In the last post on Agile we discussed the flaws of the traditional linear marketing process.  So what could be the alternative? This is where we can find inspiration from an unlikely source – the military. John Boyd was perhaps the most prolific fighter pilot the US has ever known. He was an unusual character, a self-described maverick with an aversion to authority, which didn’t always work in his favor in the military. His nickname was 40 seconds Boyd because he would bet any pilot $40 that he would be on their tale in 40 seconds. But he was also an intellectual, who studied the history and theory of warfare. He was one of the main architects behind the F16 which was smaller and lighter than its predecessor the F-15. It was the most agile plane of its time and agility, as Boyd had shown was all that mattered in warfare. After Boyd finished his work on the F-16 he devoted most of his time to developing a framework for agility which he called the OODA loop. OODA stands for Observe/Orient/Decide/Act and to Boyd it was more than a simple summary of the steps human beings go through to make fast intuitive decisions. He thought of it as a general framework for gaining competitive advantage. If you constantly run through your OODA loop you can outmaneuver you’re your competition by getting “inside their OODA loop”. Your decision and action become their observation and by making fast decisions you can essentially control their OODA loop.

Boyd’s concepts are surprisingly applicable to marketing. A simple variation of his loop describes how a modern marketing function can organize itself to achieve maximum agility.

Sense instead of Observe

Marketers need to keep all their senses open – not only their eyes but also their ears. In this digital world consumers are giving marketers constant cues about what they need and want. Consumers have always done this but now marketers have the ability to capture these cues at scale. This is because consumers live on platforms that generate data. They write about their needs and opinions on social networks. This is where we can learn from listening to them. They also demonstrate their needs through what they purchase and how they engage with companies. This where is simple observation can help us get to know our customers better. Marketers have a broad range of tools available that can capture all this data and analyze it. Those who keep their senses open now have the ability to get a pretty complete picture of what some have called a customer’s “digital body language”. Reading the digital body language and responding to it is what agile marketers do better than their competition.

Orient

The senses will provide you with a constant stream of raw data about what your customers do, want and need. This data needs to be processed to orient it for decision making. This happens by putting new data in the context of what we already know about the customers and what we are trying to achieve with them. This stage involves 3 main steps – Customer Insight, Content Inventory and Connection. We first develop in depth Consumer Insight through processing the data that comes in through the senses. At the same time we make sure we have a Content Inventory that lists and categorizes everything we have to offer to the customer in response to their needs. Finally we make the Connection between customer and content based on what we know they need and what we have to offer them.

Create instead of Decide

A fighter pilot’s spectrum of potential decisions is pretty limited – up, down, left, right, faster, slower – that’s about it. In marketing however things are more complicated. We not only need to decide, we also have to create. The previous stage might identify content gaps or it may expose areas where we have content but where it is not of the right quality. These issues need to be addressed in the creative process. What is different about how content is created in this agile system is how it is connected to the other stages in the loop. It is constantly informed by the information we are getting by our customers. That information can not only give us new insights that can lead to new ideas, it can also give is fast feedback on which ideas are working and which aren’t. This feedback loop can really change the creative process itself, which becomes more experimental and iterative. The agile principle of deliver today and adapt tomorrow can be applied to the creative process. Less time spent on planning and more on real time in market testing.

Interact instead of Act

The end goal is the customer interaction that drives value. These interactions can happen anywhere at any time. The explosion of marketing channels has provided marketers with 2 crucial challenges : within-channel optimization and cross-channel integration. Every time a new channel opens up, marketers need to master it to ensure the interactions are happening in the most optimal way. This is where deep expertise in search, social media, video distribution, e-mail marketing, … is crucial. Within-channel optimization requires deep specialty channel expertise and an ability to create channel specific content. The second challenge is cross channel integration. How can you make sure that all touch points are synchronized in a way that ensures a consistent customer experience across every channel. This is where marketing automation platforms can play a vital role in managing the complexity of all customer touch points.

With Sense Orient, Create and Interact the acronym might have changed to SOCI but in spirit it is still an OODA loop.

