GTM Modeling: Knowing where to invest across your customer experience ecosystem

24% of marketing teams are not using any kind of modeling to measure the impact of their efforts. That’s nearly a quarter of businesses still allocating marketing spend by gut feel alone. Another 35% rely on attribution methods that are rapidly falling out of favour. Only 41% use Marketing Mix Modeling (MMM).

MMM, if you’re unfamiliar, is a type of econometric model that helps brands understand how their marketing investments (primarily across paid media channels) contribute to business outcomes like sales or revenue. Like all regression-based models, MMM reveals correlation rather than causation. It estimates how strongly changes in variables (like media spend or promotions) are associated with outcomes (like sales or leads), while controlling for things like seasonality. 

MMM has proven especially effective for consumer-facing brands: studies from Nielsen and McKinsey show 20–30% ROI lift and 5–15% sales uplift, respectively, when used to inform allocation decisions.

But these models can only work with what’s measurable and what can be cleaned, structured, and fed into a regression. The more intangible things like intuitive knowledge or creative resonance that can’t be captured in clean data, sit outside the bounds of what these tools can interpret. So the model should be just one part of a broader decision-making arsenal, not a replacement for judgment or taste.

By analyzing historical data over time, the models quantify the incremental impact of each channel, allowing marketers to optimize where and how they spend. It enables better capital allocation decisions across media, pricing and promotions (which is what most MMMs focus on). Regression models like MMM are forecasting engines. By simulating different levels of investment in media or pricing, you can estimate their projected impact on sales or ROI before making a move. This shifts the model from merely diagnostic to more strategic and actionable, allowing for more informed planning and scenario testing. 

But as we’ll explore below, MMM can (and should) be expanded to evaluate other commercial levers like brand, product, or customer experience.

The Limit of Marketing-Only Models

MMM has historically been used as a media allocation tool. That’s useful, but limited. It does not capture the full customer or brand experience. For that, we would need a model that captures a company’s full Go To Market plan. 

A true GTM model should:

• Isolate uplift across multiple touchpoints

• Reflect how customers actually experience the brand

• Help shift investment across functions, not just reallocate ad dollars

There are three areas that these models ought to capture in order to deliver a more holistic impression of a brand’s activities: 

1. Culture 

The trends and conversations that customers see today influence what they’ll buy tomorrow. Whether it’s an emerging design aesthetic on Pinterest, expressions of shifting values on TikTok, or an evolution in lifestyle behaviors on Reddit, these signals surface long before they show up in traditional research or reporting. And they’re moving faster than ever. The pace at which these meanings and preferences evolve is cultural velocity. There are two reasons this matters:

First, if your GTM cycle spans months (or even years), the initial brief may no longer reflect what the market cares about by the time you launch. Tracking cultural momentum lets you adjust creative, product positioning, or campaign emphasis so you’re in sync with the market when it counts.

Secondly, for teams deploying active budgets, cultural velocity can guide short-term investment. If a theme or behavior suddenly spikes, you can shift media spend, partner selection, or influencer engagement so you can amplify your message at the time attention is growing. 

2. Customer Experience 

A clunky checkout, slow delivery, or high return rate not only influences the impression a customer has, but it can meaningfully impact their commercial contribution. 

For example, Google and Deloitte found a 0.1s improvement in site speed can result in retail consumers spending almost 10% more, while Walmart concluded that for every 1 second improvement in page load time, conversions increased by 2%.  

Likewise, we can see the impact of delivery times on repurchasing behavior.

3. Brand 

Brand builds memory, meaning, and margin. Despite a long list of sources demonstrating the impact over time (see Binet & Field, Sharp, System1, WARC, etc), brand investments often lose out to short-term tactics because they’re harder to measure. If we can model the impact of brand behavior, we can actually tie our brand-led activities to commercial impact over time and justify investment with statistical backing.

Implementation  

So, to incorporate these three areas into our modeling, what needs to happen? 

We can treat culture, experience and brand the same way we treat media: as structured inputs into a weekly regression model. Three additions to a baseline MMM would significantly expand its utility:

1. Culture Index

This is a weekly index that captures the momentum of emerging topics and themes relevant to your category. It draws from public attention data across platforms like TikTok, Pinterest, Reddit, YouTube, Google Trends and Substack. Some of this is available through APIs, others through licensed platforms or even custom scrapes of trending queries and content volume.

This index doesn’t try to measure every conversation, it just tracks the top 10–20 cultural signals that matter to your customer. The model then correlates those with uplifts in paid media performance, organic engagement, or conversion activity.

