Data Driven: Multichannel Challenges
Measuring the impact of marketing campaigns has become both complicated and enhanced in a multichannel world. Traditional methods of looking at cause and effect relationships between a set of activities and a set of behaviors no longer give marketers a complete or accurate picture of events.
Conquering the world of multichannel marketing analytics isn't as daunting as climbing K2. But if you are looking for an easy way to the top, you will be greatly disappointed.
With the right framework, however, you can make the most of this marketing challenge. By following this step-driven approach, you will be well on your way to mastering the art and science of the multichannel world.
• Make it a priority. Considerable effort goes into fully tackling multi-channel analytics. Having a clear sense of its importance may be vital to justifying the time and resources necessary to be successful. A 2009 McKinsey & Co. report highlighted two points that put this in perspective.
First, by 2011 the Internet will be used either as a research tool or as a sales channel in more than 45 percent of all retail sales in the United States. And second, consumers who shop across multiple channels (Internet, catalog, bricks-and-mortar stores, etc.) spend on average four times more annually on purchases than those who shop only one channel. That fact alone demonstrates why in today's world, multichannel analytics is important and relevant to every marketer.
• Inventory available data. The primary purpose behind tackling multichannel analytics lies in an ability to accomplish some seemingly simple tasks. These include the ability to successfully track, measure and analyze the impact of marketing activities over time and to provide the set of analytics that gives you or your organization intelligence that can be acted upon.
To accomplish this, first inventory all available data. Don't be afraid to think small here. While it is easy to focus on the most obvious data (sales revenue, conversion rates, call volume, Web traffic, etc.), there could be additional and less obvious data points that support the "why" behind the these numbers. Data also may come from sources that you have not previously explored.