Adapting to change is probably most important in new product development. Change and innovation live off one another. The last decade has seen a transformation in how companies approach innovation. This was drastically needed. According to the USPTO 59% of new product launches fail. These efforts accounted for 46% of all development resources. Even when products are launched, they usually aren’t optimized. 50% of all software functionality is rarely or never used. That’s a lot of wasted development efforts. The inefficiencies are everywhere. A survey among US car manufacturers in 2003 (Michael Kennedy – Product development for the lean enterprise, 2003) showed that their engineers on average only spend 20% of their time adding value. No wonder new product development teams everywhere have started to look at how they can do things differently. One of the biggest Achilles heels of the traditional production processes was that they were very rigid and linear. Very detailed plans were made at the beginning of the process and after that the focus was on delivering against the plan, on time and on budget. This process was not designed to adapt to changes that occur during the development cycle. With the acceleration of change described earlier, those changes started to occur more frequently, which meant that the plans that were designed at the start of the development process were often sub optimal halfway through the process.

More agile processes were drastically needed and the first to make that point were a group of software developers who got together in a ski resort in Utah in 2001. There the group decided to draw up the Agile Manifesto, a statement in reaction to the traditional software development methods that prevailed at most companies which they deemed too rigid to deal with the speed required to develop new software in the modern age.

Agile software development projects would focus on working software which is delivered in iterations throughout the project lifecycle. This continuous delivery of working software allows the developers to get fast customer feedback on an ongoing basis and incorporate it in future iterations of the product. The focus is on technical excellence and design, simplicity and regular adaptation to changing circumstances. Change is welcomed all the way up until the end of the development process. Agile teams are self-organized and highly collaborative. They prefer verbal communication over written process reports and tend to be made up of a combination of highly motivated business people and developers.

Since its inception hundreds of books have been written about Agile development and its principles have been adopted well beyond the software industry into broader engineering and project management disciplines. Several surveys have proven the value Agile software development has delivered versus more traditional development methods. The table below shows the results of one of the most recent studies which interviewed more than 3,000 respondents who had practiced Agile methods in some capacity within their organizations.

55% of respondents say that 90-100% of all Agile projects have been successful. Another 21% claim that 75% of projects were successful. Adoption of the Agile principles in software development is now wide spread.

Agile Marketing Principles

The acceleration of change in consumer behavior and product innovation, as well as the ability of customers to give almost instantaneous feedback to marketers through social networks and other digital platforms requires marketers to be at least as agile as their colleagues in new product development. A review of the literature and best practices around Agile reveals some general principles for Agile Marketing.

Agile Marketing is :

These principles, while very intuitive, are hard to implement. Most marketing organizations still make their marketing plans annually and maybe revise them once or twice a year. They develop a campaign calendar that becomes the backbone for most of their activities throughout the year. There have been some developments over the last decade. Digital communications have introduced shorter campaign cycles. Most companies are monitoring what is happening on social networks and are getting more customer feedback. And the cost of testing has dropped dramatically in digital which has seen more marketers adopt testing as a part of how they go to market. But for the most, these “more agile” marketing activities happen piecemeal rather than systematic. They also tend to be disconnected from everything else that goes on in the marketing organization.

This shouldn’t come as a surprise. Most marketing organizations have not fundamentally changed their marketing processes in the last decade. They may have added on some agile functionality but they still approach the market in a traditional linear fashion – they plan, design, create, launch and maybe measure if they have the time and the patience. If marketing wants to become a truly agile discipline, this process will need to change.

Technology, new product innovation, consumer behavior, the entire society is changing faster than ever. This poses significant challenges for companies who need to find ways to adapt to change and embrace it as a breeding ground for innovation rather than a nuisance that forces them to reconsider outdated plans. This has pushed a broad range of disciplines to radically change the way they work over the last decade. It happened in software development, new product development and engineering. The objective was simple – how can we change the way we work to become more agile? Marketing seems to have lagged behind in many ways. This is surprising as for most companies change tends to be driven by their customers. One would therefore expect marketing, the discipline closest to the customer, to be the most adept at changing.
In the next couple of weeks I’ll post a number of pieces related to marketing’s ability to adapt to change. I will not spend time asking why marketing hasn’t changed faster. I will instead focus on the opportunity marketing has to learn from the other disciplines who did go through a transformation over the last years.

