Chris Mercer: Anyone can Plug Into Analytics

Having trouble viewing the text? You can always read the original article here: Chris Mercer: Anyone can Plug Into Analytics
Is analytics really going to make a difference? Will I benefit by getting deeper into my analytics? Find out how exciting one m…

Having trouble viewing the text? You can always read the original article here: Chris Mercer: Anyone can Plug Into Analytics

Is analytics really going to make a difference? Will I benefit by getting deeper into my analytics? Find out how exciting one man can make Google Analytics. Analytics is not one of those words that inspires action. It’s not a word like “Rose” dripping with alternate meanings and romantic associations. It’s not like the word […]

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Deliver Step Change Impact: Marketing & Analytics Obsessions

Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes […]

The post Deliver Step Change Impact: Marketing & Analytics Obsessions appeared first on Occam’s Razor by Avinash Kaushik.

Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes things better – I’m borrowing the concept from mathematics and technology, from “step function”).

In those moments – common around new years or new annual planning cycles – the difference between delivering an incremental change vs. a step change is the quality of ideas you are considering. In this post, my hope is to both enrich your consideration set and encourage the breadth of your goals.

My professional areas of interest cover Customer Service, User Experience and Finance, though here on Occam’s Razor my focus is on influencing incredible Marketing through the use of innovative Analytics. To help kick-start your 2019 step change, I’ve written two “Top 10” lists, one for Marketing and one for Analytics – consisting of things I recommend you obsess about.

Each chosen obsession is very much in the spirit of my beloved principle of the aggregation of marginal gains. My recommendation is that you deeply reflect on the impact of the 10 x 2 obsessions in your unique circumstance, and then distill the ten you’ll focus on in the next twelve months. Regardless of the then you choose, I’m confident you’ll end up working on challenging things that will push your professional growth forward and bring new joy from the work you do for your employer.

Ready?

First… The Analytics top ten things to focus on to elevate your game this year…

The Step Change Analytics Obsessions List.

A1. Improve the Bounce Rate of your top 10 landing pages by 50%.

(Improving Bounce Rate results in reducing it. :))

You'll be surprised by the steep drop in Cost per Acquisition.

Google Optimize will be one of your BFFs in this quest. You’ll know you’ve moved beyond basic improvements when you start setting Custom Objectives – they require deeper thinking, which is a good sign.

A2. Eliminate 40% of the numbers from your dashboard.

Take the newly-created white space to explain what to do based on performance of 60% of the numbers that remain.

What your boss wants most this year, more than love, is to be told what the data wants her to do. Don't leave her guessing.

(Bonus, with actionable ideas: Smart Dashboard Modules.)

A3. Take your first steps towards unlocking smart algorithms.

Learn what Session Quality is in Google Analytics, then learn how to use it in your campaigns to improve conversions. In the Audiences section, go to the Behavior folder.

Learn what Smart Bidding is in Google Ads, then learn how to use it in your campaigns to improve outcomes.

Machine Learning algorithms will make our data smarter in unparalleled ways; Session Quality and Smart Bidding offer early clues about the scale and type of intellect. In both instances, it is immensely valuable to really understand how a smart algorithm uses billions of data signals to calculate likelihood of a conversion.

Across all your analytics data, algorithms will take you places humans simply can't. This should be the year you invest in an expansion in skills and practice to take advantage of these possibilities.

A4. Take a class in data visualization. It will save your life.

Anyone can make a complicated visual, it takes someone very special (you!) to draw out the essence of the story data is trying to tell.

My recommendations:

Free Courses: Data Visualization and D3.js and Data Analysis and Visualization at Udacity.
Affordable: Data Analysis and Presentation Skills at Coursera.
Occam’s Razor: Start with this one: Closing Data's Last-Mile Gap: Visualizing For Impact. And, there are five more linked to here.
 

Through all these courses remember the most important thing about data visualization: It’s not the ink, it’s the think. Obsess about improving the think, just as much as I’m encouraging you to improve the ink.

A5. Obsess about what happens after campaigns end.

In our analytics practice we tend to celebrate victory too early (at the end of the campaign) or with insufficient breadth (the full scope of impact).

Did you get customers with high lifetime value? How long did the brand lift – say Awareness – last? What was the average order value of the second purchase by people you acquire via Search, compared to those via Retail?

Is there a difference in behavior between people who signed up for email over the last year vs those who did not? What the cost of getting a retail customer to make subsequent purchases over mobile apps lower?

A6. Understand your personal impact, obsess about improving it.

Grab the revenue number for the company. Now work out how much of it is influenced by you directly. Make a note of what it is (likely to be a couple percentage max).

Double that number this year.

What are the first five things on your list?

None of them will be easy, but converting insights into action via influence rarely is. But, you don't have to stretch too far to see how amazing it would be for you (and data too!) if you double your impact.

A7. Run one super-large controlled experiment.

To prove what your Executives believe purely from their gut. Or, to disprove it.

Does Facebook advertising really work better than TV? Can you create premiumness for your brand using digital? Is a 15% coupon now better than 20% off the next purchase? Does swapping out male model posters for cute animals triple sales?

Does sponsoring a fashion show lead to an increase in brand equity? Does free pickup in store result in higher attach rates?

A8. Identify four relevant micro-outcomes to focus on in 2019

(In addition to the macro-outcome of revenue).

Businesses win when you optimize for a portfolio, because at any given time only a tiny fraction of people want to buy. Solving for micro and macro-outcomes is directly connected to the holy grail of solving for short-term AND long-term success.

Employees also become smarter when they have to optimize for more than one thing. :)

A9. Throw away your custom attribution model. Embrace data-driven attribution.

For some things, humans are already less smart than machines. Trying to guess what might be happening across millions of touchpoints on and off site, on and offline, is one of those things.

Skip the first five steps of attribution’s ladder of awesomeness, jump to DDA. From the tens of hours saved per week, figure out how to feed offline data into your data driven attribution model.

With an obsession with data-driven attribution, you are also solving for a portfolio rather than a silo. Super cool, super profitable.

A10. Hire an experienced statistician to be a part of your analytics team.

There is too much goodness in modeling that you are not taking advantage of. From segmentation models to identifying incrementality to predictive modeling to survival analysis to clustering to time series to… I could keep going on and on.

2019's the year you get serious about serious analytics.

A11. Bonus: Reporting kills, analysis thrills.

If that is true, and it is, :), then what % of time are you personally spending between Data Capture – Data Reporting – Data Analysis?

data_capture_data_reporting_data_analysis

Outsource or eliminate half of your data capture and data reporting responsibilities, and allocate it to data analysis and driving action.

You'll be surprised at the increase in your salary and bonus (oh, and the company will benefit too!).

In context of Analytics are you aiming for something special in 2019 that I've not covered above? Will you please share that with me by adding a comment? Thank you.


