What is incrementality in marketing
Incrementality in marketing means evidence that a given marketing activity over a specific period of time actually did increase sales above and beyond the level of that ‘would have happened anyway’. In other words, was your marketing spend efficient and did it drive results?
The aim is to see which channels, campaigns and ads contribute to achieving your desired marketing outcomes, and to answer questions such as:
- Whether a channel, campaign or ad actually does drive additional sales;
- How much additional sales revenue can we attribute to that channel, campaign or ad to judge whether it’s worth the effort and expense for the outcomes driven; and
- Whether a channel, campaign or ad only works in a combination with other channels, campaigns or ads.
The consequences of not getting incrementality right
In the worst case scenario, a channel, campaign or ad may be found to have zero impact on sales, or even to reduce sales, which means you would be investing in something that doesn’t deliver positive results. In other words, you’d be simply wasting your marketing budget and your time.
Where you’re able to get a detailed understanding of incrementality, you may be able to choose between different channels, and choose the one which delivers more incremental sales for the same cost, or the same sales for less cost, leading to a more efficient allocation of marketing resources. Not understanding this, can lead to an inefficient marketing mix.
Of course, no marketing is carried out in a bubble, and it may be that a marketing channel, campaign or ad, only works, or only works well, in combination with other concurrent or prior marketing activities. For example, a banner advertising campaign may have zero effect on its own, but may be found to work in combination with concerted television, radio and OOH campaigns. This may be particularly relevant where the availability of other cost effective channels is limited, such as the availability of affordable prime time TV commercial slots. So understanding this not only allows a more efficient allocation of resources, but allows you to scale your marketing.
And finally, an area where we have seen much focus in recent years, particularly with performance marketing channels, is cannibalization, where one channel drives the conversion and another intercepts it at the last stage and claims credit. This can lead to a marketer double paying, for example where a conversion was driven by a paid click in search but the customer leaves the checkout to go and find a coupon code on a coupon site to get a discount. So not only is the marketer paying twice, once for the paid search click and again for the coupon site as an affiliate, but is also giving away margin with the coupon. As well as the risk of double paying, there’s the risk of wrongly turning off the channel that actually drives the conversions, thereby reducing overall conversions.
In short, not understanding incrementality can lead a marketer to:
- Invest in marketing channels that do not work at all
- Mis-allocate marketing budgets, by not focusing on channels that work better and investing in channels that underperform, or wrongly assume a channel doesn’t drive conversions and turn it off
- Miss the fact that a channel, that appears to fail on its own, can work well within a particular marketing mix
- Pay twice for marketing due to cannibalisation effects
How we measure Incrementality
- Test group who are exposed to the marketing from the channel, campaign or ad; and a
- Control group who will not see the marketing from the channel, campaign or ad.
Customers in a cohort (i.e. individuals with similar characteristics like the date and time when they are identified and where they are at that time. For example, individuals in the United States in the month of May) are then randomly assigned to the test or the control group and the results analyzed over a set period of time. By comparing the performance of the two groups, marketers can then attempt to identify the incremental lift driven by the activities to which the test group was exposed. So if the test group generated 120 sales and a similar sized or weighted control group only 100, you could infer a 20% uplift in sales as a result of the marketing activities.
However, the clear disadvantage of this approach is that it’s backward looking. You need to carry out an experiment for a sufficient amount of time and spend enough money to get statistically significant results. And you’ll normally need a dedicated analytics team or agency and/or testing software to analyze the results and prove that they were not generated by chance.
It’s also the case that it’s increasingly difficult to ensure no cross over between the test and control groups when testing online due to privacy laws that require users to consent to cookies, the fact that users can and do use different devices and browsers which don’t have the same cookies, and not to forget anti-virus software applications that automatically remove cookies.
In some cases, it may be possible to limit testing to identified individuals who need to sign in or otherwise be identified to see an offer, for example where it’s sent by email and a user needs to sign in to a customer area. Also it may be possible to serve ads according to IP addresses, such as between one US State and another, where ads are served in New York and not in California etc.
Alternative strategies include increasing and decreasing spend over time or ‘turn off’ experiments to see how much this will affect sales, but this suffers from the lagging effect of previous marketing activities that will affect a subsequent period. And another approach is, instead of running a control group, simply to compare the brand’s results with observed industry data or averages or panel data, but this has many obvious limitations.
A more advanced form of incrementality research involves multitreatments, similar to multivariate testing, where several different combinations are tested against a control, to isolate which combinations work best. As you can imagine, this typically increases the complexity, cost and time required for the measurement greatly.
Examples of incrementality debates
Incrementality has long been a hotly debated topic in marketing, with suppliers of different marketing services strongly contending that their service does drive incremental sales. Here are some examples:
ATL or Brand marketing
With much traditional offline marketing, such as television, radio and OOH, it’s difficult to directly measure the impact as there’s nothing to click in a TV or Radio ad. Whereas, a print add or direct mail campaign can incorporate vanity URLs or QR codes, or an incentive can be offered for all those who input ‘Summer 2025’ in the landing page or text or call an easy to remember number, in many cases the effect will likely be captured by other channels, such as by sales instore or online. And so arguments for incremental value are often made by comparing sales against a baseline period where no such advertising was carried out.