The Acceleration of Change

Here’s an interesting statistic. If you look at the history of technological change and assume we will continue to see innovation accelerate in the future at the same rate as it has done in the past, we won’t experience 100 years of progress in the 21st century – it will be more like 20,000 years of progress. It was Kurzweil who made this observation in 2001 in his essay The Law of Accelerating Returns. He demonstrated how technology innovation is not changing in a linear way as most people assume, it changes exponentially. He demonstrates this with this graph which shows the growth of computing power over time.

He predicts that by 2045 the power of computers will exceed the collective power of all human brains combined.

It isn’t just technology which is changing rapidly. Innovation in general has been growing exponentially during the last 50 years. The graph below shows the number of patents issued by the United States Patent and Trademark Office (USPTO) over the last 50 years.

It is clear that companies have been trying to respond to the changes in society by stepping up their innovation efforts.

And consumers are doing their best to keep up with this change. One might argue that they are also driving the acceleration of change. The NYT showed this interesting visualization of the adoption rate of new technologies by consumers.

The steepness of the lines on the right shows the speed of new product adoption in the modern day. It took the telephone roughly 70 years to make it into 90% of the US households. Color TV and the cell phone did it in 15 years. Apple lovers are really pushing the boundaries of early adoption. Back in 2007, the original iPhone took 73 days to cross the million mark. The iPad managed the same feat in just 28 days, about 2½ times as fast as the first iPhone. It almost seems like early adoption is normal adoption for Apple products. Apple is the obvious example of a company that not only knows how to adapt to change, but also create changes in the marketplace.

Adaptive Skills as a Competitive Advantage

While Apple’s market sensitivity and speed makes most other companies look sluggish, there are other less obvious examples of companies who have realized that the ability to adapt to change can be their biggest competitive advantage. Coke is one of those companies. For a company of their size they have an incredible ability to adapt to change and bounce back from failures. It didn’t take them long to bounce back from one of the most famous marketing catastrophes ever, the introduction of New Coke on the 23^rd of April 1985. The production of the old Coke stopped that same week, which caused a national uproar. Coke had not just taken a product off the shelf, consumers felt it had taken away a quintessential piece of American culture. It only took Coke 2 months to reintroduce the old Coke as Coke Classic. Coke insiders referred to this as the second coming and consumers reacted accordingly. Their celebration of the return of their old favorite beverage made Coke’s sales roar. It even made some believe that Coke deliberately discontinued Coke classic for 2 months, something that has always been denied by company officials.

This is just one example of how Coke has adapted to change extremely well over the years. Other examples include the turnaround of Dasani, the acquisition of Vitamin Water and the acquisition of their bottlers in a response to a similar move by Pepsi. Other companies who excel in their ability to adapt to change include Zara with their “Just in Time” design system that allows their designs to make it from the sketch pad to the store rack in as little as 2 weeks. And there is Netflix who constantly revitalize their company by picking new enemies, be it Blockbuster and its late fees or illegal downloading, which it is fighting through strategic partnerships with new video distribution platforms such as the Xbox or Apple TV. These companies have shown how to turn their ability to adapt to and even create change into an unfair competitive advantage.  In the following posts we will explore how marketing can learn from other disciplines to become better at adapting to change.

In my previous post I listed some empirical evidence that shows that TV advertising is becoming increasingly effective. One of the reasons is that TV is a superior medium for driving emotional engagement. The role of emotional engagement in driving purchasing behavior has been hotly debated in the last couple of years. This is as a result of advances in neuroscience that have demonstrated that the decisions people make are often a result of what happens in their sub-consciousness. As a result the traditional linear AIDA model, where the role of communications and advertising is to move customers down a linear path from awareness to interest to desire to action by grabbing their attention, has been severely challenged. 

 

 

People have been buying and selling data about consumers for a long time.  Companies like Axciom have been doing this for years in the direct marketing business.  But recently a new breed of companies has been popping up who are acquiring and selling data about consumers in the online advertising space.  Companies like BlueKai and Datran are the modern day, digital equivalent of the Axcioms. The good old direct marketing techniques from the 80’s and 90’s are now also being used for targeting online advertising ads.  Therefore data collected about consumers can now be used for smarter targeting across all direct and digital channels.  This broader playing field will dramatically grow the size of the data reselling business in the next few years. 

