Big Data Analytics Will Displace Net Promoter Score (NPS) for Measuring Customer Experience

Our benchmark research into big data analytics shows that marketing in the form of cross-selling and upselling (38%) and customer understanding (32%) are the top use cases for big data analytics. Related to these uses, organizations today spend billions of dollars on programs seeking customer loyalty andvr_Big_Data_Analytics_09_use_cases_for_big_data_analytics satisfaction. A powerful metric that impacts this spending is net promoter score (NPS), which attempts to connect brand promotion with revenue. NPS has proven to be a popular metric among major brands and Fortune 500 companies. Today, however, the advent of big data systems brings the value and the accuracy of NPS into question. It and similar loyalty metrics face displacement by big data analytics capabilities that can replace stated behavior and survey-based attitudinal data with actual behavioral data (sometimes called revealed behavior) combined with unstructured data sources such as social media. Revealed behavior shows what people have actually done and thus is a better predictor of what they will do in the future than what they say they have done or intend to do in the future. With interaction through various customer touch points (the omnichannel approach) it is possible to measure both attitudes and revealed behavior in a digital format and to analyze such data in an integrated fashion. Using innovative technology such as big data analytics can overcome three inherent drawbacks of NPS and similar customer loyalty and satisfaction metrics.

Such metrics have been part of the vernacular in boardrooms, organizational cultures and MBA programs since the 1980s, based on frameworks such as the Balanced Scorecard introduced by Kaplan and Norton. Net promoter score, a metric to inform the customer quadrant of such scorecards, is based on surveys in which participants are asked how likely they are to promote a brand based on an 11-point scale. The percentage of detractors (scores 0-6) is subtracted from the percentage of promoters (9-10) to produce the net promoter score. This score helps companies assess satisfaction around a brand and allows executives and managers to allocate resources. The underlying assumption is that attitude toward a brand is a leading indicator of intent and behavior. As such, NPS ostensibly can predict things such as churn behavior (the net number of new customers minus those leaving). By understanding attitudes and behavioral intent, marketers can intervene with actions such as timely offers and others intended to change behavior such as customers leaving.

Until recently, NPS and similar loyalty approaches have been one of the most adopted methods to track attitudes and vr_Customer_Analytics_02_drivers_for_new_customer_analyticsbehaviors in customer interactions and to provide a logical way to impact and improve the customer experience. The prominence of such loyalty programs and metrics reflects an increasing focus on the customer. An indication of this increased focus is found in our next-generation customer analytics benchmark research, in which improving the customer experience (63%), improving customer service strategy (57%) and improving outcomes of interactions (51%) are the top drivers for adopting customer analytics. Nevertheless, while satisfaction and loyalty metrics such as NPS are entrenched in many organizations, there are three fundamental problems with them that can be overcome using big data analytics. Let’s look at each of these challenges and how big data analytics can overcome them.

It is prone to error. Current methods and metrics are vulnerable to errors, most deriving from one of three sources.

Coverage error results from measuring only a segment of a population and projecting the results onto the entire population. The problem here is clear if we imagine using data about California to draw conclusions about the entire United States. While researchers try to overcome such coverage error with stratified sampling methods, it necessitates significant investment usually not associated with business research. Additionally, nonresponse error, a subset of coverage error, results from people opting out of being measured.

Sample error is the statistical error associated with making conclusions about a population based on only a subset of a population. Researchers can overcome it by increasing sample sizes, but this, too, requires significant investment usually not associated with business research.

Measurement error is a complex topic that deserves an extended discussion beyond the scope here, but it presumes that analysts should start with a hypothesis and try to disprove it rather than to prove it. From there, iteration is needed to come as close to the truth as possible. In the case of NPS, measurement error can simply be the result of people not telling the truth or being unduly influenced by a recent experience that skews evaluations such as brand impression or likelihood to promote a brand. Another instance occurs when a proper response option is not represented and people are forced to give an incorrect response.

Big data can address these error vulnerability because it uses a census approach to data collection. Today companies can capture data about nearly every customer interaction with the brand, including customer service calls, website experiences, social media posts and transactions. Because the data is collected across the entire population and includes more revealed behavior than attitudinal and stated behavior, the error problems associated with NPS can be largely overcome.

It lacks causal linkage with financial metrics. The common claim that a higher NPS leads to increased revenue, like the presumed relationship between customer satisfaction and business outcomes, is impossible to prove in all circumstances and all industries. For instance, a pharmaceutical company trying to tie NPS to revenue might ask a doctor how likely he is to write a prescription for a certain drug. The doctor might see this as a compromising question and not be willing to answer honestly. Regarding satisfaction metrics, Microsoft in the 1990s had very low user satisfaction but high loyalty because it had a virtual monopoly. The airline industry today sees similar dynamics.