Switching gears, here are ten things to obsess about to collectively deliver a step change via your Marketing game this year…

The Step Change Marketing Obsessions List.

M1. Improve the Bounce Rate of your top 10 landing pages by 50%.

(Improving Bounce Rate results in reducing it. :))

Same as the #1 on the Analytics list. :) Far too many Marketers ignore this simple strategy to make lots more money. You work so very hard to earn attention, why then let your ads write checks your website can’t cash?

An additional delightful benefit: I find that getting Marketers to obsess about landing pages forces them to audit the user experience, something worth its weight in gold.

M2. Put up or shut up time for your social media strategy.

99.999% of corporate social media participation yields nothing.

Your CMO wants people to love your brand and organically amplify its goodness. It genuinely is a good thought. Except, a cursory glance at your social contributions show nothing of that sort over the last three years.

So, why are you spending all that money?

I recommend using that money to buying your team iPhones every Friday, I assure you that'll have a positive ROI.

Or. Focus on social media primarily as a paid media strategy. Bring the same discipline to the application of accountability to social media ads that you bring to your Display or Video ads anywhere on the web.

Here are five brand and five performance metrics that'll be your BFFs in 2019, as you social strategy lives up to that now famous mantra: Show me the money!

M3. Keep control of creativity, give up control of the creative.

Machines are much better at optimizing the latter for short or long term.

(For now) You are still better at the former – do lots of it, then hand it over to smart algorithms.

It is hard, especially for creative types who confuse creativity with creative. But, with every passing day you are harming your bottom-line more if you don’t follow the formula above.

Also consider the Machine Learning opportunities for Marketing beyond creative.

Aim to shift 25% of your marketing budgets in 2019 to opportunities that are powered by ML algorithms and rejoice at the boost in profits that results.

M4. TV works, solve for each factor that drives success.

Most TV campaigns are sold and bought based on reach (GRPs FTW!).

In my experience you should optimize for reach AND one overarching story AND creative consistency AND ensure each successfully tested creative has enough frequency to wear-in.

And, if you can't solve for three ANDs… Shift money to max out the Performance Digital opportunity, then with the left over money buy every person in your team – and at your agency – a new car. Your TV budget is big enough , and trust me when I say that giving out a new car will have very high motivational and bottom-line ROI.

M5. Seek to understand the customer journey.

What drives the first purchase? What drives the second? What drives the support calls in between? What does using the product really, really feel like? What drives advocacy?

All advertising that fails does so because the Marketer behind it understands only one sliver of the experience, then solves for that sliver with heart-breaking short-term focus.

When the Marketer understands the answers to the above questions, it influences the creative, it influences targeting, it influences retail store displays, it influences frequency, it influences product design, it influences…. it changes everything. Including profits.

Journeys are better than tinder dates.

6. Solve for intent. It is more possible and more critical with every passing day.

See-Think-Do-Care is a great intent-centric business framework, if I may say so myself, for challenging your current marketing strategy.

What intent is your current marketing content (tv, digital, ads, emails) targeting? What happens once your ads meet that intent? What meaningful content are you publishing, on and offline, to engage audiences before and after the BUY NOW (!) moment? Is your measurement aligned with the intent your marketing is targeting, or are you judging a fish by its ability to climb a tree? How do you know?

Shifting to See-Think-Do-Care is the single biggest force multiplier when it comes to your marketing. Help shift your organizational thinking to the current century in 2019.

M7. Your marketing budget allocation can be improved anywhere from 50% to 50,000%.

Allocating budgets is the hardest decision a Senior Marketer will make. Most will use strategies like Digital had 27% of budget last year, this year we should do between 28 and 30%. History, gut-feel, inter-company-politics, etc. are primary reasons why this silly mindset is pervasive across companies.

A better way? Profitable opportunity size.

I don't think you can argue with the first part: Invest where you make more profit. The second part takes a bit more work. It comes from plotting diminishing margin curves with confidence intervals. In English: How high can the investment goes before every $1 you invest returns less?

You are a Marketer, so it's unlikely that you'll plot these curves. Make it a priority for your Analytics team to do so; without them massive chunks of your budget is being flushed.

(Also, see obsession #10 on the Analytics list.)

M8. A grandmother's Marketing strategy for grandmothers only.

A bit provocative, but I want to challenge how most Marketers just make little tweaks to their strategy. The bigger the company, the more that this pernicious problem exists. Don't let that be you, and allow me to share two views that'll challenge your reality.

Here's the average time spent per day by US adults with media devices…

average_time_spent_media_devices_age

My humble description of a "grandmother's marketing strategy" is the bar on the right (65+).

It is eminently sensible for our marketing for our fellow 65+ aged Earthlings to be reflective of the implications of that right-most bar.

The problem arises when our entire marketing strategy is an extension of that right-most bar. For our entire marketing strategy to be structured on that 6:55 you see above, when our products and services are not 65+ centric is… A bit silly. Perhaps even reflective of failing our fiduciary duty.

Note the difference in total media consumption (time, place, device, more). Note the products and services your company currently offers. Reflect on this: How misaligned is your current marketing strategy?

I get really excited about something super-cool, but subtle, in the data above: The implication of the difference between active vs. passive consumption!

The difference between leaning-back and letting content wash over us vs. leaning-in and pulling content you desire is huge. It dramatically changes what your marketing should be solving for (beyond the obvious investment alignment by platforms issue).

One more reality-check for your 2019 Marketing strategy: Here's a helpful deep drive into the shifts in consumption of TV across US adults – in just six years (!!)…

us_time_spent_watching_tv

This possibly explains why Toyota's entire Marketing strategy seems to be TV-centric (with the incredible frequency of 48 per day per person here in the bay area!). It seems Toyota is only trying to sell cars to 65+ (whose TV watching has actually increased).

In 2019, resolve to align your marketing strategy with your 1. products 2. goals 3. audience, and 4. amount of expressed intent on the platform.

Credits: Originally created by Sara Fischer of Axios, the first graph is via my buddy Thomas Baekdal's newsletter. 100% of you need to sign up for it. The second chart is from the lovely team at The Economist.

M9. Suck less more.

Every campaign you are currently executing can be made to suck less – especially if you think end-to-end experience.

Ex: Expedia's emails are so long they always trigger "[Message clipped] View entire message." Suck less and maybe use my past behavior to send shorter emails so I know you care about me?

Ex: Nordstrom sends me one email a day with exclusive deals – how many clothes do they think I need? Suck less and maybe send me one a month? Or, base it on shopping patterns in store to deliver delight and not just a deal?

Ex: Macy's email I just received (titled "Resolution #1: get an extra 20% off before it ends") has promotions for Women, Men, Shoes, Bed & Bath, Kids, Juniors, Jewelry, Plus Sizes, Handbags, Home, Kitchen, Beauty. All above the fold. Below the fold: Large pictures with promotions for White Bedding, Biggest Underwear, Biggest Mattress (yes again), Best Face Forward, 25% off Adidas, Macy's presents the Edit, Fresh Pastels (the image does not make clear what this is), Free, Fast Pickup. PHEW! This can be unsucked at so many levels, with just a little bit of love and focus.

