Every month we refresh your Marketing Mix Model (MMM) so it reflects the most recent performance of your business and marketing efforts. Many customers might wonder: “If the model already exists, why does the refresh take time? Isn’t it just clicking refresh?”

A completely fair question. And the short answer is: MMM refreshes are not mechanical updates—they are scientific ones.

Each refresh is a full re-run of a statistical model across thousands of variables, millions of data points, and dozens of decisions that must be validated so we maintain accuracy, stability, and usefulness. The MMM you rely on to guide investments is only as trustworthy as the rigor behind each refresh.

This article pulls back the curtain on what actually happens every month, why timing can vary, and how you can help ensure the fastest possible turnaround.

1. MMM Refresh ≠ “Clicking a Button”

The MMM rebuilds itself from the ground up every time

Each refresh is not a light update—it’s a full new model run. Why? Because MMMs must reflect your most current reality. Businesses evolve every month: spend shifts, channels come and go, campaigns change, seasonality moves, experiments complete, and macro conditions shift.

The refresh process re-estimates:

• Incrementality of every channel

• Baseline (your underlying organic performance)

• How channels interact with seasonality

• Adstock/lag effects

• Relative efficiency rankings

• How results align with experiments that recently ended

• Any new channels, re-categorizations, or model adjustments requested

The model is not “tweaked”—it is rebuilt, retrained, and revalidated.

That rigor is what keeps the MMM useful and prevents misleading recommendations.

2. Data preparation is the hardest—and most variable—step

Before the model can run, data must be checked, validated, and corrected. This is the #1 dependency that can speed up or slow down a refresh.

What we must do each month

• Validate spend and impressions across all platforms

• Verify output metric data (often daily + geo-level)

• Ensure no missing days, mismatched IDs, or schema changes

• Reconcile manual data files (events, brand campaigns, organic channels, etc.)

• Check for anomalous values that would bias the MMM

• Ensure the same structure and naming conventions as prior months

This step alone can require substantial back-and-forth. Even small inconsistencies—like a missing column, a channel name change, or a data extraction delay—can hold up the entire process.

As noted in Paramark’s internal guidance, data sharing and data quality are historically one of the biggest sources of delay in MMM timelines.

3. Engineering + data science revalidate the entire model

Once data is clean, the engineering and data science teams run the model through a full statistical workflow:

Model training & selection

• Fit dozens to hundreds of model variations

• Test different assumptions (adstock, saturation curves, priors, seasonality structures)

• Select the best-fitting models based on statistical criteria

• Ensure results are consistent with business reality and historical trends

Safety checks

• Look for unusually high or low coefficients

• Validate that last month’s learnings still hold

• Incorporate experiment results when appropriate

• Review baseline stability

• Assess month-over-month volatility

• Ensure no channel is incorrectly over- or under-attributed

These checks are essential—your business makes real budget decisions based on MMM results, so we must avoid “false precision,” bias, or model drift.

4. Growth advisory reviews and interprets the output

Once the model is run, the Growth Advisory (GA) team performs a deep strategic review before anything is shared with you.

What GA does:

• Sanity-checks the model outputs

• Flags any unexpected shifts

• Aligns on whether changes reflect true business movement vs. data noise

• Reviews experiment performance and integrates learnings

• Prepares recommendations (e.g., experiments, reallocations, next steps)

• Writes the narrative summary you receive with each MMM refresh

This is not mechanical re-reporting—it’s expert interpretation.
It takes time because the value of the MMM comes from what it enables you to do next, not just the numbers themselves.

5. Why MMM refresh timing can vary month to month

Even with strong operational discipline, several factors introduce natural variability:

Data delays or changes

• Late delivery of platform data

• Manual files missing updates

• Schema or naming changes

• New channels or campaigns with new data structures

• Changes in spend granularity (geo, campaign, tactic)

Model changes

• Adding a new channel

• Re-categorizing channels

• Incorporating experiment results as priors

• Switching output metrics

• Updating seasonality structures or lag assumptions

All of these require additional validation time.

Business complexity

Some customers have:

• Many channels

• Multiple geographies

• Complex funnel structures

• Heavy seasonality

• Frequent strategic shifts

More complexity = more checks = longer refresh time.

6. What you can do to help MMM refreshes move faster

We love fast turnarounds as much as you do. Here’s how to help us move at maximum speed:

1. Maintain consistent data delivery

• Automate data sharing wherever possible

• Share data on time each month

• Avoid structural changes to files or schemas

• Use stable naming conventions

• If changes are required, alert us early

2. Keep manual files up-to-date

If any channel requires manual uploads (events, brand spend, revenue, etc.), timely updates are essential.