Big data analytics can show causal linkage between measurement of the customer experience and the organization’s financial metrics. It can link systems of record such as enterprise resource planning and enterprise performance management with systems of engagement such as content management, social media, marketing and sales. Collecting large data sets of customer interactions over time enable systems to relate customer experiences with purchase behaviors such as recency, frequency and size of purchase. This can be done on an ongoing basis and can be tested with randomized experiments. With big data platforms that can reduce data to the lowest common denominators in the form of key-value pairs, the only obstacles are to have the right skill sets, big data analytic software and enough data to be able to isolate variables and repeat the experiments over time. When there is enough data to do so, causal patterns emerge that can link customer attitudes and experiences directly with transactional outcomes. As long as there is enough data, such linkage can be revealed in any type of market such as wallet share in consumer packaged goods or “winner take all” markets such as automobiles.

It lacks actionable data. Often loyalty metrics such as NPS are tied to employee compensation. Those employees have a motivation to understand the metric and what action is needed to improve the score, but that is not easy due to a number of factors. Unlike quantitative metrics such as revenue or profitability, NPS and similar loyalty metrics are softer metrics whose impacts are not easily understood. Furthermore, the measurement may happen just once or twice a year, and the composition of the sample can change over time. Often what happens is a customer satisfaction team and consultants responsible for the research and analysis prepare the trend and driver analysis and share that with various teams with suggested areas of improvement and action to be taken. Such information is disseminated based on aggregated data broken out by important product and service segments and perhaps customer journey timelines. The problem is that even if employees understand the metric and how to impact it, by the time action is taken within the organization, it is not timely and not customized in an individual manner.

Big data analytics inherently has a streamlined capability to act upon data. Instead of the traditional process of reporting results and waiting months for action to be taken on those results and new results to show up in an NPS program, data can be acted upon immediately by all employees. A big reason for this is that data is now collected at a granular level for individual customers. For instance, if a customer with a high customer lifetime value (CLV) score shows signs that are precursors of switching companies, a report can be issued to show all interactions in that individual’s customer journey and highlight the most impactful events. Then an alert can be sent and a personal interaction such as a phone call or a face-to-face meeting can be set up with the objective of preventing the customer’s defection. Incentives such as a bank automatically waiving certain fees, an airline giving an upgrade to first-class or a grocery store giving a gift certificate can be recommended by the system as a next best action.  It can also be done on a more automated but still personalized basis where the individual customer can be discreetly addressed to see how he or she can be made happy. Each of the actions can be measured against the value of the customer and contextualized forvr_Big_Data_Analytics_08_top_capabilities_of_big_data_analytics that customer. In this way, big data analytics platforms can bring together what used to be separate analytic models and action plans related to loyalty, churn, micromarketing campaigns and next best action. It is not surprising in this context that applying predictive analytics is the most important capability for big data analytics for nearly two-thirds (64%) of organizations participating in our research.I wrote about these ideas a few years ago, but only recently have I seen information systems capable of disrupting this entire category. It will not happen overnight since many NPS and satisfaction programs are tied to a component of employee compensation and internal processes that are not easily changed. Furthermore, NPS can still have value as a metric to understand word of mouth around a brand and in areas that lack data and better metrics. However, as attitudinal and behavioral big data continue to be collected and big data analytics technology continues to mature, revealed behavior will always outperform attitudinal and stated behavior data. Organizations that can challenge their conventional NPS wisdom and overcome internal political obstacles are likely to see superior return from their customer experience management investments.


Ventana Research

Unit4 Defines Strategy to Disrupt ERP Market

Unit4 is a global business software vendor focused on business and professional services, the public sector and higher education. Recently company executives met with industry analysts to provide an update of its strategic roadmap and to recap its accomplishments since being acquired by a private equity firm in 2014. Unit4 is the result of successive mergers of ERP and business software companies, notably CODA and Agresso. The company is also a part-owner (with and others) of independently run FinancialForce, which sells a cloud-based ERP system built on the platform.

All vendors of business applications – especially ERP – are challenged today by a more disruptive technology environment than they have faced over the past 15 years. Unit4 is in the beginning phases of a planned evolution of its product and go-to-market strategy designed to gain share in the global ERP market. Parts of what it presented to analysts are already in place while other parts lie ahead still on its multiyear roadmap.

From a technology standpoint, the ERP software market has been in period of relative stasis since the Y2K bubble burst in 2000. Other than the arrival of cloud-based software as a service, the pace of innovation in this category has been relatively slow, especially relative to the pace set in the 1990s. Now this market is in the process of changing and organizations are deciding when to replace their ERP as I have written. The accumulation of more than a decade of small but steady incremental technology advances is giving vendors new possibilities for designing their applications. For its part Unit4 has been evolving the architecture underlying its applications to make them easier to implement (the company calls it “an elastic foundation”). It is also using Microsoft’s Azure platform to enable it to offer, for example, predictive and prescriptive analytics, mobile application functionality and intelligent process automation.

Unit4’s product and marketing strategy aims to seize opportunities provided by technology disruptions to gain share in a consolidating market.  We see three main sources of technology disruption that increasingly will be driving buyer preferences in the ERP market over the next decade.

One is the use of technologies to provide new more valuable capabilities. Here are some examples.