Ex: Even really good programs can use sucking less. Companies like Google and Microsoft have so many divisions. Each team/department optimizes for itself, emails are pretty good, hence each thinks they are doing really well. But, if you flip the lens to me – the recipient – I get a lot of email from each company. I wish someone at G/M would track Emails Sent/Humans Sent To, and reflect on the sad reality. It would create a culture of Marketing with me at the center instead of a company department – you can imagine the benefits.

I'm using email marketing as an example of activating the power of suck less because I love email marketing. It is an effective and profitable strategy. It has loads of behavioral data available. It needs a comparatively small team to execute well. Yet see how much opportunity there is to suck less at even the largest companies.

Substantially bigger opportunities to suck less exist in all other Marketing you are doing. TV. Print. Radio. Display (omg, sooooo much opportunity!). Video. Website. Mobile app. Everything else.

All you need to do is take a quick peek under the covers.

Your 10x goal for 2019: For every $1 invested in chasing a shiny object (VR ads! Influencer marketing!!!), invest $10 in sucking less in existing large clusters of your Marketing.

Profits that follow will also be that lopsided.

One last bit, culture eats strategy for breakfast. Create a quarterly Most Unsucked Team award, and celebrate this dimension of success. Incentives matter.

M10. Bring your great taste and expectations to work.

You can easily recognize when something is mediocre – even when others put lipstick on the pig and run it around the organization as the greatest success of the month.

You know what exceptional looks and feels like – you are not just a Marketer, you are an intelligent customer.

Yet, my experience is that most Marketers stay in their lane. Often, company cultures encourage that non-beneficial behavior.

In 2019, speak up.

You have great taste. Don't leave it at home when you leave for work.

Speak up.

When you see low quality work being pushed out by your Marketing organization… Create alternative mocks. Push for your version of the brand's tag line (not the generic MBA buzzword puke-fest). Ask for a better balance between Earned-Owned-Paid marketing. Politely challenge your Leader's assertion that creative x is better because he feels like it will be. Recommend experimenting with reckless ideas, instead of directly putting 30% of the budget on them. If you see lipsticked pigs being paraded around as exceptional examples, humbly, privately, flag the corrosive implication on culture to the most senior leader who'll listen to you.

Speak up.

You deserve to be heard.

When you speak, it'll give others around you the courage to speak up as well. Smart people tend to run in packs.

That’s it. :)

A slight repetition: Reflect deeply on the impact of the 10 x 2 obsessions in your unique business environment. Then, distill down to a total of ten you’ll focus on in the next twelve months. Finally, put a start and expected end date for each item. If you get through the list, you would have contributed a step change to your company’s bottom-line, and discovered unexpected personal joy.

As always, it is your turn now.

If you had already identified obsessions for Analytics and/or Marketing for the next twelve months for yourself, what obsessions did you choose? I’m super curious. Are there a couple in my lists above that would be particularly impactful in your company? Some of my recommendations are quite straight-forward, what do you think get’s in the way of focusing on them?

Please share your obsessions, tips, culture-shifting strategies, and critique via comments below.

Thank you.

The post Deliver Step Change Impact: Marketing & Analytics Obsessions appeared first on Occam's Razor by Avinash Kaushik.

The Impact Matrix | A Digital Analytics Strategic Framework

The universe of digital analytics is massive and can seem as complex as the cosmic universe. With such big, complicated subjects, we can get lost in the vast wilderness or become trapped in a silo. We can wander aimlessly, or feel a false sense of either accomplishment or frustration. Consequently, we lose sight of where […]

The post The Impact Matrix | A Digital Analytics Strategic Framework appeared first on Occam’s Razor by Avinash Kaushik.

The universe of digital analytics is massive and can seem as complex as the cosmic universe.

With such big, complicated subjects, we can get lost in the vast wilderness or become trapped in a silo. We can wander aimlessly, or feel a false sense of either accomplishment or frustration. Consequently, we lose sight of where we are, how we are doing and which direction is true north.

I have experienced these challenges on numerous occasions myself. Even simple questions like “How effective is our analytics strategy?” elicit a complicated set of answers, instead of a simple picture the CxO can internalize. That’s because we have to talk about tools (so many!), work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

Soon, your digital analytics strategic framework that you hoped would provide a true north to the analytics strategy question looks like this

digital_analytics_frameworks

The frameworks above cover just one dimension of the assessment (!). There is another critical framework to figure out how you can take your analytics sophistication from wherever it is at the moment to nirvanaland.

A quick search query will illustrate that that looks something like this…

digital_analytics_maturity_models

It is important to stress that none of these frameworks/answers exist in a vacuum.

Both pictures above are frighteningly complex because the analytics world we occupy is complex. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more.

The Implications of Complexity.

There are two deeply painful outcomes of the approaches you see in the pictures above (in which you’ll also see my work represented as well).

1. Obvious:

No CxO understands the story we are trying to tell – or, even the fundamentals of what we do in the world of analytics. Therefore, they are inclined to remain committed to faith-based decision-making and continue to starve analytics of the attention and investment it deserves.

2. Non-obvious:

Leaders of analytics organizations do not truly appreciate the wonderful effectiveness, or gross ineffectiveness, of their analytics practice (people, process, tools). You see… None of the currently recommended frameworks and maturity models aids analytics leaders in truly understanding the bottom line impact of their work. The result is analytical strategies that are uninformed by reality, and driven new tool features, random expert recommendations and shiny objects (OMG we have to get offline attribution!).

When one grasps these two outcomes – blind business leaders, blind analytics leaders – it is simply heartbreaking.

Simplifying Complexity.

The dilemma of how to simplify this complexity, to create sighted business and analytics leaders, has lingered with me for quite some time. I’ve intended to create a simple visual that absorbs the scale, complexity and many moving parts.

On this blog, you’ve seen numerous attempts by me to remedy the dilemma. To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0.  Each aimed to solve a particular dimension, yet none solved the heartache completely. Especially for the non-obvious problem #2 above.

The hunger remained.

I wanted to create a visual that would function as a diagnostic tool to determine if you are lost, trapped in a silo or wandering aimlessly. It would help you realize the extent to which analytics impacted the business bottom line today, and what your future analytics plans should accomplish.

Then one day, a magic moment.

During a discussion around planning for measurement, a peer was struggling with a unique collection of challenges. He asked me a couple of questions, and that sparked an idea.

I walked up to the whiteboard, and excitedly sketched something simple that abstracted away the complexity – and yet preserved the power of smarter thinking at the same time.

Here’s the sketch I drew in response:

impact_time_metrics_matrix_sketch

Yes, it was an ugly birth. But, to me, the proud parent, it was beautiful.