Obviously, it’s difficult to ensure that all other elements remain the same from one period to the next, and ensure that no other factors influence the results, but an approximation for the sales uplift can be made.
Online vs Offline
A common contention is that offline marketing, such as tv commercials or direct mail, influences online conversions, and that credit should be given to the offline marketing rather than the online channel. A flaw in this is that it assumes that the online ad and landing page had absolutely no influence in the conversion process, and that the presence of an online ad didn’t prevent a competitor hijacking the conversion, for example by a competitor bidding on your brand. But the main way to evidence this is to compare the baseline level of online conversions, adjusted for seasonality and day of the week, against the sales when the ad aired or the direct mail landed.
Paid vs Natural Search and Branded vs Non Branded Search
Often an online sale occurs after a branded search, which is the culmination of a sales funnel which begins with Awareness, Interest and Desire before leading to Action (AIDA). Often the top of funnel Awareness, Interest and Desire is created by thought leadership content found in natural search or from non branded paid search terms. However, once a searcher has identified the solution he or she wants, branded search terms like ‘where can I buy X’, ‘best price for X’ will often get the sale. This is often the case where a company feels forced to bid on its own brand in paid search to defend against competitor bidding. So in other words, the branded search was not incremental but merely part of a conversion funnel that was initiated elsewhere.
Without understanding the value of the content marketing in initiating the sale, a marketer could mistakenly reduce spend in this area and increase spend on branded search. The related field of attribution marketing is often used to fairly attribute responsibility for sales across different channels in these cases.
Coupon and Voucher Code sites
One of the most hotly contested areas in the incrementality debate concerns the effect of coupon and voucher code sites, whereby a customer who came from a different channel, upon seeing a field to input a discount code, can be tempted to abandon a checkout to try and find a coupon or voucher code in order to get a discount code. In the worst case scenario, that potential customer might get distracted by another offer and never come back but, even if not, that customer could then be wrongly attributed as having come from the coupon or voucher site, which may involve the payment of an affiliate commission and likely involves a sacrifice of margin due to the discount.
Tactics to avoid this have included trying to make the coupon code input box less visible, for example by putting it to the left hand margin in a form with fields on the right, with faint text and no border or simply as a link that needs to be clicked. Here the idea is that a visitor who’s come to the site armed with a code, will actively look for the input box, whereas a regular visitor will not notice it. Another tactic is to offer all visitors a code from the brand itself on the landing page. While this sacrifices some margin, it removes the risk of the visitor leaving the site and not coming back, and avoids paying unnecessary affiliate commissions.
As you can imagine, the coupon and voucher code sites vehemently defend their position, claiming that they have such huge traffic that they can and do drive lots of new incremental traffic to brands’ websites, and often the presence of the discount code is what actually convinces a new customer to give the brand a try. And in comparison, this new traffic greatly outweighs any cannibalization.
Retargeting Ads
Similar arguments surround the value of banner retargeting, particularly when a brand believes that its customers typically require several visits to the website before buying. And as the potential customer intended to come back again anyway, the retargeting simply interrupted that normal customer journey and took the credit for the sale. Of course, the brand is likely paying per click or per sale from the retargeting ads, and many of the retargeting ads offer extra discounts to entice a visitor back, so there’s the same double payment and misattribution argument as with coupon or voucher code sites.
Similarly, however, the retargeting providers produce arguments and studies to show that, while there is some cannibalization, the retargeting gets customers to come back who could have gone on and bought a competitor’s product, or bought the same product from another retailer, and often claim it’s the discount in the ads which got the sale. And additionally, there’s the argument that the retargeting ad shortened the time for the conversion cycle, as it brings back a potential customer sooner.
Referral Marketing
Likewise referral marketing often faces a similar accusation that it cannibalizes sales that would have happened anyway, as a person looking to, say, subscribe to an online streaming channel, will ask around the office or in a chat group to see if anyone has a referral code to hand in order to get a discount. Or worse, will go to one of the referral code sites available online and procure a code from a complete stranger, so that an unnecessary reward and incentive is paid out.
Despite academic research proving that referred in customers are more valuable, staying longer and spending more, and are themselves more likely to refer new customers, this belief persists. And as the leading enterprise referral marketing platform provider, it’s a myth that we’re keen to debunk.
So in order to do so, we recently carried out our own survey based consumer research across UK & Ireland, France, Spain, the US and Canada with and found that:
- Less than half of respondents claimed a brand had been on their radar screen before it was referred to them;
- Almost a fifth of respondents said that the brand they were referred was better than the one they were already using;
- The vast majority of people surveyed said that it was important to know that their friends also like the product or service being referred, and more than two thirds said that it was the referral that moved them from considering to buying the product or service; and
- Referral was far more likely to generate an instant investigation of a product or service compared to any other marketing channel
In other words, referrals are far more likely to generate incremental sales than cannibalize existing sales, but even in the case where a person was already considering purchasing that particular product or service, a referral can speed up the sales cycle and be the key element that moves a visitor from consideration to purchase.
What next?
If you’d like to know more about our research, or any of the topics discussed above, please reach out and we’ll be happy to share what we’ve learned.