Next Generation Marketers Want Answers Not Stats

cameron kaneLiving in the world of open rates, click rates and funnel driven conversion stats? I get it, we have to report on performance, but when do you get to do the really cool things in marketing? What happened to discovery and exploration? Why are we still looking at bar charts and tables to tell a data and performance story? Why are we blaming everything on data issues? The simple fact is, there is far more we can do with what is available today. Better ways to tell stories! Better ways to illustrate new concepts and ideas! And Better ways to look at the metrics and underlying data that supports those. Edward Tuft, a famous statistician best said this, “There is no such thing as information overload, There is only bad design”.

How do you get out of this grind and report cycle and help the business answer real questions? What are the limiters that stifle the curious human mind? In the days where Artificial Intelligence is nearing operational singularity, the difference in the future will be driven by the curious marketer and their unquenchable desire to answer questions about the consumer, the products, the connection of events, cycles and their impact on the brand connections. It won’t be about did it hit the inbox or did Gmail convert better than Yahoo domains or even product level analysis, the future will be a perpetual exploration of information and business intelligence and data visualization will be on the forefront.

Quant minds will settle for data, yet the cool thing about the new age marketer is they respond to visualization and many new marketers grew up with this stimuli and tables and worksheets are simply like bills, necessary just not inspiring. To first understand how to create change, you first need to remove the idea of One Metric That Matters (OMTM). Yes, we are all driven by Top line (sales/revenue), Bottom line (profit) and some by Waist Line (organizational efficiency), but your north star metric shouldn’t be the only things driving your company or you will struggle to be as adaptive as you need to be with your customers.

Input Versus Output Metrics

Let's break down the metrics first: Input Metrics vs. Output metrics and why they promote action and inaction. There is a great deal of talk about North Star metrics, and one metric that matters. Omnichannel marketers don’t think like this generally, but there is no mistaking what you do vs. what is your mandate. While many may look at input metrics (delivery, received, exposure, interaction, revenue), many are fixated on output metrics to justify changes in their programs (Revenue, Conversion, etc..) and complain religiously about flawed attribution models.

Output metrics help you set long term goals for the growth of your business - $10 million in revenue, 200k weekly active users, $10 million in MRR are all great examples.

Input metrics represent the actions that influence the output metric - 100,000 pageviews, 1,500 registrations, 900 upgrades from free to paid, for example.

You can’t focus exclusively on output metrics because they’re too big, too broad, and not actionable - they are a scoreboard. To win the game you need to focus on the individual plays that drive the score. Monitor output metrics to know how you’re doing, but build experiments around the input metrics you can directly influence. Input metrics are leading indicators and output metrics are lagging indicators. By definition, it can take time for the output to reflect positive or negative changes in the inputs. Output metrics can hide growth problems percolating under the surface. By the time the problem surfaces as poor results, and you recognize you have a problem, the damage is done.

Reporting Versus Business Intelligence?

With all the leading multichannel tools supporting hundreds of standard reports, parity exists on the surface. iPost with a history of agency and technology saw a trend and build software to help address the gaps faced virtually all email campaign tools; basic reporting. The ability to build customized reports, do data analysis in visual ways and allow this to happen on the fly as things change. Business Intelligence is about exploration and telling stories and making decisions, we tend to lose sight of that looking at campaign level analytics and inbox trends daily. To break this down a little:

  • Reporting shows you what happened so far and what the status is. Reports often use standard or well-known formats to help people do their jobs better or more easily on a daily (or weekly, monthly, etc.) basis. They may also focus on one specific set of data or records. For example, a daily report of all the customer online orders sold in a given day or week will help the marketing team understand what “in-market” experience had an impact.
  • Business Intelligence shows you why things happened and how to improve business performance in the future. Often using multiple sources of data, BI lets you discover data relationships you never knew existed and explore new business possibilities. For example, by analyzing online orders over a given period of time, by device type, time to second purchase, renewal trends, Average order value and SKU level breakdowns, daypart bias (night shopping, day shopping), location (in store, online, at home) as well as seasonal variances in demand,products, messaging, promotion, discounting effects impact within those time windows. Get the drift? BI is a never-ending discipline that marketers should embrace eagerly.

