During World War II, the sea and airborne invasion of France on June 6, 1944 (D-Day) had been planned for years. British, Canadian, and American airborne forces planned and rehearsed for months a precise series of glider and parachute landings that were designed to secure bridges, road junctions, and other key terrain that would enable the ground invasion forces to advance rapidly inland. The airborne invasion forces took off from England and months of planning appeared to vanish instantly. Parachute forces dropped into unmarked landing zones, gliders landed in the wrong areas, and thousands of soldiers from different units were mixed together in the night. It appeared that a military disaster had occurred. Yet, only hours later, the original military objectives were bring accomplished by ad-hoc units that faced much fiercer German resistance. Commander’s Intent had saved the day. Leaders and soldiers at all levels understood that no matter where they landed, they had to form into units and seize the bridges and key terrain. The plan was a failure, but good Commander’s Intent and superior training allowed improvisation and initiative to save the mission.

The role of a leader to express intent increases in proportion to company size and market uncertainty. At the same time, followers must thoughtfully adapt intent—expressed through missions, strategies, and objectives—at different levels of an organization, taking advantage of their proximity to market reality.

Commander’s intent was originally published in Thoughts re: Thinking on Medium, where people are continuing the conversation by highlighting and responding to this story.

Peltz said he believed the vote margin was plus or minus 1 percent, and the support he garnered from shareholders should be a sign to Taylor that he deserves a board seat. “Even if they win, which I’m not sure they did, think about what Pyrrhic victory it is,” he said in an interview on CNBC. “Everybody but the current employees voted for us. Up and down the line.”

Mr. Peltz has facts on his side. P&G has some facts too. Facts are stubborn things. Most of the public disagreement focused on strategy. There was less discussion about the secular trends at the heart of CPG disruption. Three stand out to me:

1. Technology adoption is accelerating
2. Mobile is moving faster than previous trends
3. Purchase decisions are increasingly high consideration

At the heart of the Peltz proxy fight was whether P&G should react — and react more quickly — to these trends. Let’s draw some hypotheses based on the trends above:

1. Traditional competitive advantage in R&D, brand building, and distribution is necessary but not sufficient
2. Durable competitive advantage will accrue to brands that establish direct-to-consumer relationships in terms of both CAC and LTV economics
3. Incumbents underestimate the impact of changes to the consumer journey changes — driven by mobile specifically — and risk disruption

Let’s examine each in turn.

Traditional competitive advantage in R&D, brand building, and distribution is necessary but not sufficient

Last year Unilever acquired Dollar Shave Club (DSC). At the time, here is how the NYT covered the sale:

It used to be that if you wanted to sell razors, you needed a factory, a distribution center, a sales force, a research and development team and a marketing budget. Keeping all of these functions under one roof lowered transaction costs and made operations more efficient. In part this was because of communication structures — having telephone and mail together was a necessity.

These competencies are still important. The example of DSC does not diminish the importance of brand building. In fact, in a world where almost any information is just a click away, understanding consumer needs and storytelling are more important than ever.

Disruption starts to look like death by a thousand cuts for CPG brands like P&G. Starting a business is easier today than ever before. Friction will continue to decrease. With Amazon Web Services, Braintree, Fulfillment by Amazon, you have on-demand, plug-and-play business services.

When go-to-market time and cost are decreased, brands must develop new competencies to sustain their competitive advantage. Direct-response marketing is a core competency that CPG brands have undervalued.

Durable competitive advantage will accrue to brands that establish direct-to-consumer relationships in terms of both CAC and LTV economics

Let’s turn back to DSC. In it’s coverage of the Unilever acquisition, here is how The Washington Post covered the sale:

In other words, Dollar Shave Club is accustomed to selling directly to consumers, while, Unilever, of course, is accustomed to people shopping for its brands in drug stores or in supermarkets. It’s not hard to see why Unilever would want to join forces with a brand steeped in that kind of selling. Many consumer goods lines that have traditionally sold through stores — from packaged foods to sporting goods — are giving fresh consideration to whether it’d be advantageous for them to sell online without retailers as an intermediary.

In his analysis of the DSC sale, Ben Thompson of Stratechery honed in on the economic advantage of this approach:

The net result is that thanks to the Internet every P&G advantage, save inertia, was neutralized, leading to Dollar Shave Club capturing 15% of U.S. cartridge share last year… According to the traditional way of measuring market share Dollar Shave Club only has 5% of the U.S.; the discrepancy is due to the massive price difference between Dollar Shave Club and Gillette. And yet, the price difference is the entire point: in a world with good enough products (Dollar Shave Club imports their blades from Korean manufacturer Dorco) that can be bought on zero marginal cost websites and shipped to your home directly there is no reason to charge more.

