Some commonly tracked customer metrics like Satisfaction and NPS have a notoriously tenuous, if any, correlation with business growth. In their book – the “Wallet Allocation Rule” – the authors make the case for a new measure that might be the strongest indicator yet of Share of Wallet and growth.
Growth is Number 1
Growth has been and continues to be the top priority for business leaders. And yet, few firms achieve it with any consistency. A study published in the Harvard Business Review suggests less than 8 % of a set of ~ 5,000 publicly traded companies achieved growth of 5% per year for 5 years. Other studies have found less than 25 % of companies succeed in their bid to grow consistently and profitably.
Firms can adopt one of two approaches on the path to growth – 1) acquire new customers and/or 2) get a bigger share of existing customers. The latter is most certainly the low hanging fruit. It is significantly easier and more cost-effective to increase current customers share of spend than it is to acquire new customers. This is especially so today when customers in most categories are not loyal to a firm or a brand but rather a set of firms or brands. This is true for both, consumer as well as business markets.
The key question to answer therefore is – how are customers dividing their spend among you and your competitors? In other words, what is your “share of wallet”? And how can you change it? This is the critical question, answer to which can help drive growth.
Satisfaction ≠ Growth
In a bid to achieve that elusive growth, companies hawkishly track metrics like satisfaction and NPS. In the belief – albeit mistaken – that the happier we make our customers the more business they will do with us. The truth is that there is little correlation between Customer Satisfaction or NPS and Share of Wallet. Studies show that, on an average, Satisfaction or NPS levels explain less than 1% of the variation in a customer’s share of spend, with the R squared typically 0.1 or under (on a scale of 0 to 1, with 1 being a perfect correlation).
Enter the authors of Wallet Allocation Rule. In their treatise on the topic, they make the case for a new metric that is a strong predictor of Share of Wallet. What’s more, it uses more commonly tracked metrics to arrive at a Wallet Allocation Rule Score that estimates and predicts share of wallet.
The Wallet Allocation Rule
Based on their research, the authors found that a Brand’s Share of Wallet can be predicted by a simple mathematical formula which they call the Wallet Allocation Rule:
The main factor in this formula is the relative position of a brand in the market vis-à-vis competition (Rank) and the number of brands used by the customer. Any change in rank, or number of brands, has a direct bearing on change in the share of wallet. A survey of approx. 5,500 firms across geographies and industries was used to validate this rule. Currently available metrics like satisfaction and NPS were used to determine rank. This rule suggests the following:
- Evaluation of a firm has to be in the context of competition
- Rank matters. There are financial implications to being number 1, or 2, or 3….
- Parity hurts. It is not enough to be tied for first place. Customers must have a reason to prefer your brand
- The more the brands a customer uses, the less the potential for everyone
All of the above elements are completely ignored when measuring Satisfaction or NPS.
Drivers of Rank & Share of Wallet
Upon knowing rank, the focus now shifts to understanding drivers of rank v/s drivers of NPS or satisfaction. What will make your brand the first choice for customers? As a corollary, what is driving them to choose a competitor? Getting insight into the money you currently get and the money available to be earned helps drive strategies and actions to grow share of wallet. A couple of cases cited in the book bring home this point.
Credit Unions in the US enjoy extremely high satisfaction and NPS scores – not only among banks, but all other industries. Despite this, they hold less than 10% of US deposits and 65% of their customers use more than one competing financial institution. Research reveals the key driver of satisfaction for Credit Unions is fast problem resolution & competitive fees. However, pouring resources into further improving both is not likely to grow their share of deposits. The most important driver of competing banks share, however, was Internet Banking – where they score over Credit Unions. Now this knowledge offers a potential path for credit unions to reduce customer’s perceived need to use competing banks.
The DIY home improvement market in France is another great example. This is a large market with the highest expenditure of household spend in the country of Euro 800 per year. Four major players dominate the market with shares of 37%, 19%, 14% and 12%. They enjoy very similar satisfaction levels driven mainly by the range of products offered. This obviously does not explain their difference in Market Shares. Understanding why customers choose competitors reveals more. While the dominant brand offers the best service and support, number two wins on the basis of convenience of location and the third focuses it’s offering on savvy DIY’ers and professionals. There is potential for disruption here if the runner up chooses to improve its service and support and take on the market leader.
Choose a Customer Metric that delivers growth
It is as important to understand what is driving your customers to competitive brands as it is to know how “satisfied” they are with yours. In the end, it is share of wallet that drives growth. Not Satisfaction or NPS. It behooves businesses therefore to ensure a strong return on investment in customer intelligence by choosing a metric that helps deliver growth.
The LRC Loyalty Metric – a relative measure
A big takeaway from the “Wallet Allocation Rule” is that relative performance is more important in explaining customer behavior – such as share of spend – than an absolute one. It is why at LRC we persistently factor competitive performance into loyalty measures. We have known for over 20 years that a relative measure is FAR more likely to drive a “relative” behavior, like choice, as opposed to an absolute one.