Automation is about to disrupt mature industries that compete on the quality of customer service. In this blog post, I explore how new technology will affect the way companies use the NPS (Net Promoter Score), the most frequently used customer loyalty metric. NPS methodology will change, and with it the way information flows within a company.
- IoT & Feedback Collection
- Deep Learning and Data Analysis
- Prescriptive AI and Implementation
A Quick Reminder about the NPS
Before we jump in, let’s make sure everyone is on the same page. If you’re an NPS veteran, skip this section.
The NPS is a standard metric for benchmarking customer satisfaction and loyalty. It has been proven to be a good predictor of growth. Since December 2003 when Frederick F. Reichheld (Bain & Co.) introduced “The One Number You Need to Grow”, the NPS has evolved into a management philosophy, or as Bain & Co. like to call it, The Net Promoter System®.
The NPS is a metric that is calculated with data from customer surveys. It is useful for both B2B and B2C environments, especially in industries where there is a strong emphasis on customer service. At its core is a “magical question” that has proven to produce data closely correlated with future growth: “How likely is it that you would recommend [brand] to a friend or colleague?”. Respondents answer on a scale from “0 (not at all likely)” to “10 (highly likely)”. When the data is analyzed, the respondents are categorized as the chart below shows. This process gives out a handy little number that can be assessed easily: above 0 is good, and above 50 is excellent.
This way of measuring customer loyalty was revolutionary. It has since inspired many companies to build operational systems using this benchmark. A NPS survey can include multiple follow-up questions that gather more granular data. The company can then use the data to build a customer-centric culture by taking measures such as setting goals at all organizational levels, tying it to benefits, or using it to compare the performance of different branches. While the concept is simple, implementing this way of operating requires executive commitment, operational discipline and technology. Let’s explore some of the technological trends that will affect the NPS in 2017.
AI technology will be responsible for automating processes that were historically labor intensive and expensive: data collection, analysis and implementation across different departments. The disruption will come when some companies will be able to close the customer feedback loop and accelerate it using technology. There is no doubt companies operating in this way will become market leaders. Here is a closer look at the technologies that will cause the most changes…
IoT & Feedback Collection
We’re going through a period of “physical-digital integration”, especially with the rise of the IoT (Internet of Things) that offers new opportunities of feedback collection. As we get more used to talking to things around us, we’ll probably start sharing how we feel: “Alexa, I’m sad my parcel hasn’t shipped yet”. Gathering this kind of data in real time will offer a lot of benefits for companies. The data is recorded as it occurs, so there’s less bias creeping in by self-reporting long after the events occurred. Presumably, the user giving the feedback is logged into an account which means that data can be tracked from multiple touch points simultaneously. It will be easy to achieve closing the feedback loop by naturally following up with the user: Alexa says: “Did you get your parcel on time?”.
Deep learning and data analysis
Deep Learning is already changing the way we process both structured and unstructured data.
Algorithms are becoming better at finding correlations by themselves, which will result in better data cross-referencing. Intelligent centralized systems will allow NPS survey responses to be cross-referenced in real-time with purchases and referrals. The data will be highly accurate because it will not be biased by cultural differences or individual personality traits.
The application of deep learning technology in natural language processing has evolved to the point where text analytics accuracy is over 85%, allowing for questions to be analyzed on a large scale. This means that it’s easy to go through thousands of additional comments from the NPS survey, revealing the “Why” behind the score. This information will be useful across the company, from executives to product managers to the salesforce on the frontline.
This capability also means that surveys will change. Customers can write what matters most to them directly, without having to use multiple questions to get to what’s truly relevant.
Prescriptive AI and Implementation
Prescriptive AI technology will create an opportunity for companies to automate the kind of high-end customer service only found in high-end service providers. The kind of customer service that reacts to your needs even before you know your needs. Previously, I used an example of complaining to Alexa about the speed of parcel delivery. Imagine you need your parcel because it’s someone’s birthday. Prescriptive AI will not only be able to piece together the parameters of your order and figure out why you’re nervous, but will also be able to give a nudge to the shipping department to prioritize your delivery. AI system will be able to aggregate user data and act on it by sending recommendations to different departments, thus breaking down the silos.
Gearing up for the NPS Battle
As more industries mature and compete on customer experience, companies have an amazing opportunity to leverage emerging technology to close the customer feedback loop and increase the velocity of information gathering analysis and implementation. I have gone through some of the opportunities new technologies offer to businesses that are willing to explore and invest in a vision where each customer can have a personalized experience.