3. Flag and align Growth Advisory on major business changes proactively

Examples:

• New channel launches

• Campaign restructures

• Platform migration

• Funnel metric changes

• Geo expansions

• Pricing/promotions changes

• Major channel strategy changes

This helps us anticipate what will shift in the model and prepare adjustments.

4. Align on channel groupings in advance

Last-minute requests to split or combine channels require a full re-run.

5. Provide experiment data promptly

If a recent experiment just ended, we need clean results to incorporate into the next MMM.

FAQ: What does it mean when Paramark asks us to update this month’s manual sources before the MMM refresh can start?

It usually means Paramark is waiting on the last customer-managed inputs needed to treat the month as ready for modeling. Even when automated sources have already landed, a refresh may still pause until manual sources are updated and reconciled.

Common examples include channels such as influencer, events, organic initiatives, offline programs, partner-delivered files, or other spreadsheet-managed inputs that do not arrive through a fully automated feed.

When Paramark asks for those manual updates, the fastest path is to confirm:

• which reporting period is now complete,

• which manual channels or tabs were updated,

• whether the numbers should be treated as final or still preliminary, and

• whether the file structure or channel naming changed from prior months.

The reason this matters is simple: if one manual source is still missing, incomplete, or formatted differently, Paramark may hold the refresh rather than start from a partial month and risk rework, misattribution, or a misleading readout.

So when you hear that the MMM refresh cannot kick off until all manual data is in hand, the safest interpretation is that the team is protecting the quality of the model by waiting for the final customer-managed dependency to be finalized.

FAQ: When will I get this month’s MMM refresh?

The honest answer is that the exact delivery date can vary from month to month. A standard monthly refresh often lands after the latest month’s data is complete, validated, and reconciled, but Paramark does not treat the calendar alone as proof that a model is ready to deliver.

In practice, your refresh is usually delivered only after four things are true:

• all required data sources for the month have landed,

• any manual files have been updated and checked,

• the model has been rerun and validated,

• and the Growth Advisory team has reviewed the output and prepared the readout.

That means a question like “When do we get our August model?” is best understood as “When is August fully ready for a high-confidence refresh?” If data arrives on time and nothing unusual needs investigation, delivery is faster. If data is late, a manual source needs updating, a schema changed, or the team is validating an unexpected shift, delivery can move out.

If you are asking because you have a planning deadline, budget meeting, or leadership readout coming up, tell your Paramark team that date directly. In some cases, they may be able to share a directional timing update or an early read on what is on track versus what is still being validated, even if the final refreshed model is not ready to deliver yet.

FAQ: Should Paramark hold an MMM refresh while we sort out newly launched or newly listed channels?

Usually, yes if those channels are truly missing from the model inputs — and not necessarily if they are already present in the paid data and only need verification or remapping.

The key distinction is whether Paramark is dealing with a real modeling dependency or a naming / mapping check.

• If a channel is genuinely new or missing, the team will often pause before finalizing the refresh. Adding a net-new channel can change spend totals, channel definitions, priors, and the interpretation of the month, so pushing ahead too early can create rework or a misleading readout.

• If the channel is already flowing through paid data, the right next step is usually to confirm where it lives in the current mapping rather than assume the refresh must stop. In that case, Paramark may be able to continue once the team verifies whether the spend is already captured under an existing channel, campaign group, or naming convention.

A practical way to think about it is:

• Missing from inputs = likely hold until confirmed.

• Already in the data but unclear where = investigate mapping first.

The fastest way to unblock this is to share a concrete list of the channels in question and clarify:

• whether each one is net new, renamed, or already included under another paid-data label,

• whether historical months need a backfill or reclassification, and

• whether the change affects only reporting labels or the actual modeling structure.

Once that is clear, Paramark can make the right call: continue the refresh with confidence, or hold briefly to incorporate the missing channels correctly before the model is rerun.

FAQ: What should we include when Paramark asks us to update monthly event data?

If events are one of your manual sources, the goal is to give Paramark a clean, month-complete event log that can be reconciled alongside paid and other manual inputs before the MMM refresh begins.

A strong monthly event update usually includes one row per event with these fields:

• Event name

• Event date

• Attendee count, if available

• Spend associated with the event

• Event type (for example, trade show, conference, hosted event, or another internal category)

• Location such as state/city when your team tracks it

If your team uses a monthly manual-data canvas or spreadsheet, a practical events tab often uses columns like Event, Date, Attendees, Spend, Event Type, and State/City. Filling those same fields consistently for each completed event is usually enough to unblock reconciliation before the MMM refresh begins.