So with so many companies buying and selling data about consumers, what really determines the value (and therefore the price) of a data point?  I think there are 3 drivers.

1. Predictive Power

The 1^st driver is Predictive Power of the data point.  Let’s say for example that I am a manufacturer of drills and that I am trying to purchase data points that will help me identify whether a consumer is interested in buying a drill.  And let’s assume that I can choose between the following 2 sets of data points.

Most people would agree that the data points in set 1 are more valuable for a drill manufacturer than those in set 2.  This is because of their natural correlation with someone’s likelihood to purchase drills.  This example is very straightforward.  If you had to determine the Predictive Power of a 100 different data points however, you would have to build statistical models that predict the likelihood of someone buying a drill based on all 100 data points.  Those that enter the model have a high Predictive Power which can be quantified by the lift they generate in the models.  Whether you build statistical models or not, the principle is that data points with a high Predictive Power will improve our prediction of whether a consumer will be interested in buying a drill and, as a drill manufacturer, I am prepared to pay a higher price for them.

2. Recency

The 2^nd driver is Recency.  This is really a special case of Predictive Power but I want to call it out separately as it has become an increasingly important driver.  In a digital world people often reveal real time what their intentions are.  Knowing whether a person has searched for drills on Google, whether they have clicked on a banner for drills or whether they have seen a drill related video online can be very powerful.  These data points generally outperform the more traditional data points that are listed in the example above because they are direct indications of a consumer’s interests and needs at a certain point in time.  For these self disclosed data points, Recency is very important.  When someone searches for a drill on Google then that is very valuable information if I can target that person immediately.  However, if I know that someone searched for a drill 3 months ago then that single observation in itself is a lot less valuable.  The predictive power of self disclosed data points starts to decline minutes after the observed event.  Because of the disproportionately high value of very recent data we anticipate most of the future innovation to focus on capturing multiple events real time and shortening the cycles between observed events and the ability to use that knowledge for targeting.  This is already happening on advertising exchanges through the introduction of Real Time Buying. 

3. Exclusivity

The final driver is Exclusivity.  Let’s use the same example and let’s assume that I can only buy the data points in set 1.  Let’s also assume that I have built a statistical model and have determined that the general predictive power of the number of hammers a consumer owns is far more predictive than the other 2 data points.  I would be prepared to pay a relatively high price for data on hammer ownership.  Now consider an alternative scenario where one additional data point is available: the number of nails a person uses per year.  Let’s assume that the general predictive power of nails consumption is almost as high as that of hammer ownership.  The availability of nails consumption will have an effect on the price I am prepared to pay for hammer ownership.  It’s the basic laws of supply and demand.

In the next few years the buying and selling of data will undoubtedly become a lot more streamlined.  When that happens, the market drivers described above will increasingly determine the price companies are willing to pay for information about their consumers.  Consumers on the other hand will get a much more transparent view of the value they are generating by allowing companies to collect their data.  Who knows, maybe they’ll even be able to claim their share of the pie.

Last week Ogilvy announced a partnership with Marketshare Partners, a marketing science company based in LA with offices in NY, San Francisco, Chicago and London.  I had been looking for a while for a partner who could help us with econometric modeling, the type of analysis that can help you tie overall marketing spend to business outcomes and can help you determine how much to spend on marketing and how to allocate it by product, segment, geography and medium. 

I won’t go into the details of why Ogilvy chose Marketshare Partners or what the joint offering exactly entails.  This would not be the right place for it.  However I did speak to a lot of different companies in this space over the last couple of months and I thought I’d share my thought on how they differentiate from each other.  Hopefully this could be useful when you are looking for a partner - if you need one just give me a call

I started with laying out the various tasks involved with running an econometric modeling project that would result in a marketing investment strategy.  These steps are listed below.

Data Gathering : you need to collect data on marketing spend and performance from a wide range of sources.

Data Aggregation : you need to put all that data in one place where it can be accessed easily by analysts.

Statistical Modeling : you need to build the statistical models that link marketing spend to business outcome.

Optimization : you need to use those models to determine what the optimal marketing spend and allocation is.

Scenario Planning : you need to build several investment scenarios that demonstrate the impact of different investment strategies on business performance.

Dashboards & Management Systems : you need to give non technical users access to the data and scenarios through decision support tools.