It took a sixteen hour direct flight to Singapore for the squiggly sketch to come to life – where else, in PowerPoint!

The end result was just five slides. As the saying goes: It's not the ink, it's the think.

I want to share the fully fleshed out, put into practice and refined, version of those four slides with you today. Together, they’ll help you fundamentally rethink your analytics practice by, 1. understanding data’s actual impact on your company today and, 2. picking very precise and specific things that should be in your near and long-term analytics plans.

The Impact Matrix.

To paint a simple picture of the big, complicated world of analytics, the whiteboard above shows a 2×2 matrix.

Each cell contains a metric (online, offline, nonline).

The business impact is on the y-axis, illustrated from Super Tactical to Super Strategic.

The time-to-useful is on the x-axis, illustrated from Real-Time to 6-Monthly.

Before we go on… Yes, breaking the x-axis into multiple time segments creates a 2×5 matrix, and not a 2×2. Consider that to be the price I’ve paid in order to make this more actionable for you. :)

Diving a bit deeper into the y-axis… Super Tactical is the smallest possible impact on the business (fractions of pennies). Super Strategic represents the largest possible impact on the business (tens of millions of dollars).

The scale on the y-axis is exponential. You’ll notice the numbers in light font between Super Tactical and Super Strategic go from 4 to 10 to 24 to 68 and onward. This demonstrates that impact is not a step-change – every step up delivers a massively higher impact.

impact-time-metrics-matrix-shell-sm

Diving a bit deeper into the x-axis… While most data can be collected in real-time now, not all metrics are useful in real-time.

As an example, Impressions can be collected in real-time and they can also become useful in real-time (if actioned, they can have a super tactical impact – fractions of pennies). Customer Lifetime Value on the other hand takes a long time to become useful, over months and months (if actioned, it can have a super strategic impact on the business – tens of millions of dollars).

Here is a representation of these ideas on the Impact Matrix:

impact-time-metrics-matrix-framing_sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

Impressions can be used in real-time for decision-making by your display, video and search platforms (e.g., via automation). You can report Gross Profit in real-time, of course, but doing so is almost entirely useless. It should be deeply analyzed monthly to yield valuable, higher impact actionable insights. Finally, Lifetime Value will require perhaps the toughest strategic analysis, from data accumulated over months, and the action takes time to yield results – but they are magnificent.

Pause. Reflect on the above picture.

If you understand why each metric is where it is, the rest of this post will fill you with euphoric joy rarely experienced without physical contact.

The Impact Matrix: A Joyous Deep Dive.

In all, the Impact Matrix contains 46 of the most commonly used business metrics – with an emphasis on sales and marketing. The metrics span digital, television, retail stores, billboards, and any other presence of a brand you can think of. You see more digital metrics because digital is more measurable.

Some metrics apply across all channels, like Awareness, Consideration and Purchase Intent. You’ll note the most critical bottom line metrics, which might come from your ERP and CRM systems, are also included.

Every metric occupies a place based on business impact and time of course, but also in context of other metrics around it.

Here’s a magnified view that includes the bottom left portion of the matrix:

impact-time-metrics-matrix-close-up_sm

Let’s continue to internalize impact and time-to-useful by looking at a specific example: Bounce Rate. It’s in the row indicating an impact of four and in the time-to-useful column weekly. While Bounce Rate is available in real-time, it is only useful after you’ve collected a critical amount of data (say, over a week).

On the surface, it might seem odd that a simple metric like Bounce Rate has an impact of four and TV GRPs and % New Visits are lower. My reason for that is the broader influence of Bounce Rates.

Effectively analyzing and acting on Bounce Rates requires the following:

* A deep understanding of owned, earned and paid media strategies.

* The ability to identify any empty promises made to the users who are bouncing.

* Knowing the content, including its emotional and functional value.

* The ability to optimize landing pages.

Imagine the impact of those insights; it is well beyond Bounce Rates. That is why Bounce Rate garners more weight than Impressions, Awareness and other common metrics.

When designating a metric as a KPI, this is your foremost consideration: depth of influence.

With a better understanding of the Impact Matrix, here’s the full version:

impact-time-metrics-matrix-complete-sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

As you reflect on the filled out matrix, you’ll note that I’ve layered in subtle incentives.

For example, if you were to compute anything Per Human, you would need to completely revamp your identity platforms (a strategy I’ve always favored: Implications Of Identity Systems On Incentives). Why should you make this extra effort? Notice how high those metrics sits on the business impact scale!

Other hidden features.

The value of voice of customer metrics is evident by their high placement in context of the y-axis. Take a look at where Task Completion Rate by Primary Purpose and Likelihood to Recommend are, as an example. They are high in the hierarchy due to their positive impact on both the business and the company culture – thus delivering a soft and hard advantage.

You’ll also note that most pure digital metrics – Adobe, Google Analytics – sit in the tactical bottom line impact. If all you do day and night is just those metrics, this is a wake-up call to you in context of your actual impact on the company and the impact of that on your career.

At the top-right, you’ll discover my obsession with Profit and Incrementality, which form the basis of competitive advantage in 2018 (and beyond). Analyzing these metrics not only fundamentally changes marketing strategy (think tens of millions of dollars for large companies); their insights can change your company’s product portfolio, your customer engagement strategies and much more.

The matrix also includes what is likely the world’s first widely available machine learning-powered metric: Session Quality, which you’ll  find roughly in the middle. For every session on your desktop or mobile site, Session Quality provides a score between 1 and 100 as an indication of how close the visitor is to converting. The number is computed based on a ML algorithm that has learned from deep analysis of your user behavior and conversion data.

Pause. Download the full resolution version of the picture. Reflect.

It is my hope that the placement of each of the 46 metrics will help you add metrics that might be unique to your work. (Share them in comments below, add to our collective knowledge.)

With a better understanding of the matrix, you are ready to overcome the two problems that broke our hearts at the start of the post – and do something super-cool that you did not think we might.

Action #1: Analytics Program Maturity Diagnostic.

Enough theory, time to some real, sexy, work.

The core driver behind creation of the Impact Matrix was the non-obvious problem #2: How much does your analytics practice matter from a bottom line perspective?

YOU matter if you have a business impact. You’ll have a business impact if your analytics practice is sophisticated enough to produce metrics that matter. See the nice circular reference?

:)

In our case we measure maturity not by evaluating people, process, and layers upon layers of tools, rather we measure maturity by evaluating the output of that entire song and dance.

Answer this simple question: What metrics are most commonly used to make decisions that drive actual actions every week/month/more?

Ignore the metrics produced as an experimental exercise nine months ago. Ignore the metrics whose only purpose is to float along the river of data pukes. Ignore the metrics you wish you were analyzing, but don’t currently.

Reality. Assess, reality. No point in fooling yourself.

