Driving Outcomes Through
Data-Driven Processes
In the early 2000s, Kodak was at the top of the photography world. Internally, Kodak’s leaders aspired to “lead the digital imaging revolution,” but their behaviours told a different story. They continued investing in traditional film while competitors advanced in digital photography. By the time Kodak tried to catch up, it was too late, the market had shifted. The company filed for bankruptcy in 2012.
This example illustrates a common problem in both personal and organisational development: the disconnect between stated goals and the actions required to achieve them. Kodak wanted the outcome, digital leadership but were unwilling to adopt the behaviours necessary to realise it.
Success in strategic execution does not come from what we claim to want, but from what we consistently do. Organisations that thrive are those that align their ambitions with behaviours they can control and sustain. To achieve sustainable success, businesses must transition from outcome-obsessed planning to behaviour-led execution. Focusing on controllable actions rather than uncontrollable outcomes mitigates the risks of inertia, misjudged progress, or demotivation due to external conditions.
The Power Of Behavioural Consistency
The saying “it takes 15 years to be an overnight success” rings true for most people we perceive as having instant success. Consistent, small efforts compound over time to produce significant results. Whether it is writing a book, building a relationship, or growing a business, success is often the result of persistent behaviours rather than a single, monumental effort.
In the business world, however, the focus is often on outcomes, Key Performance Indicators (KPIs), sales targets, and other goals that may not be entirely within an individual’s control. For instance, setting a sales target of $12 million might seem motivating, but if a salesperson is already driven and working hard, this target might not push them further. Instead, it could lead to stress, especially when external factors like market conditions are unfavourable.
A more effective approach is to focus on behaviours that are within the team’s control. For example, instead of setting a sales target, you might ask your sales team to contact 50 companies each week. This behavior is measurable, actionable, and within their control. Over time, improving the process around this behaviour will lead to better outcomes naturally.
However, it is important to consider the consequences of the behaviours you incentivise. If you reward salespeople solely for acquiring first calls, they may ask for a lot of meetings with people not yet ready to buy.
Just as it takes effort to build a relationship, it takes continuous effort to sustain it. In business, this principle applies to maintaining customer relationships, market position, and overall performance.
A common mistake is to change behaviours once a certain level of success is achieved. For instance, a company might focus on acquiring high-profile clients at the expense of nurturing existing relationships. This shift can lead to a decline in the established market and a loss of what made the company successful in the first place.
Aligning Behaviours with Goals
One of the most critical components of operational success is the selection of the right behaviours, those that directly support strategic objectives while remaining within an individual’s or team’s control. This alignment ensures that daily actions are not only measurable and repeatable but also meaningfully contribute to overarching business outcomes.
Start with the End in Mind
The process begins by clearly defining the intended outcomes. Whether the goal is to increase customer retention, improve onboarding efficiency, or boost sales velocity, every outcome must be traceable to a set of contributing behaviours. This avoids the common pitfall of misalignment, where KPIs are set in isolation and teams are left unclear on how to influence them through day-to-day work.
For example, if a SaaS company sets a goal of reducing churn by 20%, behaviours that might support this include proactive client check-ins within 7 days of inactivity, usage pattern reviews every 30 days, or personalised onboarding workflows based on role segmentation. These are specific, actionable steps that have a plausible causal link to the desired KPI.
Evaluate Controllability and Predictive Power
Not all behaviours are equally valuable. The ideal behaviours to focus on share two key attributes:
They are within the control of the team or individual: A marketing executive cannot control how a prospect behaves, but they can control how often campaigns are tested, how segments are targeted, or how promptly leads are handed off.
They correlate strongly with desired outcomes: This requires examining historical data to determine which behaviours are associated with performance improvements. For instance, you may find that leads contacted within 5 minutes of website form completion convert at 3x the rate of those contacted after 30 minutes. This insight can then inform behavioural targets for your BDR team.
Create Feedback Loops and Iterate
Selecting the right behaviours is not a one-time activity. Markets evolve, customer preferences shift, and internal capabilities grow. For this reason, organisations should build feedback loops that regularly reassess the effectiveness of behavioural targets against performance outcomes. A behaviour that once correlated with success may lose relevance, or a previously overlooked metric may emerge as predictive after system changes or market shifts.
