For the longest time, traditional valuation methods have been standard in the sale of merchant portfolios. Any modern seller, including ISOs, knows the conventional methods leave money on the table. The methods focus on static and oversimplified assumptions, relying heavily on historical data and often negating non-financial influences on portfolio performance. To put it bluntly, with advancements in technology, traditional valuation models for merchant portfolios don’t cut it.
With today’s emerging models, buyers can now leverage AI in the payment industry to create far more accurate, forward-looking estimations. This gives them a clearer picture of a portfolio’s value. As a result, sellers receive a fair market price for their assets in a true win-win, powered by both human expertise and data science.
Redefining Portfolio Analysis With Data Science
Modern portfolio evaluation seamlessly incorporates AI into the process. Buyers of merchant portfolios understand the limitations of conventional valuations and see where AI excels. AI tech uses advanced algorithms and analytics to automate data collection, extracting and processing vast amounts of financial data in a fraction of the time of manual methods and yielding greater accuracy.
Behavioral Segmentation
One key distinction between traditional methods and the use of AI-driven decision-making is the use of behavioral segmentation over industry codes. In older valuation models, industry codes generalized businesses, leading to a lack of specificity and less reliable data, especially in current fast-paced environments. Behavioral segmentation looks at several pieces of data unique to each business, including:
- Customer interactions
- Product usage
- Sentiment analysis
- Transaction history
- Website activity
By acknowledging the unique behaviors of each business, AI valuation models can provide improved efficiency for automation and more accurate risk assessments. The tech creates opportunities for buyers to evaluate churn risks, but it also helps sellers enable proactive interventions to retain high-value merchants and capitalize on growth opportunities.
Accurate Forecasting
For sellers, a reliance on historical data is a limitation in valuation. It skews the attrition rate and leads to potentially biased and poor cash flow projections.
AI, with its prowess for predictive analytics and penchant for vast and sometimes obscure data sets, provides a more accurate depiction of residual decay. It accounts for historical data, yes, but it also accounts for external influences on the market and specific business-focused data. By separating each business from the portfolio, AI models create more accurate and forward-thinking valuations.
With more reliable forecasts, buyers get a better estimate of how a portfolio is likely to perform. Sellers can receive a fair valuation and receive top dollar for their assets.
Assessing Value Beyond Its Historical Context
A portfolio is more than its current residuals. Despite this fact, most traditional valuation models look to residuals as the basis for a portfolio’s value. They use multiples of the number to estimate future income streams and factor in attrition rates and potential growth. This way of calculating value is limited and best applied to smaller, straightforward portfolios.
For modern, complex portfolios, especially those that ebb and flow with fast-paced markets, value has to move beyond the historical and look towards the future, call it the merchant potential value (MPV). Using AI and modern data analytics, it’s possible to look beyond a portfolio’s existing income stream to its quantifiable, latent potential for future growth that can be priced as the market stands today.
Moving past historical valuations towards an MPV approach means viewing assets as dynamic platforms for growth rather than static. These future-focused projections represent a premium that buyers are willing to pay for, and AI models showcase the projections as verifiable growth options. Therefore, buyers aren’t only paying for current cash flows, but also data-backed potential.
To clarify the MPV concept, buyers typically analyze merchant portfolios for opportunities to incorporate value-added services, like recurring SaaS software fees or business financing. For example, a portfolio with a high number of merchants already using integrated software with open APIs is demonstrably more valuable. The existing technology offers a direct pathway for introducing new revenue-generating services later. This portfolio addition adds a verifiable premium to the valuation.
Positioning Your Portfolio for Maximum Value
In today’s market, you can use a buyer’s timeline to gauge their sophistication. Modern, AI-driven valuations are fast, often taking just days to produce an accurate and fair offer. If a potential buyer quotes a process that’ll take weeks, consider it a red flag. It likely signals they use older, manual methods and may not be equipped to see your portfolio’s full, forward-looking value.
It’s crucial to work with a buyer who communicates directly. Demand access to the decision-maker who can explain the valuation data, as this is essential to assessing its fairness. In the final agreement, scrutinize the “future business” clause. A favorable clause protects your right to work with merchants again, allowing you to get paid now while preserving relationships.
Moving Forward in a Data-Driven Market
Developments in AI and data science are changing and even replacing traditional methods for merchant portfolio valuation. The new reliance on these tools is creating a transparent and dynamic marketplace where future potential is as vital as past performance. ISOs and agents who understand this new landscape are in an ideal position to unlock the real, forward-looking value of their assets.
