The last 2 chapters of the Math Marketing paper describe how you can make it work for you in order to increase accountability and to generate powerful insights.  This article on Chief Marketer (http://chiefmarketer.com/marketing-roi/0616-marketing-accountability-tips/) described 8 steps to greater accountability.  The last chapter discusses Math Marketing in the context of generating insights.

1. Build a single customer view
Owning your own data is never a bad idea. As described earlier, many companies have been building corporate data warehouses that create a single view of all their customer data since the early CRM days. This is often an expensive and labor-intensive task. But if done correctly, your database can be an incredibly valuable asset. Tesco in the U.K., for example, estimates that 16% of their margin is attributable to the knowledge they get from their customer database—that makes their database a $3.2 billion asset.  One of the key challenges in the future will be the increasing amount of customer data now generated outside of corporate systems. That data is owned by platforms like Facebook, Twitter, Google, Microsoft andYahoo!, rather than the companies themselves. We expect these companies, or even third- party data clearinghouses, to develop solutions that will enable companies to integrate these data sets with their own data warehouses.

2.   Mine your data
Your data is only as valuable as the insights you distill from it. This is what Math Marketing allows you to do. There is a whole range of techniques available, but we suggest you start with the basics and prioritize your data mining efforts based on the questions they will answer. Do you know who your best customers are? What drives your profitability? How can you find prospects with the same profile? What can your data tell you about how to communicate with customers and prospects? These questions will determine whether you need a value segmentation, an attitudinal segmentation, a lifetime value model, an anti-attrition model, a browsing typology or any other Math Marketing tool at our disposal today.

3.   Learn from search-intent modeling
Search data is rich in information. And it’s free. Search intent modeling tools mine that data and provide insight based on the search terms that consumers use to find your brands/products. This can teach you a lot about the thought process of your target audience and the words they use to talk about your brand, which can improve the effectiveness of your overall communication tremendously.

4.   Plant your listening posts
Social web platforms such as blogs, microblogs, forums, social networks, and opinion and review sites give you another relatively easily accessible source of consumer data. This data can be used to generate insights in a manner similar to search intent modeling. Listening post technology can collect data from these platforms and perform semantic analysis of the conversations that are happening there. This can give you more insight into how many people are talking about your brand, whether they have positive or negative opinions and which other brands or characteristics they associate with it.

5. Revitalize your primary research
If you want to know what consumers think, just ask them. This is how primary research works, and this is why it has been the main data source for generating insights for a long time. But today’s primary research is an entirely new discipline from what it was a decade ago. Social communities and online survey tools have dramatically cut the costs of primary research. These tools can be used for idea generation, polling and even in-depth interviews. And they can be deployed very quickly. They even give you the ability to leave the feedback channel with your customers open at all times, providing your marketing group with a constant influx of fresh insights.

6.   Simplify for increased actionability
Simplicity leads to action. This is definitely true for insights. But keeping things simple in a Math Marketing world is not obvious. Math Marketers are specialized in analyzing the vast amounts of data in our digital world. They often need to be taught how to embrace both the complexity of that data and the simplicity of actionable insights. There are plenty of techniques and tools that can help Math Marketing insights become more actionable. These range from personas and customer portraits to rulebooks and guidelines. Simplicity is key with all of them.

7.         Empower the end user
Most companies spend far too little time on this last point. They put all their effort into generating insights and occasionally formulating them into simple, easy-to-digest formats only for their work to end up collecting dust on the shelf of the head of marketing intelligence. You should develop insights with end users in mind. These can be customer service representatives, new product development engineers, creative and design teams, or anyone else who could end up benefiting from your insights. Involving them in the insight generation process can help focus your efforts and will almost certainly help increase the adoption and use of those insights. This can often be achieved through small organizational changes. Consistent communication of insights to end users can be an
easy but important first step.

The fourth chapter of the Math Marketing paper describes the current landscape of potential Math Marketing partners.

If companies need external partnerships to stay at the forefront of the Math Marketing revolution, whom should they partner with and what should they look out for? The Math Marketing partner of the future needs to be strong across three dimensions.

Broad Aperture
Math marketers must be able to look at the marketing landscape in its entirety. This includes all media and all elements of the marketing mix. They should understand the impact of marketing on overall business results. They also need the senior-level client relationships that can give them the authority
to take, and act on, this broader view.

Specialty Skills
Math marketers must have the specialty skills necessary to master the modern Math Marketing toolkit. This includes econometric modeling, data mining, statistics, Web analytics, online ad serving competencies, quantitative market research techniques, dashboard and visualization technologies, and social media analytics. They must always stay abreast of the latest developments in the Math Marketing industry; departments must also have the robust technical infrastructure to handle the large volumes of data involved in modern Math Marketing.

