Few companies are achieving advanced marketing analytics at scale despite the benefits of doing so. Analytics should offer the prospect of improved efficiency and effectiveness in demand generation management.
The dream of profitable growth for a company is potentially in reach with an analytics culture that drives both tactical and strategic decision-making. What then is holding companies back? Why isn’t analytics a core internal function at more organizations? Many other marketing services have moved or are moving in-house, so why not marketing analytics too?
The recent rush to in-house marketing services has several well-known drivers. They differ based on the type of service in question.
For media, the issues of transparency and efficiency — regaining control of one of the largest expense items in most P&Ls and rationalizing the dollars wasted by agency mismanagement and misappropriation — are paramount. Relationships are easy to build directly with media owners, available tools are numerous, and talent is abundant.
For advertising, it’s also about control where recent scandals in creative tendering, in addition to souring agency politics and relationships, can more easily be replaced with streamlined internal teams given that the technology improvements in production have significantly reduced studio requirements.
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For demand management in general, it’s about regaining control of strategy and being able to better balance sales and marketing approaches around a common internal understanding of the customer. Agencies still cling to the silos of the past. Add to that, their parental holding companies, for the most part, have proved unable to create effective, cross-functional teams for clients. In addition, today’s client-side enterprises represent large and complex enough challenges to attract highest-level talent to internal positions.
But what about Marketing Analytics? There are strong proponents to bring this function in-house as well for several reasons:
- Data is too sensitive to send outside the organization.
- Transparency and trust issues are heightened especially where analytics may be viewed as functioning within a black box, as is often the case.
- Data is too important and valuable to leave its processing and management to an external entity.
That said, the case for marketers to bring analytics in-house, in reality, is still more complex. Barriers are still present, specifically around talent, trust and technology. Here’s what they look like:
Today’s sexiest job is the data-scientist and the big tech firms attract the majority of them. Furthermore, the many branches of marketing analytics have bred niche services and corresponding highly specialized talent pools. Building large enough teams with the required leadership is a hard lure for most companies, not to mention keeping what then needs to remain an agile and independently-minded team away from the bureaucracy and protocols that often hamstring them.
Even with a strong internal team, educating and defending analytics to the larger organization can prove to be a persistent battle. Black boxes are not just the domain of vendors, internal solutions can be just as mysterious without demonstrable reassurances, openness and conviction. In fact, bringing analytics in-house can raise a potential principle-agent problem: when the measurer and the measured are under one roof.
While some companies are able to develop a healthy working relationship between analytics/research and their marketing stakeholders, others degenerate into very broken relationships. Where analytics are used to evaluate and optimize significant investments, these relationships are predictably strained with analytics teams faced with being seen as the ’internal affairs’ group.
Frequently, in order to establish an analytics function in house, significant investment in data management systems, algorithm development, process design and execution, software development, training and more are needed. Building your own modeling platform for just one application in most cases will not be worth the effort and expense. A lone development effort will also lack from any economies of scale and the benefits derived from large-scale implementation. For example, large-scale user feedback and experience-based innovation will be lacking.
Perhaps because of these challenges, three distinct types of in-housing models have emerged for companies grappling with analytics: Do It For Me (DIFM), Do It With Me (DIWM) and Do It Yourself (DIY). Each of these models originates from a separate and unique set of needs, but common to all are clients looking to the outside analytics profession for some level of support.
Following is a high-level look at each model and the corresponding needs and services, as well as additional internal resources client-side companies might typically put in place to succeed.
This does not mean outsourcing of analytics. Rather it means internal analytics teams focus instead on the internal relationships and lead project management to better engage and inform their wider organizations. Vendors are used based on their ability to offer true partnership. What’s important is how they fit with the client’s culture, subordinate to the internal agenda, bring clear and validation to the analytics, and provide thought leadership and effective sounding board.
In this model, clients focus more on understanding and validating the analytics through more of a hands-on learning and training process. The goal is to be able to fully defend the analytics and address any internal technical questions based on a detailed understanding of the data, transformations and analytics processes. Outside support is focused on providing deep knowledge, project management, supplemental hands-on analytics resources to scale production, best practices, and validated and accessible analytics platforms.
For companies who are able to build strong and deep teams, overcoming some of the barriers mentioned above, there is still a need to partner with outside experts in order to maximize their success. These companies recognize value in accessing talent and technology that is bred in the broader industry. Typical services provided by these outside partners include insights on innovations in methodologies and technology, best practices in data, benchmarks, quality assurance and software platform licensing.
While marketing continues to be a dynamic and fast-changing landscape, some elements of it are more well-known and manageable. Those elements — particularly media and advertising strategy — are now increasingly moving in-house as the principle-agent relationship with outside vendors breaks down. Marketing analytics, however, may continue to buck this trend since it requires an independent input to be credible and it’s a newer, less understood, still developing a skillset that doesn’t suffer from any other bias in its provision.
That said some level of internalization of analytics should be sought by successful companies. In order to start that journey what is needed is an assessment of where the company is today on its path to enablement; what is the desired end-state and what is the current readiness to approach any of the above in-housing models. There is no one best model of enablement, simply which is best fit for a given client.
For clients who determine DIY is their best solution, there is a graduation curve where you typically must go through phases of DIFM and DIWM in order to fully engage with and internalize the end to end analytics process. This way of learning by doing under a fast-follow approach is the best way to ensure a full transfer of capability to the internal team.
The most pragmatic approach is to try DIWM and make an assessment at that point based on where you are on the enablers of trust, talent and technology as to whether you should focus your investments to bringing it further in-house or conversely invest outside in DIFM or indeed remain with the DIWM solution. Finally, it’s worth noting that circumstances always change too and so having the flexibility to move between these solutions and to work with a partner who can meet you at your point of need as it changes is the only way to truly mitigate risk and maximize the opportunity from marketing analytics.