{"id":2062,"date":"2020-08-30T15:52:05","date_gmt":"2020-08-30T22:52:05","guid":{"rendered":"https:\/\/evocalize.com\/blog\/the-sophisticated-marketers-guide-to-incrementality\/"},"modified":"2023-05-12T14:45:49","modified_gmt":"2023-05-12T21:45:49","slug":"the-sophisticated-marketers-guide-to-incrementality","status":"publish","type":"post","link":"https:\/\/evocalize.com\/blog\/the-sophisticated-marketers-guide-to-incrementality\/","title":{"rendered":"The sophisticated marketer’s guide to incrementality"},"content":{"rendered":"
As marketers, finding ways to successfully drive business metrics and organizational goals through marketing is our mission, and continued optimization of our marketing strategy and tactics is our daily work. In the last few years Incrementality and Lift as buzzwords in marketing have started to overshadow other marketing topics, especially given the increasing focus on \u201cgrowth\u201d marketing. Because these buzzwords put a premium on incremental growth, the notion of incrementality, and the tools to efficiently and effectively measure it, have become a critical focus for marketers.<\/p>\n
The notion of incrementality has been around for a long time though. Most marketers have likely heard the 18th century quote from John Wanamaker:<\/p>\n
Half the money I spend on advertising is wasted; the trouble is I don’t
\nknow which half. – John Wanamaker<\/p><\/blockquote>\nWanamaker\u2019s conundrum gets at the heart of incrementality in the digital age and leads us to ask the following questions:<\/p>\n
\n
- Does my marketing drive actual value or just claim credit for an action that would
\nnaturally occur?<\/li>\n- How far can I scale my efforts while positively impacting bottom line business
\nresults?<\/li>\n<\/ol>\nWhat is incrementality?<\/h2>\n
Incrementality in marketing refers to the incremental benefit generated per unit of action taken. Incrementality is the lift in desired outcome (web visits, conversion, revenue, etc.) provided by marketing activity (ads run, campaigns launched, etc.). Incrementality isn\u2019t solely about assigning credit or value to a conversion (that\u2019s attribution), it\u2019s about identifying the specific interaction that moves a user from passive to active and as such drives real business results.<\/p>\n
It can be used to address the following types of questions:<\/p>\n
\n
- What happens if I increase or decrease budgets for channel X?<\/li>\n
- Is campaign Y a key contributor to my wanted outcome?<\/li>\n
- Is Z just cannibalizing my organic audience?<\/li>\n
- How much more revenue am I generating with this marketing program?<\/li>\n<\/ol>\n
Incrementality strives to identify the causal event of a conversion, allowing businesses to properly allocate budget, reduce wasted ad spend and optimize the marketing mix.<\/p>\n
Doesn\u2019t attribution indicate incrementality?<\/h2>\n
In today\u2019s world it\u2019s a must that marketers require some level of attribution for their paid marketing efforts (last touch, multi-touch attribution, etc.) and also likely provide some form of evaluation of effectiveness (ROI, ROAS, etc.). Measuring and determining the value of marketing has become a common and critical component of marketing organizations these days.<\/p>\n
Unfortunately, Incrementality generally isn\u2019t factored into existing marketing attribution or ROI calculations. We are so focused on the mechanics of HOW to measure we lose sight of the WHAT to measure. As a result marketeers often get derailed if asked about the incrementality of their efforts. You measured correctly and assigned credit logically, but how do you know your efforts fundamentally moved the needle? Are our retargeting efforts incremental? Are paid branded terms in search just cannibalizing SEO? Has the TV campaign shown a business lift? Knowing that a lead cost $10 is not as valuable as knowing that same lead generated an additional $100 in revenue. Incrementality testing helps address these questions.<\/p>\n
Identifying the incremental value of remarketing (case study)<\/h2>\n
Remarketing has become increasingly popular led by cart abandonment campaigns, recommendations based on site visit behavior, etc. Evaluating based on classic attribution metrics looks great, largely driven by the high intent of the audience (we know they are interested), but incrementality is a bit more nuanced. In our own lead generation programs remarketing new home listings to potential buyers in a given geography we initially saw mixed results, I\u2019ll provide a brief case study.<\/p>\n
