Mutli-Channel Attribution and Understanding Interaction

I'm no cosmologist, but this post is going to rely on a concept well known to astrophysicists, who often have something in common with today's marketers (as much as they might be loathe to admit it). So what is it that links marketing analytics to one of the coolest and most 'pure' sciences known to man?

I'll give you a hint: it has to do with such awesome topics as black holes, distant planets, and dark matter

The answer? It has to do with measuring the impacts of things that we can't actually see directly, but still make their presence felt. This is common practice for scientists who study the universe, and yet not nearly common enough among marketers and people who evaluate media spend and results. Like physicists, marketing analysis has progressed in stages, but we have the advantage of coming into a much more mature field, and thus avoiding the mistakes of earlier times.

Marketing analytics over the years and the assumptions created :

• Overall Business Results (i.e. revenue) : if good, marketing is working!
• Reach/Audience Measures (i.e. GRPs/TRPs) : more eyeballs = better marketing!
• Last-click Attribution (i.e. click conversions) : put more money into paid search!
• Path-based Attribution (i.e. weighted conversions) : I can track a linear path to purchase!
• Model-based Attribution (i.e. beta coefficients) : marketing is a complex web of influences!

So what does this last one mean, and how does it relate to space? When trying to find objects in the distant regions of the cosmos, scientists often rely on indirect means of locating and measuring their targets, because they can't be observed normally. For instance, we can't see planets orbiting distant stars even with our best telescopes. However, based on things like the bend in light emitted from a star, and the composition of gases detected, we can 'know' that there is a planet in orbit of a certain size and density, that is affecting the measurements that we would expect to get from that star in the absence of such a hypothetical planet. Similarly we don't see black holes, but we can detect a certain radiation signature that is created when gases under the immense gravitational force of the black hole give off x-rays.

This is basically what a good media mix/attribution model is attempting to do, and it's why regression models can work so well. You are trying to isolate the effect of a particular marketing channel or effort, not in a vacuum, but in the overall context of the consumer environment. I first remember seeing white papers about this mainly about measuring brand lift due to exposure to TV or display ads, but those were usually simple linear regression problems, connecting a single predictor variable to a response, or done as a chi-square style hypothesis test. But outside of a controlled experiment, this method simply won't give you an accurate picture of your marketing ecosystem that takes into account the whole customer journey.

As a marketer, you've surely been asked at some point "what's the ROI of x channel?" or "How many sales did x advertisement drive?" And perhaps, once upon a time, you would have been content to pull a quick conversion number out of your web analytics platform and call it a day. However, any company that does things this way isn't only going to get a completely incorrect (and therefore useless) answer, but they aren't really even asking the right question.

Modern marketing models tell us that channels can't be evaluated in isolation, even if you can make a substantially accurate attempt to isolate a specific channel's contribution to overall marketing outcomes in a particular holistic context.

Why does that last part matter? Because even if you can build a great model out of clean data that is highly predictive, all of the 'contribution' measuring that you are doing is dependent on the other variables.

So for example, if you determine that PPC is responsible for 15% of all conversions, Facebook is 9%, and email is 6%, and then back into an ROI value based on the cost of each channel and the value of the conversions, you still have to be very careful with what you do with that information. The nature of many common methods for predictive modeling is such that if your boss says, "Well, based on your model PPC has the best ROI and Facebook has the worst, so take the Facebook budget and put it into PPC" you have no reason to think that your results will improve, or change the way you assume.

Why not? Because hidden interactivity between channels is built into the models, so some of the value that PPC is providing in your initial model (as well as any error term), is based on the levels of Facebook activity that were measured during your sample period.

It's a subtle distinction, but an important one. If you truly want to have an accurate understanding of the real world that your marketing takes place in, be ready to do a few things:

1. Ask slightly different questions; look at overall marketing ROI with the current channel mix, and how each channel contributes, taking into account interaction
2. Use that information to make incremental changes to your budget allocations and marketing strategies, while continuously updating your models to make sure they still predict out-of-sample data accurately
3. If you are testing something across channels or running a new campaign, try adding it as a binary categorical variable to your model, or a split in your decision tree

Just remember, ROI is a top-level metric, and shouldn't necessarily be applied at the channel level the way that people are used to. Say this to your boss "The marketing ROI, given our current/recent marketing mix, is xxxxxxx, with relative attribution between the channels being yyyyyyy. Knowing that, I would recommend increasing/decreasing investment in channel (variable) A for a few weeks, which according to the model would increase conversions by Z, and then see if that prediction is accurate." Re-run the model, check assumptions, rinse, repeat.