Take the subset of metrics that actively drive action, and change the font color for them to green in the Impact Matrix.

For a large European client with a multi-channel existence, here’s what the Impact Matrix looked like after this honest self-reflection:

impact-time-metrics-matrix-analytics-program-savvy-sm

More of the digital metrics are green, because there are more digital metrics period. You can see the company’s marketing strategy spans television and other offline advertising, including retail.

You’ll likely recognize many of these metrics as the one that your analytics practice outputs every day. They represent the result of a lot of hard work by the company employees, and external analytics partners.

We are trying to answer the how much does the analytics practice matter question. You can see that more sharply now.

For this company most green metrics cluster in the bottom-left quadrant, with most having an impact of ten or under (on a y-axis scale of 1 to a ). There is one clear outlier (Nonline Direct Revenue – a very difficult metric to compute, so hurray!)

As every good consultant know, if you have a 2×2 you can create four thematic quadrants. In our case the four quadrants are called Solid Foundation, Intermediate, and Advanced:

impact-time-metrics-matrix-analytics-program-maturity_sm

For our company, the maturity of the analytics practice fit mostly in the Solid Foundation quadrant.

Is this a good thing?

It depends on how long the analytics practice has been around, how many Analysts the company has, how much money it has invested in analytics tools, the size of their agency analytics team, so on and so forth.

If they have a team of 50 people spending $18 mil on analytics investment each year, over the last decade, with 12 tools and 25 research studies each year… You can now infer that this is not a good thing.

Regardless, the Impact Matrix now illuminates clearly that highly influential metrics are underutilized. These are the metrics  that facilitate deeper thought, patience and analysis to deliver big bottom line impact.

Recommendation Uno:

Conduct this exercise for your own company. Identify the metrics actively being used for decision-making. Which quadrant reflects the maturity of your analytics program? With the investment in people, process, tools, and consultants, are you in a quadrant where your bottom line impact is super strategic?

Recommendation Dos:

Identify your target quadrant. In this instance the company could move bottom-right and then up. They could also move top-left and then top-right. The choice depends on business strategy and current people, process, tools reality. This should be obvious; you always want the Advanced quadrant lit up. But, you can’t go from Beginner to Advanced directly – evolution works smarter than revolution. (If your Solid Foundation quadrant is not lit up, do that first.)

Recommendation Très:

Create a specific plan for the initiatives you need to undertake to get to your next desired quadrant. You’ll certainly need new talent, you’ll need a stronger strategic leader (less ink, more think), you’ll need to identify specific analytics projects to deliver those metrics, and you’ll most definitely need funding. Divide the plan into six-month segments with milestones for accountability.

The good news is that it is now, finally, clear where you are going AND why you are going there. Congratulations!

Recommendation Cuatro:

Start a cultural shift. Share the results of your assessment, the green and black reflection of the current reality, with the entire company. Inspire each Marketer, Finance Analyst, Logistics Support Staff, Call Center Manager, and every VP to move one step up or one step to the right. If they currently measure AVOC, challenge them to move to Unique Page Views or Click-thru Rate. It will be a small challenge, but it will improve sophistication and, as you can see in the matrix, the impact on the bottom line.

Most companies wait for some Jesus-Krishna hybrid to descend from heaven and deliver a glorious massive revolution project (overnight!). These never happen. Sorry, Jesus-Krishna. Instead, what it takes is each employee moving a little bit up and a little bit to the right while the Analytics team facilitates those shifts. Small changes accumulate big bottom line impact over time.

So. What’s your quadrant? And, what’s your next right or next up move?

Action #2: Aligning Metrics & Leadership Altitude.

When offered data, everyone wants everything.

People commonly believe that more data means better results. Or, that if an Agency is providing a 40 tab, font size 8, spreadsheet full of numbers that they must have done a lot of work – hence better value for money. Or, a VP wants two more histograms that represent seven dimensions squeezed into her one page dashboard.

If more data equaled smarter decisions, they would be peace on earth.

A core part of our job, as owners of the analytics practice, is to ensure that the right data (metric) reaches the right person at the right time.

To do so, we must consider altitude (aka the y-axis).

Altitude dictates the scope and significance of decisions.  It also dictates the frequency at which data is received, along with the depth of insights that need to accompany the data (IABI FTW!). Finally, altitude determines the amount of time allotted to discuss findings.

Managers have a lower altitude, they are required to make tactical decisions – impacting say tens of thousands of dollars. VPs have a higher altitude, they are paid a ton more in salary, bonus and stock, because they carry the responsibility for making super strategic decisions – impacting tens of millions of dollars.

This problem has a beautifully elegant solution if you use the Impact Matrix.

Slice the matrix horizontally to ensure that the metrics delivered to each leader are aligned with their altitude.

impact-time-metrics-matrix-leadership-levels_sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

VPs sit at decision making that is squarely in the Super Strategic realm – on our scale ~40 and higher. This collection of metrics power heavy decisions requiring abundant business context, deep thinking and will influence broad change. Analysts will need that time to conduct proper analysis and obtain the IABI.

You can also see that nearly all metrics delivered to the VPs arrive monthly or even less frequently. Another reflection of the fact that their altitude requires solving problems that will connect across orgs, across incentives, across user touch points, etc.

So. Are the metrics on your VP Dashboards/Slides the ones in Super Strategic cluster?

Or. Is your analytics practice such that your VPs spend their time making tactical decisions?

Below the VP layer, you’ll see metric clusters for slightly less strategic impact on the company bottom line for Directors. The time-to-useful also changes on the x-axis for them. Following them is the layer for managers, who make even more frequent, tactical  decisions.

The last layer is my favorite way to improve decision making: Removing humans from the process. :)

Recent technical advancements allow us to use algorithms – machine learning – to automate decisions made by metrics that have a Super Tactical impact. For example, there is no need for any human to review Viewability because advanced display platforms optimize campaigns automatically against this metric. In fact an expensive human looking at reports with that metric will only slow things down – eliminating the fractions of penny impact that that metric delivers.

Recommendation Cinco:

Collect the dashboards and main reports created by your analytics practice. Cluster them by altitude (VP, Directors…). Identify if the metrics being reported to each leadership layer are the ones being recommended by the Impact Matrix.

For example: Does your last CMO report include Profit per Human, Incremental Profit per Non-line Channel, % Contribution of Non-line Channels to Sales? If yes, hurray! Instead, if they are reporting Awareness, Consideration, Intent, Conversions, Bounce Rate… Sad time. Why would your CMO use his or her valuable time making tactical choices? Is it a culture problem? Is it a reflection of the lack of analytical savvy? Why?

Put simply, the big and complicated is not so big and not so complicated. This simple analysis will help identify core issues that are stymieing the contribution data can make to smarter, faster, business success.

Recommendation Seis:

Kick off a specific initiative to tackle automation. If data is available in real-time and useful in real-time, there are algorithms out there that can make decisions for humans. If there is a limitation, it is all yours (people, bureaucracy, connection points, etc.).

