Every Figure Must Be Good, Bad or Ugly

You get to hear “actionable insights” whenever analytics or roles of data scientists are discussed. It may reach the level of a buzzword, if it hasn’t gone there already. But what does it mean?

That's one ugly number
That’s one ugly number

You get to hear “actionable insights” whenever analytics or roles of data scientists are discussed. It may reach the level of a buzzword, if it hasn’t gone there already. But what does it mean?

Certainly, stating the obvious doesn’t qualify as insightful reporting. If an analyst is compelled to add a few bullet points at the bottom of some gorgeous chart, it has to be more than “The conversion rate decreased by 13.4 percent compared to the same period last year.” Duh, isn’t that what that plot chart is saying, anyway? Tell me something we can’t readily see.

And the word “actionable” means, “So, fine, numbers look bad. What are we supposed to do about it?” What should be the next action for the marketers? Should we just react to the situation as fast we can, or should we consider the long-term effect of such an action, at this point? Shouldn’t we check if we are deviating from the long-term marketing strategies?

Many organizations consider a knee-jerk reaction to some seemingly negative KPI “analytics-based,” just because they “looked” at some numbers before taking action. But that is not really analytics-based decision-making. Sometimes, the best next step is to identify where we should dig next, in order to get to the bottom of the situation.

Like in any investigation, analysts need to follow the lead like a policeman; where do all of these tidbits of information lead us? To figure that out, we need to label all of the figures in reports — good, bad and ugly. But unlike policework, where catching the bad guy is the goal (as in “Yes, that suspect committed a crime,” in absolute terms), numbers in analytics should be judged in a relative manner. In other words, if the conversion rate of 1.2 percent seems “bad” to you, how so? In comparison to what? Your competitors in a similar industry? Last year’s or last quarter’s performance? Other similar product lines? Answering these questions as an analyst requires full understanding of business goals and challenges, not just analytical skillsets.

Last month, at the end of my article “Stop Blaming Marketing Problems on Software,” I listed nine high-level steps toward insight-driven analytics. Let’s dig a little further into the process.

Qualifying numbers into good, bad and ugly is really the first step toward creating solutions for the right problems. In many ways, it is a challenging job — as we are supposed to embark on an analytical journey with a clear problem statement. During the course of the investigation, however, we often find out that the original problem statement is not sufficient to cover all bases. It is like starting bathroom renovation in a house and encountering serious plumbing problems — while doing the job. In such cases, we would have no choice but to alter the course and fix a new set of problems.

In analytics, that type of course alteration is quite common. That is why analysts must be flexible and should let the numbers speak for themselves. Insisting on the original specification is an attitude of an inflexible data plumber. In fact, constantly “judging” every figure that we face, whether on a report or in the raw data, is one of the most important jobs of an analyst.

And the judgment must be within the business context. Figures that are acceptable in one situation may not be satisfactory in another situation, even within the same division of a company. Proper storytelling is another important aspect of analytics, and no one likes to hear lines out of context — even funny ones.

It may sound counterintuitive, but the best way to immerse oneself into a business context is to figure out why the consumer of information is asking certain questions and find ways to make her look good in front of her boss, in the end. Before numbers, figures, fancy graphics, statistical methodologies, there are business goals. And that is the key to determining the baselines for comparisons.

To list a few examples of typical baselines:

  • Industry norm
  • Competitors
  • Overall company norm
  • Other brands
  • Other products/product lines
  • Other marketing channels (if channel-driven)
  • Other regions and countries (if regional)
  • Previous years, seasons, quarters, months, weeks or year-to-date
  • Cost factors (for Return on Investment)

Then, involved parties should get into a healthy argument about key measurements, as different ones may paint a totally different picture. Overall sales figure in terms dollars may have gone down, but the number of high-value deals may have gone up, revealing multiple challenges down the line. Analysts must create an environment where multi-dimensional pictures of the situation may emerge naturally.

Some of the obvious and not-so-obvious metrics are:

  • Counts of opens, clicks, visits, pages views, shopping baskets, abandonments, etc. Typical digital metrics.
  • Number of conversions/transactions (in my opinion, the ultimate prize)
  • Units sold
  • # Unique visitors and/or customers (very important in the age of multichannel marketing)
  • Dollars — Total paid, discount/coupon amount, returns (If we are to figure out what type of offers are effective or harmful, follow the discounts, too.)
  • Days between transactions
  • Recency of transactions
  • Tenure of customers
  • Cost

If we conduct proper comparisons against proper baseline numbers, these raw figures may reveal interesting stories on their own (as in, “which ones are good and which ones are really ugly?”).

