Surfacing Prime Companies for Investment with CE Alternative Data

Summary

Welcome to the CE Scout Index. The Scout Index provides a glimpse into the power of Consumer Edge’s credit and debit card transaction data for actionable private company deal sourcing. The Index was constructed by modeling historical Private Equity and Venture Capital deals in search of  a clustered target range of historical spend. This spend range is used to filter through over 14,000 brands tracked by Consumer Edge, highlighting private companies where spend data alone puts them on the cusp of being the next big deal.

For private investors, deal sourcing can feel very restrictive in the current environment.  As a Private Equity or Venture Capital partner, trying to wrap your arms around all of the companies, let alone figuring out which ones are within your size target, can be daunting.  Insight often comes from data sources that are more proxies than direct measures of company size like job listings or github code commits.  Or even more indirect measures like time since a smaller seed or angel round of funding – assuming those rounds are even picked up by popular industry databases. 

Consumer Edge provides direct credit and debit card transaction data that reflects a strong corollary to consumer spend at even the smallest brands.  However, with 14,000 brands in the US alone, looking brand by brand at spend trajectories requires time and skill for interpretation, and the data doesn’t fully capture spend for thriving DTC businesses that may have a wholesale component. With these challenges in mind, Consumer Edge developed easy-to-use spend numbers accessible via either a machine-readable data feed or our Discover dashboard to  filter for companies that have tracked spend within a certain range. Used in conjunction, the Scout Index provides search parameters that can be customized based on the target deal size.

More specifically, when parsing through hundreds of historical deals pulled from PitchBook, a pattern emerges among CE-tracked companies. Those that have represented the bulk of closed deals over the past few months are clustered within relatively narrow spend bands as tracked by our data.  This implies that much of the normalization necessary to go from CE Transact data spend to true company revenues as surfaced by a due diligence is consistent across deal worthy companies. Searching within these bands reveals the CE set of companies well positioned for investment – companies whose spend puts them squarely within range of comparable firms who have recently successfully raised funding.

High-Level Methodology

Considering that many private firm transactions have an extended timeline from the initial discussions to the finalization of a deal, three different time periods were evaluated for the index. The spending of a company was assessed over the following windows: 364 days, 182 days, and 91 days prior to the deal’s closure. This was done to determine which timeframe would be most predictive of future funding.

•  364 days: This period provides a sufficiently long timeframe to capture the early stages of an investor’s interest in a target company or when they begin to take a more serious look. It also offers the necessary historical data for businesses influenced by seasonal spending peaks.
• 91 days: This timeframe considers the period of due diligence, during which an investor closely monitors sales to ensure the company’s growth trajectory remains on track.
• 182 days: This intermediate period is included to enhance the analysis, allowing for the understanding of any effects that fall between the 364-day and 91-day windows, and to observe when companies start to stabilize or exit their given timeframe.

Interestingly, the same companies cluster at different levels throughout the three time periods. 

Finding the Clusters

To identify the specific ranges for these clusters, a PitchBook query was executed to generate data on all companies that had transactions between January 1, 2023, and May 16, 2024, within consumer DTC industries. Companies were isolated based on their tagged presence in the CE Transact US data. Using the USA1 + USA2 eMax panel, daily historical spend was pulled for each of these companies. The spend was then summed over the 91 days, 182 days, and 364 days leading up to and including the day of PitchBook’s Deal Date. Companies were ranked by total spend within each window separately. A “cluster” was defined as any group of consecutively ranked companies where the spend of the rank n company was within 20% of the spend of the rank n-1 company. The 20% differential was not pre-determined but was based on natural clustering observed in the data. Both the 182-day group and the 364-day group had a 22% increase between two companies in the ranking, resulting in a split into two clusters.

91 Day

182 Day

364 Day

Each quarter, Consumer Edge publishes a thematic subset of companies that have recently fallen within these bounds to provide concrete reference points for the types of companies that might be coming up in screens for deal sourcing using transaction data.  These quarterly updates also incorporate any recent deal activity, and provide insight into underlying trends in private investments.  

Rejected Hypotheses

Growth as a Variable

Growth was rejected as an explicit variable to build the index due to its implicit inclusion via the consistent scaling factors between the 364, 182 and 91-day periods. The inclusion of all three time periods made looking at alternative growth measures such as year-over-year growth redundant and raised the possibility of too many collinear variables unnecessarily complicating the model.

Relevance of Transaction Count

Transactions were also rejected as a significant variable because there was no clear relationship between the level of transactions for a company and the completion of a deal.  This implies that private funding is more dependent on revenues than a need to cover costs, as costs are more likely to be tied to transaction growth and number of items sold (sales personnel, advertising, manufacturing, compute, etc.)

Industry Effects

Industry effects also appeared minimal, although this may have been due to a small scope of industries selected for the original sample. For spend to be captured in Consumer Edge data, the companies under review must be primarily direct-to-consumer, and therefore the initial list of deals that were inputs into the model were limited to consumer-facing industries. Within this subset, there did not appear to be any clustering by industry or vertical. 

Confounding Factors

Cause vs. Effect

It does need to be caveated that investors in private companies often express concern over a “pump and dump” phenomenon. This refers to private company behavior wherein investor interest becomes a catalyst for a one-time spending boost on marketing and other unsustainable levers to temporarily drive sales and a corresponding increase in valuation based on a revenue multiple. As this circumstance is difficult to back out of the data and likely to be present even unconsciously as a Hawthorne effect (although the original study has been questioned), we simply accept its existence as a variable driving the spend band progression.

Seasonality

Tracking spend band progression from 364 to 182 to 91 days removes the normalization of seasonal businesses that had been an attractive feature of focusing on 364 day spend bands. Congruent to the speculation regarding pump and dump above, there may be specific patterns in when seasonal business accept funding (for instance just at the peak of their seasonal spend). 

See the Scout Index in action – download the CE Scout Report on baby apparel and gear brands poised for private investment now.

Notes

This methodology is a top-down approach. The index is not meant to assign a statistical probability that companies of a certain size will participate in a funding round. It does not include many other relevant variables that would be needed for a comprehensive analysis. Rather, it is meant to provide guideposts for investors looking to source new deals based on company DTC consumer spend.