Recommending products to friends, family and neighbors has been around since, well gosh, the beginning of time…The science of measuring recommendations is something relatively new. About 8 years ago, Fred Reichheld of Bain & Company developed a metric called Net Promotertm. The Net Promoter Score is obtained by asking customers a single question on a 0 to 10 rating scale: “How likely is it that you would recommend our company (product/service) to a friend or colleague?” Based on their responses, customers are categorized into one of three groups: Promoters (9–10 rating), Passives (7–8 rating), and Detractors (0–6 rating). The percentage of Detractors is then subtracted from the percentage of Promoters to obtain a Net Promoter score (NPS.)
Since 2003, many large companies have adopted this metric as a way to easily measure customer loyalty. Since the score is so simple, it is easy for employees to understand, companies to measure over-time, and customers to answer the question. The score has been fairly controversial as well, with respected market researchers argueing that really understanding customer behavior and using knowledge of it to predict the future takes more than just asking one, scalar question.
At Attensity we’ve made some very exciting advances when it comes to Net Promoter. The first is related to “catching customers in the act.” Wouldn’t it be great if you could be alerted as to when customers are recommending you and your products? Well in social media, this is possible. As new reviews and recommendations are posted, we can do a lot of things with this data:
- Immediately alert users as to when the customer is recommending or not recommending (“catching them in the act” – or well, just after),
- Queue the review to a responder to either add to the review or even thank the customer for the review,
- Aggregate the content from the promoters and detractors to give users(marketers, customer service teams, product developers) a better understanding of user opinion,
- Even analyze the behavior of recommenders to understand if it can predict other behaviors like future purchases and future recommendations.
Another advancement is related to enabling companies to get a better understanding of the score. We call it the “why” behind the score. The problem with the score alone is that it is fairly one dimensional. Many of our customers have come to us and said – “can you tell us why customers are or are not recommending us?” Is it a product issue? A customer service issue? An employee issue? An experience issue? “What do we need to do to fix the problem?” While the score provides a great metric to track over-time, it lacks the granularity to provide the action steps needed to make changes. This screen shot shows reasons why customers purchase and recommend. We have a series of reports that shows the “why” so that users can drill in and see all of the dimensions that impact the score.
In order to be able to do this type of “why” analytics – we aggregate customer comments found in both social and directly communicated sources like email, survey verbatims, etc. We pull each permutation of how a customer might articulate a recommendation or a critique. This screen shot shows how we aggregate “would not recommend” issues into a group so that users can then query as to the “why” behind the score.
Are you able to get to the details behind your satisfaction scores? Do you know why your customers give you good or bad scores? Recommend you to their friends and family? We can tell you!
Trademark: Net Promoter, promoter and detractor are trademarks of Bain & Company and Satmetrix.
Photo Credit: By dmangust
