Editor's Note: Like any marketing program, tracking the performance of your mobile marketing program is critical in order to deliver the best business results. But because mobile marketing is new territory for many brands, established benchmarks to help you understand what good performance actually looks like can be tricky to find and define. Plus, not all mobile program goals are the same.
For guidance towards results-driven success for your mobile marketing program, download our playbook here. For broader guidance on measuring customer retention and loyalty, keep reading.
Loyalty research is common among businesses that strategically manage customer experience (CX). The prevailing assumption is that loyalty is integral to business success.
But how do you know if a customer is truly loyal, from acquisition through renewal? Is it as simple as asking a single survey question like "likelihood to recommend" to accurately gauge future intent?
Net Promoter defined.
Criticizing traditional research as being overly complex without adding value, Net Promoter theory argues that asking customers their likelihood to recommend plus one open-ended follow-up question enables companies to reliably gauge their long-term health.
By definition, Net Promoter is calculated as the percentage of customers who are promoters minus the percentage who are detractors. Thus, at the individual level, each respondent is discretely categorized as a promoter or detractor on the basis of a 'Top Box/Bottom Box' categorical break. Companies became so convinced that one question was all they needed, some re-tooled entire VOC programs to adopt a single Net Promoter Score (NPS) metric.
The evidence is decidedly mixed in this area. Reichheld provides empirical evidence that suggests the metric is associated with enterprise growth. There's also a variety of anecdotal evidence from supporters showing positive results in their own business settings.
But evidence from academia is less conclusive. When Keiningham et al. examined linkages among satisfaction, loyalty and growth in 2007, data showed a combination of VOC metrics universally outperformed NPS when predicting later behavior. More recent research by Zaki et al. established the unreliability of NPS (and overall satisfaction) as single loyalty measures within complex organizations.
Has NPS been over-hyped?
There's little question that Net Promoter has intuitive appeal and, at first blush, appears to be a viable solution to an ongoing research industry struggle -- how to effectively predict customer behavior using attitudinal measurement approaches. Numerous proponents have supported the idea that likelihood to recommend is the only attitudinal metric that links consistently to company performance. But has the concept been over-hyped? Is it really always the best choice for understanding your customer experiences?
In support of the concept, we found that subscribers of a national web provider that gave low likelihood to recommend ratings were more than three times as likely to leave the provider within a six-month window.
On the flip side, we also saw likelihood to recommend revealed as a weaker predictor than other satisfaction and loyalty metrics. For one business-to-business equipment manufacturer, perception of the value received is the most potent survey-based predictor of revenue. And for a retail bank, likelihood to recommend was the poorest predictor of prospective customer-level behavior.
So, what's the verdict?
As straightforward as NPS is in theory, the volume of mixed results calls into question the veracity of making an all-encompassing suggestion that any survey metric has a near-perfect relationship with customer intent.
1. One size never fits all.
While there may be some status in the idea there's one best answer to a problem, the truth is that linkage between attitude and behavior is not easy to prove. As far back as the late 1960s, psychologists were deeply concerned that attitudes did not always relate to behavior. Research continues to this day, but a resounding finding has been one you might expect: Attitudes link to behavior in different ways, under different conditions.
Every organization is different, industries vary greatly, relationships are always unique. Know your organization, know your business model, and understand that metric selection and research design must fit the business. Never force your company into a prefabricated research approach.
Always-on, digitally enabled lifestyles require a fresh look at research options. Today, it's pretty common for a customer to interact with a non-human. Digital disruption and the experience economy introduce CX enablers like AI, machine learning and bots.
Be sure memories created during all interactions are positive and customize VOC measurement appropriately so omnichannel journey impacts are evaluated and quantified.
2. Blind faith in any metric is risky.
The wide disparity of business models suggests performing testing in your own organization before deciding which metrics to use to track health. Maybe a satisfaction rating best predicts revenue growth and retention rates. Or maybe customer effort is the best metric to use. If you don't test multiple measures, you may end implementing something sub-optimal, or even detrimental, to business process.
Start by conducting a beta-test with multiple measures.Take the data, statistically link it to behavioral and organizational data, and analyze which metric best predicts behavior. If you skip this step, you may end up spending a significant amount of time on something that offers little predictive insight.
3. Use the right metric at the right times. You may need more than one.
Another best practice is to avoid relying on a single metric as your only indicator. After all, loyalty is a complex issue. Why would we expect to capture all of its nuances with a single question? We don't use one indicator to assess our individual health and we shouldn't use one question to measure the health of a company. Best practice in measurement theory suggests using multiple indicators to represent multi-dimensional constructs.
There are several research advantages to this approach including increased reliability, increased scoring dispersion and better coverage of underlying factors. More pragmatically, our practice has shown that composites and multiple measures of loyalty better predict behavior than do single indicators. The argument against this approach is that they can be difficult for business stakeholders to comprehend. Given today's emphasis on balanced scorecards and tracking complex key performance indicators for performance and compensation, creating executive understanding of how to use more than one metric is not insurmountable.
When picking a key metric, make sure its meaning and interpretation are consistent from the bottom of the organization to the top. It must be relevant at the customer level and the enterprise level, plus everywhere in between.
4. Be sure the measure works top-down and bottom-up.
When creating your loyalty metric for performance tracking, make sure that its interpretation is consistent from the bottom of the organization to the top-meaning that it must be relevant from the customer level to the enterprise level.
Net Promoter is a loyalty metric best suited to aggregate-level organizational performance tracking. This constrains the customer-level segmentation of individuals based on their attitudinal loyalty and potentially lessens the precision of targeted customer marketing efforts.
On the other hand, raw scores on loyalty items have the same meaning and metric at the individual survey level as they do rolled up to the enterprise level. This consistency can be an advantage in client implementation and education efforts.
As practitioners, we have a responsibility to educate client partners and present them with options. NPS may work well for some business situations, and not others.
Having flexibility and domain expertise to present a comprehensive view of the effect that several CX metrics have on business results is effective at preventing blind faith in any trend. Encouraging open, frank dialogue during program design leads to better business outcomes. Providing pros and cons of a variety of approaches ensures success.