In addition to the economic advantage, consumers are increasingly reachable. VC has recognized this trend for several years. A16Z partner Jeff Jordan shared his thesis that betting narrow can be big after their investment in Walker & Company:

Today, the Internet (and social media) enables us to target these segments more precisely. There’s an important nuance here, however: This targeting is not about narrowing per se, but about reaching and including segments that weren’t addressed before… That’s why our newest investment is e-commerce company Walker & Company, which was founded by Tristan Walker to build a modern personal care brand for people of color. Globally, this is not a narrow audience given that a giant percentage of the world’s populations are people of color.

In a world where small upstarts can serve unmet needs more quickly than large incumbents, category leaders must invest more heavily in direct-to-consumer marketing competency. This starts with understanding changes to the consumer journey.

Incumbents underestimate the impact of changes to the consumer journey changes — driven by mobile specifically — and risk disruption

McKinsey released their consumer decision journey (“CDJ”) research in 2009 highlighting huge shifts in consumer behavior. The average skin care consumer in 2008 had 1.5 brands in their initial consideration set but added +1.8 during their evaluation. With two-thirds of evaluation touchpoints happening on digital, consumers were demanding information.

Eight years later, these trends continue. Take search as an example. Changes in beauty search reflect changes in purchase behavior. Consumers are craving assistance. While digital’s share of wallet has grown over the years, it’s outpaced by the opportunity and capped by historical trends. As a byproduct of last-click attribution and pressure on budgets, advertisers have increasingly focused on “lower funnel” auctions. It’s not a recipe for growth.

This proxy fight is just one skirmish in the broader war. From mattresses to socks to private labels, new brands are being built in category after category — disruption starts small, it’s easy to disregard, and, with the benefit of hindsight, it always seems obvious. Get out the popcorn.

But while the academic research is intellectually interesting my passion is centered on how models are adopted in the real-world and less concerned with accuracy. How do companies manage lifetime value? How transparent are models to key stakeholders? What insights exist into high-value customers? What tools are made available to acquisition and retention teams? Do value-based KPIs exist across silos? Does lifetime value carry hidden risks and how should we mitigate them? Let’s explore some of these questions.

Bill Gurley has been around tech and venture for a while and brought some wisdom to this debate several years ago in a long post titled The Dangerous Seduction of the Lifetime Value (LTV) Formula:

As long as the sum of the discounted future cash flows are significantly higher than the SAC [Subscriber Acquisition Cost], then people will argue it is warranted to “push the accelerator,” which typically means burning capital by aggressively spending on marketing… The LTV formula, when used correctly, can be a good tactical tool for monitoring and comparing like-minded variable market programs, especially across channels. But like any model, its proper use is entirely dependent on the assumptions used in that model. Also, people who have a hidden agenda or who confuse a model with reality can misuse it… Seduced by the model, its practitioners often lose sight of the more important elements of corporate strategy, and become narrowly fixated on the dogmatic execution of the formula.

Amen! While I am one of the lifetime value loyalists Bill mentions, his skepticism is met with open arms. We could spend days better understanding his entire post so I’d like to focus on one key point around how the drivers of lifetime value “tug” at one another and what that means for marketers:

This may be the single most important issue and it lies at the heart of why the LTV model eventually breaks down and fails to scale ad infinitum… If you try to raise ARPU (price) you will naturally increase churn. If you try to grow faster by spending more on marketing, your SAC will rise (assuming a finite amount of opportunities to buy customers, which is true). Churn may rise also, as a more aggressive program will likely capture customers of a lower quality. As another example, if you beef up customer service to improve churn, you directly impact future costs, and therefore deteriorate the potential cash flow contribution.

Many marketers approach growth and profitability from the perspective of opportunity costs, as two ends of a spectrum with trade-offs to be accepted and managed. Does quality (“profitability”) always come at the expense of quantity (“growth”) though?

Margins of safety in marketing allow us to exploit the spread between price and value

Price is what you pay, value is what you get. Smart marketers can learn from smart investors. Past performance is no guarantee of future success but each individual customer can be framed as a unique investment, taken with a reasonable margin of safety in the same way value investors approach stocks and bonds.

