The DoubleThink » data

Agile Marketing Part II – Learnings from product development

Dimitri — Tue, 16 Nov 2010 19:41:01 +0000

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.

The Agile Manifesto (2001)

Source: http://agilemanifesto.org/

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.

Agile Delivers

Source : VersionOne – 3rd Annual Survey: 2008, “The State of Agile Development”, Conducted: June-July, 2008

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 :

Sensitive	Keep your senses open to your customers. They define what value means for your organization.
Adaptive	Don’t stick to outdated plans – expect change and uncertainty and respond accordingly.
Lean	Eliminate waste in the marketing process by focusing relentlessly on the creation of value as defined by your customers.
Fast	Be quick without hurrying. Value is created at the “point of sale” – not the “point of plan”
Iterative	Deliver today – adapt tomorrow. Test, observe, learn and iterate.

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.

Music is Math

Dimitri — Sat, 11 Sep 2010 12:34:01 +0000

I have been pretty obsessed with a little side project over the last couple of weeks which explains why activity on the left column on this blog has been low … . It all started about a month ago when a colleague in London told me about the Korg drum computer app on the iPad which transforms the iPad into a wicked beat machine. I was immediately hooked. After a couple of days of beat making I decided I was ready for something bigger and I started looking into other music software. That’s when a very kind Best Buy employee introduced me to Reason. My life will never be the same again!

I have no musical background, have never played an instrument and never went to music school. I was a complete novice when I picked up Reason 3 weeks ago. A couple of Youtube reason tutorials later, I launched my own little one man band on myspace.

The software is incredible. It allows you to make any sound imaginable, from horns to electric guitars, from techno synthesizers to monk chants and crickets. These instruments play notes on different tracks which you can then combine in a mixer. The picture below shows a Reason screenshot with a song that is played on 12 different tracks.

It also has a number of drum computers you can use to make your own beats or you can select from a library of hundreds of pre made loops. The possibilities seem endless and while you can make things extremely complicated (Reason is used by pro’s like the Prodigy), I found myself finishing my first song in just 2 days.

What quickly became clear in this process is how similar making music is to analytics. While I was composing and arranging, it felt like I was using the part of my brain I use while writing code in SAS. That shouldn’t be surprising. The theory about the similarities between music and mathematics goes back all the way to Plato. There is even a wikipedia entry devoted to the subject. The screenshot above shows has the structure of a database. Every track is a file of data (notes) that is linked through the rhythm and the beat.

Analytical skills came in really handy in interpreting the notes, especially when you have never had musical training. I quickly discovered how useful midi files are. Midi files are similar to mp3 files but rather than containing the sound waves, they contain the musical instructions for the instruments (the notes, the rhythm, the velocity and length of the notes, …). You can read them automatically into Reason and they show up like different tracks on a screen like the one shown above. You can then select which instruments you would like to play which tracks and then move blocks of notes around to compose and arrange different variations of the song. A lot of midi files can be downloaded from the web.

Not every song is available on midi though. When I wanted to make a dance version of one of my favorite 90’s grunge songs I couldn’t find the midi for the song anywhere. I was stuck. So I started looking for a solution and came across Widisoft, a software package that reads in sound files and translates them to midi files. Here is a screenshot of my grunge song translated by widisoft. It is remarkable how similar it looks to the punchcards that came out of the first computers. It’s data in it’s purest form!

At first sight the screenshot looks like random dots on a couple of bars. But when you listen to the song and look at the data at the same time you quickly start to see the patterns that identify which data points correspond to which sequences and instruments. Some intuitive pattern recognition allows you to split this data stream into different organized tracks pretty easily. This creation of order out of data chaos is very similar to what data analysts do when they analyze large data sets.

I decomposed the data, mapped the different tracks to new instruments, put it all on a house beat, rearranged the sequences and recorded Slaphappy. Fans of early 90’s Seattle grunge might recognize some of the riffs.

