A Different Approach to Revenue Estimates Leveraging Alternative Data

Explore a fresh approach to revenue forecasting using alternative data, by focusing on the customer journey rather than short-term trends

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Welcome to the Data Score newsletter, your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of alternative data insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. Through my extensive experience as a purchaser and creator of data, I have gained a unique perspective that allows me to collaborate with end-users to generate meaningful insights.

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In the realm of investment, successful alpha generation¹ can often hinge on the effective use of alternative data to generate an edge. This article offers an approach to leveraging alternative data, transcending the common usage of transaction data for predicting quarterly revenue. Instead, focus on a wider array of datasets that offer insights into customer acquisition and retention, viewed through the prism of a customer acquisition funnel.

Traditional use of credit card data is seeing diminishing returns

The feeling from the majority of investors on the buyside is that it’s getting harder to generate alpha from individual US-focused transaction data providers without more creativity.

Transaction data has historically been the dataset type that investors have relied on to understand the trends in revenue before the companies report. There are multiple credit and debit card panels² available in the US, as well as receipt panels (e-receipt and physical), which generate a lot of use. The feeling from the majority of investors on the buyside³ is that it’s getting harder to generate alpha from individual US-focused transaction data providers without more creativity.

Photo by CardMapr.nl on Unsplash

One approach to generating more alpha would be to combine multiple transaction datasets whose panels complement each other in coverage (different geographic exposure, different consumer cohort exposure), providing more accuracy in forecasting revenue compared to the financial market potentially relying on only one dataset. There is value in this approach of creating a bigger mouse trap, but it is still the same data-use strategy focused on estimating the upcoming quarter’s revenue. Since many investors follow this strategy in terms of investment duration and generating signals using various datasets in isolation, the ability to generate alpha may still be pressured by competing firms using similar strategies from similar transaction data.

Instead of following the traditional strategy around the quarter, I would propose a different approach. While monitoring the point of purchase is important because it’s the critical culmination of any business's ability to monetize their goods or services, there are digital signs throughout the customer journey to understand the underlying trends that are on the path to purchase. Collecting, cleansing, and enriching these digital markers makes it easier to catch inflection points early to get ahead of multiple quarters of results beyond the upcoming quarterly results.

Apply the customer acquisition funnel framework to alt data

Understanding the customer journey from awareness to loyalty is a universal paradigm employed by corporations worldwide to guide decision-making and influence consumer behavior.

Customer acquisition funnel: The customer acquisition funnel is a model that represents the journey a customer goes through from the initial stage of awareness about a product or service to becoming a loyal customer. There various approaches to the funnel with different labels used. However, it typically consists of stages like awareness, engagement, trial, and loyalty.

Corporations around the world use this framework to make decisions about how to influence consumer behavior by growing awareness, stimulating trial, generating repeat usage, and ultimately generating loyal customers. Aligning this paradigm with multiple alternative data types enables us to identify early trend inflection points or weaker conversion areas that could hinder future growth if not addressed by company management. In addition to being able to monitor critical KPIs that corporations care about and improve forecasting ability, the framework provides the ability to ask more targeted questions of management to gauge their future decisions to grow the top of the funnel and improve conversion to loyal customers.

The next sections explain the logic and offer practical advice on how to match the data to the specific levels of the customer acquisition funnel.