We have been taught there is a fundamental tension between growth and profitability — that decreasing marginal returns are a fixed assumption between volume and efficiency— but analytics gives us the power to find quality without sacrificing quantity. Two assumptions underlie this opportunity:

1. There’s no such thing as the “average customer” and since all customers are unique we assume value is highly variable. Said another way, the Pareto Principle holds for customer value.
2. For a variety of reasons — some technical, others political — marketers today rarely use lifetime value effectively. Almost 60% of marketers admit an inability to calculate lifetime value and less than 40% of marketers use revenue-based measures lifetime value as their primary success metric. Even fewer segment “below the average” to cohorts; let alone individual customers.

From the perspective of acquisition marketing, it follows from these assumptions that the spread between price and value is highly valuable. When we say “quality of quantity” we only mean the shift in mindset that, as a marketer, your goal is to maximize this price-value spread across a set of customers, influenced through different marketing channels during their consumer decision journey.

While Gurley challenged executives against worshiping at the altar of lifetime value, he also acknowledges that, from an investor point of view, not all revenue is created equal. From an operator point of view the lens of quality is similar: predictability, profitability, and diversity all matter to profitable growth.

… and how should you actually do all this? Every business will have nuances but the foundation is a persistent connection between your data warehouse and digital analytics platform. Confidence in your data goes a long way too!

With this link in place you can explore the digital predictors of customer value, build expected value models, export data to data management platforms, activate using different marketing channels, and more. Easier said than done so in my next post I’ll explore how you might size the opportunity for your business as well as some lifetime value “hacks” you can quickly implement to increase quantity of quality!

Earlier this year Google partnered with Forrester to explore these issues and understand the challenges faced by top marketing and analytics executives. Key among the take-aways was the need to link marketing and overall business objectives:

The sophisticated marketers in our study outperformed their peers in achieving their revenue goals. Fifty-three percent of sophisticated marketers stated they adhere to well-established marketing metrics that tie directly to business objectives, compared with only 18% of less sophisticated marketing organizations. These top organizations are more than three times more likely to hit their goals than other marketing organizations, and nearly 20% beat their revenue goals by over 10%.

How should marketers react? Among the recommendations — again, an obvious recommendation that is too commonly overlooked — is to think about analytics tool usability through the lens of all stakeholders; not just marketers:

Not everyone needs to have access to everything, but everyone must have access to the key data, analyses, insights, and recommendations required to do their jobs. Savvy marketers will leverage their marketing measurement platform to define stakeholder-specific analyses, views, and visualizations of current (even real-time) data so these stakeholders can most easily understand key insights and take appropriate action.

I thought back to this quote this morning as I read McKinsey’s latest research on the need to lead in data and analytics. Several key differences emerged between leading and lagging organizations that complement the results of the Forrester study:

… some of the biggest qualitative differences between high- and low-performing companies, according to respondents, relate to the leadership and organization of analytics activities. High-performer executives most often rank senior-management involvement as the factor that has contributed the most to their analytics success; the low-performer executives say their biggest challenge is designing the right organizational structure to support analytics.

Leadership and executive sponsorship matter. Just as the Forrester research found that high-performing organizations are 3X more likely to hit their business objectives, McKinsey found they are almost 3X more likely to be sponsored by the CEO. You start to see themes emerging around organizations that have succeeded at turning analytics into a source of competitive advantage:

While these conclusions are obvious, they are also a valuable checklist for organizations looking to turn analytics vision into reality. Decisions made today — a single analytical stack versus point solutions, decentralized versus functional organizational structures, and so forth — affect the degree to which analytics can be a driver of success or just another buzzword. If you’re serious about analytics, take note.

How unusual is Twitter’s revenue clip? Out of more than 100 technology companies with at least $2 billion in revenue over the last 12 months, just one had a faster pace of revenue growth than Twitter in the most recent quarter, according to Bloomberg data.

Shira is right. But comparing Twitter to a basket of 100 technology companies paints a slightly misleading landscape. These are companies with different business models and, more importantly, at different points in their life-cycle. So I was curious and took a deeper look: relative to Twitter’s peers at the same age, how does their revenue and growth compare? Cohort analysis is not just for product and marketing analytics and makes this comparison simple; in theory if not practice.