If we play with them a little more, more interesting stories will spring up. Simply, start dividing them with one another, again, considering what the users of information would care about the most. For instance:

  • Conversion rates — Compared to opens, visits, unique visitors (or customers), mailing counts, total contact counts, etc. Do them all while at it, starting with the Number of Customers, divided by the Number of Total Contacts.
  • Average dollar per transaction
  • Average dollar per customer
  • Dollar generated per 1,000 contacts
  • Discount ratio (Discount amount / Total dollar generated)
  • Average units per transaction
  • Revenue over Cost (good, old ROI)

Why go crazy here? Because, very often, one or two types of ratios don’t paint the whole picture. There are many instances where conversion rate and value of the transaction move in opposite directions (i.e., high conversion rate, but not many dollars generated per transaction). That is why we would even have “Dollar generated per every 1,000 contacts,” investigating yet another angle.

Then, analysts must check if these figures are moving in different directions for different segments. Look at these figures and ratios by:

  • Brand
  • Division/Country/Territory/Region
  • Store/Branch
  • Channel — separately for outbound (what marketers used) and inbound (what customers used)
  • Product line/Product category
  • Time Periods — Year, month, month regardless of the year, date, day of the week, daypart, etc.

Now here is the kicker. Remember how we started the journey with the idea of baseline comparisons? Start creating index values against them. If you want to compare some figures at a certain level (say, store level) against a company’s overall performance, create a set of index values by dividing corresponding sets of numbers (numbers in question, divided by those of the baseline).

When doing that, even consider psychological factors, and make sure that “good” numbers are represented with higher index values (by playing with the denominators). No one likes double negatives, and many people will have a hard time understanding that lower numbers are supposed to be better (unless the reader is a golfer).

Now the analyst is ready to mark these figures good, bad and ugly — using various index values. If you are compelled to show multiple degrees of goodness or badness, by any means, go right ahead and use five-color scales.

Only then, analysts should pick the most compelling stories out of all of this and put them in less than five bullet points for decision-makers. Anything goes, for as long as the points do matter for the business goals. We have to let the numbers speak for themselves and guide us to the logical path.

Analysts should not shy away from some ugly stories, as those are the best kind. If we do not diagnose the situation properly, all subsequent business and marketing efforts will be futile.

Besides, consumers of information are tired of the same old reports, and that is why everyone is demanding number geeks produce “actionable insights” out of mounds of data. Data professionals must answer that call by making the decision-making process simpler for non-analytical types. And that endeavor starts with labeling every figure good, bad or ugly.

Don’t worry; numbers won’t mind at all.

Is It Ever Good to Be Bad?

If you were to ask Miley Cyrus the question in this headline, the answer would be “Oh, yeah.” But if you look at album sales for her chronological counterpart, Taylor Swift, compared to Miley’s since she went “twerking,” the answer is clearly “no.” It took a year for Miley’s most

Miley Cyrus vs Taylor SwiftIf you were to ask Miley Cyrus the question in this headline, the answer would be “Oh, yeah.” But if you look at album sales for her chronological counterpart, Taylor Swift, compared to Miley’s since she went “twerking,” the answer is clearly “no.” It took a year for Miley’s most recent album, “Bangerz,” to reach 1 million in sales, and Taylor Swift’s most recent one, “1989,” hit 1.2 million in just one week. That was Taylor’s third album to sell 1 million copies within a week.

So if positive personas, values and public behavior sell more records, why do the politicians keep upping the volume and intensity of negative campaign ads?

According to Wesleyan Media Project research from 2013, presidential campaign ads hit a record new high in 2012 for volume and for negativity. Interesting, given that further research by Dowling, Conor M.; Wichowsky, Amber, as printed in the American Journal of Political Science in 2015, shows that voters actually punish politicians for negative ads.

But do we really punish negative advertisers? Consciously, it’s fair to say that most people claim to reject negative ads, maintaining that we are not swayed by mudslinging personal attacks and we make choices at a higher intellectual level. Yet, unconsciously, those negative messages, repeated over and over and over, get into our heads and linger longer than we might know. Because 90 percent of our thought is unconscious, according to Gerald Zaltman, a Harvard Business School neuromarketing pioneer and author of “How Customers Think,” those lingering, and likely dormant thoughts might, have a different response to negative ads than the leftover 10 percent that guide what rolls off of our tongues.