By embedding these review cycles into operational governance, organisations can continuously optimise their behavioural strategies to stay aligned with both their goals and their operating environment.
“You do not rise to the level of your goals. You fall to the level of your systems.”
James Clear, Atomic Habits
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Mapping Processes
Towards Behaviours
Once you have selected what are the right behaviours you wish to encourage and track, you then need to implement and map processes. This is easily one of the most challenging tasks in business, primarily because it is inherently tedious and difficult to get right.
The first thing to consider is that employees often worry that process mapping might lead to them losing their jobs, even if that is not the case. It is uncomfortable for anyone to have someone watching over their shoulder, and it can disrupt their workflow. As such it is crucial to handle this process with care and ensure everyone understands that the goal is to improve the process, not to replace them.
Where to Begin
The first step in any process mapping is to uncover the variables that you control.
Scenario: You want to optimise the cold outreach process on linkedin. The variables you control are:
Target audience (e.g. industry, role, company size) Start by defining your lead criteria. This could involve creating segments based on industry, job title, or other relevant factors. Your process should include steps for continuously refining these criteria based on feedback and results.
Message frequency and scheduling cadence. This might include setting rules for initial contact, follow-ups, and final attempts before moving a lead to a different status. (e.g. “cold lead”).
Message content tailored to personas. Develop templates for your outreach that can be customised for each segment. The process should also include a review or feedback loop to regularly update and improve these messages based on their effectiveness.
Sequencing of touchpoints across platforms.
Next, Factor Out Edge Cases
When you start mapping out processes, it is crucial to remember that the real world is inherently complex and unpredictable. There are countless variables and possibilities, and it is impossible to account for every single outcome right from the start. This is why it is essential to focus on the most common scenarios, the situations that occur 90% of the time, when you are beginning to map out and optimise your process. By focusing on these, you can create a streamlined and efficient process that works well most of the time.
In our example, if you are optimising a sales process, begin by focusing on the typical customer journey that most of your clients follow. This might include the most common objections they raise, the usual questions they ask, and the standard sequence of interactions that lead to a sale. Edge cases, are still important to record. Initially, it is sufficient to acknowledge these edge cases and have a basic plan for dealing with them as they arise. Often, these scenarios can be addressed on a case-by-case basis. This approach prevents you from overcomplicating your process with too many contingencies that might never be needed.
However, if you start to notice that a particular edge case is happening more frequently, it’s a signal that you need to pay closer attention to it. When a previously rare situation starts becoming more common, it may require its own policy or a more detailed part of your process. For example, during the COVID-19 pandemic, many organisations suddenly had to address new questions and challenges, such as whether employees should come into the office. What started as a rare concern quickly became a common one, necessitating a formal process or policy.
One of the most important things to remember when developing or refining a process is to keep it simple. A process that tries to account for every possible outcome will quickly become overly complex and difficult to follow. If a process is too complicated, people are less likely to use it correctly, or they might abandon it altogether.
Avoiding Misinterpretation: Behaviour vs. Signal
When you are considering the effectiveness of your processes, it is crucial to think deeply about behaviours, signals, and the underlying philosophy that guides your actions. These elements are interconnected and understanding their relationship can significantly impact how you approach decision-making, especially in areas like sales, marketing, or customer engagement.
Say your guiding principle is that you do not want to waste time or resources on people who are not genuinely interested in what you are offering. Issues arise when you start to conflate signals, like whether someone has read a message, with actual behaviours, such as their level of interest or intent to engage. It is very tempting to make assumptions based on these signals, but doing so can be misleading and in some cases, counterproductive.
For instance, if you are sending messages to potential leads on LinkedIn, you might decide that after sending four messages with no response, the person is not interested. That seems reasonable on the surface. However, this approach assumes that the lack of response is a clear signal of disinterest, which may not always be the case. What if the person has read your message, but their LinkedIn settings prevent it from showing up as “read”? Or what if they are indeed your ideal target but simply prefer a different communication channel, like email or a phone call, over LinkedIn? These scenarios illustrate the danger of making assumptions based on limited data. While assumptions can help streamline your decision-making process, they can also lead you to prematurely discard valuable prospects or misinterpret their behavior.