Just as important as the fields themselves is consistency from month to month. Use the same tab or template structure, keep naming conventions stable, and make it clear whether the month is final or still subject to updates. If a field is unavailable for a given event, it is better to leave a clearly understood blank than to shift the structure of the file.

If your team uses a shared monthly manual-data canvas or spreadsheet, the fastest path is usually to update the events section there rather than create a brand-new format. That helps Paramark reconcile the latest month quickly and reduces the chance that the MMM refresh is delayed by preventable formatting questions.

In short: when Paramark asks for event updates, they are typically looking for a complete, consistently formatted list of that month’s events and related metadata so the manual source can be validated without extra back-and-forth.

FAQ: We got a reminder asking individual owners to update this month’s manual data. What should each owner do?

If Paramark tags specific channel owners and asks for this month’s manual data, the request is usually about finishing the last customer-owned updates needed to unlock the refresh, not about re-explaining the entire data setup from scratch.

The most helpful response from each owner is to confirm four things clearly:

• the month or cutoff date covered (for example, data through the end of August),

• whether their assigned tab, sheet, or channel section has been updated,

• whether the numbers are final or still expected to change, and

• whether ownership changed and someone else should now be the point person.

If your team uses a shared monthly data-pull canvas or spreadsheet, the safest pattern is for each owner to update only the channels they own, keep the structure unchanged, and reply promptly if a source no longer belongs to them. That avoids chasing the wrong person and helps Paramark re-route gaps quickly.

If an owner cannot finish by the requested date, the next best update is not silence — it is a short status note explaining what is still missing, when it is expected, and whether Paramark can proceed without it. That gives the team a real chance to decide whether the refresh must wait or whether the missing piece is non-blocking.

In practical terms, these reminders are usually asking channel owners to do one of three things: update their section, confirm it is complete, or identify the correct new owner. The faster that happens, the faster Paramark can decide whether the month is ready to model.

FAQ: What should we check if digital spend or impressions are missing from the files Paramark ingests?

If Paramark says digital spend, impressions, or cost data are not showing up in the files it ingests, the first goal is to figure out whether the issue is upstream in the exported files or downstream in ingestion, mapping, or validation.

The fastest path is usually to confirm five things clearly:

• Whether the missing values are absent from the source files themselves (for example, GCS, parquet, warehouse export, or shared flat files), or whether they exist there and are only missing once Paramark reads them.

• Which reporting period is affected and whether the month is still partial, preliminary, or expected to backfill later.

• Whether the issue affects both spend and impressions or only one field, since that often narrows the problem to a specific export rule, column mapping, or transformation.

• Whether any schema, column names, file paths, table names, or campaign labels changed recently, even if the change looked minor on the customer side.

• Whether the gap is limited to certain channels, markets, dates, or inventory types rather than the entire paid dataset.

In practice, those checks help separate two common situations:

• The values never made it into the shared files. In that case, the next step is usually for the customer or their data team to fix the export, backfill the missing period, and confirm when the corrected files are final.

• The values are already present in the shared files. In that case, Paramark typically investigates ingestion logic, schema expectations, mapping, or validation rules before deciding whether the month is ready to model.

A practical status update that helps unblock the team quickly is: what dates are affected, whether the source files already contain the missing values, what changed recently, and whether a corrected backfill is coming.

So when Paramark flags a digital-spend gap, the safest interpretation is not automatically that the data is lost. It usually means the team needs to verify whether the break happened before the data reached Paramark or after it entered the ingestion and validation flow.

FAQ: What should I do if a spend number looks wrong in a newly delivered MMM refresh?

If a refreshed MMM has already been shared and you notice that a spend figure looks wrong, the most helpful next step is to flag the exact discrepancy clearly rather than assume the model itself is broken.

A useful update to Paramark usually includes:

• which month, market, and channel are affected (for example, US audio in August),

• the value shown in the refresh versus the value you believe is correct,

• whether the corrected number is now final or still subject to change, and

• whether this is only a source-number correction or might also reflect a mapping, grouping, or classification issue.

That context helps Paramark quickly distinguish between a few different situations:

• the source data itself changed after the refresh was prepared,

• a manual input was incomplete or misstated,

• the spend was present but mapped to the wrong place, or

• the issue is only in how the readout is being interpreted, not in the underlying model inputs.

In practice, the safest assumption is that a spend correction may require verification before anyone treats the refreshed readout as final. Sometimes the fix is limited to a reporting clarification. Other times the corrected input can change how that month should be interpreted and may justify a follow-up update from the Paramark team.

So if a number looks off, the fastest path is to share the exact corrected figure and scope of impact. That gives Paramark what it needs to determine whether the issue is a simple data correction, a mapping question, or a reason to revisit the delivered refresh.