The advertising industry has evolved significantly over the years, with the emergence of new technologies and trends. Social influence and word-of-mouth marketing continue to play a significant role in the industry, influencing consumer decision-making and driving sales.
AdAge reported that the US ad industry grew by 7.5% in 2021, with total ad spending reaching $259 billion. The report also notes that digital ad spending now accounts for more than half of all ad spending in the US.
To keep up with the ever-changing landscape, brands and agencies continually look to improve the accuracy of advertising models by incorporating data such as social data to further understand, attribute and correlate consumer conversations to predict business results by leveraging analytics and machine learning to derive actionable insights. Incorporating social influence data into these models can be crucial, especially as we know social conversations can directly and indirectly impact consumer purchases.
What is social influence?
Social influence is unique in two ways: the data sets available to us and our experience in incorporating social data into marketing models. Engagement Labs has the most extensive ongoing measures of word-of-mouth (WOM) about brands dating back more than a decade. Our offline data is a key component of our TotalSocial data and analytics platform, which is crucial for modelers. We have developed a data transformation and scoring method that applies to our WOM data and to social listening data from social media, blogs, forums, and other sources. These methods allow brands to distill wide-ranging data into eight distinct metrics that are highly predictive of brand outcomes.
Social data can directly drive consumer purchases, but it can also work indirectly in combination with other marketing activities. Understanding this two-step process is vital to successfully incorporating social influence data into models. We have learned that a substantial part of the impact of paid media is a result of WOM.
The current state of social influence
According to McKinsey, consumer conversations about brands have a significant impact on the purchase decisions of other consumers. McKinsey estimates that word-of-mouth is the primary factor behind 20% to 50% of all purchasing decisions. This shows the immense power of social influence in driving sales and improving marketing ROI.
IDC, on the other hand, predicts that by 2025, worldwide spending on AI systems will reach $97.9 billion, up from $37.5 billion in 2019. This shows the increasing adoption of AI technologies in various industries, including advertising.
Impact of social influence on consumer decision making
Extensive research shows that both real-life and social media conversations are critical pathways for consumers in their purchase journey. Our research, as we have reported in the MIT Sloan Management Review, reveals that conversations on average drive about 20% of sales. Social influence, both offline and online, is very substantial and should not be overlooked by brands. While this is the average, our research shows that each brand has its social DNA. The way social influence works for one brand can be quite different from the way it works for another, even for brands in the same category.
Understanding what drives a brand and how big the opportunity to drive incremental sales is key to maximize performance. Doing so can potentially unlock and demonstrate tens of millions of dollars in incremental sales.
How to incorporate social influence data into predictive modeling
Discovering and obtaining streams of quality data and know-how for incorporating social data into both new and pre-existing models is key. Experience to integrate it with brand and/or media data that reflects and best represents the most important KPIs to reveal the metrics that drive business outcomes. Then, for brands who have their analytics programs, Engagement Labs can work alongside their modeling team to help them incorporate social influence data (online and offline) to strengthen the predictive power of their models.
Here are a few recommendations for brands and businesses on how social influence data can impact their marketing and sales strategies:
Integrate word-of-mouth to predict sales
Social influence is a crucial component to test when constructing forecasting models. By incorporating offline and online conversations about brands into predictive models, businesses can gain insights into how social influence impacts their sales.
Gain insight into the mechanisms driving sales improvement
Quantifying marketing’s amplification via social influence is essential for calculating marketing’s full ROI. Businesses can identify the marketing activities that drive the most word-of-mouth conversations and optimize their marketing strategies accordingly.
Pinpoint the consumers most likely to talk about and recommend your brand
By identifying the groups most prone to talk about and recommend a brand, businesses can prioritize target market and audience segments to optimize their marketing strategies.
Gain new insights into competitors
TotalSocial reveals the extent to which positive or negative conversations about other brands in the marketplace impact your brand sales. Businesses can gain valuable insights into their competitors and optimize their marketing strategies to stay ahead of the competition.
Social influence and word-of-mouth marketing continue to play a significant role in the marketing and advertising industries. By leveraging emerging technologies like generative AI and combining it with Engagement Labs’ TotalSocial data and analytics platform, brands and businesses can gain a better understanding of social influence and how it impacts their marketing and sales strategies. This, in turn, can help businesses optimize their marketing strategies, improve marketing ROI, and drive business outcomes.
This article originally appeared on the Engagement Labs blog; reprinted with permission.