Human behavior is highly variable and context-dependent. People may respond differently based on timing, the communication channel used, or even their current workload. By incorporating multiple touchpoints in your process, you allow for these variations and reduce the risk of misinterpreting a single signal as a definitive behavior.
Creating A Flow Chart
When you are mapping out a process, especially something complex like sales or customer engagement, using a flowchart is one of the most effective methods. A flowchart visually represents the sequence of steps, decisions, and actions that make up the process. But the key to a successful flowchart is not just its structure, it is about keeping it as simple and clear as possible.
A complicated flowchart can overwhelm the people who need to follow it and may even lead to them abandoning the process altogether. The goal is to strip the process down to its essential elements, removing anything that is not absolutely necessary.
One thing we advocate for is to include your reasoning behind decisions directly in the flowchart. While some might argue that a flowchart should be strictly procedural, we believe that making your thought process transparent can be incredibly valuable. By doing this, you are not just laying out steps to follow, but also providing context and rationale for why certain steps exist.
Including assumptions and reasoning helps prevent one of the most common complaints people have about processes: that the rules do not make sense or are not clear. When people do not understand the reasoning behind a rule or a step in the process, they are more likely to deviate from it, either intentionally or unintentionally. This deviation can go unnoticed until it causes a problem, at which point it becomes difficult to address because the underlying issue is a lack of understanding, not just a failure to follow directions.
Once you have simplified the flowchart as much as possible, test it out in real-world scenarios. Observe how well it works in practice and be open to feedback. It is rare to get a process perfect on the first try, so be prepared to iterate and refine the flowchart based on what you learn during testing. This might involve further simplification or adjusting certain steps based on how they’re working in practice.
Look For Bottlenecks
Once you have mapped out your process, the next step is to start identifying areas where you can optimise. One key aspect to focus on is identifying bottlenecks, those points in the process where progress slows down or stops altogether.
For instance, consider a scenario where you are in the middle of a sales cycle, and you receive a technical question from a prospect that you cannot answer on your own. If there is only one person in your organisation who can address that question, and they are frequently busy, this creates a bottleneck. This person, often a subject matter expert (SME), becomes a critical dependency, and any delay on their part can slow down the entire process.
To address this, you might consider implementing a protocol to minimise the impact of such bottlenecks. For example, if the SME has not responded within a day, you could proactively communicate with the prospect, saying something like, “I do not have the answer to your question right now, but I can arrange for a call with our expert.” This keeps the conversation moving forward and shows that you are actively managing their request, even if you don’t have the immediate solution.
Look For Redundancies
The next crucial aspect to focus on is identifying and eliminating redundant steps, those activities within your process that do not add value or make sense. These steps often exist simply because “that is the way things have always been done.” By critically evaluating each part of your process, you can significantly streamline operations and improve efficiency.
This issue of redundancy is often compounded by a culture of resistance to change. In many organisations, especially those with a long history like manufacturing, there is a mindset that if something is not broken, it does not need fixing. The idea is that if a process has been working well enough, there is no need to risk changing it. However, this approach can lead to inefficiencies and missed opportunities for improvement. Just because a process functions does not mean it is optimised. It is essential to question whether each step in the process still serves a vital purpose or if it is merely a holdover from an earlier time.
“Nothing is less productive than to make more efficient what should not be done at all.”
Peter Drucker
The Data You Need
To Inform Your Process
A common pitfall is the creation of process is the need to track the progress of that new process creates excessive administrative tasks. This is particularly true if you rely on spreadsheets or manual logs to track interactions and responses.
For instance, let us say you are running an outreach campaign where you send messages to a large number of people. If you do not have an automated system to track who received which message and what their response was, you are left with the cumbersome task of manually recording this information. This often means creating and maintaining a spreadsheet where someone needs to log each interaction: which person received which message, on what date, and what their response was. Not only does it consume a significant amount of time, but it also introduces a high risk of errors. This is why having an automated way in which to track and collect data is so important.
Mapping Out Your Data Sources
With the process now laid out, we must now see which data sources are at play. What exactly do we mean by a data source? Essentially, it is anything that generates information; this could range from a website to a software tool, a file, or any system that collects and stores data that aids in our decision-making process.
