Social is informing all marketing disciplines today and the real-time nature of social data is ideal for driving conversions.
Since the heyday of the infomercial, direct response (DR) campaigns have been a significant driver of revenue, and today, in the age of social media, they still play a major role in the pursuit of conversions. It has been proven that many verticals can achieve better results with social than display advertising, so it makes sense that leveraging social for DR campaigns can deliver results, sometimes even rivaling those of search.
Social media and the wealth of data that comes with it is still a beast to be wrangled for many brands. But that data is actually what makes social such an ideal channel for direct response. The real-time data gleaned from social media is actionable across organizations from sales to customer service, and, of course, marketing, to drive conversions. Monitoring social actions, reactions, brand interactions, and reacting to them in real time is one of the best ways to deliver desired responses. For example, customer service can quickly respond to a customer issue with a targeted offer and drive a sale. Marketing can develop DR creative directly related to likes, check-ins, brand comments, and other indicators of importance.
But with all that data, the key to making social work for DR is investing in a comprehensive data strategy on the front end to optimize your campaigns. Many brands make the mistake of only optimizing as they go along, which is of course important. However, to get the most out of social DR campaigns, it's important to implement data strategies including active monitoring and analysis, as well as view tags, click tags, and pixels at every stage of the "conversion funnel."
Here are a few basic steps for making sense of the social media noise and using it to optimize your DR campaigns:
As with any marketing campaign, mapping your social DR program to clear-cut and specific goals is key. Some campaigns are geared toward driving purchases while others may focus on conversions such as profile completion or email list signup, and different tactics are better suited for each goal. For instance, examination of our campaign data has provided empirical evidence that the more difficult conversions are (e.g., for big ticket items like auto purchases, or infrequently purchased items like financial services), the more targeting plays a role in the conversion rate. In campaigns designed to drive simpler conversions such as clicks or impressions, the creative actually is more important than targeting.
It is also important for advertisers to develop thoughtful multi-touch attribution models for their products. Multi-touch attribution models enable advertisers to give "credit" to every ad that led to the conversion rather than simply crediting the last ad that the consumer sees. This allows for a more holistic view of performance of each component of a campaign - display, search, mobile, social, etc. - and enables better overall campaign optimizations. Only by understanding and measuring the effectiveness of social platforms in the overall digital context can advertisers understand the value of social advertising to their DR efforts. Proper multi-touch attribution models are also critical to support optimal bidding models, optimal targeting, and lookalike expansion models to help advertisers achieve scale with social DR.
Though our mailboxes continue to be flooded with direct mail flyers, and infomercials aren't going away anytime soon, the new evolution of direct response is social media. Social is informing all marketing disciplines today and the real-time nature of social data is ideal for driving conversions. It can be done without crippling your resources and it can be done at scale. Just be sure not to jump in feet first; lay the foundation for a social DR program with the right tools and data strategies, optimize on the front end as well as throughout, and measure accurately and comprehensively.
Image on home page via Shutterstock.
Dilip is CEO and co-founder of Compass Labs. He previously led Google's mobile ads business and ran PayPal's risk and fraud management, financial services, and compliance. Dilip has co-founded and led two successful start-up companies -CashEdge and CommerceSoft - after stints at McKinsey and Goldman Sachs. Dilip has an MBA from the Harvard Business School, M.S. in electrical engineering from Rice University, and a B.Tech in electrical engineering from the Indian Institute of Technology, Madras.
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