Marketing & Data Priorities for a New Business

Note: Apologies, some hiccup with Blogger caused an old, unfinished version of this to be posted.  I think I captured the gist of it, and filled in the missing words.

In my career, I have worked with some very large, mature companies while on the agency side, and in-house with a small-to-medium sized business, though closer to small at the time when I joined.  I bring this up because a colleague of mine recently decided to leave our thriving enterprise to join a start-up, as one of the 'first five.'  I think that everyone considers what it would be like to work for a company from the very beginning, no matter his or her profession, and so it got me thinking about what I would do in that position.

I would guess that for many people, myself included, the attraction of working for a start-up as an early employee isn't about the beer cart, the foosball Fridays, or even the stock options, but about the ability to come in and practice our particular craft on a tabula rasa.  Deep down, almost everyone who is good at what they do thinks that they could be a little bit better if not burdened by the ghosts of business past.  Every company evolves over time, but like a city, new growth is inevitably on top of the old, no matter the lengths that you go to clear the site.  I've never met a developer who hasn't been frustrated by old code, an SEO who understands why a website was built a certain way, or a DBA who would design a database the way he found it.

Obviously, I think that it probably goes without saying that most of these legacy problems are not the result of incompetence, but rather a combination of a lack of foresight, and the normal "things that happen" over time.  We can assume that most of the people who build the legacies that the rest of us inherit have good intentions, but lack the luxury of building a foundation that will stand the test of time.  The key, for our hypothetical selves at start-ups as it was for our real-life predecessors, is to wed the long-term concerns with the immediate business needs of the fledgling company.

So as a thought exercise, here are some considerations and strategies that I would prioritize if I found myself moving to a brand new company:

Advertising:
Think about your target audience, and how they gather information.  Chances are, budget will be an issue, so the most important things will be efficiency and extremely narrow targeting.  There is always a natural progression of one platform at a time, but that's a mistake.  The evidence all points to a multi-platform strategy from the beginning.  I hate to fall back on a cliche like "synergy," but it is easier than actually explaining the math.  The point is, launch your content, social, paid social, and paid search all at once, even if you only do a limited amount of each one.  Coordinate them, because they amplify one another, and you will maximize the effect of your spend.

• Know your audience
• What need does your product fill, who has that need?
• If you had that need, how would you go about satisfying it? Try it.

• Make sure that you have a keyword strategy that looks beyond CPC to CPA/CPE or whatever your target user action is
• Note that this means you will have to track everything FROM THE START

• Know your budget, and what will fit into it
• Set some money aside for testing, maybe 10% at first
• Start with very tight keyword groups, be proactive with matchtypes and negative keywords, and watch any GDN or YouTube spend carefully
• Careful targeting is better for conversions and budget, so start with only your own country

Content:
Produce as much of it as possible, make it valuable, don't make it sell your own product.  Mix your mediums, make sure that you think about the life cycle of each piece, and how you can distribute it.

• Ensure that you have a place for content to live permanently, preferably on your site.  You want a link that can live forever
• Use your content as a source of advertising material, from keywords to messaging.  If you find yourself saying something a lot in your content, it's probably important in your industry

SEO:
This is one is simple on the face of it, but also one of the easiest matters for a new company to overlook and one of the hardest things to plan for as it grows.  Generally speaking, your best bet is to everything right, but here are a few ideas that you can start with:

• Make sure that every page that has clear focus
• that you explain that focus on that page with at least a few hundred words worth of text
• and that you repeat that focus very concisely in the metadata  
• Make sure that your URL structure is logical 
• clear hierarchy in the sitemap based on importance of the page
• hyperlink deeper pages to appropriate higher-level nav pages using relevant anchor text

Data Collection:
Set yourself up with a Google Analytics account immediately.  It doesn't matter if you plan to use another web tracking platform down the road, you want to make sure that you are measuring out of the gate.  This is free, and it will join to your AdWords account to give you significant targeting benefits.

