When Attribution Models Are Just Fancy Excuses

We've all been there. Your Google Ads performance is looking shakier than a house of cards in a hurricane, ROAS is dropping faster than your confidence in Q4, and suddenly everyone's an attribution expert. "It's the attribution model," they cry. "We need to switch to data-driven!" "First-click is clearly wrong!" "What about view-through conversions?"

Hold on. When did attribution models become the marketing equivalent of "the dog ate my homework"?

The Great Attribution Theatre

Attribution modelling has morphed from a useful analytical tool into an elaborate performance art piece where marketing teams spend more time justifying their existence than actually improving results. The harsh truth? Most attribution discussions aren't about better measurement: they're about better excuses.

Here's the thing: attribution models were originally designed to help you optimise campaigns, not to provide convenient explanations for why your performance marketing isn't performing. Yet somewhere along the way, attribution became marketing's security blanket, the sophisticated-sounding reason why budgets should stay the same despite declining returns.

When Models Become Convenient Narratives

The most telling sign that attribution has become your fancy excuse? You're spending more time explaining your model than improving your campaigns. If you find yourself in endless Slack debates about whether that conversion should be credited to the display ad they scrolled past three weeks ago or the search ad they clicked yesterday, you've officially entered excuse territory.

The Correlation Trap

The biggest attribution myth is precision. Teams argue over fractional percentages in models based on incomplete data, making budget decisions on foundations shakier than a Christmas pudding. Just because your attribution model shows that video campaigns have a 23.4% assisted conversion rate doesn't mean video campaigns are driving 23.4% of your revenue. Correlation isn't causation, but correlation with fancy charts makes for compelling board presentations.

The Defensive Posture Problem

Attribution transforms from optimisation tool to justification mechanism when teams use it defensively. Instead of asking "How can we improve performance?" you're asking "How can we prove this budget allocation was correct?" The model stops being about finding truth and starts being about defending territory.

This defensive stance emerges because attribution had to answer everyone's questions: the board's questions about performance, finance's questions about ROI, and sales' questions about lead quality. No single measurement system can bear that much weight without buckling into excuse-making territory.

Common Attribution Excuse Patterns

Cherry-Picking the Convenient Data

Ever noticed how attribution discussions tend to focus on the metrics that support predetermined conclusions? This is cheap inventory bias in action: when marketers attribute credit to campaigns with lower-priced products simply because those products converted better, ignoring that conversions were likely due to price, not marketing brilliance.

For e-commerce brands, this often manifests as over-crediting campaigns that drive sales of clearance items whilst under-crediting the brand awareness campaigns that made customers consider your store in the first place.

The Digital Tunnel Vision Excuse

Digital signal bias is perhaps the most common attribution excuse in e-commerce. Teams focus obsessively on trackable online behaviour whilst ignoring how all marketing efforts influence both online and offline consideration. Your Google Ads campaign might get credit for a conversion, but what about the podcast ad that made them aware of your brand last month?

This tunnel vision becomes an excuse to ignore channel interdependencies. "Our Google Ads are performing brilliantly according to our attribution model" sounds great until you realise your brand awareness campaigns have been switched off for three months and you're essentially harvesting demand that other channels created.

The "In-Market" Blind Spot

Here's a favourite excuse: attributing conversions to campaigns when customers were already ready to purchase. Your attribution model credits that Display campaign with a conversion, but the customer had been researching your product category for weeks and was going to buy from someone anyway. Your ads didn't create demand; they just happened to be there when demand converted.

This in-market bias becomes an excuse to avoid the harder question: are you actually influencing purchase decisions, or just intercepting customers who were already convinced?

The False Precision Problem

The most dangerous attribution excuse is the myth of accuracy. Marketing teams will argue over whether a campaign contributed 47% or 52% to a conversion when the reality is that no attribution model is remotely that precise. Most aren't even directionally accurate, but the charts look impressive in presentations.

This false precision creates elaborate justifications for budget allocations based on fundamentally unreliable data. You're making strategic decisions using numbers that appear authoritative but lack genuine accuracy. It's like using a broken compass and arguing about whether you're 2 degrees off course whilst heading confidently in the wrong direction.

When Reality Gets Blurry

Attribution becomes a full-blown excuse when the model becomes the story and reality gets pushed aside. Teams start optimising for attribution metrics rather than business outcomes. You'll hear phrases like "according to our attribution model" more often than "according to our revenue data."

The ultimate attribution excuse is using complex frameworks to avoid acknowledging fundamental strategic misalignments. Rather than admitting that your creative messaging might be off-target or your product-market fit needs work, it's easier to blame the measurement methodology.

The E-commerce Reality Check

For e-commerce businesses specifically, attribution excuses often mask deeper problems:

• Product issues: Instead of acknowledging that your conversion rate is dropping because your product photos are mediocre, you blame the attribution model for not properly crediting awareness campaigns

• User experience problems: Rather than fixing your checkout flow, you argue about whether email marketing or paid search deserves credit for recovered abandoned carts

• Competitive pressure: Instead of addressing why customers are choosing competitors, you focus on attribution discrepancies between channels

What Attribution Should Actually Do

Attribution models should help you answer specific optimisation questions, not provide universal explanations for performance. Good attribution practice involves:

• Using models to guide specific tactical decisions, not defend entire strategies

• Acknowledging the limitations of your data rather than over-claiming precision

• Focusing on directional insights rather than exact percentages

• Testing attribution assumptions through incrementality studies

The goal isn't perfect measurement: it's better decision-making. If your attribution discussions aren't leading to clearer action items for campaign improvement, you're probably in excuse territory.

Moving Beyond Excuse-Making

The shift from attribution-as-excuse to attribution-as-tool requires uncomfortable honesty. Start by asking different questions:

• What decisions are we actually trying to make with this data?

• Are we using attribution to optimise or to justify?

• What would we do differently if this model was completely wrong?

Stop treating attribution models as infallible truth machines and start using them as imperfect but useful decision-making aids. The goal isn't to find the perfect model: it's to find insights that actually improve your marketing performance.

Your attribution model might be sophisticated, but if it's not making your campaigns better, it's just a fancy excuse with extra steps. And frankly, your ROAS doesn't care how clever your measurement methodology sounds in meetings.

The best attribution approach is often the simplest one that leads to the clearest actions. Because at the end of the day, your customers aren't buying your attribution model; they're buying your products.