When collected, these contextually based dyadic relationships form what we call geofenced communities. Geofencing is the influence marketing tactic of identifying where the prospective customer is in the purchase life cycle, and the profiles and roles of micro-influencers who impact their decision-making process as filtered in various situations and situational factors. Charting customers in this manner allows us to identify true influencers and create influence marketing campaigns that are less likely to be disrupted by situational influences and factors. Further, it helps define the content marketing strategies and highly targeted calls-to-action that are more likely to convert prospects into customers in each scenario.
To illustrate this concept, let’s explore the case study of a computer manufacturer that sought to increase its sales of laptops to college students. Having identified a growth opportunity in this category, the marketing and R&D departments developed a product at the right price point with the features and software most applicable to this audience. The following sections explain the steps taken in the Customer-Centric Influence Marketing Model.
Step One: Community Identification
Social media monitoring and analytics software was used to identify communities engaged in discussions around the keywords: “laptops,” “college applications,” “college tuition,” and “student loans.” User profiles were displayed around the keywords in a tag cloud along with other keywords that the software identified as related to the main terms. A social graph was drawn using the connected people around the chosen terms and both community and user profiles were saved. A thorough vetting process that scrubbed each audience group included clearing out the fake or little used profiles and the identification and categorization of user profiles within each group.
For example, in the discussion community that formed around “college tuition,” the marketing team identified profiles that included college applicants/high school seniors, college applications/adults, parents of high school seniors, the business’s employees, competitors’ social media accounts, competitors’ employees, official brand accounts for colleges, college alumni, media and blogger profiles, and so on.
Step Two: Situational Analysis and Factors
Next, contextual analysis was performed to identify the nature of the conversations occurring among each community to identify the situational influence that might be at play. For example, within the “college tuition” discussion community, two situational influences were identified: economic (associated to cost of tuition in a tightening economy) and cultural-socio groupthink (“cool-factor” associated with certain brands). Next, marketing analysts applied the localized situational factors (or combinations of factors) that might impact a purchase decision. These included, among others, personal factors, such as the household income and availability of funding; emotional factors, such as the distance college was from parents’ home; environmental factors, such as the location where discussions were occurring (e. g., on school forums or on social networks); physical location (geography) of those identified; and so on.
Step Three: Identification of Customer and Micro-Influencer
This analysis allowed the marketing team to understand, among those in the online community, who the decision makers were, such as parents of teens applying for college or adult students returning to college. With each group profiled, the community social graph was reoriented around the decision maker to help identify who their micro-influencers were, as illustrated in Figure 5.5. Profile data pulled from data suppliers such as Kred and PeopleBrowsr, collected data such as degrees of separation, relationship status, sentiment of discussions, and traditional analysis including past knowledge, experience, and market research were used to further segment the profiles of the micro-influencers.
However, to develop the correct influence marketing strategy and corresponding communication and promotional tactics, they went one step further. As highlighted by the situation formulas denoted in each quadrant of Figure 5.5, the marketing team matched the situation(s) with the situational factor(s) that might impact the purchase decision.
As an example, in Figure 5.5 the formula for the top-left quadrant breaks down like this:
- Situation A: Economic Situational Analysis: Cost of tuition was unaffordable by those in the community identified.
- + Factor 3: Personal: Household income of parents below national average.
- + Factor 4: Environmental: The availability of funding for students, geography of students and available colleges.
- + Factor 7: Timeline: More than 12 months from college application due date.
Figure 5.5.Geofenced communities
With decision makers identified and each social graph filtered by the possible situations that impact their decision making, the marketing team was able to craft the appropriate influence marketing campaigns that targeted the micro-influencers at key moments in the online conversation. Situational forces determined the urgency of influencers’ recommendations and impacted the conversion ratio from those same recommendations. In the end, the geofencing exercise increased the number of warm leads and the overall sales of the product within the target audience.