For the other layers, action will depend on what the problem is. It could require new leadership in the analytics team, it could require a shift in company culture, or it could require a different engagement model across layers (managers, directors, VPs). One thing adjusting the altitude will certainly require: Change in how employees are compensated.

As you notice above, the strength of the matrix is in it’s ability to simplify complexity. That does not mean that you don’t have to deal with complexity – you can be more focused about it now!

Action #3: Strategy for Analytical Effort.

One more slicing exercise for our matrix, this time for the analytics team itself.

Analytics teams face a daunting challenge when figuring out what type of effort to put into tackling the fantastic collection of possibilities represented in the Impact Matrix.

That challenge is compounded by the fact that there is always too much to do and too few people to do it with. Oh, and don’t get me started on time! Why are there only 24 hours in a day??

So, how do we ensure that each has an optimal analytical approach?

Slice the matrix vertically along the time-to-useful dimension…

impact-time-metrics-matrix-analytical-effort_sm

[You can download an Excel version of the Impact Matrix at the end of this post.]

For any metric that is useful in real-time, we have to unpack the forces of automation. Campaigns can be optimized based on real-time impressions, clicks, visits, page views, cost per acquisition etc. We need to stop reporting these, and start feeding them into our campaign platforms like AdWords, DoubleClick etc. With simple rules – ranges mostly – automation platforms can do a better job of taking action than humans.

If you are investing in machine learning talent inside your team, even narrowly intelligent algorithms they build will learn faster and surpass humans quickly for these simple decisions.

With the day-to-day sucking of life spirit reduced, tactical impact decisions automated, the analytics practice has time to focus on metrics that have a longer time-to-useful and need deeper human analysis to extract the IABI.

For metrics available weekly or within a few weeks, reporting to various stakeholders (mostly Managers and Directors) should adequately inform decisions. Use custom alerts, trigger threshold targets and more to send this data to the right person at the right time. For weekly time-to-useful metrics, your stakeholders have enough tactical context that you don’t need to spend time on deep analysis since the metrics inform the tactical decisions.

More role clarity, a thoughtful shift of the burden to the stakeholders, and more free time to focus on what really matters.

For where time-to-useful is in the month range, you are now truly heading into strategic territory. Reflect on the metrics there – challenging, strategic, Director and VP altitude. It is no longer enough to just report what happened, you need to identify why it happened and what the causal impact is for the why factors. This will yield insights that will have millions of dollars of potential impact on the company. That means, you’ll need to invest in ensuring your stories have more than just insights but also include specific recommended actions and predicted business impact. Amazingly, you’ll have just as much text as data in your output (that’s how you know you are doing it right!).

Finally, we have the pinnacle of analytics achievement. Our last vertical slice includes metrics that measure performance across customer segments, divisions and channels, among other elements. This is where meta-analysis comes into play, requiring even more time, with even more complex analytical techniques that pull data into BigQuery or similar environments where you can do your own joins, unleash R, use statistically modeling techniques (hello random forests!) to find the most important factors affecting your company’s performance.

The distribution of your analytical team’s effort across these four categories is another method of assessing maturity as well as ensuring optimal impact by the precious few analytical resources. For example: If most of your time is occupied by providing data to decision-makers for metrics in the Automate and Reporting vertical slices, you are likely in the beginner stage (and not having much impact on the business bottom line).

Recommendation Siete:

Find an empty conference room. Project all the work your team has delivered in the last 30 days on the screen. Cluster it by Automated, Reporting, Analysis and Meta-Analysis. Roughly compute what percentage of the team’s time was spent in each category. What do you see? Is the distribution optimal? And, are the metrics in each cluster the ones identified by the Impact Matrix? 

The answers to these questions will cause a fundamental re-imagination of your analytics practices. The implications will be deep and wide (people, process, tools). That is how you get on the road to true nirvanaland.

#sisepuede

At the core of the Impact Matrix is the only thing that matters: the business bottom line. Using two simple dimensions, impact and time-to-useful, you can explain simply three unique elements of any successful analytics practice. The reflections are sometimes painful, but bringing them to light allows us to take steps toward systematic improvement of our analytical practice.

That’s the power of a 2×2 (or a 2×5)!

Here’s an Excel version of the Impact Matrix for your personal use. 

As always, it is your turn now.

When your CMO asks, “How effective is our analytics strategy?”, what’s your answer? How simply can you frame it? What are the primary inputs to your near and long-term analytics evolution plans? If your VPs are getting the metrics in the Advanced quadrant, what strategies have been effective in getting you there? If you’ve successfully implemented pattern matching and advanced classification meta-analysis techniques, care to share your lessons with us?

Please share your feedback about the Impact Matrix, and answers to the above questions, via comments below. I look forward to the conversation.

Thank you.

The post The Impact Matrix | A Digital Analytics Strategic Framework appeared first on Occam's Razor by Avinash Kaushik.

Breaking Silos: Passive Consumption + Active Engagement FTW!

Today something complex, advanced, that is most applicable to those who are at the edges of spending money, and thus have an intricate web of internal and external teams to deliver customer engagement and business success. The Marketing Industrial Empire is made up of number of components. If you consider the largest pieces, there is […]

The post Breaking Silos: Passive Consumption + Active Engagement FTW! appeared first on Occam’s Razor by Avinash Kaushik.

Today something complex, advanced, that is most applicable to those who are at the edges of spending money, and thus have an intricate web of internal and external teams to deliver customer engagement and business success.

The Marketing Industrial Empire is made up of number of components.

If you consider the largest pieces, there is the internal (you, the company) and the external (agencies, consultants).

If you consider entities, you’ve got your media agency, your creative agency, your various advertising agencies, your website and retail store teams, your analysts, marketers, advertising experts, the UX teams, campaign analysts, fulfillment folks, the data analysts who are scattered throughout the aforementioned entities, the CMO, CFO, and hopefully your CEO. And I'm only talking about the small portion of your existence that is your marketing and analytics.

Whether you consider the large, simplistic perspective (internal – external) or the more complex entity view, it’s really easy to see how things can become siloed very quickly.

It’s so easy for each little piece (you!) to solve for your little piece and optimize for a local maxima. You win (bonus/promotion/award). It is rare that your company wins in these siloed existence.

That’s simply because silos don’t promote consideration of all the variables at play for the business. They don’t result in taking the entire business strategy or the complete customer journey. Mining a cubic zirconia is celebrated as if it is a diamond.

Heartbreakingly, this is very common at large and extra-large sized companies. (This happens a lot less at small companies because of how easily death comes with a local maxima focus.)

So how can you avoid this? How do you encourage broader, more out-of-the-box thinking?

This might seem simplistic, but sometimes it helps to give things names. Naming things clarifies, frames, and when done well it exposes the gaps in our thinking.

