Zero Moment of Trust Drives Brand Loyalty

Zero Moment of Trust is a critical factor in earning consumer respect and loyalty via your digital brand presence. Zero Moment of Trust is akin to Zero Moment of Truth (ZMOT), which is Google’s concept related to how consumers retrieve and process information via a digital device and make a purchasing decision therefrom. What trust indicators can you leverage across your Web and mobile brand presence to imbue a sense of trust when consumers interact with your brand? This presentation discusses the key trust indicators that you need to pay attention to when considering how your brand appears to consumers.

Trust indicators in social network marketing

Jeremiah Owyang explains the concepts and value of social networks from a marketing perspective in an easily digestible manner. Yang et al (registration required), Battiston et al, and Hill et al discuss the scientific underpinnings of these topics. Juxtaposing these discussions against one another leads to some interesting insights with respect to social media marketing.

Yang notes that in 1967, Stanley Milgram demonstrated that mutual acquaintances drive social network strength. As Yang elaborates:

“[T]he probability that two of someone’s friends know one another is much greater than than the probability that two people chosen randomly from the population know one another.”

Yang illustrates the concept of this theory by pointing to the success of Hotmail, which grew from 0 to 12 million users in 18 months.

Battiston explores how “trust” factors between actors in a social network affect the dynamics of recommendations in that social network.

“Trust plays a crucial role in the functioning of such socio-economic networks, not only by supporting the security of contracts [sic?] between agents, but also because agents rely on the expertise of other trusted agents in their decision-making.”

What Battiston drives towards is that trust-based modes of recommendation have an inverse relationship to traditional modes of recommendation, which are primarily based on the volume of recommendations as opposed to the value of recommendations. Battiston argues that trust-based (or value-based) recommendations are inherently better at promoting more satisfying results to actors within a social network.

This, in turn, promotes the propogation of sub-group cultures to form within the social network. And as non-trustworthy agents drop out of the network (because prior recommendations did not fulfill specific trust elements as dictated by the requesting actor), the sub-group refines itself overtime. As more sub-groups are defined within a social network, “network neighbors” emerge amongst members of these sub-groups, where these network neighbors operate as conduits between different sub-groups.

Yang demonstrates that sub-group performance, in terms of marketing results, out-performs all others (this was measured in terms of traditional transaction response rate metrics).

Accordingly, marketers must seek out sub-group network neighbors. These individuals are the brand influencers and advocates within a social network. Jerimiah Owyang has an excellent post on the visual display of this information. Leverage Software has developed a product which likely can visually display these sub-group cross-over individuals, thus making the selection of influencers and advocates easier. Perhaps these individuals would be great focus group candidates, “real time” collaborators in product development initiatives, etc?