‘Social Spillover’ From Targeted Customers A Powerful Campaign Amplifier

Optimizing interactions based on the connections between users will play a new role in their ability to connect with consumers and drive additional return on marketing spending.

‘Social Spillover’ From Targeted Customers A Powerful Campaign Amplifier

Driving ongoing customer engagement and retention using personalized, cross-channel campaigns and experiences remains a mission-critical focus for consumer-facing brands in the new year.

In Gartner’s CMO Spend Survey 2016-2017, seven out of the top eight marketing budgeting priorities listed by CMO respondents address technologies and channels focused on unifying and enriching the digital customer journey. But 2017 won’t just be about delivering one-to-one interactions at scale to your targeted users.

No, it will be about truly understanding the ripple effect of personalized campaigns throughout your customer’s broader social network.

In October 2016, researchers at Columbia University and HEC Paris partnered with Amplero to publish a study, “Beyond the Target Customer: Social Effects of CRM Campaigns,” which researched the behaviors of nearly 6,000 mobile customers at a major North American carrier. Leveraging machine learning-powered automation to deliver targeted contextual messaging to key users, the study found that social connections of targeted customers increased usage and were less likely to churn—despite having neither a campaign targeted toward them nor being offered any direct incentives.

Specifically, the research showed a social multiplier of 1.28. That is, the effect of the campaign on first-degree connections of targeted customers was an additional 28% beyond the effect of the campaign on targeted customers.

While good marketers instinctually recognize the ripple effect a good campaign can have throughout customers’ networks, now they have data points to back it up.

So what does this mean for marketers in 2017?

Unified Customer Profiles And Attribution Models Need To Broaden

It’s said that you can’t truly know someone unless you know his or her community. Although many brands are still struggling to integrate disparate first- and third-party data sources to understand their customers, companies that thrive will leverage this capability to measure and optimize the impact of their marketing by mapping customers’ influence within their social spheres.

Columbia University professor and study co-author Eva Ascarza points out the risks for companies that ignore a targeted user’s sphere of influence. “Marketers are missing out on a substantial piece of the pie if their customer relationship marketing campaigns only consider the profitability generated by their targeted customers,” Ascarza said. “CRM campaigns also need to include and measure the changes in activity among those connected to the targets.”

Influencer Optimization Will Be Transformed

Today, most targeted campaigns are based on straightforward behavioral triggers or profile dimensions. For example, a customer has indicated buying signals through engagement of web or in-app content and is delivered an email offer for 10% off a purchase.

However, through the aggregation of behavioral data, it’s now possible to map customer influencer networks that identify not only the number, strength, and intensity of connections, but also the potential influence a customer has over the behaviors and actions of those connections. Based on this research, marketers can amplify their efforts by targeting their largest influencers and empowering them to spur their friends and connections to take the desired action, leading to significant results, including incremental revenue that directly hits the bottom line.

While traditional “influencer marketing” focuses on press, industry analysts, or users with large social media followings, marketers will discover clusters of influencers that allow them to multiply campaign efficacy with lower investments. Understanding which customers have the strongest pull on their external networks should be top-of-mind for marketing leadership—particularly in subscription-based verticals, such as telecommunications, gaming, and media.

In one real-world example, a prominent mobile carrier has an underutilized referral program. Using this new influencer optimization technology, it was able to model usage behavior and social interactions of existing customers with the goal of identifying those that could influence others within their social spheres. These influencers were defined with the following conditions:

Once identified, these influencers received messages designed to produce a ripple effect through their networks, resulting in the action of additional users (influencer connections) being added to the subscriber base via the referral program. Within the first 90 days of the program, the carrier saw nearly a 1% lift in referral rates over the control group experiencing “business as usual” broad-brush marketing.

Churn Prevention Will Become More Complex—And More Powerful

The Columbia University study also cited an amplification of negative experiences throughout a user’s broader network. This insight places a greater accountability on the marketer to deliver valuable customer experiences or risk observing the fallout resonating throughout a customer’s social sphere. Mistargeting is worse than not messaging a customer at all!

In addition, marketers will be able to leverage machine learning technologies to predict churn further in advance based not just on behaviors exhibited by a single user, but by their social networks as well. In the study, targeted retention campaigns had an incrementally positive effect on churn beyond the targeted user. At Amplero, we use this effect in both directions: to identify customers for which there would be socially magnified fallout were they to churn, and to retain customers in whose networks an influential customer has just churned.

While most marketing leaders are working on bridging the gap from traditional marketing to delivering one-to-one personalized interactions at scale in 2017, optimizing those interactions based on the connections between users will play a new role in their ability to connect with consumers and drive additional return on marketing spending.