• Don't just stick to the out-of-the-box defaults with GA, spend the extra time to make it fit your needs
• Activate ecommerce tracking if need be, and add the little bit of code (seriously, like one line) needed to gather demographic data
• Add events and goals to customize your map of the customer journey
• are there key pages you want to track? forms? videos?
• think as you build your site, about every key interaction you will have with visitors from discovery through whatever signifies success, and be ready to track each step

Don't feel limited to Google Analytics for your data collection and housing, either.  It's great for one part of the interactions you will have, but there is more to your business than your website, and you will want information from other sources as well.  MS Access will do if you have loads of CSV files or similar data that will overwhelm Excel, but eventually you may need to move to a SQL database, or other RDB.  Maybe you want to skip that, and go straight to NoSQL, given the direction that the business work is moving in.  With AWS and Google Cloud, storing data in a non-relational manner and then accessing it freely with MapReduce jobs is becoming easier and cheaper every day.

The point is, track everything.  Collect all of the information that you can from the get go, because you will always think of things down the line and wish that you had tracked them all along.  Starting with day one, you will be asking questions about your customers and your business that will have profound effects on your marketing strategy and execution.  Give yourself every chance to make informed decisions whenever possible.

There may be more to follow this, but I wanted to get it out before the new year.  Good luck in 2015, especially if you work at a new business!

Don't limit yourself

Ad Viewability Matters, But Let's Not Overreact

The issue of display impressions, both how they are measured after delivery (in terms of tracking effectiveness for marketers) and how they are measured at the point of delivery (in terms of accountability for publishers), is whether anyone actually sees them.  There are really two problems here, both of which the IAB (Internet Advertising Bureau) has tried to address recently, though it remains to be seen if they can have any effect.

The first issue, and one that has been well-publicized over the last year or so, is the prevalence of non-human traffic hitting website servers.  Depending on who you believe, this traffic, whether from deliberately fraudulent bot networks or innocent (though equally irrelevant) spiders and other web crawlers, makes up anywhere from 15% to 50% of server requests, the implement by which impressions are "served."

The second issue is that even if the site visit is from a real person, there has historically been little or no attempt made to determine if the user was ever able to see the ad itself.  For instance, if a page loads but the particular inventory that was purchased is below the fold, and the user then clicks a link in the header to leave the page, an ad impression would be recorded despite the fact that it never appeared on the screen.  The New York Times ran an article about this not long ago, which helped bring attention to a wider audience, but largely covered existing topics of discussion.

The IAB's attempt to define "viewable" impressions and instruct the marketplace to deal only in those impressions that fit the description (“must be in the viewable portion of an internet browser for a minimum of one continuous second to qualify as a viewable display impression”) is well-meaning, but unfortunately fraught with problems.  Basically, if those conditions either aren't met or can't be guaranteed, they recommend that publishers don't sell that inventory and that media buyers don't pay for it.  And it is here that we marketers run into a bit of a conundrum.

As digital marketers, one of our big selling points for years, in terms of justifying budgets, was the fact that digital is just so much more measurable than traditional advertising channels, which in turn meant that we were comfortable being more accountable.  Over time, it became clear that display ROI wasn't quite so cut-and-dry as search and other direct response marketing, but we were quick to assure those who control the purse strings that it was ok, because we could still measure the impact.  Using a variety of modeling techniques and brand lift studies, we claimed that we could show the benefit of display advertising on other channels, whether it was increasing searches on our brand terms or improving conversion rates on page for those exposed to a display ad.

So now that we find ourselves balking at the fact that we may, in fact, have been paying for bogus traffic all along, we are in something of a bind.  Some marketers are suggesting that we should simply not purchase inventory from those publishers and networks that can't promise "viewable" impressions now, but I don't see that as feasible.  For one thing, a lot of people selling inventory, especially targeting niche audiences, simply don't have the technology built into their ad serving to track and guarantee such things.  More importantly, however, the idea of cutting off a channel for this reason has a real logical flaw to it.

