a better term for two reasons. First, it adds drama. When someone unsubscribes, you’d believe you weren’t getting any more clicks from them. B*tch, bye!

    And secondly, I had then and still have zero affection for the word disaffection. But it’s her baby, and a rose is a rose by any name. (Clever guy, that Shakespeare).

    So if you’re a campaign marketer, start using it, regardless of its inferior name. You’ll thank me. And Melinda.

    Melinda is currently an Associate Principal for the Salesforce Marketing Cloud, and I predict she will laugh heartily when she reads this. I do hope so. I miss talking shop with her!

  • Make your charts more like maps

    Make your charts more like maps

    Pecha Kucha is Japanese for “chit-chat.” I’ve talked about the presentation style on my personal blog.

    In a Pecha Kucha, the speaker is restricted to just 20 slides, and only 20 seconds per slide. It’s a 6 minute, 40 second burst of information. As for the one embedded below, here’s some context: Think of the various maps in your life. Mine come to mind easily. I see one every time I go home.

    I have a map of the world on the wall of my apartment, with pins showing places I’ve visited and others for where I’d like to visit next, once this Covid nightmare ends. As you can imagine, it drives decisions and actions.

    Another map is one we all have. It’s the small map in our phones, powered by GPS. That map drives decisions and actions every day.

    But if you’re responsible for helping your bosses or clients understand data, what about those data dashboards or charts easily leads them to take action? In my experience, truly actionable charts are few and far between!

    All too often our charts do not drive action

    Why is that? In this Pecha Kucha, I explore that question, and show you how you can drive more consensus and action with your data visualizations by choosing and customizing charts to be more map-like. I pull two examples from my decades of experience as a marketing analytics consultant.

    The data is all made up, but reflects real life business challenges, and how making your charts more like maps can help to bring your bosses or clients to decisions. And isn’t that what data-driven decision making is all about?

    Enjoy.

  • How to lie with data visualizations, Part 2

    How to lie with data visualizations, Part 2

    This is a follow-up to my post from last month. In that Part 1, I wrote about how shifting the ranges for heat maps and starting bar charts at numbers greater than zero can deceive. In this installment, I want to introduce you to the greatest source of lies in data visualization: Cognitive biases of their authors.

    I started my earlier post with an example torn from the headlines. I will again below.

    If you follow the latest news in the U.S. about Covid, you may have seen this graphic shared over the weekend, included as evidence for emergency approval of convalescent plasma as a treatment for the infection. The evidence was provided by FDA Commissioner Stephen Hahn. His chief graphic is shown in the above image.

    The graphic  appears to show a 37% reduction in deaths for Covid patients receiving infused plasma from donors who have survived the infection. Famously, Tom Hanks and his wife donated their plasma to help the research being conducted world-wide.

    Rounding down to 35% in a press conference, “A 35 percent improvement in survival is a pretty substantial clinical benefit.”

    It is, but that isn’t what the data shows.

    After an outcry from what seemed like just about everyone in Covid research whose name was not Stephen Hahn, he apologized. The drop in mortality was from roughly 11% to 7%, which is a drop of somewhere around 4%, not 37%. It was also from research on a small sample size, weirdly without a control group, among many other concerning factors.

    In an administration that is arguably more politicized than any in recent history, the impulse is to say this U.S. government official lied. Strictly speaking, he most certainly did — in both his words and his supporting data visualization. But the motivation could be less nefarious — or at least, more common.

    Cognitive biases can make us lie to ourselves

    Our brains are not the perfect instruments we delude ourselves into believing. Consider the Free Brian Williams episode of Malcolm Gladwell’s podcast Revisionist History about the fallibility of human memory. Or more relevant to this instance of (self-)deception, consider this five-minute excerpt of a talk I co-presented in 2018 with Chad Sanderson at Adobe Summit in Las Vegas. It was called Profit Through Personalization. In that talk I explained why clear, data-focused hypotheses are an important protection a data scientist has against cherry-picking or distorting data without even knowing it.

    Everyone wants to report successes. But science is hard, and replication of test results is a persistent problem. In addition to clear hypotheses, we can also conduct an exercise called preregistration to save us from ourselves.

    In conclusion

    In my first installment I talked about how easy it is to distort numbers by using bad or lazy visualizations. In this second and last installment, I want to remind you that a far more pernicious murderer of the truth is your own best intentions. I’m willing to give Mr. Hahn the benefit of the doubt that he wasn’t so much lying as practicing wishful thinking, which blinded him from many clear contraindicators of his conclusion — including the fact that randomized trials of convalescent plasma at scale is dead simple to conduct. They’re indeed being done elsewhere in the U.S., such as at UCLA — where Tom Hanks donated his plasma — and in other countries around the world. Yet no country has rung the bell on such a decisive victory over this horrible virus.

    Let his humiliation be a lesson to us all.