Wednesday, July 25, 2007
Many marketers are looking for alternatives to traditional marketing to create buzz around their brands. We're not suggesting that CPG brands totally eliminate traditional marketing; using word-of-mouth tactics alongside more traditional methods has always been important for brands. The difference is technology and a willingness to test new campaign types, be they blogs, viral campaigns, or other methods that drive awareness.
- Advertising is best predictor of buzz: After analyzing blog buzz volume, ad spending, purchase intentions and actual product sales, researchers found that the best predictor of buzz for a newly launched CPG is a large advertising budget.
- Greater media spend equals greater buzz: On average, the top 10% of products with the most buzz spent nearly $20 million on paid media for the launch. In contrast, the companies that generated the next 40% of blog buzz spent an average of $15 million; the companies that generated the bottom 50% spent an average of only $5 million.
- A few products dominate CPG buzz: Not all CPG subcategories and products generated buzz at the same level. In fact, 10% of brands accounted for 85% of total CPG buzz in the study. Over-the-counter (OTC) drug brands have higher buzz, partly driven by consumers' higher level of involvement with them. Edgy brands were also among the top 10% of products with the most buzz.
- Buzz precedes sales peaks: Buzz tends to occur very early in relation to a new product launch, with peaks in buzz preceding peaks in sales two-thirds of the time in the launches studied.
- Buzz enhances sales forecasting accuracy: For the select products that generate substantial buzz, this study provided the first-ever evidence that buzz volume can positively influence sales. In a regression-based sales forecasting experiment, incorporating actual buzz levels resulted in a meaningful accuracy improvement to forecasting models, by as much as 20%.
- Brand uniqueness prompts buzz: Beyond media spend and distribution, category familiarity (as indicated by higher purchase frequency) and product distinctiveness show value when attempting to anticipate or predict buzz.