Here's the deal, the product (display impressions) hasn't changed significantly, we are simply more aware of shortcomings than we were before.  So, if we are going to suddenly tell those to whom we marketers are accountable (boss or client) that we should no longer be buying certain inventory that we used to be, we are forced to acknowledge one of two things:

1.) The product has always not been worth what we have been paying for it, and thus all of our past attempts to demonstrate its value through various methods have been either inept (bad), or deliberately deceitful (very bad).

or

2.) The product always has always been worth purchasing for all of the reasons that we said it was, and still is, but we are recommending shutting off that valuable channel for what essentially amounts to moral reasons.

Since the whole point of most measurements for display had to do with the results, not the delivery itself, what we should be able to say is something like the following:

"Even with what we now know to be only 65% viewable delivery of purchased impressions, we have still been able to achieve [X, Y, and Z beneficial results] on our display campaigns.  While we will continue to work with our publishers and providers and encourage them to improve their delivery methods, recent industry findings have done nothing to change the core value returned by past campaigns, nor do they suggest any reduction in returns on future campaigns, even if circumstances remain the same.  As such, we do not recommend any change in current investment in this channel."

Some marketers seem to be tempted to cut off their noses to spite their own faces, but this is a wrong-headed approach.  There is a solution here, and it is a market solution.  In the same way that demographic information and services like comScore have allowed inventory sellers to specify their audience and separate into tiers that are reflected in pricing, so too will these provably "viewable" impressions command a premium.  This new information may give media buyers a little more leverage over pricing with some publishers/networks, but it doesn't give us the ability, or need, to re-write the history of our past results.  Think about it this way: if display has been as effective as it seems despite so many served impressions never being seen, there is basically nowhere to go but up.

I for one, however, will certainly be looking for a lower CPM next month from those providers who can't make the '50 %on screen for one continuous second' guarantee.

Income Inequality is the New Market Inefficiency (aka "Marketing Moneyball")

I think that there is a good chance that the new market inefficiency is income inequality. 

                There is no question that the distribution of wealth, as well as income in this country has grown more uneven over the last couple of decades.  Whether or not you care about this or find it “bad” is irrelevant to this discussion and a topic I am not going to touch, but the fact that the gap is widening is not a debate, it’s a matter of public record (the government is good at keeping track of other people’s money).  All I care about here is what the effect is on brands and advertisers.

Here is a table of income distribution in the US over the last 30 years:

	Top 1%	Next 19%	Bottom 80%
1982	12.80%	39.10%	48.10%
1988	16.60%	38.90%	44.50%
1991	15.70%	40.70%	43.70%
1994	14.40%	40.80%	44.90%
1997	16.60%	39.60%	43.80%
2000	20.00%	38.70%	41.40%
2003	17.00%	40.80%	42.20%
2006	21.30%	40.10%	38.60%

                What we are seeing is that the bottom 80% of the country has seen their share of income decrease by 20% over a period when the US as a whole saw strong economic growth as well as a population increase.  The amount of purchasing power lost when over 200 million people see their relative income decline is staggering.

Before you say that it is misleading because it is relative to the total growth in wealth, the answer is that it’s not.  Professor G. William Domhoff of UC Santa Cruz pointed out that from 1983 – 2004:

“Of all the new financial wealth created by the American economy in that 21-year-period, fully 42% of it went to the top 1%. A whopping 94% went to the top 20%, which of course means that the bottom 80% received only 6% of all the new financial wealth generated in the United States during the '80s, '90s, and early 2000s (Wolff, 2007).”

How does this all tie back to brands?  I mean, there is more money in the country, so does it matter who is spending it?  Well, that depends on your brand.

BMW is going to be just fine.  The number of people who had the buying power to get a luxury car remains the same as before, even in a down economy.  If you are a brand that relies on a broad consumer base from the upper-middle class and down however, there is a good chance that this is a paradigm shift, rather than just a short-term cycle.

Brand managers are not economists, so it is understandable that a lot of them would look at poor sales data over the last few years and think, “Well, the economy is down, so everyone hurts, but we will come back with the recovery.”  There are several problems with this though, and the biggest being that despite what the man on the street might think, there has been positive, though slow, growth for the last several quarters.  Like our hypothetical brand manager though, this assumes that the growth is evenly distributed, but the reality is that it is focused on several sectors. 

