Metrics Are Easy; Insight Is Hard
September 28, 2012 Editor 0
Big data is great. But we should consider that we’ve actually had more data than we can reasonably use for a while now. Just on the marketing front, it isn’t uncommon to see reports overflowing with data and benchmarks drawn from millions of underlying data points covering existing channels like display, email, website, search, and shopper/loyalty — and new data streams such as social and mobile engagement, reviews, comments, ratings, location check-ins and more.
In contrast to this abundant data, insights are relatively rare. Insights here are defined as actionable, data-driven findings that create business value. They are entirely different beasts from raw data. Delivering them requires different people, technology, and skills — specifically including deep domain knowledge. And they’re hard to build.
Even with great data and tools, insights can be exceptionally tough to come by. Consider that improving Netflix’s recommendation-engine accuracy by about 10% proved so challenging that only two teams — of tens of thousands from over 180 countries competing for the $1 million prize — were able to hit the goal. Or that, despite significant work to improve online display ad targeting, the average clickthrough rate (and, by implication, relevance) still remains so low that display ads on average receive only one click for every 1,000 views. That is, the vast majority of people who see the ad don’t think it’s interesting or relevant enough to click on
When they are generated, though, insights derived from the smart use of data are hugely powerful. Brands and companies that are able to develop big insights — from any level of data — will be winners.
Here’s a four-step marketing data-centered process that doesn’t stop at the data, but focuses instead on generating insights relevant to specific segments or affinity groups:
1. Collect. Good data is the foundation for the process. Data can be collected from sources as varied as blogs, search, social network engagement, forums, reviews, ad engagement, and website clickstream.
2. Connect. Some data will simply be useful in the aggregate (for example, to look at broad trends). Other data, however, is more actionable if it’s connected to specific segments or even individuals. Importantly, the linking of social/digital data to individuals will require obtaining consumer consent and complying with local regulations.
3. Manage. Given the speed and volume of social interaction online, simply managing big data requires special techniques, algorithms and storage solutions. And, while some data can be stored, other types of data are accessed in real-time or only for a limited time via APIs.
4. Analyze and Discover. This part of the process works best when it’s a broadly collaborative one. Using statistics, reporting, and visualization tools, marketers, product managers, and data scientists work together to come up with the key insights that will generate value broadly, for specific segments of customers and, ultimately personalized insights for individual customers.
Consider these insights — drawn from detailed studies and data analysis — that are being used by us and others to deliver value today:
Friends’ interests make ads more relevant. Based on the evaluation of social-graph data and clicks, companies such as 33Across have found that showing ads based on friends’ similar interests can substantially raise ad click/conversion rates.
Sometimes it’s okay if people hate your TV show. A television network commissioned Ogilvy to look at the relationship between social media buzz and ratings. An analysis of thousands of social media data points and Nielsen ratings across 80 network and cable shows identified ways to help predict ratings changes and find the specific plot lines and characters that could be emphasized in marketing to drive higher viewership. One insight was that it’s critically important to look at data differently by show and genre. As an example, for some reality and newly-launched cable shows, both love and hate — as long as there was lots of it — drove audience ratings.
Social media works best in combination. Measuring the actual business impact of social media and cross-media interactions (beyond just impressions) is in the early stages and could have perhaps the most profound impacts of all on making marketing better and more efficient. For example, by exploring panel-based data on brand encounters by socially-engaged customers in the restaurant industry, Ogilvy and ChatThreads found that social media was very effective in driving revenue from this segment. However, this effect was strongest when social media were combined with other channels such as traditional PR and out-of-home media. Exposure to these combinations drove 1.5x to 2x increases in the likelihood of revenue gains.
Each of these insights works because it is actionable and generates value. Each one provides a concrete road map for making marketing more effective and efficient. And applying each insight creates value that both brands and consumers can appreciate.
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