A customer benchmarking engine is an emerging technology which uses an artificial intelligence approach to automate the reasoning that underlies data-driven benchmarking. Its benefits are discussed here, there, and elsewhere. Briefly, it uncovers comparative insights on customers which empower customer-focused employees to be more proactive, or which are shown directly to those customers as a premium information service. The business benefits include churn reduction, market differentiation, extra revenue, and deeper customer relationships.

But, automated customer benchmarking doesn’t always make sense. So where does it? Here I’ll summarize the criteria that we’ve learned from clients, trials, conferences, discussions, and analysis.

Data. A single organization collects data on its business-customers’ traits, behaviors, business outcomes, and feedback, e.g., via surveys. Lack of data on customer outcomes narrows the scope of the insights, which may still have internal value for account management. Also, the organization should not be contractually prohibited from performing comparative analysis across customers, appropriately anonymized if the resulting insights are to be shown to customers. Evidently, the data shouldn’t be wrong or mostly missing.

Motivation. The organization should be B2B because consumers (B2C) are generally less motivated to improve, because they are less driven by external stakeholders. The same lack of strong motivation may be found if the B2B organization serves very small businesses, which are less prone to carry out performance analysis: if they are tiny but making money, then life is good, and if they’re losing money, there are more-urgent issues to address. Think of your small neighborhood restaurant, for example.

Also, the business process that the organization supports with its services should not be seen as a utility, meaning that customers only care that the service be available when they need it, and little or nothing more. Think of an internet connectivity service, for example.

A strong positive indicator of motivation is when customers themselves ask the vendor organization how they’re doing compared to other customers, where they could improve, etc.

Comparability. In principle, benchmarking only makes sense if the benchmarked entities are comparable. It makes little sense to benchmark an elephant against an armchair and an airplane. Comparable doesn’t mean identical or even similar, just productively worthy of comparison. For example, a business consultancy that brings the smartest people in the world to fix whatever problem you have, whether it’s a leaky roof, runny nose, or buggy software, won’t have comparable customers. An HR SaaS company does have comparable customers, even if its customers range from the Fortune 500 to startups and in between, because HR has common elements across companies of any size or industry: employee motivation, compensation, tenure, promotion, recruiting, dismissal, etc. Comparability is a judgment call, but most B2B vendor organizations do have comparable customers, otherwise it would be hard for them to scale their business.

Scale. A customer benchmarking engine is a powerful tool that scales beautifully with the number of customers. But, just as a search engine is probably overkill if you only possess 50 documents, or a receptionist is overkill if you have 5 employees, benchmarking 50 customers likely isn’t worth the trouble, even though the engine will do its job. Given the tradeoffs, we believe that about 150 is the right minimum number of customers for automated benchmarking to make sense.

It’s worth citing some false disqualifiers which are wrongly believed to invalidate customer benchmarking, automated or not. (1) Customers need not be concentrated by industry or segment, much less be competitors, since one is benchmarking the customer’s business process that is supported by the vendor organization’s service, not benchmarking the customer’s overall market performance. (2) The data suitable for benchmarking is rarely scarce. For example, if a given metric (employee satisfaction, say) is potentially insightful, then so is the quarterly change in that metric, since it expresses a trend. Ditto for the change when compared to the same quarter last year. Thus, the insightful metrics are easily tripled, based on changes over time, as we’ve discussed elsewhere. (3) Data need not be perfect; it never is. And the end-result of imperfect data is not a plane crash, but a misleading insight, which tends to be caught and discarded before significant action is undertaken.

Now let’s summarize the four qualifiers data, motivation, comparability, and scale in a single brief sentence:  A customer benchmarking engine makes sense for B2B organizations that generate rich data on its 150+ non-tiny customers as a by-product of its non-utility-like, repeatable service.

Who are these organizations?  B2B SaaS (software as a service), Industrial Internet of Things, BPO (business process outsourcing), Managed Services Provider, and 3rd-Party Administrator, are generally good matches if they fit the other criteria.

Automation doesn’t always make business sense, especially when the enabling technology lies outside one’s own organization, which circumstance always involves a coordination cost. But automation scales well and can enable things or insights that don’t yet exist. Apart from the benefits discussed elsewhere, this article shares what we’ve learned about where the emerging technology of customer benchmarking engines makes sense.

Raul Valdes-Perez