Learning to Read the River

Dimitri — Fri, 06 Aug 2010 15:15:29 +0000

Last week Ogilvy launched their new thought leadership program called the Red Paper series. I was fortunate enough to be able to write the very first one – Learning to read the river. The paper describes how all the data that is generated today provides a huge opportunity for companies to grow their business. It describes 4 building blocks companies need to put in place in order to extract business value from the data : Ergonomic Measurement, 3 Step Insight, The Single Enterprise View and the Math Marketing Organization.

You can find the paper here. Hope you like it!

Read the river_ogilvy_red_paper

View more documents from Ogilvy & Mather Worldwide.

The US Census – Mother of all Surveys

Dimitri — Wed, 23 Jun 2010 21:56:38 +0000

Imagine this – a Census worker walks on dirt road deep in the woods somewhere in North Michigan. After half an hour she reaches a bungalow, the 1^st sign of life she’s seen in a while. Just as she pulls out her new 2010 Census form she sees a bag hanging from the doorknob. In it she finds a Census from 2000 … .

This is the story Dr Robert Groves used at the beginning of his presentation at the ARF Audience Measurement Conference in NY yesterday. He runs the US Census Bureau, the government bureau in charge of surveying every household in the US once every 10 years. The Census is used to obtain the vital demographic statistics that are necessary for government planning. Dr Groves’ story and subsequent presentation showed that it is probably one of the biggest logistical exercises in the world.

The scale of the operation is pretty incredible. The government sends out the questionnaires to 140 million households and looks for a 100% completion rate. In this year’s Census 72% of the US households mailed their survey back. That is the same response rate they had when they did the last census 10 years ago. Any direct marketer would dream of having response rates at the same level of 10 years ago so this was a phenomenal achievement. A 1% drop in response rate would have cost the Government $80 million. This is because if you don’t respond by mail, they will send around someone to your house to ask you to fill out the questionnaire. They have a small army of 600k census workers following up on the 47 million households who didn’t respond. This in-person follow-up visit costs the government on average $57. (This includes the costs of handling the 300 assaults on census workers committed by anti-government minded non-responders!). The cost of a response by mail is only 42 cents so with these economics it became crucial for Dr Groves’ team to drive up the response rate.

They did this in a number of ways. First they made the questionnaire shorter which always drives up response rate. Then they added 2 touches to their communications cadence. They sent out a pre-announcement card before the questionnaire was sent and they sent out an extra reminder after the 1^st questionnaire was sent. This apparently consumed the entire printing capacity of the US!! They also paid a lot of attention to language. The questionnaire came in 6 languages and they had completion guides for an additional 59 languages! They also activated 250 thousand grass roots organizations who would work in the local communities to ask people to fill out the questionnaire. This according to Dr Groves was one of the main contributors to the high response rate.

Then they did a marketing campaign which cost them $350 million, an investment they could easily show the positive ROI of (all they needed to achieve break even was a 4% increase in response rate …). This was a fascinating campaign, perhaps the biggest multicultural campaign ever. It included a K-12 campaign designed at informing children of immigrant families about the Census. These children are often the 1^st to learn English in the household and can therefore be the main influencers. The campaign was run by a consortium of ethnic agencies who had to buy media in 28 languages, including Farsi, Armenian, Uruguayan etc. I saw Khmer videos, Indonesian outdoor ads and native Hawaiian posters. I have never seen localization at that scale.

Equally impressive was the use of real time tracking, modeling and optimization. Dr Groves’ team implemented daily tracking studies and performed statistical analysis every night to determine the effect of their media investment on awareness and behavioral intent. This allowed them to identify pockets of the population that were lagging behind. 18-24 year olds were an example. They tend to think the Census is not for them – that it is only for old people … . The team saw this very quickly and acted immediately by sending out targeted messages and by activating grass roots organizations. Dr Groves said the government saved 100’s of millions of dollars through real time tracking and optimization.

