{"id":3175,"date":"2020-07-26T09:49:45","date_gmt":"2020-07-26T13:49:45","guid":{"rendered":"https:\/\/new.jeffgreenfield.com\/?p=3175"},"modified":"2020-07-26T10:28:34","modified_gmt":"2020-07-26T14:28:34","slug":"are-the-hurdles-for-outcomes-based-measurement-surmountable","status":"publish","type":"post","link":"https:\/\/jeffgreenfield.com\/are-the-hurdles-for-outcomes-based-measurement-surmountable\/","title":{"rendered":"Are The Hurdles For Outcomes-Based Measurement Surmountable?"},"content":{"rendered":"

\"\"Today, marketers are being held accountable for their organizational bottom line more than ever before. With transparency increasing across marketing departments and businesses, brand leaders are being asked to track every dollar spent and demonstrate to finance leads how they are impacting revenue.<\/p>\n

This reality, in tandem with marketer concern about how many of their ads are actually being seen by human beings \u2014 and prominent researchers like Dr. Augustine Fou outlining a bleak outlook for dollars wasted due to digital ad fraud \u2014 has pushed both viewability standards and outcome-based attribution measurement to the forefront.<\/p>\n

The Biggest Hurdle<\/p>\n

True outcomes-based measurement means being able to look holistically at your marketing channels, while filtering fraud and viewability at the individual user level and accurately connecting devices across the user journey. The key challenge for agencies, sellers and brands seeking outcomes-based measurement, at scale, is the lack of an omnichannel measurement platform with reliable protections in place. Accurate outcomes-based measurement requires a gold-standard multitouch attribution (MTA) solution complete with the bells and whistles of filtering fraud and viewability across devices to determine true incrementality.<\/p>\n

Marketers need a lot of information because they\u2019re looking for proven cause and effect metrics and essentially putting their jobs on the line and telling finance, \u201cWhen will you give me more money? I\u2019m spending it on X. It\u2019s going to drive Y more dollars, and it will generate Z more profit.\u201d So, with those stakes in mind, it\u2019s important to be absolutely certain that your predictive models will perform as promised.<\/p>\n

Set Realistic Outcomes<\/p>\n

Much of marketing today is predicated on key performance indicators (KPIs), such as views, engagement and clicks, that are not always directly tied to revenue. For example, an auto manufacturer like Ford or Toyota might be focused on driving leads for their dealers. But, technically, that lead is not worth anything in terms of revenue.<\/p>\n

With MTA \u2014 identifying an individual and stitching together a consumer\u2019s journey anonymously \u2014 marketers are able to accurately tie that lead to revenue. Those automakers are then also able to ascertain which individuals end up buying the most expensive cars and track their outcomes in terms of revenue to actual action.<\/p>\n

And that\u2019s really what the goal is here!<\/p>\n

There is always a path to outcomes \u2014 even for notoriously difficult industries like pharmaceutical brands. With today\u2019s advanced MTA technology, you can track almost any KPI back to real revenue with the right platform. And, at the end of the day, we have to remember that leads don\u2019t mean anything to finance, but revenue certainly does.<\/p>\n

Know What Can\u2019t Be Measured And Create Workarounds<\/p>\n

Some sectors, like consumer packaged goods (CPG) brands, may require sampling to get information that cannot be obtained at the user level, but outcomes-based measurement is possible. Additionally, in a scurry for user-level data, I have seen a lot of CPG companies now moving very aggressively to have direct relationships with consumers \u2014 take Unilever\u2019s acquisition of Dollar Shave Club, for example. Other resourceful CPG brands, like Tide, have cleverly worked around their lack of user-level data by launching and acquiring dry cleaning businesses around the country, enabling a direct one-to-one consumer relationship.<\/p>\n

Industry Standards Are Necessary<\/p>\n

Right now, every company has its own standards, and there\u2019s no consensus in place for marketers or their agencies. Until industry attribution standards are agreed upon, wide-scale adoption of outcomes-based advertising approaches will be slower than marketers desire.<\/p>\n

It\u2019s challenging for companies delivering metrics to agree on standards and change the way that they do business for the greater good. Some attribution and outcome-based measurement businesses will struggle to adapt to the standards mandated by the industry. To prepare yourself for those new standards and develop outcomes-based measurement in your own organization, start with the following:<\/p>\n

Perform A Geo-Based Hold-Out Test: This is often considered the gold standard, where you test one media channel versus your baseline in similar regions to demonstrate the incremental value of running a specific media channel to your organization.<\/p>\n

Determine Your Time To Financial Impact: Extend your hold-out test for up to 90 days (or longer) to calculate how long it takes for consumers who are engaging with your media to have a financial impact on your organization. Remember that some channels may impact higher in the consumer funnel and may take up to six months or longer to have an impact.<\/p>\n

Eventually, my prediction is that we should anticipate a shakeup in the attribution space as the inevitable takes place. In the meantime, you and your business should do everything within your power to stay up to date on these changing expectations.<\/p>\n

This article originally appeared in Forbes<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Today, marketers are being held accountable for their organizational bottom line more than ever before. With transparency increasing across marketing departments and businesses, brand leaders are being asked to track every dollar spent and demonstrate to finance leads how they are impacting revenue. 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