These 5 Factors Will Tell You When to Refresh Your Marketing Mix Model

At a glance:

To determine the right Marketing Mix Modelling (MMM) frequency, brands should weigh five key factors: data quality, business speed, analytical capability, decision cadence and the need for stability versus responsiveness. While high-quality data and automated pipelines enable monthly or always-on models, companies lacking these resources should stick to a quarterly cadence aligned with financial planning cycles to ensure outputs are actionable.

These are questions I’m asked by marketers on an increasing basis. In addition to knowing how often they should run MMM, brands are also asking if keeping it “always on” is the right approach.

The answer (drumroll please) is that it largely depends on your business cycle, planning periods, ability to respond to change, data cadence, goals and budgets. Sorry to be anticlimactic, because there is no one perfect answer. But here I’ll outline some general rules of thumb to determine frequency, and the specific factors that will influence which is right for your business.

Which frequencies are recommended for which purpose?

Monthly is suitable if you have high data quality, fast-moving markets, frequent campaign changes or need near real‑time optimization (e.g., ecommerce, retail with weekly promotions, etc.). Monthly updates require automated pipelines to deliver effective and careful regularization and to avoid volatile recommendations.

Quarterly is a good default for most businesses who have both the media investment levels throughout the year and the required budget to deliver the analytics. Quarterly updates balance responsiveness to changing market conditions, campaign cadence and sufficient data volume for stable models. They let you capture seasonality, new campaigns, pricing changes and external factors.

Biannual is suitable for slower markets, stable media mixes or when data is limited. Less frequent updates reduce model maintenance cost but limit the opportunity to react throughout the year, making it tougher to maximize the immediate impact of emerging opportunities.

Event‑driven updates are the hardest to set up efficiently. Being able to rebuild or refresh the model promptly when major structural changes occur (e.g., large product launches, channel additions, pricing shocks, pandemics or significant creative/strategy shifts) can provide great opportunities to be as agile as possible, allowing you to maximize the impact of emerging opportunities or to negate competitor impacts. This can be delivered ad hoc with minimal setup. However, for clients with a portfolio of products, establishing “always ready” data feeds improves agility without requiring you to constantly measure everything.

What five factors do I need to consider to determine MMM frequency?

Now that you know which frequencies are recommended for which purpose, it’s time to weigh different factors that can determine which frequency is right for you:

1. Data quality and volume: Frequent updates need robust, timely data (i.e., sales, spend by channel, marketing metrics, pricing, distribution). Low volume or noisy data favors less frequent updates.
2. Business speed and complexity: Fast-moving categories and many short campaigns benefit from more frequent models. Complex channel ecosystems need more attention.
3. Analytical capability and automation: Monthly refreshes require automated ETL, model pipelines and governance. If you lack that capability, quarterly is more practical.
4. Decision cadence: Align model cadence with planning cycles (monthly finance reviews, quarterly planning, annual budgeting) so outputs feed decisions when needed.
5. Stability vs. responsiveness: More frequent updates increase responsiveness but can introduce noise. Use smoothing, priors and holdout validation to maintain stability.

So, is “always on” right for you? Continuous, near real‑time updates can be valuable, but are not universally necessary. An always-on approach is appropriate if you have:

1. High-frequency, high-quality data streams and automated modelling infrastructure
2. Rapidly changing campaigns or channels where quick re-allocations materially affect outcomes
3. A culture and governance that can act on frequent recommendations

A practical approach is to start with quarterly updates as a baseline, then supplement with monthly refreshes for high-impact channels or markets that require rapid agility. Automate data pipelines and implement quality checks before increasing cadence. Adopt a hybrid strategy that pairs an “always‑on” monitoring layer (like dashboards) with scheduled full MMM rebuilds and event‑driven rebuilds when there are shifts in performance. Finally, be transparent with stakeholders about model uncertainty and stability. To validate your recommendations, pair MMM outputs with tactical experiments like incrementality tests and holdouts. 

In summary, balance the value of more timely insights against data, capability and cost. For most organizations, quarterly updates combined with event‑driven rebuilds and an “always‑monitor” layer offer the best mix of stability and responsiveness. If you can support it technically and operationally, move toward more frequent updates where they materially improve decisions.