The recent market troubles provided a volatility that muddied the economic waters to a degree, obscuring long-term trends by drawing focus to the post-2008 environment, focused on housing and finance.  The problem is that overall GDP growth and wealth creation is no longer increasing the buying power of the widest part of the consumer base in this country, and brands need to recognize this.

Think about it this way:  You make Tide, or Gain, or some other name brand laundry detergent.  Total amount of money in the system is increasing, but primarily flowing towards a small number of people who already hold a disproportionate amount.  The vast majority of your consumers have actually seen their real buying power (based on income levels pegged to an inflation index) decrease, so they move to cheaper store brands, or buy your product only when there is a coupon/discount offer.

For your brand to just break even, the top 20% of earners in America would have to suddenly start consuming more of the same product, without adding any new consumers.  So the well-to-do family, which has gone through 1 bottle of Tide per week forever, suddenly has to start using 3 of them per week.  Rich babies will need to start dirtying their diapers at a much higher rate inexplicably.  This isn’t going to happen.

We have seen an explosion in interest in savings, discounts, and couponing.  There are huge blogs dealing with the subject, and even multiple television shows.  Cable subscriber rates fall along with telephone landlines, lagging by ten years.  The important thing is realizing that this behavior is not symptomatic of short-term economic slowdown, but long-term trends that started well before the banking crisis.

Growth will slowly increase over the next 6-12 quarters, and unemployment will slowly drop, but probably not to pre-2008 levels any time soon.  Meanwhile, population continues to increase, almost entirely in the bottom 80% of the income scale, which still possesses the lion’s share of purchasing power in this country.  For a lot of brands, krazycouponlady.com is more relevant to their consumers than BMW, and they need to embrace that.  When the economy comes back, they can’t be surprised that their sales never fully returned, and that their profit margins actually shrank.

The flipside of this is that there is a huge opportunity for brands that recognize the shift and respond to it first.  If General Mills stubbornly tries to stay the same, and cover their cereal boxes with QR codes that drive to an altered-reality experience (which is not cheap), while Kellogg suddenly cuts overhead and production costs, accepts a slimmer margin but positions themselves as the middle ground between store brands and premium brands, they will reap the benefits. 

The majority of buying power as a market group has shifted down a step, roughly 20%, compared to the post-WWII era which saw the growth of the middle class and a large industrial/manufacturing sector when many marketing practices and brand identities were established.  We have entered a new reality, and the brands that accept this first will have a vital head-start in dominating the “new middle-class.”  Advantage is gained by exploiting market inefficiency, and failure to differentiate between overall economic market conditions versus buying power demographic shifts is that inefficiency.

Is Marketing Really "Data-Driven?" Pt. 1

               No matter what you call it, the clear trend in marketing today is towards a model that depends on consumer data collected digitally to inform both online and offline media strategy.  Terms like “data-driven” and “fast-moving data” are bandied about, conjuring up an image of an agile, precise campaign that links brands to individuals, rather than demographics.  Marketers know that the shift from art to science is already in progress, and I should say that I wholeheartedly agree with this approach.

                The problem is that there is a danger in a job only half done, and at times I fear that we as an industry talk about “data-driven marketing” like experts, but that there is no rigor to the approach.  Additionally, using digital data to inform traditional media, both in terms of planning and creative, when the same statistical approach isn’t applied to those channels, will return misleading results.  No matter how cleverly you apply your digital learnings to traditional performance, if the metrics by which we measure TV are inaccurate, or not properly tied to business goals, then we risk just painting a picture that is different but no more insightful.

                To truly claim a data-driven approach, you need to collect data at every step of the marketing process systematically, and analyze it methodically, adhering to sound statistical procedure.  Just as importantly, you need to know what data to gather, and how it helps you to achieve your goal.

                Let’s start looking at an example of how this can affect measurement at every level of a campaign.  Starting with the broadest, what is the goal of marketing?  To increase the sales/services provided of the client.  How is that measured?  Brand loyalty?  Market share?  Sales in dollars?  Profit?  Units sold?  The first thing that an advertising agency has to do (ideally) is identify what the client goals are, and frankly, the media agency should be the one that determines the goal, as it is part of the marketing process.