Strategic Consulting and Planning : you need to help decision makers use these tools for decision making.

These are generally the tasks at hand when you run an investment optimization project that uses some science to get to the answer.  These tasks map perfectly to the market differentiators that started to emerge throughout my conversations with the various vendors.  This is illustrated in the diagram below.

Data Access : Some vendors had access to special data sources through alliances they had with 3^rd part data vendors or search engines.

Automated Data Preparation : Data preparation can use up a lot of your resources on projects like these.  Some vendors had automated a lot of these steps so that they could do the data gathering and aggregation described above faster, better and more cost effectively.

Unique Algorithms :  The algorithms have been around for decades so you would think all econometric modeling skills are pretty level.  However, you would be surprised how much variance there is in the quality of the statistical modeling between different vendors.  Make sure you look underneath the hood and talk to who is actually doing the modeling.

Decision Support Tools : Some vendors have packaged their tools in dashboards and scenario planning and decision support tools.  These tools can be extremely useful for helping the end users adopt some of the scientific evidence into their decision making.

Value Added Services : This is probably the most important area.  I have seen so many modeling projects collect dust on the shelf of the marketing intelligence managers.  Building models is easy.  Using them for decision making always seems to be the hardest.  You should always look for a partner who can help you with both.

Hopefully some of these learning can be useful for you at some point.  The reality is that far too few marketers are using econometric modeling to really understand the impact of their marketing efforts on their business.  As a result, they end up making multi-million dollar decisions on pure gut feel.  If there is anything this recession has taught it’s that that will ultimately get you into trouble!

I recently wrote a paper on Math Marketing and Ogilvy was kind enough to make it available on their website.  I will posts pieces of this paper on our blog over the next couple of days.  The paper starts with a history of Math Marketing, which I posted earlier.  The second chapter describes some of the Math Marketing challenges we are all facing today.

Although Math Marketing’s possibilities seem endless, there are some substantial challenges ahead. They come in the form of fragmentation, myopia, data deluge and the talent crunch.

Fragmentation

It is interesting to note that all of the techniques developed during the various stages in the history of Math Marketing are still being used. Mass marketing techniques such as econometric modeling are still around, as are CRM techniques which, arguably, have become even more important now that they can be applied to digital data. Digital mathematical tools are also evolving every day, and because of their incredible growth in marketing, an extremely high level of specialization is required to master them. This has caused a high degree of fragmentation in the Math Marketing world. Nobody is offering a complete range of analytical services and tools and, as a consequence, nobody provides the full 360 Degree picture.

Myopia

In our digital world, we can receive feedback on campaign performance almost instantaneously. We no longer need to wait weeks or months to attain sufficient data points to make a judgment on whether something worked or not. This opens up a number of opportunities for the real-time optimization of campaigns. But it also means that the focus of analysis has shifted to the short term. In the days of mass marketing, econometricians often had to look at the impact of marketing over multiple years in order to get sufficient data points. This meant that they analyzed both short-term and long-term effects. Today’s focus on short-term results is reinforced by the pressure most CMOs are under these days to demonstrate short-term ROI. Their performance is increasingly being measured by the same KPIs that are used by CFOs and CEOs. These tend to be short-term financial metrics, which are forcing many CMOs to shift their emphasis from the long term to the short term.

Data Deluge

While the volumes of data have increased exponentially and the Math Marketing tools have grown considerably, the biggest challenge remains the translation of mathematical insights into recommendations and actions that can make a direct impact. The math can be extremely complicated and it can rely on millions of data points, but ultimately the recommendation will have to help marketers improve their everyday decision making. Marketers need to contact the people most likely to buy, determine how much to invest in them, find them through the appropriate channel, serve them with the right offer, and make sure creative and content are enticing so they stand out from the glut. All too often math marketers lose sight of this. They are driven by what is technically possible rather than by what will make a real difference.