Access to Data
Math marketers need access to data. Traditionally, most data was owned and stored by clients. While clients still internally house the bulk of transactional and customer-level data, the digital era has seen other islands of data being created outside of clients’ corporate data warehouses. Microsoft and Google got in the ad-serving game with their acquisitions of aQuantive and DoubleClick, respectively. They are rapidly gathering online ad-serving data that can be used to optimize online ad spend. Online media agencies are investing in data infrastructures that enable them to get feeds from ad servers and store them themselves. Search engines have the most powerful source of search data for needs-based targeting. But very few companies have integrated this digital data with their customer data warehouses. It will be interesting to see in the next couple of years who will own what data and who will be creating the next-generation, single-customer views that will combine media exposure and interaction with search, website behavior with transactional and demographics data, all in one place.

There are various players in the Math Marketing area:

Media companies have always used mathematics to make decisions about investing media budgets. Most of them have econometric capabilities and are investing in ad-serving, data-warehousing infrastructures, which will allow them to mine that data for optimization and give them a considerable competitive advantage. They are predominantly focused on media decisions and have historically had a more transactional and less strategic view. This was usually reflected in client relationships, where media agencies tend to have less board-level access. This is changing rapidly, however, with media companies aggressively building their strategic capabilities. If they can attract the talent and own the data, they will be in a great position to answer the ROI questions of the future. However, there will always be the perceived issue of the “fox guarding the henhouse.” Clients will always have a suspicion that media agencies will favor recommendations that might benefit media agencies first.

Integrated communications agencies have traditionally had strategic relationships with clients through brand-planning teams. Some of them have built strong strategic services departments over the years. Teams like these will be able to take a broad view of ROI, and look at the impact of all 360 Degree marketing communications on a brand holistically. Their analytics teams also tend to have the technical skills to analyze large volumes of data. However, with new channels being introduced continuously, the teams will need to keep investing in new specialists who understand the inner workings of these channels well enough to use the data to answer the ROI question. (This technical knowledge might come more naturally for media agencies who know the details for media execution purposes.) Ogilvy’s strategic services group in New York, for example, is comprised of a team of 150 planners and consultants specializing in brand planning, marketing and digital strategy, and analytics. The integrated communications agencies usually get access to data through clients and partnerships with media agencies. Very few communications agencies store their own data, which could put them at a disadvantage versus others in the category.

Management consultants have entered the marketing ROI arena as an extension of their broader strategic services. For them, marketing represents perhaps the last area of expansion. By the very nature of what they do, they tend to take a broad and holistic view and probably have more senior client relationships than any other player in the category. This can give them the authority to tackle the marketing ROI question holistically. They are also often seen as more impartial than media and communications agencies. One of their main challenges is that they are often too far removed from the day-to-day execution of marketing campaigns, which means they often lack the in-depth technical knowledge of today’s marketing channels required to analyze ROI. Some of them are ramping up their services fast, though, as demonstrated by Accenture’s acquisition of digital multivariate testing company Memetrics in 2007. Management consultants usually do not have direct data access and therefore rely on clients to share data with them.

As mentioned earlier, Google and Microsoft entered the marketing effectiveness area with their acquisitions of the major ad servers in the industry. With the digitization of all media, they will soon hold the majority of all marketing effectiveness data. They have the capability to handle the largest volumes of data, and have mathematicians and engineers who, if focused on marketing effectiveness, could have the ability to do just about anything. They are not positioned strategically and therefore lack the broad aperture required to become real ROI partners, but they have the size and the financial power to change this quickly.

These main players will be supported by specialized companies to help them solve certain pieces of the overall ROI puzzle:

Agencies with a strong CRM and technology focus, such as Epsilon, Acxiom, Targetbase, Unica and Dunnhumby, have a great ability to mine high volumes
of customer-level data. Some of them also have easier access to data, as they tend to manage clients’ data warehouses. They have mastered the art of optimizing one-to-one communications, but they are almost never responsible for the entire marketing mix, which usually prevents them from looking at marketing ROI holistically.

Marketing research agencies help quantify ROI through surveys and panels. Since they are primarily the providers of research data and insights, some of them have developed extended ROI capabilities in brand measurement and econometric modeling. Millward Brown’s Optimor is a great example of such an extended capability.

Finally, there are analytics vendors who specialize in just one area of analytics. This category includes web analytics vendors such as Omniture and WebTrends; marketing mix specialists such as Market Share Partners, MMA and Hudson River Group; Financial social media analytics vendors such as Nielsen BuzzMetrics, Visible Technologies and TNS Cymfony; and many more. These players usually have partnerships with some of the players mentioned above and, in some cases, have even been acquired by them.

The diagram below summarizes the current Math Marketing landscape, with the main players plotted on a grid based on their ability to have a broad aperture and on their specialty skills.

A true ROI partner will have a combination of both and would therefore appear in the top right corner. No one is there at the moment. However, it is clear that the main contenders are management consultants, integrated communications agencies, media agencies, and Google and Microsoft.

