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Marketing transformation: the importance of culture, leadership and talent
1L’art de la gestion de projet2Un projet à succès commence par une bonne gouvernance3Cascade, agilité, demandes de changement?

Marketing transformation: the importance of culture, leadership and talent

  • TECHNICAL LEVEL

This first article in this series on marketing transformation, published a few weeks ago, covered the importance of data for effecting marketing transformation, while the third installment, soon to be published, will draw everything to a close by discussing what technology can bring to this process.

This second article will focus on three aspects: cultureleadership and talent. As early as 2010, in Analytics: The New Path to Value published in the MIT Sloan Review, Steve Lavalle and Michael S. Hopkins listed what was in their estimation the three biggest obstacles to the adoption of analytics within organizations:

  1. a lack of understanding of the way analytics should be used to improve a company’s business performance;
  2. a lack of focus on the part of management due to multiple, divergent priorities; and
  3. an internal lack of skills.

Unfortunately, after almost twelve years, these three organizational barriers continue to hinder the success of analytics projects. Thomas H. Davenport wrote two popular books in which he discussed the level of organizations’ analytics maturity using his DELTA model (Data, Enterprise, Leadership, Targets, Analysts), to which he added two other “TA” elements (Technology, Analytical techniques). Table 1, taken from his book Competing on Analytics: The New Science of Winning, Updated, with a New Introduction, illustrates the key aptitudes needed to be successful in your analytics initiatives. 

 

 
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Leaders in marketing (since we are speaking here specifically about transforming the function of marketing) have a tendency to concentrate on data and technology. But you still need to integrate other aspects into your thinking in order to ensure that analytics are supporting your marketing functions in an integrated way. Personally, after many years spent in analytics, I still believe that the elements of the DELTA model reside at the heart of transforming data into a valuable asset for an organization.

The importance of leadership

It would be utopian to believe that one marketing analyst could lead an entire organization into accelerating its marketing transformation merely through data. Rather, this type of shift requires significant financial, human and technological resources. The return on investment for this kind of project could take as long as two years, or even more.

Given these facts, project sponsors must undeniably be of a “C-level” calibre. For big organizations, we’re talking about the Chief Executive Officer (CEO), the Chief Marketing Officer (CMO), the Chief Technology Officer (CTO), Chief Data Officer (CDO), vice-presidents or other people in similar roles.

I should make the following minor clarification on the role of the CDO: The CDO isn’t necessarily a technical expert. This individual must of course understand the technology and its utility, but this person is first and foremost a business leader who knows how to transform data into valuable assets for an organization.

The four or five essential actors (according to organizations) who must lead the way are the CEO, CMO, CTO, CDO, and in some orgs the Chief Digital Officer (also referred to as the CDO). Without the CEO’s consent, it will be hard to make these projects a major priority at the company. The CMO and CDO (and the other CDO, the Chief Digital Officer) are main players within the organization and must ensure they obtain their budgets and resources from the CFO and CTO. The same combination of people could be created amongst vice-presidents or people in similar roles in some organizations.

The takeaway is that this type of project takes a fairly high level of involvement within an organization. The sponsors need to be influential and seasoned politicians (by which I mean they have the ability to navigate an organization’s internal politics). Emphasis should be placed on the economic or social benefits of this type of project to foster alignment.

 

The importance of culture

Organizational culture can either facilitate or complicate data initiatives. The Chief Data Officer (CDO) must attentively promote the development of a fact-based culture within the organization, since this will facilitate the launch of data initiatives.

Culture through education

Educating marketers to maximize the use of data is a good way of facilitating adoption of a culture focused on data. According to Gartner, 80% of data strategies created before 2023 will integrate the idea of data literacy to accelerate value generation. This idea encompasses the need to understand the data, their origin and their practical use in decision-making. With all of the recent changes in legislation (the GDPR in Europe, CCPA in California, and Quebec’s recently adopted Bill 64) and MarTech technologies (cookie and IDFA apocalypse), the idea of data literacy will become more important within the industry since it can help marketers traverse the seismic shifts that have affected their activities. Nuances in the interpretation of data are also of critical concern: Understanding why Google Analytics, Adobe Analytics, Facebook Ads and Campaign Manager don’t attribute sales the same way in their reports is essential to analyzing performance.

The necessity of having a culture of experimentation and continuous improvement

A culture of innovating through rapid experimentation is necessary for speedily transforming data into valuable assets. This concept is very popular at companies like Google and Amazon, because it’s easier and faster to attribute causal relationships between certain elements using experimentation (particularly through A/B testing). As an example, discovering insights about your customers or the performance of a campaign must be systematically followed by launching rapid testing, which enables you to validate that your insights represent a true lever for performance.

The first goal of testing is learning. The important results extracted from these tests are then used to optimize the company’s operations, as well as improve its products, services and business performance.

Just to remind you, when we talk about the idea of value in the context of data, we’re mainly referring to the application of insights (intelligence) to specific marketing uses (ad targeting, personalization of customer experience, product promotion, designing a new offer) in order to affect business performance.

Online video game companies are experts in testing pilot versions in a certain market before deciding whether the game will be launched everywhere else. The first goal of testing is to validate hypotheses, but also to minimize business risk related to the global launch of a functionality or product. Digital is an environment that’s conducive to such rapid testing.

Continuous improvement is essential to support superior performance because it forces the organization to analyze its blind spots and find ways of mitigating their impact.

It’s wiser to focus on a prototype (a minimum viable product) and to iterate your process thereafter than to undertake big projects costing millions of dollars that haven’t even proven themselves. Basically, an iterative approach enables you to test, adjust and, above all, learn.

The complexity of the technological environment and the financial constraints common to the post-COVID-19 era we’re currently experiencing are forcing companies to be cautious and minimize risk-taking. Start-ups are adopting this model because they’re agile, but this culture is also necessary at big organizations to be able to accelerate their shift towards a digital culture.

Poor data quality is often an obstacle to adopting a data-based culture

The importance of data quality to facilitate the adoption of a data-based culture is often underestimated. Incorrect, outdated or context-free data harms the ability of marketers to quickly make the right decisions. Focusing your efforts (in terms of both time and money) on obtaining high-quality data is nothing less than essential. Here’s a reminder of the main qualities you should look for:

  • Accessible: Is it easy to obtain?
  • Complete: Does it respond to the user’s needs?
  • Precise: Does it represent reality?
  • Consistent: Is it represented the same way on other platforms?
  • Valid: Is it in the right format?

Timely: Is it still relevant? Is it available when needed?

 

The importance of talented people

All vendors of analytics platforms will tell you that you need the 3 Ps to be able to realize your analytics ambitions: platforms, processes and people.

People are the real creators of value. They’re not only responsible for orchestrating the use of platforms, but they are the only ones capable of defining and executing a strategy, whatever that is.

The kind(s) of expert(s) needed for your data-led marketing transformation project can be summed up in the following profiles:

types-of-talents

 

The biggest challenge for organizations is acquiring and keeping these kinds of professionals. You must also promote the development of an environment in which these individuals can continue to learn, flourish and increase their skills.

This concludes the culture, talent and leadership section. The next article will focus on technology that supports data-led marketing transformation.

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