Talent Crunch

Good Math Marketing talent is extremely hard to find. First of all, math marketers need math skills, and math talent is scarce in the U.S. According to the OECD PISA test, which tests 15-year-old students in different countries, the U.S. ranks 35th in the world in terms of math literacy. In The World Is Flat, Thomas Friedman outlined how the lack of engineering and math skills among the U.S. population is having detrimental effects on U.S. competitiveness in the global marketplace. John Kao describes in Innovation Nation how the U.S. math education system lags behind that of most other developed countries, most notably China. However, math skills alone are not enough. Math marketers also need to have an affinity for marketing and a desire to work in what can be highly creative “right brain” marketing environments where very few of their colleagues may understand, let alone know how to apply, math. Hence, math marketers must be able to use advanced mathematical techniques and explain their findings in a marketing context to a nontechnical audience, an extremely rare combination of skills.

Does this mean there is no place for supply-side financial metrics that try and simplify how marketing works by adopting the more linear concepts non marketing executives feel comfortable with?  No, on the contrary, these metrics can be extremely useful.  They can help break down the barriers that often exist between the marketing and other parts of the organization.

Non marketing executives often think marketing as a discipline lacks business understanding and accountability and that it has goals that are not aligned to the corporate agenda.  As a result marketing is often marginalized.  Adopting the boardroom vernacular can be an important first step to breaking down the barriers.  This is exactly what supply-side financial metrics can achieve. 

Peter Doyle thinks a focus on shareholder value creation for example can help marketing gain more credibility among non marketing executives.  He thinks marketing should adopt shareholder value as the single metric to demonstrate the performance of its efforts.

Here is how shareholder value analysis (SVA) works.  It is based on the 4 key principles that are at the foundation of Finance as a discipline :

• The importance of cash flow : an investment’s return should be evaluated by looking at the future cash flow it will generate.
• The time value of money : cash today is worth more than cash tomorrow
• The opportunity cost of capital : the return an investor could get if they would have invested their money somewhere else should be taken into account
• The concept of net present value : future cash flows should be discounted by the opportunity cost of capital to calculate their value today.

If you are not interested in the detailed calculations of SVA skip the next paragraph.  I just wanted to include some of them to demonstrate that they really aren’t very complicated and that the principle can be illustrated with a very simple spreadsheet model.  Here is how SVA works in one of Doyle’s hypothetical examples :

In this example the company’s sales and net operating profit after tax (NOPAT) can be found in the “Base” column.  The board has to evaluate a marketing strategy that is expected to generate an annual sales growth of 10%.  The investment would be 40% of the incremental sales and the opportunity cost of capital is 10%.  The discount factor would therefore be 1/(1+10%)ⁱ where I =  1,2, … is the year.  The cumulative present value of the 5 year cash flow is 16.8 million.  Doyle uses the standard perpetuity method to calculate the value of the investment after the 5 years.  This assumes that the return of the investment will be equal to the opportunity cost of capital after the 5 years.  The value associated with this is called the residual value and it equals NOPAT divided by the opportunity cost of capital. 

The investment according to this SVA will generate an incremental 16.8 million of shareholder value.  Assuming there were 20 million shares outstanding this would mean that the investment increased the share price by 88c.

Doyle builds on this example by running a couple of scenarios that illustrate how shareholder value can be grown through a couple of strategies :

• Increasing the level of future cash flow generated by the investment
• Accelerating the cash flow generated by the investment
• Reducing the perceived risk associated with the investment
• Increasing the sustainability of the competitive advantage as a result of the investment
• Increasing the options for longer term growth as a result of the investment (Doyle combines the latter 2 in one “effect on continuing value”

SVA is not the silver bullet that will answer all our accountability questions – again, there is no single metric that explains marketing’s performance in its entirety. Neither does it help us quantify the anticipated effect of our marketing investment on future revenue streams.  That remains the hard part that has to be answered through measurement, historical analysis, econometric modeling and in market testing. 

However, SVA does help in a number of ways. 

• It forces marketers to consider longer term effects.
• It makes the case for alternative strategies for proving the value of marketing, most notably the impact on price elasticity.
• It uses very straightforward math that can be done in a simple spreadsheet.
• It translates marketing results in a metric that is understood outside of the marketing community and thereby breaks down the language barriers that often exists especially between the marketing and finance communities.

I think that once SVA has helped prove the value of marketing and the lines of communication between marketing and other parts of the organization are open, SVA should be replaced as possible by metrics that are rooted in the reality of how demand is created.  This will be a combination of financial and brand equity indicators.  These are the metrics that really help companies improve their marketing decisions.