Analytics: Description, Prescriptive Or Predictive?

What type of answers do you want? This is the key to which approach you take, how you think about using machines and algorithms to help explore scenarios. Remember one thing, answers don’t necessarily drive a result, they may simply create new questions. Having a malleable mind during this process combined with an intense curiosity to explore human behavior are the cores of what makes customer-centric marketing cultures.

Descriptive analytics is mostly characterized within many Big Data discussions today. You have a lot of data but don’t know how to define it, organize it or tabulate it. We also interchange this term with "reporting." It's valuable information as a foundation, but it doesn’t tell you much about why the result happened or what might happen in the future.

Predictive analytics is what's getting so much attention today. Here, you use data from the past to predict the future. Skeptics hammer this concept because they insist correlation doesn't mean causation. You might have the propensity to buy a hot dog from 7-Eleven at the same time you're buying a Gatorade and a lottery ticket, but the reality of me buying a hot dog on my next visit is zero.

Babson College Prof. Thomas Davenport says it best, "You don’t need to imply causation to apply predictions." You're simply predicting a likelihood of an action. For example, a certain type of customer might respond better to a certain type of email or product recommendation on a mobile device at home versus at work or during lunch on the run.

This is an important concept to understand whether you are using a model, a recommendation engine or simple business rules based on past behaviors. Predictions are just that -- choices --and in a transient world moving faster, you’ll need to rely more and more on these. How far you stretch them is your RISK model.

Prescriptive analytics is what I think about on long walks. This is often where cause-effect analysis meets the real world. We mask this as "testing" in the marketing world. We all know the No. 1 rule of testing: You must have a hypothesis to test against.

Think of this area like a doctor writing prescriptions: fine if you are treating the flu, but if you are trying to ascertain the relationship between increased purchase, profit and number of ad exposures over a given period of time by channel and segment, this becomes almost unfathomable operationally without a quant-geek team spending months on it in the back room. These are the exploration areas where you ask what you really want to know about the customer or market.

Analytics And Reporting Are Great, but Data Visualizations Are How You Tell Stories With Data.

The ability to create smart data visualizations was once a nice-to-have skill. But in today’s world where the amount of data is overwhelming, creating and communicating through compelling data visualizations is a must-have skill for managers.

It Shows The Relationship To Data. Looking at raw data in all its numerical, tabular, row-and-column glory is far from the most intuitive way to understand it. A good data visualization will immediately illustrate how data points are connected to one another and provide insights more quickly. Indeed, studies show that using data visualization tools can more quickly get you the information you need – up to 28% faster, even.

It Depicts Relationships Throughout Time. Depending on the application, a good data visualization can indicate patterns and trends across large data sets, showing connections that arise as time passes.

It Tells A Story. The narrative style of presenting data in a narrative manner is gaining traction all over the world. A good data visualization will be able to use causality and storytelling to send a message that cuts cleanly through the raw data, just like a moral in one of Aesop’s fables. Results matter but stories are your legacy and future

A few really cool data visualizations, not related to marketing, but cool anyway.

“Most of us need to listen to the music to understand how beautiful it is. But often that’s how we present statistics: we just show the notes, we don’t play the music.” - Hans Rosling

The Future And Beyond

Many discussions on the future are rooted in the questions you want to answer, how often you want the answer, from whom you want the answer and how you want to apply the answer to your business. The importance of knowing this is what iPost has applied for and built into their products and business. We have more data, more transient consumers, more tools to choose from, and are expected to be faster and smarter than we were yesterday, all with the same budget.

Ten years ago, it would take two to three months to build a predictive model. Now we have hundreds of thousands in play, even some that self-learn.

Five years ago, compiling a simple cross-channel report was a multi-functional effort. Now we have real-time dashboards that we can configure on the fly.

Last year we dreamed of automation and optimization with a high degree of confidence in the outcomes. Now we can apply machine learning to in-market programs to take potential error out of what we know works. Tomorrow is here. As this quote from engineer and author W. Edwards Deming implies: "In God we trust; all others must bring data"

iPost has produced a Maturity Model Whitepaper as supplemental material to this article, to download click here