The Predictive Power of Emotions

Authors: Steven Walden & Qaalfa Dibeehi

A lot of companies talk about how important it is to emotionally engage customers, to make them ‘feel loyal’ not just ‘act loyal’. But where is the empirical evidence that emotions really do make a difference? If we use KPIs like Customer Satisfaction and Recommendation (the Net Promoter Score® question), should we even be bothered with emotion?

We decided to test the ‘predictive power of emotions’ over a sample of 2,503 interviews using customer data grouped into three categories. These categories reflect perceived emotional value, from falling into a traditional hedonic value of service category to a more functional and hence, seemingly, less emotional category. In brief these categories encompass:

Activities perceived to be high in emotional value – Here we focused on one experience high in perceived positive emotions (in-resort vacationers) and one experience focused on high negative emotions (injection of diabetes drugs).

Activities perceived to be mid range in emotional value – Here we looked at experiences that are less hedonic but are associated with a future payoff (i.e., booking a vacation and receiving educational services.

Activities perceived to be low in emotional value – Here we included experiences that tend to have a low emotional charge i.e., using a credit card for mundane purchases and two B2B experiences: purchasing large IT systems and transporting goods by ship; B2B traditionally being perceived as a non-emotional, logical environment.

We then measured the effect of emotion in explaining customer satisfaction (CSAT) and recommendation using a peer-reviewed emotion scale (Emotional Signature®). Crucially the dataset associated with this scale had the benefit of holding ‘emotional’ as well as consumer quality judgements on an experience. Hence, the effect size statistic could be used to see whether the inclusion of emotion better predicted the variance in satisfaction and recommendation in comparison to quality judgements of the experience alone without emotion.

The results are presented in the figure where we have plotted the effect size or the marginal predictive power of emotions by the three emotional engagement categories. In essence, the effect size tells us how much improved are we at predicting the customer KPIs when we include emotion in the equation (versus not including it)

Here it was determined that in each category the inclusion of emotion increased the ability to explain customer satisfaction and recommendation by comparison to considering quality judgements on the experience alone. Hence by not including emotional measures, resources are potentially being misallocated and ‘value’ left on the table.

We of course expected to find that emotions would have the greatest predictive power in the high emotional engagement category. However, it turns out that the lower the emotional engagement in the  customer experience , the greater the role emotions play in predicting satisfaction and recommendation! The predictive power of emotions on the two key customer KPIs for low, medium and high emotional engagement experiences was around 35%, 25% and 5% respectively.

Implications

1. This provides empirical evidence that emotions are impactful on key customer KPIs regardless of the emotional engagement category. So measure your emotional ROI before you allocate resources.
2. Companies should not depend on personal beliefs of whether something is emotional or not when they measure an experience. High emotional engagement experiences does not necessarily equate to high predictive power of emotions on customer KPIs.
3. Organisations that have significant low emotional engagement experiences should pay the greatest attention to emotions as their effect accounts for more than 35% of satisfaction and recommendation.