Beyond last-touch: Why predictive insights and data-driven attribution matter in eCommerce

By Prem Bhatia, CEO and Co-Founder, Graas

Today, over two thirds of shoppers use more than one channel to complete an online purchase. These channels include search engine results, social media sites, reviews, branded storefronts, and more. This poses a critical challenge for all businesses: how does one evaluate the effectiveness of various marketing strategies when these strategies overlap at more than one point?

The traditional last touch attribution method, which gives all the credit to the last action preceding the purchase, unfortunately, missing out on touchpoints and insights that might have influenced the purchase decision of the consumer – like a quick look over the brand’s Instagram account to find valuable user-generated content (UGC), paid ads or reviews.

Last-touch attribution fails to accurately represent the customer journey.
Brands engage with customers through multiple touchpoints, making it crucial to accurately measure the effectiveness of these interactions. Multi-touch attribution models address this need by providing an understanding into how each touchpoint contributes to overall success.

While data-driven attribution provides valuable insights into past customer behavior, predictive analytics takes this a step further by forecasting future trends and customer actions. This helps businesses strategize for better conversions. Using multi-touch attribution data of your previous quarter(s), you can pinpoint exactly what channel, ad campaign, or persona targeting worked (and what didn’t) to build a fool-proof plan for your next quarter.
With the constant evolution of eCommerce, the ability to look at what your customers will need tomorrow has become the need of the hour.

AI-powered predictive analytics enables simulations that provide marketers with recommendations, allowing them to run experiments that reallocate or increase marketing budgets, conduct personalised marketing activities at scale and ensure campaign profitability.

Personalized recommendations are another value of predictive analytics that has received much attention. Of the revenues earned by Amazon, 35% is a result of its hyper-personalized recommendation engine – which is entirely built on predictive analytics. Brands can track customers’ recent activity on the company’s website, previous purchases, or even gender to target the right product at the right time. This kind of personalization can lead to a remarkable boost in the conversion rates and the average order value.

Attribution science and predictive models are changing the way businesses operate in a way that was thought impossible before. While attribution models help understand the impact of past marketing efforts, predictive analytics uses this information to forecast future performance and guide strategic decisions.

However, implementing these sophisticated models comes with its own set of challenges. One of the primary hurdles is the difficulty in unifying fragmented data across multiple marketing channels. Customer information is often stored differently in mobile apps, websites, social media, and even in brick-and-mortar shops. Bringing together this multitude of data into one analytical database is not easy, especially given the non-standard data structures that make it challenging for AI models to provide accurate insights.

Another significant barrier is the lack of technical expertise. Building such sophisticated attribution models requires specialised skills in data science, machine learning, and marketing analytics, which can be hard to find and expensive as well. The alternative, of course, is to use off-the-shelf attribution tools that are available in the market.

The benefits of adopting data-driven attribution and predictive insights outweigh the challenges. As eCommerce evolves, multi-touchpoint customer journeys are now the norm, requiring businesses to use AI-driven data models to accurately attribute conversions to the right channels and invest in those that yield the best ROI. By blending machine learning’s predictive power with human intuition, businesses can enhance decision-making and drive sustainable growth. The new era of eCommerce has arrived—one that leverages AI and machine learning for marketing attribution and predictive analytics to fuel success.