The image above is “The British Beehive”.  19^th century British caricaturist George Cruickshank’s used it to describe the hierarchy of labor in Victorian England.  Each profession is assigned an individualized place in the social order.  Where does math marketing fit into your organization and how do you organize your Math Marketing efforts?  The third chapter In the Math Marketing paper discusses the organizational challenges.

In their book The Four Pillars of Profit-Driven Marketing, authors Leslie Moeller and Edward Landry touch upon an interesting area: how should companies organize themselves to become ROI-driven organizations? The authors claim that being good at analytics is not enough. Though fundamental, analytics is only one of the four pillars needed in order to succeed.
The other three are:

Decision-support tools: tools that automatically make the insights from analytics available to decision makers throughout the organization.

Process: full integration of analytics and decision-support tools in all stages of the marketing process, from target setting to planning to execution to postevent analysis.

Organization: an organizational framework that ensures support and resources are in place in order to implement the process.

The authors are right—analytics is doomed to fail unless the following three ingredients are in place: the tools to disseminate the insights from analytics, the process that makes sure analytics is not an afterthought and organizational support. Most companies would acknowledge this. Very few, however, are actually taking the necessary steps to make sure these requirements are met. In my experience, companies struggle the most with the fourth pillar—the organizational framework to support the ROI mind-set. According to Moeller and Landry, there are three main components to establishing the right organizational framework:

1. Leadership: the ROI agenda needs to be driven by CMOs who are well versed in the science of marketing.

2. Center of excellence (COE) : companies need to develop a COE that is the headquarters of the analytical skills required for marketing ROI.

3. New job definitions, training and hiring profiles : all layers of the organization need to be trained and involved in establishing marketing ROI.

Of the three components above, leadership is often the easiest to put in place. Marketers who understand marketing ROI and analytics are much better equipped to prove the value of marketing to the organization. They are also more likely to speak the same language as CEOs and CFOs. With the average tenure of CMOs now at 28 months (versus 53 months for CEOs), there is increasing pressure on marketing departments to hire more accountable marketers who understand ROI and analytics, and will therefore be the most successful and rise to the top automatically.

Establishing a COE for Math Marketing is difficult, and it may become a lot harder in the future. Most companies have some analytical capability in place. It usually resides within a marketing intelligence group that sits within a broader strategy group, often (though not always) as part of the marketing organization. Most of today’s marketing intelligence groups are heavily focused on market research and econometric modeling. They have grown and matured during the years of mass marketing, when research and econometrics were the primary ROI tools. As mentioned earlier, that has been changing rapidly over the last 15 years due to the CRM and digital revolutions described above, which brought with them an explosion of analytical tools. Today’s multichannel analytics team must consist of at least one web analyst, a website optimizer, a social metrics expert, a database marketer, a search analyst, a quantitative market analyst, a qualitative market researcher, a media analyst, a digital media analytics expert, an audience researcher, an econometrician, a data miner and a PR measurement specialist. I do not know of a single company that has a COE staff with this variety of skills. The diagram below illustrates the variety of analytical  skills required in our multichannel world.  It is the skills matrix for Ogilvy’s Analytics department, a group of more than 200 Math Marketing specialists worldwide.

Furthermore, with new analytical applications being developed at the speed of light, the COEs need to evolve their capabilities continuously. Add to that the incredible scarcity of analytics talent mentioned earlier and we understand how it is virtually impossible for companies to scale internal capabilities that will be able to meet all the ROI challenges of tomorrow. Although the ROI function will never be able to be outsourced completely, the role of external partners is destined to grow in the future, and the right organizational framework will need to be established to support multipartner relationships.

The biggest challenge companies face is to have all layers in the organization involved in establishing marketing ROI. Even if one were able to establish a COE (internally or virtually through partnerships) that has people with all the necessary skills, its value would be limited if it were isolated from the rest of the organization. The main objective of a COE should be to make decision makers, who usually reside outside of the COE, smarter. The COE should provide them the insights, knowledge and tools to make smarter decisions independently. Therefore, if a COE is very successful, it will make itself obsolete. This, of course, has political implications. Very few COE teams feel comfortable with this, and most have a tendency to keep knowledge and expertise to themselves to a certain extent, if only to maintain their status as experts and the job security that comes with it. This is a very natural reaction that can result in tension between COE teams and other parts of the marketing organization. There are two things that can prevent this from happening.

First, it should be clear that the main goal of the COE is to have others in the organization adopt the insights it produces and improve their decision making as a result of doing so. Members of the COE should have this written in their personal performance goals and should be incentivized accordingly.

Second, COEs should strive to automate the cutting-edge work they are currently doing so that it becomes common practice and, at the same time, be given the freedom to continuously push the ROI agenda and pursue opportunities to reinvent themselves. This will require ongoing investment in training and R&D.