User efficiency is increased by greater automation of repetitive tasks (especially in finance and accounting departments). In addition, many legacy ERP systems have gaps in their architecture or their design that require manual process steps, process interventions (that require input rather than requiring it by exception) and manual data transfers. Another aspect of automation is reducing the need for data entry. For instance, an individual’s appointments booked in Microsoft Outlook can be reused for billing. Some of the built-in automation will be designed for vertical industries to reflect their specific requirements.

Overall effectiveness can be enhanced by use of more advanced predictive and prescriptive analytics as an integral part of a transaction-processing application such as ERP. These techniques can improve the quality of decisions that individuals make in executing transactions. Unit4’s strategy is to create vertical-specific advanced analytics to address the needs of these businesses. Effectiveness also can be improved by embedding in-context collaboration capabilities, which I have written about. That is, such software is “aware” of what an individualvr_bti_br_technology_innovation_priorities is doing and, for example, provides ready access to the specific colleagues that the user may need to contact at that moment and enables them to share all underlying data and documents that might be relevant under the specific circumstances (such as a master contract or previous instant messages). Our benchmark research on business technology innovation shows that collaboration ranks second in importance behind analytics as a technology innovation priority. Collaborative capabilities in software will multiply over the next several years as software transitions from the rigid constructs established in the client/server days, which force users to adapt to the limitations of the software, to fluid and dynamic designs that mold themselves around the needs of  users. Business is an inherently collaborative process anyway, so such capabilities are important to the productivity of business software users.

The user experience is improved by rethinking its design and organization of the screens. Unit4 aims to improve the mental ergonomics of working with its applications. The redesign reduces screen clutter, facilitates navigation across screens to complete a task and enhances graphics to make interactions more pleasing and efficient.

vr_Office_of_Finance_01_ERP_replacementUnit4 encapsulates these existing and prospective improvements in the slogan “self-driving ERP.” One element of this metaphor is reducing the amount of effort and attention required of individuals to handle mundane repetitive chores. The other is that by using built-in analytics that can spot potential issues and opportunities in the data, individuals will be able to spot and take action on situations that have the highest payoff. The company hopes to extend the capabilities of its ERP software beyond a simple transaction processing engine to include differentiated capabilities to run businesses more -intelligently.

In addition to providing scope for product differentiation, offering organizations far more than a like-for-like replacement of their existing software may provide an incentive to replace existing software sooner. One impact of the slow evolution of technology on the ERP market is that, as shown in our Office of Finance research, on average, companies are holding on to their ERP software a year longer than they did a decade ago.

A second technology that is already disrupting the market is the increasing adoption of cloud-based or hybrid-cloud-based ERP systems by Unit 4’s key target buyers: larger midsize companies in business and professional services, government and higher education. These sorts of organizations tend to have less capable IT staffs and smaller IT budgets than large public companies. This affects the performance of their systems because the software and hardware are not always kept up to date. For these buyers, a cloud-based product can deliver better performance than they currently have at a lower total cost of ownership.

The third disruptive technology approach is permitting end users to configure the ERP application without having to modify its code. Cloud-based applications that are designed to be used in a multitenant environment must be flexible enough to appeal to the widest possible audience. This requires an architecture that enables individual organizations to readily configure processes and make adjustments without altering the underlying code. It also means having industry-specific or even micro-vertical capabilities built into the system. Vendors that want to offer their software in a multitenant environment have to do this, but it is useful even in an on-premises or private cloud deployment because it can reduce the effort and expense of deploying the software. Properly executed, this approach makes the software more adaptable to how a company does business, rather than forcing an organization either to live with the software as is or pay significant fees to modify it to meet specific requirements. Unit4 was already heading in this direction before the change in ownership and management.

The management team also has been tackling internal issues and revamping its go-to-market strategy, essentially completing the integration of the various software companies. The company will invest in promoting a single master brand for visibility.  Product naming has been simplified to “Unit4” plus a functional label construction (such as “Financials,” “Professional Services” and “Consolidation”). This will apply across the board except for “Business World,” which has good recognition on its own. Some once local or regional products such as Travel and Expenses are now available worldwide. Unit4 is increasing its exposure in North America, increasing its sales coverage where it has had a limited presence, as well as focusing its European sales efforts in the U.K., France and Germany.

Technology and innovative software design will drive consolidation of the ERP market over the coming decade. Unit4’s  management team has made necessary changes to its sales and marketing management. Its strategies are sound and essential to its long-term success in this market environment. Combined they reflect a formula that successful business applications vendors will use to gain advantage in the newly dynamic ERP market. The company is well-positioned to achieve its objectives from product and market standpoints. At the least, Unit4 has the potential to grow faster in its fragmented markets by taking share from smaller vendors that do not have the critical mass to make the necessary investments in products, sales and marketing. (By analogy, this is similar to what happened with a long list of DOS business applications that did not have a recurring maintenance revenue stream to fund redevelopment on Windows.). However, its strategies are not unique. For that reason (and I hate to state the obvious), Unit4’s management will need to execute its strategy well. To ensure that it gains sufficient market share to sustain a competitive position, it will need to innovate faster than its competitors in shorter product cycles and execute in the field consistently.


Robert Kugel – SVP Research