Ruthann Lariscy, professor emeritus at the University of Georgia who focuses on studying political advertising, suggests those negative thoughts do linger in our minds and have a lot more influence on Election Day than we want to admit, to ourselves and especially to anyone else. As Lariscy, points out in a recent article she wrote featured on CNN.com, we process negative information a bit more to help us better understand the implications of the message and that longer contemplation time enables it to register deeper into our psyches. Thoughts that linger longer, even passively, often resurface at later times to influence our behavior, says Lariscy, who refers to this process as the “sleeper effect.” Per her article, we tend forget the negative things one politician says about another and move on. But come Election Day, when we are standing in the election booth with ballot in-hand, something triggers that negative energy associated with claims made in the past and, in a lot of cases, that is when we punish politicians by voting against that bad memory, even if we don’t recall all the details or the source.

While Lariscy calls it the “sleeper effect,” I refer to it as the “survival effect.” Just like other species on this great Earth, we humans are programmed for survival and that deep-rooted and dominant DNA strand affects much of what we do in our daily lives, and has a lot of influence on our attitudes and opinions. Once something has negative energy associated with it, we unconsciously go into survival mode, and start to feel anxious or uneasy without really knowing why, in many cases.

We get a good example of how negative energy impacts our unconscious drivers from the Iowa Gambling Task. This task, which the University of Iowa originated in the 1980s, teaches us that our unconscious responds to negative energy and affects our behavior well before our conscious mind does. Participants in the study were given $2,000 and four decks of cards. The task was to play cards from the four decks, and earn money or lose money accordingly. Two of the decks had high risks for loss, while the other two had a greater chance of earning rewards. Participants were hooked up to monitor stress responses while playing the game. Among the healthy participants, signs of “unconscious” stress showed up after flipping over just 10 cards. It took between 40 and 50 flips for the unconscious mind to catch up! That implies that it takes our conscious mind four and five times longer to catch up with the attitudes, conclusions and drivers of our unconscious minds!

My conclusion from the above study is that our unconscious minds are wired to recognize stress and threats to our survival quickly and, as a result, put us in “survival” mode when we don’t consciously realize it.

How does this impact advertising? In terms of conscious statements about intent to vote or purchase from a brand, there’s likely not much change. But in terms of unconscious drivers that impact 90 percent of our thoughts and behavior, it suggests a great deal: Negative energy associated with your brand or products can impact sales down the road.

Negative energy can come from statements made by competitors questioning your integrity, ability to keep promises made and even financial stability. It can also come from using colors that create unconscious feelings of anxiety vs. those that put our minds at ease and create a sense of trust. And it can come from us, in the form of bad ads that leave one to question our values and our familiarity with what matters most to our customers.

GoDaddy, more known for its bad Super Bowl ads, perhaps, than its great customer service (which it has, by the way), is an example. For years, GoDaddy has tried to use shock value during Super Bowl games to get people talking about the brand. That goal is achieved, as the ads continue to pepper the top of the “Least Effective” ranks, in terms of generating persuasion, relevance, watchability and other results, as measured by Ace Metrix. In 2014, GoDaddy achieved No. 1 and No. 4 spots for the least effective Super Bowl ads with the “Body Builder” and “I Quit” ads. In 2015, the company went with one designed for even more shock value by having a beautiful top model sucking face with a quintessential unattractive nerd. GoDaddy claims that that kissing ad generated its best Super Bowl scores yet. And, per Mashable’s report on the ad’s results, its best sales day ever, with increases of 45 percent for a single product.

While “shock value, off-beat” ads might work great for short-term gains, in this case it clearly didn’t work for long-term sustainability. Forbes, on Oct. 30, 2015 — a few short months after the ad’s debut — reported that GoDaddy has posted a $71.3 million dollars earnings loss. Per prior years of running weird Super Bowl ads, GoDaddy reported a $200 million net loss in 2013 and, in June 2014, listed its total indebtedness as $1.5 billion. Clearly, there’s a lot more at stake here than advertising, but its fair to say that a brand’s persona and the energy it puts out does have an overall impact. Again, compare Miley Cyrus to Taylor Swift. Two talented female artists who write and perform songs based upon their personal values and those they want to project to the world.

Recently, Bloomingdale’s released an ad that suggested “date rape” for holiday partygoers. After much backlash and media attention, which likely upped its overall brand awareness scores at the time, the brand apologized. Only time will really tell if this likely ploy for attention will impact sales, not just this holiday season but down the road when shoppers can choose similar products from other retailers who don’t engage in doing bad for others while trying to gain good for themselves.

Lesson Learned
What you put out in this consumer-driven world of ours comes back. Maybe not immediately, but eventually it does. Because our conscious minds might “sleep” on bad energy; but in time, our survival DNA brings it back to the forefront of our unconscious drivers of behavior, and often influences us to choose “good” over what feels “bad.” Because success for any brand, large or small, lies in long-term sales, not short-term spikes, it’s easy to see that in this world of ours, in politics and in business, good does and will triumph over bad!