5 Signs It’s Time to Invest in Incrementality

We are often asked, “Do I need to invest in an incrementality practice if my business is this size?”. Brands tend to understand the importance of an experimentation practice but have a hard time deciding when it's worth investing in one. Creating an incrementality practice is an investment - a time investment for the team setting up the experiment, a performance investment considering the media holdouts, and a monetary investment if you are partnering with a 3rd party like Haus to get it done. We share below when the value of learnings can significantly outweigh the costs.

This blog will walk you through 5 common signs that indicate it is definitely time to start investing in incrementality.

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1. You spend on more than 1 or 2 marketing channels

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When you are spending on just one or two major channels, it's likely that you can see the effects of these channels on your business level metrics. For example, if you only spend on Meta and Google and you decide to ramp up spend on Meta, then you should see your business level sales or revenue increase immediately. However, as you add more channels into your mix, it becomes increasingly difficult to decipher where the impact is coming from. It's time to start an incrementality practice when there’s more variables at play. Running experiments will help you better understand what tactics are driving the most impact and where there are pockets of inefficiencies that can be reallocated to more effective initiatives. 

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2. You spend more than $1M a month on marketing tactics 

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Even if you are spending on just Meta or Google, if you are spending more than $1M a month it's a good idea to test and learn which tactics within these channels are most efficient and effective. With incrementality testing, you can better optimize your strategy on these channels. You can learn if PMax is more incremental when brand terms are included or excluded or if Advantage Shopping+ is more effective than a standard conversion campaign. With over a million dollars of spend going out the door each month, it’s important to verify performance and not rely on faulty platform or last-click attribution. 

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3. Your business is omni-channel

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As businesses expand outside of direct sales into marketplaces like Amazon or retailers like Target, the picture of marketing impact becomes increasingly difficult to decipher. Maybe you test a new media channel and don’t see an immediate lift in DTC, but it had an effect on Amazon or retail sales. This can be hard — sometimes impossible — to track in platform reporting. Or maybe Amazon starts bidding on the same terms as your DTC ecomm team so you see a drop in search performance but can’t tell if it's truly due to Amazon. If this issue rings true to you, it's certainly time for an incrementality practice. When you run experiments with Haus, you can estimate the impact of media on all sales channels using the same methodology.

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4. You’ve hit a growth plateau 

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Are you struggling to drive growth to the next level? If so, it's likely time to start your experimentation practice. Incrementality testing can help you conquer this plateau and increase growth velocity by identifying pockets of opportunity and areas of inefficiencies in your media mix. It’s not directional or correlative like attribution. Incrementality shows the true causal impact of tactics on your business’s bottom line.

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5. When MMM and MTA aren’t quite cutting it

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Media mix models (MMM) and MTA both rely on correlation rather than causation so they can lead you astray. MTA methodology is breaking down as privacy laws increase and MMM takes too long to get an actionable read. The good news is that you can use incrementality experiments to calibrate your models for more accurate results. Since incrementality testing is causal rather than correlative, these reads act as an anchor in MTA and MMM. The more you calibrate to incrementality results, the better the models become. 

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If you are hitting any of these 5 signs, it's time to start answering your big business questions once and for all with incrementality and experimentation.

Request a demo here and we will help you get started!