Can Route-to-Market Expertise Be Codified?

In my previous article, I argued that digital Route-to-Market has made enormous progress in visibility. The next challenge is decision-making.

Technology can increasingly tell organisations what is happening across outlets, distributors, customers and field organisations. The harder question is what should happen next.

That raises a more fundamental question: Can the knowledge experienced Route-to-Market practitioners use to make those decisions actually be codified?

Can decades of judgement about segmentation, coverage, distributor performance, channel economics and field execution be translated into methodologies, diagnostics, decision rules and technology-enabled workflows?

The answer is not simply yes or no. Some elements of RtM expertise are highly codifiable. Others still depend heavily on context, experience and judgement.

Understanding the difference may become increasingly important as RtM technology moves from reporting towards recommendation and action.

Route-to-Market expertise translated into diagnostics, decision rules and commercial actions.

Much of expert judgement is already more structured than we think.

Experienced RtM practitioners rarely make decisions purely from intuition. Over time, they develop patterns. They know which questions to ask, which combinations of indicators signal a problem, and where the trade-offs sit between service, growth and cost.

Consider outlet coverage. An experienced practitioner might assess:

outlet potential;
current sales;
cost-to-serve;
geography;
order frequency;
channel characteristics;
available sales capacity.

From these inputs, they form a view on how an outlet or segment should be served. The final decision may require judgement, but much of the reasoning behind it can be made explicit. That is the starting point for codification.

From expertise to methodology

The first step is turning experience into a repeatable method.

Instead of asking: “What would an experienced RtM manager do?”

we can ask:

“What questions would they consistently ask before making this decision?”

Distributor assessment is a good example.

An experienced practitioner is likely to look at areas such as:

market coverage;
sales capability;
execution standards;
financial health;
management capability;
data quality;
operational discipline;
willingness to invest.

These can be translated into defined assessment criteria. However, that does not remove judgement. Rather, it creates a consistent framework within which judgement can be applied.

The same principle can be used in outlet segmentation, distributor selection, territory design, cost-to-serve, field-force sizing and customer prioritisation.

Once expertise has been structured into methodology, it becomes much easier for technology to use it.

From methodology to diagnostics

The next step is diagnosis. A dashboard can identify a problem. A diagnostic framework can start helping explain why it is happening.

For example, imagine a distributor whose numeric distribution is falling. The system might ask:

Is the market itself contracting?
Is outlet coverage declining?
Has salesforce productivity fallen?
Is call completion deteriorating?
Is the outlet universe accurate?
Are distributor economics constraining investment?

These questions form a diagnostic tree. Depending on the answers, the likely cause, and therefore the right intervention, begins to narrow.

Instead of simply telling the business: “Distribution is down.” technology starts helping answer:

“Why is distribution down?”

That is an important step towards decision intelligence.

From diagnostics to decision rules

Some RtM choices can go further and be translated into decision rules. Take distributor performance.

Strong market opportunity + weak execution capability → capability-building intervention.
Strong opportunity + persistently weak execution + limited willingness to invest → reassess distributor suitability.
Low opportunity + high cost-to-serve → reconsider the service or coverage model.

Or consider, to take another example, outlet coverage.

A high-potential outlet with attractive economics may justify direct field coverage.

A lower-potential outlet with high servicing costs may be better served through telesales, digital ordering, wholesale or distributor coverage.

The exact thresholds will vary by market and business. But the important point is that the logic behind the decision can often be made explicit.

From decision rules to technology-enabled workflows

Once methodology and decision logic are explicit, they can sit inside technology. For example:

A distributor diagnostic can become a guided workflow.
An outlet-segmentation methodology can become an automated scoring model.
A coverage review can trigger recommended changes to visit frequency.
A cost-to-serve model can suggest a different service approach.

The technology is no longer simply storing data. It starts to orchestrate a decision process:

Signal → Diagnose → Recommend → Decide → Execute → Measure

For example:

Signal: field productivity drops in a territory.
Diagnose: the system reviews outlet density, travel time, call completion, potential and route design.
Recommend: adjust territory boundaries and move selected low-value outlets to a lower-cost service model.
Decide: the commercial team applies market context.
Execute: routes and outlet assignments are changed.
Measure: performance is tracked to see whether the intervention worked.

This is where RtM expertise becomes more than advice. It becomes part of a repeatable operating system.

But not everything should be reduced to an algorithm

Codification has limits. For example:

Route-to-Market decisions often involve factors that are difficult to capture fully in data.
A distributor relationship may have strategic importance.
A seemingly inefficient route may protect a critical competitive position.
An outlet may have low current sales but strong future potential.
A market may be changing faster than historical data can reveal.

There are also situations where several commercially reasonable choices exist and management must decide which trade-off to accept. The goal should therefore not be to eliminate human judgement. Rather, it should be to use technology to make routine reasoning more consistent and allow expert attention to focus where context genuinely matters.

So, the future is unlikely to be: human judgement or technology.
It is much more likely to be: structured expertise + technology + human judgement.

Why codification matters

Codifying RtM expertise creates several benefits.

Consistency Good practice becomes less dependent on who happens to be making the decision.
Scale Expert thinking can be applied across more markets, territories, distributors and outlets.
Speed Problems can be diagnosed and responses considered more quickly.
Capability building Commercial teams learn through the decision process rather than simply receiving an answer.
Institutional knowledge Expertise becomes an organisational asset rather than remaining largely in individuals' experience.

And perhaps most importantly:

Technology enablement Methodologies and decision logic can be embedded in analytics, workflows, recommendation engines and AI.
As RtM platforms move closer to decision-making, their differentiation may increasingly depend not only on the technology itself, but on the quality of the commercial logic embedded within it.

RtM intellectual capital may become more valuable in a digital world

Historically, much RtM expertise has been delivered through people.

The methodology may sit behind the work, but value is often realised through experienced practitioners interpreting the situation and advising the business. Digital technology creates another possibility.

Expertise can increasingly be captured in scalable forms:

diagnostic frameworks;
assessment tools;
scoring models;
benchmarks;
decision trees;
workflows;
recommendation engines;
AI-enabled decision support.

This changes the strategic value of domain expertise. A methodology is no longer simply a tool used by a consultant. It can potentially become part of the operating logic of a digital platform. And the better that logic becomes, the better the technology becomes at moving from visibility to meaningful action.

The real opportunity: codifying the questions, not just the answers

Codification does not always mean programming a fixed answer. Often, the more valuable thing to capture is the decision process:

What should we examine?
Which variables matter?
What relationships should we test?
What warning signals should trigger further investigation?
Which interventions should be considered?
Where is human judgement required?

Experienced practitioners are valuable not simply because they know the answer. They know how to frame the problem. Capturing that reasoning may be one of the most important opportunities in the next phase of RtM technology.

Looking ahead

Digital RtM has already made information dramatically more accessible. The next stage is making commercial reasoning more scalable. That means turning tacit expertise into methodology, methodology into diagnostics, diagnostics into decision logic, and decision logic into technology-enabled workflows.

Not every RtM decision can, or indeed should be automated. But much of the reasoning behind good RtM decisions can be made more structured, repeatable and scalable. As technology moves from reporting towards recommendation and action, that structured domain knowledge may become one of its most valuable ingredients.

The question is therefore no longer simply: Can Route-to-Market expertise be codified?
Increasingly, the more interesting question may be: Which parts should we codify first?

In my next article, we will explore what RtM technology and AI actually need to know before they can make commercially useful recommendations. In the meantime, I would really appreciate your comments.