At the end of the presentation we got a sneak peak at some early results. The big trends shouldn’t come as a surprise. The population will grow from 309m to 439m in 2050. 20% of all households will be over 65 years old in 2050. There will be significant growth in the Hispanic population from 8% in 1980 to 30% in 2050. The Asian population will grow significantly as well. Apparently 47% of the national fertility in the next decades will come from minorities. We could also see the emergence of a bi-modal income distribution with an increasing gap between 90th and 10th income percentile – $138k vs $12k.

The US Census is a remarkable project. In terms of scale it is maybe only rivaled by the Indian Census which, as I found out on my trip to Bangalore last week is scheduled for the first time in 2011. Imagine surveying a billion people, a large portion of which live in remote, underdeveloped rural areas. I wouldn’t want to be in charge of that job!

Next Generation Geo Marketing

Dimitri — Wed, 09 Jun 2010 12:00:06 +0000

A friend just sent me this beautiful visualization of where people are taking pictures in London. You can clearly see the high traffic areas around the tourist hotspots. It’s a great use of the type of data people are generating by going about their everyday lives. It also shows how this data could become really practical for marketers trying to find out who is where at what moment in time. I am sure the picture would change if we were to look at certain times of day, days of week, seasons or even profiles of the people that are taking pictures. The view would be very different for people who live in London vs those who don’t for example.

This type of location based behavioral data could give marketers very valuable information about when they need to be where in order to surround their target audience. It seems this data would be able to provide much more detailed information than what is currently available. While this particular application only looks at where people are taking photographs, other location based services like Foursquare generate tons of data about what people are doing where. Foursquare already automatically shows their users which locations are trending (ie becoming popular) real time.

The application of location based behavioral data will go well beyond location based targeting on mobile devices. I believe it will soon become the basis for the next generation of geo marketing tools. Retailers for example could use it to determine where to locate their stores and media planners could use it for planning their outdoor advertising. We could even use this new type of data to profile zip codes based on the activities of the people who live there. These zip code profiles could be used in the same way marketers are now using socio demographic profiles of zip codes for targeting. The current profiles are pretty static since they are based on more traditional sources like the Census. The behavioral location based profiles could be much more dynamic and a lot more detailed. A company like Foursquare, if it wanted to, could actually already very easily make these zip profiles available to marketing professionals. This would make them a real competitor to established 3^rd party data vendors like Acxiom and Experian.

The Value of Stories

Dimitri — Fri, 07 May 2010 21:01:18 +0000

What is the actual value of a story? Are people prepared to pay more for something if there is a story attached to it? It turns out that they are. That is the outcome of a very original experiment by writer / NYT columnist Rob Walker. I saw Rob speak about his Significant Objects project, a project which I think might have generated a very significant dataset.

Here is what Rob did. He purchased a bunch of random objects on Ebay, most of them for less than $4. He then distributed the objects to his friends, fellow writers and artists and asked them to write a short story about the object they were given. He then put the objects back up for sale on ebay with the story in the description to see if people would pay more for the object now there is a story attached to it.

It turned out they did. When he sold the first 100 objects, he essentially sold $120 worth of objects for $3,612 – a 2,776% significance markup as he calls it. The object in the pictire above was the highest seller of the 1st wave of products he sold – $193 for a statue he initially bought for $3. He even sold a 25c plastic banana for $75.

So people were prepared to pay a lot more than the initial value of the storyless object. In fact the difference was so big that Rob concluded that the real value was in the story – the object was merely the vehicle for the story.

That’s very interesting and it obviously gives us a view in how brands work. But what I find most interesting about the experiment is the dataset it created that showed the exact monetary value of stories. I would love to analyze that data to see what really drove the value of the story. Was it the object, the synergy between the story and the object, the reputation of the writer or are there any attributes in the story itself that give us clues about what creates its value.

The data is available on the website. I might play around with it if I find the time. Here is the video of Rob Walker’s presentation at PSFK.