Today, I want to name two of the most common silos in large and extra-large companies, in the hope that it’ll force you to see them and subsequently abandon siloed thinking and solve for a global maxima.

Name abstract ideas, draw pictures, deepen appreciation, take action.

Could not be simpler, right? :)

Let’s go!

The Advertising Ecosystem: Passive Consumption.

I'm randomly going to use Geico as an illustrative example because the frequency at which they are buying ads means that every human, animal, and potted plant in the United States has seen a Geico commercial at least once in the last 6 hours (contributing to Geico’s business success).

Typically the ads we see are the result of the external creative and media agencies, and their partners in the internal company team/s.

Geico purchases every kind of ad: TV spots, radio ads, billboards (OOH), digital displays (video, online,– social media), print (magazines, newspaper, your cousin's Christmas letter), and so much more.

The teams naturally gravitate towards optimization and measurement that spans their individual mini-universes.

Was that a great ad? Can we test different spending levels in that market? What is the best way to get people to remember the delightful gecko? Can we automate the placement of display ads based on desired psychographics?

Did we get the TRPs that we were shooting for? What was the change in awareness and consideration? What was the reach/frequency for the Washington Post? How many impressions did our Twitter ads get, and how many people were exposed to our billboards?

These are important questions facets of, and delivery optimization of, the advertising. Questions like these, and adjacent others, tend to drive the entire lives of creative and media agencies/teams. For entirely understandable reasons. Siloed incentives delivering siloed local maxima results.

I cannot stress enough that these results can be positive (for the ad business and, in this case, the sales of insurance products). And yet, as a global maxima person it does not take a whole lot of effort to see a whole lot of opportunity if both the siloed incentives can siloed execution implied by the above questions can be changed.

Here’s an incredible simple way that every human seeking global maxima can look beyond the silo: “So, what happens after?

As in, what happens after the finite confines that are the scope of my responsibility/view?

To see that, the first step is to paint a picture that illustrates the current purpose (your silo), and then give it a name.

Here’s that picture for the example we are using, and the name I gave it is “passive consumption.”

passive_consumption

Over 90% of advertising is passive consumption. This means that the ad is in front of the human and they may see it or not see it.

Even on the platforms where interactivity is at its very core (Instagram, Facebook, YouTube, etc.), almost all of the advertising does not elicit any sort of interactivity. If you look at the percentages, almost no one clicks on banner ads, a small percentage on search ads, and you need only speak with a few people around you to see how many people actively engage with TV ads vs. run to the bathroom or pull out their mobile phone the moment forced-watch TV ads come on.

Keep in mind, this is not a ding against passive consumption or the hard work done by Geico's agency and internal teams. Blasting ads on TV does cause a teeny tiny micro percentage to buy insurance – a fact provable via Matched Market Tests, Media Mix Models. The teeny tiny micro infinitesimally small number of views of brand display ads will cause outcomes. (Hold this thought, we’ll come back to that in a moment.)

So, what is the passive consumption challenge?

First, how far the vision of the creative and media agencies/teams will see (thus limiting success – global maxima). Second, trapped in the silo the vision for what will be measured and deemed as success.

The first is heartbreaking. The second ensures the death of any long-term impact.

Let me explain.

With over 90% passive consumption…. Well, passive… Smart media and advertising agencies/teams will primarily use post-exposure surveys to measure awareness (what companies provide car insurance) and consideration (which brands you would consider).

The brilliant agencies will also measure elements such as purchase intent (how likely it is that you'll consider Geico as your next car insurance provider) and likelihood to recommend (how likely is it that you'll recommend Geico to your family and friends).

All of these metrics will cause surveys to be sent via various mediums to people who've seen the TV ads, the banners on Facebook, and the video ads on YouTube. And a subset of users who were not exposed to the ads. Usually, there is anywhere between a few hundred to a thousand survey responses that will end up providing a statistically significant sample.

The scores from these responses are presented in weekly, monthly, or quarterly meetings. Segmented by marketing activity, they are the end-all be-all justification for media spending. Snapchat increased aided awareness by +23%, let us spend more there. Or, billboards in Georgetown and Austin shifted purchase intent by +2%, we should triple our spend in Chicago.

Every measurement and optimization initiative is based on this cocktail of metrics. Thus delivering a positive, but local, maxima.

Even the next best innovation in media will be based on results from the same metrics cocktail. Thus delivering a little more positive, but still local, maxima.

Why not global maxima?

Because success is determined by, innovation is driven by, measurement that is self-reported feelings.

That name captures the actual thing that is being measured (feelings) by the metrics above, and where the data comes from (self-reported) after being exposed to our advertising.

This will help your company, your agencies, understand limits. Limits in terms of what’s happening (mostly, passive consumption) and what data we are looking at (all post-exposure and self-reported).

Limits in measurement that incentivize solving for a local maxima.

Let me repeat one more time. Passive consumption measured by self-reported feelings does drive some success – else Geico would not be the financial success it is. In the short-term some campaigns are trying to drive long-term brand influence or causing a shift in public opinion or simply to remind people your brand still exists as a choice. All good. Self-reported feelings are wonderful. Appreciate that even in those cases where you are not trying to drive short-term sales, if all you have are feelings converted into metrics… You are limiting imagination.

An obsession with just passive consumption by your agencies and internal teams delivers 18 points of success. I’m saying if you think global maxima, remove limits, you can do 88 points!

The Business Ecosystem: Active Engagement.

Getting those additional 70 points success requires breaking the self-imposed creative/media/advertising silo and caring about the human behavior if people lean-in instead of passive consumption – when they take an action (a click, a phone call, a store visit).

Time to draw another picture, and give this behavior a name.

I call it… drum roll please… Active Engagement!

active_engagement

Some people, between 0.01% to 10% (so rare!), who see Geico’s online ads will visit a Geico retail store or Geico's website.

People are actually doing something. They are walking into your store, talking to an agent, picking up the literature, calling you on the phone, clicking on to your site, watching videos, comparison shopping, and more. This is all human behavior that your tools can report for you.

A small percentage will end up buying insurance – mazel tov! –, providing perhaps the most valuable data.

The lucky thing about active engagement is that, in addition to self-reported feelings, you also get tons of highly-useful quantitative data representing human behavior.

I call this type of data: Observed Human Behavior.

If you are a part of an creative, media, or an internal company team, you have two powerful issues you can solve for: passive consumption (happens most of the time) AND active engagement (happens some of the time).

Likewise, you can seek to understand performance using self-reported data where the people reflect on how they feel, along with behavior data that represents what they actually do.

The combination of these two factors deliver the much needed Global Maxima perspective.