2. Experience Quality Index

This is a composite score created from customer experience data. Metrics that most CX, product or ecommerce teams are already tracking. Depending on your specifics, it could include:

• Funnel Friction: Bounce rate, cart drop-off, scroll depth, time on checkout

• Operational: Delivery delays, return rates, tracking issues

• App/Site Stability: Page load time, app crash frequency, form errors

• Perception Signals: CSAT, NPS, post-purchase sentiment

Each of these is normalized, tracked weekly and combined into a single model input. But unlike a typical ops dashboard, EQI isn’t just a diagnostic, it’s tied to commercial outcomes. By using regression to correlate dips in EQI with conversion rate, repeat purchase or CAC:LTV ratio, the model can elevate CX  to a lever of strategic investment. There’s also the opportunity to incorporate predictive churn scores and behavioral intent signals to deepen the EQI’s predictive value. 

3. Brand Momentum Index

Brand signals are typically dismissed as too fluffy to model. So in order to find a workable solution for regression models, we can create an index comprised of two components:

• Brand Momentum: a composite of brand equity trackers, Google Trends, earned coverage and share of voice.

• Activation Flags: binary markers for big brand moments such as product drops, pop-ups and events or creator partnerships. 

  
  

Noise

Digital signals, especially on social, can be messy. Spam, bots, and short-lived hype cycles can distort what’s really going on. To mitigate against this, it’s best to source from trusted platforms, use cleaned datasets, combine multiple sources into composite indices, smooth them over time and look for consistent patterns. This takes diligence, so it’s not just a set-and-forget play. 

Above: GTM Model Framework 

Model considerations 

While a GTM model extends beyond traditional MMM, it still relies on regression techniques that are fundamentally correlational. That means it can show strong associations between inputs and commercial outcomes, but it can’t prove causality on its own. To build trust in the results, each model should be accompanied by other techniques such as holdout validation or multicollinearity checks. In more mature setups, this can be complemented with causal inference methods like geo experiments. But even without these layers, a well-structured and consistently refreshed GTM model offers a major step forward.

What this unlocks for GTM teams

With these inputs, marketing teams can look beyond media optimization and unlock a true decision intelligence engine for their GTM strategy. It enables:

• Brand and CX teams to quantify their contribution 

• Marketing to shift from channel efficiency to commercial effectiveness

• Execs to see tradeoffs and simulate scenarios across product, media and experience (not as silos) 

While this approach sharpens day-to-day decisions, its real value shows up in quarterly planning. By quantifying the contribution of the full GTM plan in a single model, leadership can make more confident trade-offs, such as shifting budget across teams, rebalancing GTM channel spend, or timing major pushes based on forecasted impact. 

Getting started

You don’t need a data science team to begin. Start with a simple regression using existing media and sales data, then gradually layer in one or two new inputs before advancing to something more sophisticated. The key is treating culture, CX and brand as measurable inputs, not afterthoughts. The complexity can evolve over time.

An applied example.  

Let’s say you’re a skincare brand.

You’ve just launched a new brightening serum targeted at younger consumers. The campaign includes Meta and TikTok spend, a dermatologist influencer partnership, a 15% launch promo, and a new educational landing page. Three weeks in, sales are soft. Paid performance looks flat. The media team may suggest pulling spend. The sales team may want to discount. The brand team may be angling for another influencer collaboration.

A GTM model provides a clear story. Like a traditional MMM, your model includes:

• Paid media spend and formats (e.g. TikTok, Meta, YouTube)

• Promotions (e.g. 15% launch offer)

• Pricing fluctuations

• Retailer vs. DTC sales splits

• Baseline sales and seasonality

From this, you already know:

• Your TikTok spend is your most elastic channel, but only during launch windows.

• Email is showing diminishing returns

• The 15% promo had only a marginal uplift: ROI negative when factoring in margin hit

That’s where a typical model would stop. But your GTM model adds three more signals: Culture, Experience, and Brand.

Your Culture Pulse Index, which tracks weekly demand signals around ingredient trends, self-care rituals, and TikTok discourse, spiked two months before your launch. The conversation was peaking early then flattened as you launched. Your media pulse missed the cultural moment. But now, a new conversation around “skin barrier repair” is trending. That insight prompts a creative pivot to align your messaging with what’s peaking now, not two months ago.

Your Experience Quality Index picks up that cart abandonment jumped with app performance dipping for Android users. The model shows that even with strong media, conversions fall 4% during low EQI weeks. So the problem isn’t the campaign. It’s CX friction. Rather than cutting media, you prioritize fixing the app.

Your Brand Momentum Index shows a clear pattern: branded search volume increases in the week following a physical activation like your recent LA pop-up. The uplift doesn’t come during the event, but lags by 7–10 days. This lag effect helps you time follow-up campaigns, SEM or targeted emails to capitalize on renewed interest, rather than letting momentum fade.

With traditional MMM alone, you’d optimize media and pricing and maybe push another promo. With GTM modeling, you learn to:

• Time your media to cultural spikes

• Fix experience bottlenecks that suppress conversions

• Scale the right brand activations

You still get the core MMM value: elasticity across channels, promotional lift and spend optimization. But you gain a richer picture of why certain levers work (or don’t), and where to shift capital across marketing, CX, product, or brand.