The ads were extremely compelling and relevant given our ability to leverage consumer intent info from past site visits and integrate into dynamic product ads across a number of ad platforms. The standard measure of success looked great with cost per lead and ROAS much better than our traditional new audience campaigns. So much so that we began shifting even more budget into the remarketing campaigns.<\/p>\n
In measuring the incrementality of the campaigns however, it was a little bit shocking to see that we were in low double digits for truly incremental conversions. More importantly, the marginal costs of the leads generated were not consistently ROI positive despite some fairly significant revenue calculation on the true value of the leads. We kept experimenting with tweaking creative, segmenting and isolating audiences (prior channel, recency, similar audiences, geography, etc.), and of course measuring incrementality. We learned a few things along the way:<\/p>\n
4 Key Learnings from Measuring the Incremental Value of Remarketing<\/h3>\n
\n
- In remarketing recency is key. While conversion was much greater for more recent visitors, incrementality was lower. The opposite was true for longer horizon visitors or \u201clapsed users\u201d.<\/li>\n
- In our business, geography had a profound impact given the established value of a given lead in a specific market and marginal cost\/marginal revenue calculations (In real estate the value of a lead can vary considerably largely driven by
\npotential commission on home value). Some markets would never be profitable while others offered a great deal of headroom. National campaigns were blending results and under optimizing our marketing efforts.<\/li>\n- Even though the metrics for remarketing were great, shifting too heavily to remarketing exacerbated our incrementality issues as we spent more and more effort remarketing existing users and existing marketing channels. It\u2019s important to maintain a pipeline of new users in each marketing channel\/ad network.<\/li>\n
- Remarketing impacted LTV. The remarketing program increased the volume of leads by a particular user (our business model valued each lead) which increased the Average Revenue per User (ARPU) which we identified by comparing the incrementally of users and leads.<\/li>\n<\/ol>\n
Based on these insights and numerous others we were able to adjust our marketing spend to optimize for marginal cost of each lead by specific audience segments defined by geography, recency, and site behavior. The process provided valuable insights into not only our marketing efforts, but user behavior, our product experience, and our business model.<\/p>\n
How to measure incrementality<\/h2>\n
There are three common ways to measure incrementality.<\/p>\n
Marketing Mix Modeling (MMM)<\/strong> is an important practice in which historical regression can be used to derive incrementality, but this assumes the future will behave much like the past, so things like seasonality, a rapidly changing market, intense competition, etc. can impact reliability.<\/p>\n
\u200d
\nMulti-Touch Attribution (MTA)<\/strong> is a great framework for attribution, but is often challenged in today’s ad environment dominated by \u201cwalled garden\u201d networks, current pixel tech (complicated by increasing privacy concerns), and multiple users’ presences across numerous devices. Attribution alone also often does not account for \u2018immeasurable\u2019 contributions like brand equity, offline marketing, and existing organic behavior.<\/p>\nThe most common and generally recommended approach for measuring incrementality is a Design of Experiments (DoE) test<\/strong>.<\/p>\n
How to measure incrementality with a Design of Experiments (DoE) test<\/h2>\n
In a typical Incrementality test, the target audience is segmented into a test group (exposed to the ads) and a control group (suppressed from seeing the ads or shown a \u201cplacebo\u201d\/Public Service Ad) and the conversion lift on the test group is compared to the conversion lift on the control.<\/p>\n
For example: Your test group saw 2% conversion whereas your control group saw a 1.5% conversion. So (2% – 1.5%) \/ 1.5% = 33% incrementality in conversions. Then of course you\u2019ll then have to determine whether the 33% incremental conversions were worth the cost of the particular marketing effort.<\/p>\n