POV

"Life is a game of inches. One inch too short or too fast, and you don't quite catch it." Al Pacino - Any Given Sunday.

If life is a game of inches, success must be all about the intuition required to be ready at the right time in the right place.

How often have you unconsciously made sense of seemingly disconnected events? For example, a stock ticker going up, a brand becoming relevant or an artist climbing the charts.

Those intuitions start from a moment of clarity built on the collection of our memories. Then, traveling through our hippocampus, memories and logic form a coherent story that we call a hypothesis. Our hypotheses are not always correct, of course, but we tend to only remember those that make us say, I knew it!

The question then becomes: How much more successful would we be if we somehow classified and stored our attempts and shortened the time between intuition, idea, hypothesis and execution of our marketing ideas?

The Cost of Being Slow

Suboptimal Time to Action in business and life affects us all at one point or another: frustration when someone beats us to the punch, a distinct fear of missing out, or, worse, missed business opportunities due to the obsolescence of an initiative.

Looking back at the 1200 organizations we studied over the last two years, we witnessed myriad factors contributing to the elongation of the Time to Action. However, the isolated elements could arguably all be wrapped up into three main categories: governance, technology and data.

Most of the organizations we observed showed similar characteristics. All have:

• Invested in monolithic legacy systems
• Fragmented user and employee experiences
• Little widespread understanding of data
• Business analysts buried in weekly reporting or other Excel-intensive tasks

These characteristics are all signs of our early 2000s corporate behaviors clashing with 2022 business needs.

On one hand, we have inflexible multi-tenant IT architecture and processes fighting against the desire to build agile "business first" governance structures. On the other hand, business teams need to mature their approach towards much broader adoption of data and look at implementing a governance and budgets culture that favors micro-innovation, or what Gartner refers to as Citizen Development.

Diagnose Before Acting

Why are data, business analysis, operations and technology so important? 

To help us frame the answer correctly, a mix of questions from our Maturity Assessment survey can come to our aid:

• On a scale from 1 to 10, how confident are you that the teams managing your weekly marketing initiatives can access primary data quickly enough to make the most of an opportunity?
• On a scale from 1 to 10, how confident are you that your teams understand and apply the suitable data models to their decision-making?
• How long does it take on average for an idea to become a hypothesis? And for a hypothesis to become a business initiative?
• Once a decision suggested by a model is accepted, what is its process of implantation? (People and roles, technology and dataset access)
• On a scale from 1 to 10, how confident are you that the Master Data Management tools you use can easily activate your hypothesis into action? 

Behind the answers to these questions, there are hundreds of stories that can inform the trained eye. Led by a Decision Management expert or a senior Citizen Developer, any team responding to this questionnaire could assess the current state, agree on a roadmap of priorities, and design a path of least resistance.

Breaking Down the Time-to-Action Process

What are we exactly doing here? By dissecting the Idea to Action journey, we can identify and isolate the low-hanging fruits of easily accelerated decisions. In addition, the more minor victories we can prioritize will motivate staff and give the rest of the organization the confidence necessary to gradually invest more resources into implementing this "behavioral" innovation.

More on “quick wins” here

In a recent project we conducted, a Tier 1 FMCG company experienced difficulties managing the forecast of multiple product lines. Pushed by product leaders and divisional CMOS, the group CFO sought expert support to improve the efficiency of his marketing budget distribution and the region's inventory assignment plans.  

For 12 months, the CFO engaged in a forecast optimization exercise where he empowered his C-level peers with more real-time data to move from idea to hypothesis but also identified with the CMOS the pockets of savings (campaigns optimization, transportation optimization and more), that saved the company $360M over two years in Europe alone. 

The ROI of this initiative started to emerge only eight weeks after its start. By applying a genuinely incremental design and an agile approach—and with the help of the decision management experts—the CMO and CFO identified small, incremental pieces of decisions within more significant decisions where time to action improve. 

The Devil is in the Details.

Senior leadership’s vision, a culture of management innovation and agile micro-budget management all contributed to this Tier 1 FMCG company’s overhaul success.

These three factors helped the organization make real-time, smaller, gradual decisions on things like inventory assignments or promotions management. In addition, it helped upgrade the job of roughly 70 people to perform value-adding activities, rather than over-expending time on Excel. Together, these changes helped the organization create a culture of data-backed hypotheses, not just opinions.

Real-time, fact-based budget distribution only fueled the success of this project. With management’s willingness to explore which micro-decisions could be accelerated, they were able to efficiently implement emerging master data management tools.

A company with a myopic ROI or an old-school Kanban mindset would have failed. A company that sees innovation as a "job" of the Product or the IT team would have likely only implemented the “new shiny object” with little context or backing. A management style that doesn't see business analysts at the center of its innovation would have created pockets of isolated tests rather than rethinking how data fuels real-time decisions throughout the organization.

While impossible to create a precise sequence of events without incurring hindsight bias, the moments of truth we identified in this use case are:

• A distinct openness to solving issues with a management and process-first attitude.  
• The selection of partners with more experience in decision management, data science, and design thinking, rather than the same old "domain knowledge."
• The creation of a cross-departmental, micro-budgets funded COE that educated the teams in "Train the Trainer" sprints.
• A willingness in the CFO to seek many small, quick wins.

As a result of this evolved Modus Operandi, distributed teams throughout the organization started using data science terminology during their meetings. They mastered the identification of the attributes influencing a model and its business outcome, as much as an understanding of the difference between systems of intelligence, systems of engagement, and systems of record. More on distinguishing between the different types of systems here.

Increasing the speed of a team's time to action requires management's willingness to devolve decisions, a partner willing to teach and complement a team's skillset and simple data management tools that can sit on top of clunky legacy systems. All of these together create the perfect recipe to make the integration between a hypothesis and its implementation faster.