{"id":2100,"date":"2017-08-10T16:42:09","date_gmt":"2017-08-10T21:42:09","guid":{"rendered":"https:\/\/getgood.com\/roadmaps\/?p=2100"},"modified":"2017-08-10T16:42:09","modified_gmt":"2017-08-10T21:42:09","slug":"roi-and-influencer-marketing","status":"publish","type":"post","link":"https:\/\/getgood.com\/roadmaps\/2017\/08\/10\/roi-and-influencer-marketing\/","title":{"rendered":"ROI and Influencer Marketing"},"content":{"rendered":"

Continuing my series\u00a0about the trends in influencer marketing: The rise of ROI. <\/em><\/p>\n

In the early days, when blogs and social were new and shiny, return on investment was\u00a0\u201csquishy.\u201d As influencer marketing matures, so too do the expectations, and the measurement models.<\/p>\n

Measurement models are shifting from soft\u00a0\u201cpotential reach\u201d to firmer engagement models, and a better understanding of true awareness (eyeballs, lift) across all\u00a0platforms, not just the easier-to-measure website ad impressions and content views. The best predictive\u00a0models look at both awareness and engagement, to provide the necessary context for brands trying to decide which type of content\u00a0will best deliver to the marketing plan.<\/p>\n

How many people see<\/b> the content across all opportunities – native, social, pageviews. defines REACH or\u00a0AWARENESS. How many people act<\/b> on it (clicks, likes, shares) is ENGAGEMENT.<\/p>\n

Engagement rates will continue to be important, especially as we are increasingly able to link social actions with purchases through longitudinal studies like Nielsen Catalina, and foot traffic studies that can link a social visitor with a real life visitor, but these are expensive, and more likely to be used by\u00a0large CPG and retail\u00a0advertisers with big budgets. Not universal. Yet.<\/p>\n

In the meantime, while we wait for the nirvana of proving engagement drove purchase\u00a0\u2014\u00a0knowing<\/b> whether someone who read that blog post last\u00a0year,\u00a0purchased it this year, we will rely on brand lift studies from Nielsen, Millward Brown and others,\u00a0and\u00a0first party reporting combined with original research. OpenUp<\/a> is a start-up doing\u00a0interesting work in the space\u00a0of\u00a0linking digital engagement with content to\u00a0eventual\u00a0purchase.<\/p>\n

Advertisers want to understand the return on branded content, including influencer marketing,\u00a0in the same way they evaluate their other advertising activities. Cost Per View is emerging as an\u00a0AWARENESS\u00a0metric alongside the click-through rate\u00a0and the\u00a0effective CPM that advertisers use to evaluate the overall\u00a0efficiency of a media plan.<\/p>\n

Re-visioning Measurement: A model for digital content marketing\u00a0<\/strong><\/p>\n

When a marketing tactic is new, we tend to be a little forgiving when it comes to measurement. It is simply not possible to be first to market, and also have a case study to evaluate before you make your decision. We operate on gut, on past experiences that are similar, out of a desire to experiment with the new tactic. We monitor and measure, but it is to establish a benchmark, not against a benchmark.<\/p>\n

As the tactic evolves and matures, however, a body of work begins to emerge. Successes and failures, near misses and home runs, all combine to give some indication of what works. And what doesn\u2019t. We are at that inflection point with content marketing, and particularly with influencer marketing. Benchmarks are emerging left, right and center.<\/p>\n

Problem is \u2014 many of these benchmarks are either the very simple pageview and click-through-rate (CTR) we started with or defined by the different technology platforms people are using, thus hard to compare with each other. In some cases, they measure things because they can, not because the measure is useful or relevant, a criticism I have oft levied at Google Analytics.<\/p>\n

In addition to CTRs, content program benchmarks tend to rely on views (page, video, slide) to show reach, and comments and earned social to demonstrate engagement. At publishers that scale content through native, native CTRs get added to the mix. This is a good start, but volume based measurements don\u2019t allow you to compare tactics with different budgets. More budget nearly always delivers more volume.<\/p>\n

Adding to the complexity, Facebook and the other social platforms report in the context of their platform – likes, comments, shares – and are more than a little opaque unless you are the account owner. This makes it challenging for advertisers trying to understand their earned media. We can count it, we just have a harder time understanding the person who shared it.<\/p>\n

Plus things change. Not every day but it feels like it.<\/p>\n

We need to simplify to make the data we collect useful to marketers. Capture key points that let us understand the success of a particular campaign and the component tactics AND compare the campaign to other campaigns, the tactics to other tactics.<\/p>\n

Simplify. Standardize.<\/strong><\/p>\n

Every marketing tactic we use has an awareness and an engagement component. We want you to pay attention and then do something. Isolate those and look at them separately to understand the performance of each tactic against its goals. You can also aggregate each measure to understand how the overall campaign performed.<\/p>\n

Views \u2014 Awareness<\/em>
\nWhile reach will always be important as a general gauge for awareness \u2014 the potential or available audience for a message \u2014 we need to move past who MIGHT see something, and evaluate our campaigns based on who actually did. To standardize across all platforms, we use views and actions as proxies to estimate the engaged audience of digital content.<\/p>\n

Actions \u2014 Engagement<\/em>
\nAbsolute numbers are great to understand the VOLUME of your social engagement, but if you want to compare tactics, you need to use rates. This corrects for size. Our old friend the Click-Through-Rate<\/a> is still strong here, and lets us compare all our tactics against a single measure. But, it isn\u2019t the only useful RATE we can calculate.<\/p>\n

We can also look at an overall engagement rate for a campaign, defined as Total Engagements\/Total Reach.<\/p>\n

For content, look at the content engagement rate. Of the people who read something, how many shared it with others? Or simply commented.<\/p>\n

Content Engagement Rate = Actions\/Views<\/strong><\/p>\n

For video, the video completion rate (completed views\/total views) remains an important measure, but it underestimates the success of the content. Looking at the ratio of viewers who watched at least 25% of the video (or more than 10 seconds on Facebook) gives a more accurate measure of the video success. You can also look at content engagement rates for video.<\/p>\n

Social is a bit more squirrely when it comes to standardized measurements across platforms. We have reach and engagements, but we don\u2019t always have access to actual viewers of a social action due to platform and cost barriers. If we own the channel, we have better data, but influencer data depends on whether the platform allows third-party access, and if so, how much it costs to get and use it.<\/p>\n

Right now, I am intrigued by content and sentiment analysis as the path to understanding message penetration on social. Because both paths \u2014 audience and content analysis \u2014 are on the pricey side, we collectively tend to rely on engagement metrics to understand results on social. We have the data, and we can efficiently compare across platforms.<\/p>\n

I recommend looking at two measurements here:<\/p>\n