Here are some findings:

• Twitter was founded in March of 2006 and is approximately 38 quarters old. Yahoo and Facebook reached this milestone in Q3 of 2004 and 2013, respectively. Different macro environments entirely but comparing the same vintage is a good start…
• Yahoo revenue grew fastest with strong quarters such as +253% year-over-year growth in quarter #38. Lapping post-bubble comps is one thing but looking at trailing year growth between quarters #31–34 and #35–38 still has Yahoo leading the pack with +63 year-over-year compared to Twitter at +41% and Facebook at +33%.
• Yahoo added $2B incremental over four quarters compared to only $820M at Twitter. Facebook’s growth is more impressive when you consider their base was 4x the size of both Twitter and Yahoo and by quarter #38 their run rate was almost $7B.
• Looking further into the future of Facebook and Yahoo shows how quickly they ramped sales at this point in their life-cycle. Facebook’s growth actually accelerated for several more quarters and Yahoo’s rapid growth continued for several years before peaking in 2008.

It’s clear to me that TWTR is at a key inflection point and, while most conversation centers around their product, profitability, and user growth challenges, it remains to be seen whether they will be able to reignite sales growth.

Charts below. You be the judge.

Changing the Twitter conversation (again) was originally published in Keeping Stock on Medium, where people are continuing the conversation by highlighting and responding to this story.

While Moneyball was a critical success it also made sports analytics more accessible to the general public. Digital marketing is not likely to see similar box office success but practitioners can learn a lot from the adoption of analytics in sports — the history and evolution of different measures, the emotional reactions, the continued resistance.

Receptiveness to advanced analytics in basketball and beyond

Let’s look at basketball usage rate: a measure of an individual player’s offensive contributions. FiveThirtyEight identified four competing ways to calculate the same measure:

One small problem: Nobody seems to agree about what exactly usage rate is, or should be, or how it is calculated. Many analytics-minded observers don’t even know there are different, competing versions of the statistic in popular use, much less that each variant has its own philosophy about what it means to “use” a possession. For a term so common to the modern hoops lexicon, that’s more than a little strange. So let’s have ourselves a little history lesson and learn much more than you ever wanted to know about usage rate, in all its permutations.

Sports are inherently complex but, even if analysts were to agree, would coaches and general managers take analytics into account when making key decisions? The adoption of analytics is all over the map with skeptics like the Lakers, Redskins, and Phillies leading the anti-statistics charge. Statisticians will continue to develop measures like usage rate, Real Plus Minus, Wins Above Replacement, and more but sometimes — recognizing that analytics is a means to an end — evolution is easier than revolution.

Steph Curry, offensive production, and the evolution of an assist

Steph Curry is one of the top NBA players according to many pundits and many metrics. But Curry’s assists are down 20% year-over-year from 7.7 to 6.3 per game despite a small increase in minutes per game. What gives? It’s easier to understand Curry’s assist change when adding “hockey assists,” a/k/a secondary assists. On that measure Curry’s production is up 55% year-over-year from 1.8 to 2.8 per game.

The hockey assist is exactly what it sounds like: the pass before the pass, the assist of the assist; it’s a common hockey metric that basketball analysts can now measure using the SportVU camera system. Perfect? No. Simple? Yes. The same people who are scared by advanced analytics can easily understand and trust the hockey assist.

Curry looks like less of an enabler on the surface until you peel back the first layer of the onion. His production has shifted to something that was historically more difficult to measure.

The parallels to digital marketing are striking. Like last clicks in digital marketing, points have always been the key measure of basketball success — they’re the offensive conversion. Where formulas like WARP are akin to Media Mix Models, hockey assists are like path to purchase analysis. In both instances, attribution depends on measurement and, as new data becomes available, analysts can work backward from the conversion — or point or goal or other success measure — to understand what causes outcomes.

Simplicity matters: hockey assists are the stepping stone to more advanced analytics

Where sports still have measurement challenges that preclude attribution, marketers can see relatively complete digital paths with some tagging, assumptions, and elbow grease. As an analytics practitioner I challenge myself to remember that analytics is a means to an end. If change is that end, hockey assists and similar metrics in digital marketing like impressions-assisted conversions are often more effective influencers than advanced analytics.

Almost everything is better than last click attribution. As media consumption continues to change rapidly, can we wait for perfect measurement and perfect attribution models? Are we sacrificing business results for academic and intellectual disagreements? The next time you think about criticizing an attribution model, remember the hockey assist. Simplicity is powerful.

Advanced attribution modeling is used to track and score the effect of multiple marketing events leading up to an event… It is also more expensive and time-consuming to manage. Worse, the lack of industry consensus on what makes a good measurement model further hinders marketer adoption. For those reasons, multichannel attribution modeling is not widely used. eMarketer estimates that only 30.1% of US companies that are engaged with more than one digital marketing channel currently use multichannel attribution models.

