Hamilton AI is built to consolidate marketing measurement
“SHOW ME THE MONEY”, shouts Tom Cruise in his beloved movie, Jerry McGuire. And that is precisely what Hamilton AI is designed for.
CMOs – and not least CFOs – have long desired a measurement model that could both identify and predict the business outcomes of marketing spending.
Questions still linger like:
1) What are the exact marketing contributions to top-line growth or baseline sale?
2) What is the contribution per channel and touchpoint in the customer journey?
3) What is the optimal balance between sales tactics versus branding initiatives?
4) How should my marketing and media plan look like in terms of optimal spend?
Previously, these questions have been addressed in different ways by two different set of measurement practices, Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA)
The Shortcomings of previous marketing measurement methods
The econometric Marketing Mix Modeling – typically estimating the sales effect of each media category and other factors – is not really suited for today’s marketing landscape. It’s too high-level. With a whole new swath of digital media publishers, more granularity is called for.
MMM also hides interdependencies between channels and touchpoints. It requires a large data volume. And being time-consuming and expensive, it is also quite infrequent in use.
Total Marketing Modeling is the way forward
Imagine if you could build a more unified marketing measurement model for identifying the effects hierarchy throughout the customer journey. Both on- and offline – on a very granular level, down to individual media publishers.
A model where you work your way down from attributing the effects of both paid media categories and other economic factors down to individual media publishers. The objective being to achieving an actionable plan that let you optimize marketing investments down to each touchpoint against either fixed budgets or set business targets. Hamilton AI first determines the contribution of each marketing and media investment using probabilistic modeling. Then it uses simulations to recommend the optimized media plan generating the best possible outcome.
Hamilton generates a network model that connects all media spend – and other data like interest rates, pricing, distribution, competitive activity levels, weather – to the sales KPI. When the connections have been made, it’s possible to predict business outcomes from the media effect of different scenarios. All the way down to publisher level.
On the operational level the marketing or media manager will be able to select the optimal spend allocation between marketing channels and media publishers. Either from a sales target objective (how much do I need to spend) – or a budget constraint (how much can we sell).