Your voice reveals your subconscious

Dimitri — Tue, 06 Apr 2010 16:27:25 +0000

I met a slightly creepy yet really interesting company yesterday. Voiceprism has a technology that analyzes sound waves generated by the human voice. This gives them the ability to listen to an individual for 10 minutes, establish a baseline voice pattern and then detect the deviations from the normal frequencies. These deviations have proven to be indicative of emotions such as excitement, stress, delight, anger, …

This reminded me of the biometrics Innerscope looks at to gauge emotional engagement. The voice could be an additional data point that can give us clues about what is happening in the subconscious. The main differentiator for this technology is its ability to listen in on thousands of phone conversations. This means it can be used at scale in a cost effective way. I haven’t seen another technology that has the potential to capture data from the subconscious for potentially thousands of consumers.

They talked to me about their work with insurance companies where they analyze claims calls and flag insurance claims that should be investigated further based on the stress levels of the callers. They were basically doing automated lie detection. This apparently helped insurance companies dramatically reduce fraud levels.

I think there could be other interesting applications, especially in quantitative research where it is often hard to quantify true emotional engagement through analyzing what people are saying. How they are saying it can now apparently give us valuable clues as well. Click here to see a video of how the technology works.

TV’s emotional impact

Dimitri — Fri, 12 Mar 2010 20:34:26 +0000

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.

Robert Heath has published extensively on this topic and has shown that the role of emotions in the decision making process are very important. In a 2009 Admap article about how TV builds brands he said :”Early advertising models that addressed the role of emotion in advertising reflected the thinking of the time, which was that ‘conscious thinking’ leads to ‘feeling’, which leads to ‘attitude change’, which, in due course, leads to a purchase decision. They were not to know at the time, but they got it seriously wrong. Feelings and emotions are processed much more quickly than thoughts.” Heath has used brain scanning technology to show that we usually make decisions up to 1/5th of a second before we are aware of them. He says that “we always form an attitude about a decision through emotional and subconscious rational processing before we start to consciously and actively ‘think’ about it. So our conscious thinking tends either to support the decision or counter argue it. There is empirical evidence […] showing that the presence of brands inhibits processing of product attributes and encourages consumers to use shortcut to validate their brand choices.” This is illustrated in the diagram below.

If people use brands as subconscious short cuts, then brands need to be built through communicating at the subconscious level.

This is where TV is especially powerful. Contrary to other media such as print that require conscious processing of information (ie reading), TV more than any other medium can work at the subconscious level by evoking feelings through the uses of visuals and sound. This is why TV is such a powerful medium for creating brands. It does this through emotional communication. And Binet and Field have demonstrated, in the study of the IPA databank I mentioned in my previous post, that emotional campaigns consistently outperform rational campaigns in terms of driving sales, share, price elasticity, loyalty and penetration.

Google Analytics Intelligence

Dimitri — Mon, 01 Feb 2010 12:00:17 +0000

This is a bit of a geeky post so apologies in advance. But I have been meaning to write about how fantastic I think Google Analytics Intelligence is. If you are not sure what I am talking about have a look at the video below.

I love it for a number of reasons. First of all it finally embeds very basic analytics best practices in web analytics software. It has bothered me for years that some of the most sophisticated web analytics packages did not even have the most basic significance tests in their standard reports. I have often seen people draw conclusions on the back of results that were statistically insignificant. GA Intelligence will really help avoid this.

Second, it automates some of the mundane analytical tasks. A few rather pessimistic analytics professionals have asked me whether this was the beginning of the end for analytics as a profession – the start of the complete automation of analytics. I don’t see that happen anytime soon! If the basic, repetitive tasks are being automated then that would free up time for analytics professionals to use their skills much more efficiently on more advanced work. There is plenty of that work to be done.

The most important reason why I love GA Intelligence is the seamlessness with which they have incorporated the significance tests in their reports. It is a fantastic example of how to distribute the ability to do more complex statistical analysis among a broader non-technical audience. And the alert system is very elegant as well.

I love it!

What should I pay for a piece of data?

Dimitri — Mon, 18 Jan 2010 13:00:30 +0000

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.

Set 1	Set 2
Number of hours spent on DIY per week	Number of vacations taken per year
The number of hammers owned	Interest in water sports
Size of the house owned	Age

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.