                Why is that?  Let’s look at the list of client goals that I mentioned above, all of which at first blush appear to be totally normal, reasonable ways to judge a marketing agency, but all of which have some issues from a statistical and/or business standpoint.

Brand Loyalty: This is probably the worst measure for a number of reasons, over and above the fact that it is a vague concept.  Anything that is survey or panel based can be looked at, but the methodology and sampling issues make it less scientific. 

Market Share:  Better than brand loyalty, but because the information has to come from a number of outside sources makes the gathering of this data ponderous, and more importantly there is a long time lag for reporting.

Sales (dollars): On its own this number is somewhat useful because it is an absolute 1-to-1 value, but it really should be adjusted to account for the market environment of the client’s particular category, rather than taken raw.

Profit: Terrible.  I don’t think that anyone would actually measure a company’s marketing success based on profits, but it is something that clients think about and a useful illustration about what stats you don’t want.  Too many uncontrolled variables go into profit and revenue numbers.  If a company sells more product but the cost of raw materials increases as well, it shouldn’t be factored into any measure of advertising.

Sales (Units): This is probably the best way to measure overall advertising success over a long period of time, once again normalizing the number to the broader market conditions.  By using sales in units you remove some of the variables around pricing and competitive environment (aside from the ones that can adjusted for).

The key to any good statistical measure of success or ability is removing as many uncontrolled variables as possible, and not crediting/blaming advertising for things it can’t control.

Since I often use baseball as examples for statistics and how to use them, the perfect analogue here is using ‘wins’ to judge a pitcher.  Conventionally, people looked at wins to determine how good a pitcher is, but that number is quickly falling out of favor, because it has very little statistical relevance to how well a pitcher performs.  Think about it, if a pitcher gives up 5 runs but his team scores 8, he gets a win.  If another pitcher gives up 2 runs but his team is shut out, he gets a loss.  Who did a better job?

The lesson is not that we shouldn’t measure things and use the data as much as possible, but that not all stats are created equal, and that we need to make sure that what we are collecting is telling us what we think it is.  Right now I would say that marketing is getting good at amassing data, but still extremely infantile in terms of manipulating it properly.  We are still at the stage of evaluating pitchers based on wins, as it were.

Some of this is also based on assumptions, and how many of them are based on traditionally held marketing beliefs that we take for granted, despite never seeing empirical evidence for them.  Every marketer should be a gadfly.  Poke holes in theories or justifications that don't make sense.  If you see a test that doesn't account for uncontrolled variables in the results, point it out.  If a conversation is centered around an idea that everyone accepts but no one has proved, ask why.  

Next up, it might be worth looking at TV, and the relationship between digital/social and offline, in order to challenge some of the preconceived notions.

We Need to Make Digital Measurement Easier

[Editor’s Note:  Sorry for the long layoff, I am going to be better about posting starting today]

To be clear, I don’t mean that we need to make it easier for us digital marketers, but that we need to make it easier for the brand representatives that we have to report to.

When I go through plans and recaps for marketing programs, the problem becomes very clear.  People who have been dealing with traditional marketers for a long time expect just a few things from TV, print, and radio: Reach and Frequency.  These are estimates, and they are provided by the people selling the media, so they don’t need to be calculated by those buying it.

It’s simple, and clean, though it does nothing to tell you about the effectiveness of the channel after the fact.  How many people saw the ad, and how often.  Move on.

Sometimes you will see a brand lift study built into a buy, which basically just consists of polling some consumers to see how the ad made them feel (with varying degrees of scientific rigour).

Then we get to digital, and suddenly the performance metrics increase exponentially.  The breadth and depth of data that we have available to us in the digital space is both a blessing and a curse in that sense.

First of all, we subdivide “digital” into myriad channels of increasing specificity.  There is display, search, social, in-text, and more.  Each of these sub-channels has multiple ad unit types, and in turn, each ad type has multiple statistics that can be tracked.

(For instance, display ads can be static units or interactive units (and static units can be further broken down by size, so there are standard banners, skyscrapers, etc.), and so you have reach in terms of unique users, then interaction rates, time spent in the ad unit for rich media, click through rate, video plays in unit, and more.))

