‘Loyalty’ is a soft word in business – similar to ‘culture’ and ‘leadership’ – in that we intuitively know it matters but have difficulty quantifying how much it matters. A new era of metrics, modeling, and accounting is ahead of us as we head into the era of ‘consumer capitalism’ where a customer base becomes a company’s primary long-term asset.
The below post is a very simplistic, illustrative example of how the topic works conceptually. It is meant for educational purposes and is not specific to any business or brand in-particular.
100 years ago, loyalty was the only thing holding businesses back from bankruptcy. In an era where telephones were in their infancy and the passenger car was a novel innovation, businesses built trust with local customers by sourcing high-quality products and delivering on a high standard of service. In turn, they were rewarded with customer loyalty and word-of-mouth marketing. As the Baby Boom began, many of these businesses hit their stride and began decades of expansion in their local and national markets.
It wasn’t until we began to enter the era of LBOs (leveraged buyouts) and Wall Street’s tricks that we began to see the beginnings of the ‘global brand.’ Propelled by large amounts of liquidity and ruthless standards, the goalposts suddenly shifted from customer-centric to shareholder-centric.
Without getting into a philosophical debate about the purpose of business, it became clear that dumping loads of capital behind business leaders whose unilateral focus was to ‘maximize shareholder value’ shifted executive incentives away from customers and towards shareholders. Cost-cutting, outsourcing, and everything else linked to the globalization business model soon became the ‘trends.’ Price became the ultimate variable, and as a result, accountants and finance departments began working towards shifting the bulk of its work towards maximizing short-term, quarterly earnings.
Now in the digital age where you can measure and track everything in business, we are starting to see a shift back to loyalty. Not in a hokey advertising way, but in a way that customer loyalty itself becomes an asset, one that informs the expected future cash flows and ultimately the value of the business in the future.
Performance can be tracked in a number of ways. For example, Net Promoter Score (NPS) is standard way for marketing teams to measure customer satisfaction with a given product or service. You will often see this type of NPS survey delivered by email or in-app when they ask you ‘how likely are you to recommend this product/service to a friend?’
There will be different benchmarks for NPS depending on the industry, but generally, it can be assumed that a business with many Promoters (ie. loyal, enthusiastic customers) will see strong growth and financial performance. Naturally, NPS is not a perfect metric and has many flaws; however, it is a starting point and certainly an indicator for expected future performance.
A lot of research is now focusing on how such metrics (NPS, etc.) correlates to financial performance. Given that several avant-garde public companies have begun to disclose certain loyalty metrics (obviously the ones they deem to be favourable), there is a new body of academic and capital market’s research being conducted to better understand behavioral and transactional metrics about a given company’s customer base.
In theory, this data can then be modeled and essentially become predictive of future performance. Questions abound, however, as to what that data will look like and how it will be standardized. To start, we highlight below a simple benchmark that could be applied to almost every business (product or service), before diving into more advanced analytical models.
Customer-Based Corporate Valuation (CBCV) – Financial Modeling
The work by Peter Fader – who sold Zodiac to Nike and is now running Theta Equity partners – focuses on using transactional logs in non-subscription businesses to model future cashflows using certain assumptions.
“What do we need to implement CBCV?
The model consists of 4 interlocking submodels governing how each customer of a firm will behave:”
Certain elements like customer acquisition and basket-size model will be easier to predict than others such as customer retention and how frequently customers will transact with a firm. We broke down the CBCV paper written by Fader et al. in some depth in an earlier post, which uses advanced statistical modeling and analytics to calculate CBCV for publicly traded firms.
This assumes that companies have both the data available and the finance and accounting teams in place to break that data out into their quarterly reports. While the more rigorous calculations are really only feasible for medium and large-size firms, the basic premises can be applied to even small firms who are collecting customer data using CRM (customer relationship manager) software and transactional logs.
From a capital market’s perspective, this type of data can be broken out to help find the future stars and diagnose deficiencies in the current portfolio.
Overall, the CBCV is a new and novel approach to calculating the value of a customer base (and thus a firm) using advanced statistical and analytical techniques. Finance departments of the future may begin playing around with their own customer models, but for now most of this data is held in the marketing and executive branches. This creates its own set of challenges that will need to be addressed in creative ways going forward.
Customer Capitalism – Accounting
It is always impossible to predict when these types of ‘loyalty metrics’ will become standard in financial disclosures of publicly-traded companies. Not only do financial standards boards have to determine what metrics are required, they then have to determine how those metrics will be integrated into current standards and enforced (ie. make them auditable). Furthermore, some jurisdictions will decide to implement them before others and then the typical problems in accounting (ie. non-GAAP vs. GAAP) will emerge.
For now, it is enough to start thinking about how to storify this data in a concise way. Different industries have their own unique types of customer bases, and within those industries, each company has its own style and strategy for acquiring and retaining customers.
Ultimately, painting a story about customer acquisition and retention can be done with a specific set of fairly simple metrics (ie. new customers, repeat purchases). However, as was discussed above, looking at your top 20% of customers and their purchasing behaviors can sometimes lead to the most insightful data points. This type of Power Law (typically the top 20% drive 80% of the volume) is visible in some businesses more than others, but analyzing the top subset of a customer base (ie. based on volume or goods purchased) almost always leads to actionable insights for the executive team.
Whether those insights will be required (or strongly recommended) to be disclosed public investors or to private investors is TBD (to be determined).
Nonetheless, we have seen in this post that customer loyalty is a future performance indicator, and understanding it in greater detail can have benefits for the business in the short, medium, and long-term. The 1st step is to analyze it, the 2nd step is to put one or two metrics around it that can be tracked, and the 3rd step is to shape those metrics into the narrative that gets discussed among executives, investors, and potentially even public markets.
Overall, we are seeing loyalty metrics becoming more sophisticated as businesses and investors alike seek to better understand how this data can inform strategy and predict future performance. Much work is to be done, but this field may help us unlock insights and reshape frameworks around the pursuit for profit in the global economy.