Marketing Attribution in the Age of AI: Understanding the Customer Journey.

Artificial intelligence and machine learning provide marketers with powerful new tools to understand how customers interact with brands throughout their journey. Traditional attribution models that relied on basic last-click analysis are no longer sufficient in today’s complex multi-channel world. By analyzing massivе amounts of customеr data with AI, markеtеrs can gain unprеcеdеntеd insights into how diffеrеnt markеting activitiеs and touchpoints influеncе purchasing dеcisions ovеr timе.

AI helps uncover the true customer journey

What was once a mystery is now becoming clear through AI. Traditional approaches assumed a linear path from awareness to purchase, but the reality is far more complicated. Customers may interact with a brand through dozens of channels, like search ads, display ads, email, websites, apps, retail stores, word-of-mouth referrals from friends, and more, before purchasing. 

AI technologies can map out the complete non-linear customer journey by applying advanced machine learning algorithms to analyze patterns in this vast pool of customer behavioural data. They can identify key influencers, measure the impact of different touchpoints, and understand how influence shifts between channels depending on where customers are in their decision-making process. This provides invaluable insights to optimize cross-channel marketing strategies and deliver compelling experiences.

Attribution models evolve with new data and algorithms

AI constantly enhances attribution modelling capabilities as more performance data and contextual information become available. Initial models focused on single-touchpoint analysis or simple multi-touch attribution. Now, models have become far more sophisticated, powered by new data sources like mobile usage, website tracking, product review information, and even external economic indicators. 

Advanced algorithms like deep learning are also employed to discover deeper patterns and relationships not evident from simple sequential or regression analysis. Attribution models developed through AI represent a continual evolution and will grow more accurate as additional behavioural observations are collected and programming is refined. This allows marketers to optimize their credit allocation across channels in near real-time based on ongoing customer interactions.

Personalized experiences across all touchpoints

With a granular understanding of individual customer journeys, AI enables highly targeted, personalized experiences. Attribution data reveals what influences each customer segment at distinct phases—whether it is product reviews that drive consideration. These targeted ads generate initial interest or influencer recommendations that clinch the sale. AI also identifies subtle differences in journey patterns based on demographic or usage characteristics. 

This level of personalization was not possible with aggregate analytics alone. Now, marketers can precisely orchestrate the right mix of messaging, offers, recommendations, and assistance at each customer touchpoint to guide them efficiently along their unique path to purchase. The result is a far more engaging, optimized omnichannel experience that enhances loyalty and lifts revenues.

Advanced Attribution Models Enabled by AI

Marketers are constantly challenged by the complex online customer journey, where it is difficult to attribute conversions to specific marketing touchpoints accurately. Advanced Attribution models enabled by AI are helping solve this problem by analyzing massive customer datasets and online interactions through sophisticated algorithms.

• Algorithmic Attribution

Algorithmic attribution models use advanced machine learning algorithms to analyze thousands of data points in customer journeys. By identifying patterns in interactions that typically led to a conversion compared to those that did not, the algorithms can determine the most likely influence of each touchpoint. This removes much of the subjective human judgment involved in simpler attribution methods. 

• Data-driven Attribution

With the abundance of behavioural data now available from various online and offline sources, data-driven attribution is emerging as the future. Linking customer identities across channels allows attribution models to understand a person’s full path to purchase, not just their activity on one platform. This provides much richer insights than isolated channel-level views. Unifying scattered data necessitates privacy-protecting technology but, when done rightly, delivers ultra-personalized experiences. 

• Position-based Attribution

Position-based methods attribute conversions based on when interactions occurred in the customer journey.

Ø First-interaction attribution solely credits the first touchpoint before a conversion. However, this ignores the influence that subsequent interactions may have had.

Ø Last-interaction attribution assigns all influence to the final touchpoint only. But many interactions together could have gradually persuaded the person to buy.

Ø Middle-interaction attribution distributes credit equitably among all touchpoints between the first and last. It assumes all intermediate steps hold equal importance. Nevertheless, certain middle interactions may matter more than others.

Conclusion

Artificial intelligence is revolutionizing how marketers understand the customer journey and allocate credit to various touchpoints influencing purchasing behaviour. Advanced attribution modelling powered by AI provides a transparent view of the complex, non-linear paths that customers take. This allows optimization of cross-channel strategies to deliver personalized experiences that effectively guide customers through their decision-making process. As AI capabilities advance with new data sources and algorithms, attribution will grow ever more accurately, helping marketers maximize returns on investment across all marketing activities based on their actual impact on customers’ purchasing behaviours over time.