That is how you shatter silos. The creative agency has to care about how ads perform in their labs, in the real world, and what kind of online and offline behavior the creative is driving (end-to-end baby!). The media agency has to care about the creative and where it needs to get delivered (recency, frequency FTW!), and the bounce rate (70% ouch, 30% hurray!) and profit from each campaign. The retail experience team, the call center delight team, and the site experience team will break their silo and reach back into understanding the self-reported feelings data from the media agencies and the ideas that lead to the creative that delivered a human to them.

Everyone cares about the before and after, solving for the overall business rather than their little silo. Passive consumption plus active engagement equals global maxima. Or, self-reported feelings plus observed human behavior equals global maxima.

: )

Here’s a massively underappreciated benefit: It also encourages every employee – internal and external – to take full credit for their impact on the short and long-term effects of their effort.

It is rare to see this happen in real life, even at top American and European companies.

What’s usual is to see the three silos between creative agencies, media agencies, and company internal team. There is usually further sub-segmentation into passive consumption teams (also lovingly referred as brand agencies/advertisers) and active engagement teams (performance agencies/advertisers). The further sub-sub-segmentation into products and services (depending on the company).

They then quickly fall into their respective measurement silos, solving for the local maxima.

Change starts with naming things and drawing pictures. Gather the key leaders at your company and agency partners. Show them passive consumption and self-reported feelings along with active engagement and observed human behavior. Talk through the implications of each picture. Ask this influential audience: What can you contribute to when it comes to breaking silos?

I have yet to meet a single company where simply drawing the picture did not result in a dramatic rethinking of focus areas, responsibilities, and ultimately priorities.

Accelerating Success: Five Quick Changes.

Once you have that discussion, what should you do to truly cause a significant change in behavior?

Five Es form the core of the strategies that I end up using (please share your's via comments below). They are:

1. Expand the scope of data your employees use.

For the people who buy your television ads, include both store and website traffic data. Break the shackles of GRPs and Frequency.

For people buying your display ads on Facebook, include page depth, bounce rate, as well as micro-conversion rates for those campaigns. Break the shackles Awareness and Views.

For people buying your videos ads on Hulu, complement Hulu's self-reported feelings metrics with user behavior and conversion rates.

And continue going in this fashion.

2. Expand the incentives structures for your employees.

Most marketing employees, both internal and external, undertaking passive consumption initiatives are rewarded for cost per TRP, effective reach, awareness and consideration increases, etc. Whatever this bucket as an employee incentive, it can stay.

Consider adding one or two KPIs from active engagement. For example: Store visits, phone calls (as a result of that increase in consideration). Website visits, loyalty, micro-outcomes, and 25 other easily-available observed human behavior metrics are available to you pretty much in real-time.

For people who own responsibility for your stores, call center and website, take a metric or two from passive consumption and make it a small part of their incentive structure.

People respond to what they are compensated with, or promoted for. Use it to solve for a global maxima in the company and its customers.

3. Expand the time horizon for success.

This is really hard.

You buy 100 TRPs, it’s expensive, and the executives tend to start badgering you for immediate results.

The problem is that self-reported feelings data takes time, and since at least 90% of passive consumption leads to no immediate active engagement, all this does is incentivize bad behavior by your agencies and employees. Long-term objectives are thrown onto the chopping block and long-term strategies are judged on short-term success – which immediately ruins the campaign’s measurement. Oh and the audience being bombarded by your ads that are trying to deliver short-term outcomes from long-term creative and campaigns… They despise you because you are sucking, they can see that, and they instantly realize your are wasting their time.

No matter how much your wish, a Chicken won’t birth a Lion’s cub.

If you want short-term success, define the clearly as a goal, pick the right short-term self-reported feelings metric and observed behavior metric, now unleash your creative agency and their ideas (on that short-term horizon), then plead with your media agency to buy optimal placements, and ensure the retail/phone/web experience is not some soft and fuzzy experience, rather it is tied to that clear goal and success metrics. Sit back. Win.

If you want long-term success… Same as above, replace short with long. How amazing is that?

4. Expand the datasets that teach your smart algorithms.

If you’ve only visited this blog once in the last 12 months, or read just one edition of my truly amazing newsletter :), Marketing <> Analytics Intersect, it is quite likely I have infected you with the passion to start investing in machine learning in order to bring smart automation to your marketing and user-experience initiatives.

If you are following my advice, make absolutely sure that you are not training your algorithms based solely on passive consumption, self-reported feelings data. It is necessary, but not sufficient.

Rich observed behavior data will provide your algorithm the same broad view of success as we are trying to provide the humans in #2 above. In fact, the algorithms can ingest way more data and complexity. Thus allowing them to solve for a super-global maxima compared to our humble abilities.

Every algorithm is only as smart as the data you use to educate it. Don't short-change the algorithm.

5. Expand leadership comfort level with ambiguity.

For your TV efforts, there are limits to what you can measure. You have self-reported feelings data, and usually that’s about it. If you have a sophisticated world-class measurement team, you may be running some controlled experiments to measure one or two elements of active engagement observed human behavior data.

For YouTube or Hulu on the other hand, you’ll have additional self-reported feelings data, and if you follow my advice today, plenty of directly-causal observed human behavior data at your disposal.

Get very comfortable with this reality, and execute accordingly.

When some executives are not comfortable with this reality, they typically end up gravitating towards the lowest common denominator. Even in regards to strategies where more is possible (digital), they just end up using self-reported feelings data for everything.

I do understand why this is; executives are pressed for time, so the executive dashboard needs only one metric they can compare across initiatives. This instantly dumbs-down the intelligence that could help contribute to smarter decisions.

Kindly explain this to your executives, share with them the value of being comfortable with a little ambiguity that comes from using the best metric for each initiative type.

We can achieve smarter global maxima decisions if we just use different metrics in some instances.

Closing Thoughts.

The larger the company, the harder it is to solve for a global maxima. Companies need command and control. Companies worry that people are going to run wild in 15 different directions. Companies need to reward an individual, that means creating a finite role that can be defined and measured at a small level. Companies add layers upon layers to manage. Companies create org clusters (divisions). And, more.

Every one of these actions forces a local maxima. Every human can see their few pixels and have no idea what the image looks like.

Even if then the company progresses little by little, they’ll run out of luck one day. Worse some nimble small company – that does not yet have to worry about all of the above – will come eat your breakfast first, then dinner and then lunch.

The lesson in this post applies across the entire business, even if in this instance it is applied to marketing and advertising.

Paint a picture of what the local maxima execution looks like in your division – or better still company. Give these pieces a name. Then, figure out, like I’ve done above, what the connective tissue is that’ll incentivize global maxima thinking and execution.

Carpe diem!

As always, it is your turn now.

In your specific role, are you solving for the global maxima or a local maxima? How about your creative and media agencies? Your internal marketing or product teams? Has your company done something special to ensure that teams are considering both self-reported feelings and observed human behavior? Is there a magic metric you feel that’ll encourage each piece of the business success puzzle to solve for a global maxima?

Please share your wisdom, tips and secrets to success via comments below.

Thank you.

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