“Where does INCRMNTAL fit within the advertising tech stack? “
This question is one that we are often asked when presenting marketers and marketing analysts with our platform. When working with large companies, we are often asked this question by the procurement teams.
The answer is never a straight answer for us, as companies use INCRMNTAL for different use-cases, measuring anything and everything from small campaign changes on TikTok, to making strategic decisions such as planning ad spend, or cutting redundant ad spend.
An analogy we often use is: INCRMNTAL is like a measuring tape. It’s a multi-functional tool.
This article addresses the most common platforms we get asked if we replace.
MMPs and attribution providers track impressions, clicks, and users.
These platforms create a match between ad engagements (impressions / clicks) and conversions (installs, registrations), using a deterministic ID (IDFA, GAID, Cookie) or a fingerprinted ID (using a combination of IP, location, device details).
More recently, these platforms act as a layer on top of privacy centric attribution methods (such as SKAdNetwork) simplifying the setup process, and adding a layer of reporting on top.
MMPs and attribution platforms are great as they provide near real time feedback loop allowing advertisers to make decisions around creative, placement, and audience optimization, but they also have several constraints:
INCRMNTAL doesn’t replace the MMP. It improves the measurement stack by allowing you to measure the true value of paid marketing activities.
What an MMP is not great at is measuring the value of marketing activities, as the mechanics of measurement (giving credit to a single ad engagement) completely ignores the funnel a user goes through before becoming a customer.
An analogy we like using for this is that using an MMP to make marketing budget decisions is like managing a football team where you only pay the person scoring the goals, firing the rest of the players…
Internal data science teams are tasked with various projects and developments, often more related to a company’s own product, rather than for the company’s marketing team or marketing operations.
Often, we will meet a data science person who’s only tasked with marketing related tasks, but usually those tasks will be related to LTV Predictions, product change simulations, or hyper personalization using AI.
Marketing science will sometimes be tasked to conduct an ad hoc analysis, by using a geolift experiment, or by using a framework such as causal inference. But those will be sporadic, and often inconclusive.
INCRMNTAL provides marketers, marketing analysts, and data scientists with a platform measuring the incremental and marginal performance of any marketing activity, or a change in marketing they would like to analyze further.
Yes and No.
Media Mix Modeling (or MMM in short) is an econometrics top down method of identifying correlation between ad spend and marketing performance.
The main use-case for MMM is creating a budget plan, testing the plan, and creating a new plan.
An MMM will require historical data, substantial calibration and customization to become accurate over a long period of time. An MMM relies on correlations, and is often fitted using MMM priors and posteriors, which may include opinions and biases such as “what we believe is the ROI from TV ad spend?” or “what do we believe was the contribution of black Friday vs. the 200% spend increase we made?”.
INCRMNTAL uses a bottom up approach to measure marketing performance, tracking marketing changes and using changes in combination with seasonality and external variables to measure performance of channels, campaigns and ad groups.
Unlike MMM, and given the approach, INCRMNTAL can also be easily validated using a number of ways.
The main differentiator between INCRMNTAL and MMM is that MMM’s main use-case is creating scenario plans, while INCRMNTAL covers all use-cases of MMM, but different from MMM, INCRMNTAL allows Advertisers to do more.
Our platform’s mission is to help companies evolve from counting clicks to measuring value. Most of our customers do use the platform to identify efficiencies they can unlock – either by reducing redundant ad spend (advertising spend that gets credit, but does not deserve the credit it gets), or by scaling the right channels yielding the best average and marginal results.
In the past, measuring incrementality required substantial manual effort, and was only done sporadically, or accidentally.
INCRMNTAL build a completely new way to measure incrementality continuously, without relying on user-level data, and without requiring a single experiment. The platform is ready to go on day one of the integration.
You can read more in our Whitepaper.
If you want to learn more about our platform, schedule a time to speak with us here: incrmntal.com/demo
Maor is the CEO & Co-Founder at INCRMNTAL. With over 20 years of experience in the adtech and marketing technology space, Maor is well known as a thought leader in the areas of marketing measurement. Previously acting as Managing Director International at inneractive (acquired by Fyber), and as CEO at Applift (acquired by MGI/Verve Group)