Continuing our example of mapping a sales process. You may find the data inside of a CRM, an email tool, & linkedin. Often to get a complete picture, you will need to integrate multiple data sources and here is where it gets interesting, and perhaps counterintuitive. While the natural instinct might be to gather as much data as possible to cover all bases, the reality is that more data is not always better. What you really want to do is identify the smallest, most impactful set of data that can effectively inform your decision.
This approach is crucial because one of the biggest pitfalls for large companies, especially those with significant resources, is the tendency to over-collect data. They often shift from considering what they need to know to make a decision, to what would be nice to know. This leads to an overwhelming amount of data that not only complicates the decision-making process but can also result in decision paralysis. With too much information, it becomes increasingly difficult to discern which data points are actually relevant and critical to the decision at hand. The result is a slower decision-making process, increased costs, and in some cases, confusion rather than clarity.
So, the strategic takeaway here is to focus on simplicity and relevance. By honing in on the most essential data sources, those that have the biggest impact on your decision-making, you can streamline your processes, reduce unnecessary complexity, and avoid the costly mistake of data overload.
Identify Leading Indicators
Once you have identified the key data sources, the next step is to focus on “leading indicators.” These are the variables within your process that you can actively control or influence, and that have a direct impact on the outcomes you’re aiming to achieve. The idea is to pinpoint the factors that, when adjusted, will lead to meaningful improvements in your process and ultimately drive better results.
To illustrate this, we will take the example of cold contacting potential clients on LinkedIn, a scenario many of us are familiar with. In this case, there are numerous variables you could consider when deciding who to contact. These might include the size of the company (small vs. large), the industry they operate in, the job titles of the individuals, or even demographic factors like gender. Each of these variables could potentially influence the success rate of your outreach, but not all of them will be equally important.
This is where your existing data (your first-party data) comes into play. You can analyse the data you have already collected from past outreach efforts to identify patterns and trends. For instance, you might look at your historical data and discover that a significant percentage of the prospects who converted into customers were product managers, while very few marketing managers showed any interest. Armed with this insight, you can make informed decisions about where to focus your future efforts.
Extract, Centralise, Model
Your Data Points
Once you have mapped out your processes and identified the key data sources and leading indicators, the next crucial step is to centralise this data. This involves extracting the relevant data, modelling it, and integrating it into your decision-making framework. The process of modelling the data is essentially about overlapping various data sets, applying your bespoke business logic and ensuring that the data is structured in a way that directly supports the decisions you need to make.
This step is more than just gathering data, it is about refining and transforming that raw information into actionable insights. Depending on your specific needs, you might automate certain aspects of this process, feeding the data into algorithms that can make real-time decisions. Alternatively, the data might be displayed on dashboards where human decision-makers can analyse it and take action. Either way, the process of modelling the data is essential, as it turns abstract numbers into meaningful, decision-driving insights.
Applying Data To Your Processes
One of the most powerful aspects of data is its objectivity. Data, in its purest form, does not lie. It provides a clear, unbiased reflection of reality, assuming you have structured and interpreted it correctly. This is particularly valuable when you are trying to optimise a process because the data does not come with opinions or preconceived notions. It simply tells you what is happening, allowing you to identify which parts of your process are working effectively and which are not.
Once you have objective data points integrated into your processes, they serve as a feedback mechanism that can continuously inform and refine those processes. This is where the power of data-driven decision-making really comes into play. By focusing on the variables you can control, data allows you to conduct A/B testing, an invaluable tool for fine-tuning your approach. Over time, as you continue to test and optimise, you can narrow down your approach to a point where you know with high confidence what the result will be.
This approach shifts the focus from worrying about Key Performance Indicators (KPIs) to concentrating on the processes themselves. When your processes are consistently being improved based on solid, data-driven insights, the KPIs will naturally follow.
Conclusion
Success lies not in obsessing over distant outcomes but in refining the processes and behaviours that are within your control. When these are informed by objective, strategically relevant data, organisations are empowered to scale sustainably, adapt rapidly, and outperform consistently.
At 173tech our mission is to help businesses turn complexity into clarity, enabling them to map, measure, and optimise every layer of their operations. The combination of behavioural precision and data integrity is not just a competitive advantage; it is the foundation of modern, scalable success.