You can measure attributes of the ads themselves, like click through rate, impressions, cost per impression/click, etc., and you can also measure on-site actions and behavior, like conversions, bounce rate, time on site, and so on.

We haven’t even talked about the social metrics like Facebook likes, tweets, +1s, ‘conversations, and additional followers/friends.

The upside of all of this is that obviously the data gives us visibility and optimization options that traditional marketers can only dream of.  The downside is that we are actually held to performance standards unlike traditional offline media channels, and moreover, that the people who we report to get lost in all of these metrics.

Traditional media channels don’t provide brands with much in the way of data or measurement options, and maybe the answer is that they should be forced to come up with better ways to justify their value.  More likely however, we as digital marketers need to find ways to simplify our reporting.

This may mean actually giving brands less raw data, and it’s possible that Pandora’s box has been opened and it is too late.  However, I think that the only possible outcome is the creation of a weighted composite number that is based on an equation taking into account a variety of metrics across digital channels, pegged to an index.  The million dollar problem is just figuring out how to do it, but you can bet that I will be working on it, as I am sure others are.

Expect a 'part two' of this entry in the future.

If It Ain’t Broken, Why Innovate?

 

This article, entitled “TV isn't broken, so why fix it?” (link) appeared on CNN this past week. I have to say, I fundamentally disagree with almost everything about it.

The primary idea that I have an issue with is that because TV (and are we talking about the device or the delivery format?) isn’t completely without utility or appeal, no one should be trying to improve it. What if Kennedy had looked at the moon and said, “Hey, no one has ever been up there, so why should we go?”

Seriously though, I find it hard to believe that a guy writing a ‘Business Insider’ piece for CNN’s Technology segment could be so opposed to progress and innovation. The companies he takes shots at for trying to improve the consumer experience with their televisions? Apple, Google, and Microsoft. Those are some pretty good track records when it comes to developing new and popular ways to interact with technology, and I would imagine that one of them could very well strike upon the key to the next stage in TV evolution.

In criticizing their past attempts to innovate in the space is his second majorly flawed position (after “maintain the status quo”), which is basically, “if these companies have failed to achieve this breakthrough in the past, they should just quit trying.” Really? Once again, this doesn’t feel like someone who should be writing about business or technology.

Then the piece meanders for a bit while the author goes back and forth between talking about TVs as a piece of technology and TV as a service provided by cable companies without recognizing the distinction, followed by some unsubstantiated assumptions about TV consumers and some weak anecdotal or hunch-based evidence.

“But I'm going to go out on a limb here and say that most TV viewers simply won't care enough about any of this stuff to shell out $1,500 for a new Apple TV, or spend a few hundred bucks and countless hours fiddling around adding a new box to their TV set and figuring out how it works.”

Oh yeah? Those being the same people who waited in long lines to pay $400 for a slightly newer version of a cell phone they already had? Why am I picturing the guy who wrote this as an 80-year old man whose grandkids have to constantly set his VCR clock?

“But normal people don't think about TV that way. TV is passive. The last thing we want to do is work at it.”

What defines “normal people?” How do you know what they think about TV? More importantly, what have you seen in the last few years that doesn’t suggest that people are looking for more control, more personalization, and more interactivity?

Perhaps the biggest fallacy at all is that TV hasn’t already been revolutionized several times in recent memory. To him the massive old box upon which he enjoyed black and white westerns as a child is somehow closely related to the HD plasma flat-screen which is currently streaming Netflix via an HDMI connection from my laptop. A year or two ago I was setting recordings weeks in advance so that I could watch my favorite shows after I returned from vacation, and skip the commercials while I was at it. I no longer adjust the rabbit ears when my picture is blurry, and the channel options went from 14 to 1,400 in my lifetime.

“That's why we love TV just the way it is. If it ain't broke, don't fix it.”

If people had said that 20 years ago, I would consider TV extremely broken. Speaking as a marketer, I do actually find a lot of problems with television, but even as a viewer I would be happy to see change. As it is, there is much room for improvement, and while lack of imagination may be something that the author suffers from, I’m glad that there are people in the industry who don’t share that narrow-mindedness.

As soon as I calm down enough to climb off this soapbox, I will submit my resume to CNN. It seems like they are in desperate need of some technology writers.