Given slow adoption, it’s no surprise that many smart marketers and analysts recommend data-driven attribution as a simple solution to this complex problem. Suneel Grover of SAS. Ken Chaplin of TransUnion. Mike Osborn of HomeAway. The list goes on. Here’s how Google’s Adometry views data-driven attribution.

The logic behind data-driven attribution is compelling: combine big data with statistical models to isolate incremental impact using individual customer paths. Simple! Ignoring the 14% of marketers who find last click attribution “very effective” — likely advertisers blessed with simple purchase paths and simple media mixes — there are immediate opportunities to increase performance using models that more accurately attribute incremental value. But is data-driven attribution the right answer for your organization in 2016?

The bull case: how data driven attribution can transform your marketing organization

Data-driven attribution delivers three key benefits to marketers:

1. Reduction of waste improves efficiency and drives bottom-line profit;
2. Insight into your most effective campaigns translates to new investment and drives top-line revenue;
3. Clear illustration of cross-channel interaction can break down silos and increase cooperation between brand and performance teams who historically operate independently.

Customer journeys are shrouded in the fog of war but multichannel attribution — with executive support and good project management — can help pierce that fog. Successful data-driven attribution requires long-term capital investments but, compared to building inhouse models, requires relatively smaller investments in people. This is a material concern in today’s shallow market for analytics talent; hiring data scientists is increasingly difficult. For these reasons and more, data-driven attribution is a winning recipe for many marketers.

The bear case: how data-driven attribution can mislead your marketing organization

Like most things in life, data-driven attribution is easier said than done. At best, data-driven attribution is complex and expensive but directly increases businesses outcomes. At worst, data-driven attribution is a feedback loop for bad strategy that constrains marketers from taking calculated risks. As you evaluate the potential of data-driven attribution for your organization I encourage you to keep two principles in mind.

Garbage in leads to garbage out. When an attribution model does not show uplift your first reaction should be to examine strategy and tactics, not cut investment. Are we reaching consumers throughout their decision journey? Is our content and creative aligned with context? Mobile illustrates this problem well. The mobile gap to parity — 8% of ad spend on 24% of media consumption — is starting to close as marketers better understand cross-device journeys and put new measurement tools to good use. Attribution models might suggest devaluation of mobile but smart marketers read between the lines.

There is no single source of truth. It’s easy to lose sight that attribution is a means to an end, not an end in itself. To use another cliche: when you have a hammer, everything looks like a nail. Rigorous controlled experiments, deployed consistently, are an easy way to validate model output. No attribution model, data-driven or otherwise, will tell you the value of the strategy you don’t adopt. Experiments can help your team to develop institutional knowledge and take calculated risks on new strategies or ideas.

Is data-driven attribution better than last click? Almost always. Will data-driven attribution solve all your marketing challenges? Of course not! Reasonable expectations — about attribution generally and data-driven attribution specifically — are a smart starting point to cut through the hype. Here’s hoping 2016 is the year of attribution.

Measurement comes before attribution

It’s useful to think about measurement and attribution as separate but related concepts. Measurement is the physical act of choosing what events to track and then aggregating sets of data from web, mobile apps, call centers, and other channels. Attribution is fed by measurement and, like anything in life, garbage in leads to garbage out. Whereas attribution can be done iteratively, measurement is usually a centralized process to ensure consistency and build trust with consumers of data and analysis. Worse than an inability to measure key business outcomes is the insular and ineffective culture that results from lack of trust in data. To make attribution work measurement is a necessary prerequisite.

Prioritize your first attribution dots

For organizations looking to both develop momentum and create durable institutional knowledge, how do you approach dozens of competing prioritizes? Let me suggest several straightforward attribution projects:

1. Estimated conversions for insight into the efficiency of mobile search;
2. Brand Lift to understand how creative resonates throughout the customer decision journey;
3. Ghost ads to quantify the incremental contribution of display ads, allowing your team to approach view-through conversions with confidence;
4. TV attribution to measure the second screen effect and start to build a cross-channel pictures of how your marketing programs compliment each other.

While these projects are tactical, they also align with CMO priorities such as mobile and video. They all have the benefit of requiring minimal upfront measurement work — most marketers can use these as quick wins to build attribution momentum, start a culture shift toward data-driven decision making, and secure more resources for larger initiatives. Before you boil the attribution ocean, try connecting two dots first.