Wanted: marketing models for the whole customer journey
Most brands just scratch the surface of customer targeting
Digital channels have transformed marketing over the past 15 years by allowing brands to use algorithms to get the right message to the right prospect in the most opportune moment. Programmatic platforms such as Google and Meta’s Facebook and Instagram today offer powerful machine learning and artificial intelligence (AI) tools to help marketers autotarget online customers with the highest likelihood of conversion.
But as powerful as today’s platforms are, most brands are only just scratching the surface of intent-based marketing, which uses an individual's online data to build targeted messages and encourage consumers who are most likely to buy. The next step in the evolution of intent-based marketing is about developing advanced attribution models that cover the entire customer journey.
These models enable brands not only to accurately model consumers’ online and attribute credit for conversions to digital channels, but also to gain a view of the full funnel across online and offline channels. They enable brands to build predictive models on users’ intent, in turn helping them to optimise marketing campaigns from exposure to conversion.
Unravelling the attribution path in digital marketing
Attribution modelling tools analyse a user’s intent while they browse a website, and use these intent signals to attribute credit for conversions to digital channels and predict the likelihood of a person converting. For example, the attribution modelling tool we work with enables us to classify users as journey A, B, C, D, E or F customers, with journey A representing the highest probability of conversion and journey F the lowest.
This solution tracks 35 buying intent signals from each website visitor, including variables such as time on site, pages viewed, time stamp and previous visits. With over 10 years of refining the algorithm, the tool is able to predict each user’s level of buying intent according to their online behaviour effectively. This data empowers marketers with insights to attract more A visitors and fewer F visitors.
The journey A audience segment, for example, might make up 1% of the visits but 5% of the sales. Journey F visitors could comprise 36% of the site visits but only 7% of the sales. Attribution modelling enables us to evaluate each source or campaign to compare where customers on different journeys are coming from. Importantly, we can see this attribution data in real time.
Figure 1: An example showing how predictive modelling collects intent signals from all users based on their behaviour and groups them into different journey categories.
The solution also allows us to look at each campaign or traffic source and determine which proportion of the traffic it generates is assigned to journeys A to F.
This helps to optimise campaigns around creative executions, landing pages or user targeting, in turn allowing for improved quality of users based on their intent to convert. The approach is tailored to getting quick marketing insights that allow for on-the-fly optimisation.
One of the most exciting opportunities from this next-generation approach to attribution modelling and intent-based marketing lies in exploiting offline data. Most brands gather a wealth of customer data from in-store or call centre interactions. This data, housed in their customer relationship management (CRM) systems, is generally excluded from online attribution reporting.
The ultimate goal is to automate data flows across digital marketing, customer relationship management and analytics and attribution platforms
Moving towards an integrated online and offline marketing ecosystem
Today’s tools use application programming interfaces (APIs) to map CRM data such as e-mail addresses or telephone numbers to user data from platforms such as Meta, Google or TikTok. These contacts can be exported and used to improve marketing efficiency — provided users have opted in to allow their data to be used in this manner.
Once prospects in the CRM database have been segmented according to their propensity to buy, call centre agents can be empowered to focus their energies on the customers that are most likely to convert. Audience lists can also be fed into programmatic platforms and used for more effective cross- or upselling. Another compelling use case is using the list of high-intent users to create lookalike audiences.
With attribution tools maturing and brands starting to harness their power, we are moving towards a Holy Grail in marketing: an integrated view of offline and online customer conversions. The ultimate goal is to automate data flows across digital marketing, CRM and analytics and attribution platforms. This enables brands to match offline sales to online leads (and vice versa) and understand the attribution path from first exposure to final sale.
Grant Lapping is the digital executive at new-age solutions and systems integrator +OneX
The big take-out: The next step in the evolution of intent-based marketing is about developing advanced attribution models that cover the entire customer journey.
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