A crash course in the trends that will affect every association’s recruitment and retention planning.
Here’s your syllabus:
Spending on Mobile advertising will grow at a projected compound annual rate of 26% through 2020. Facilitated by leading programmatic ad networks, like Google, Yahoo and AOL, native advertising is projected to command a 63% share of those dollars over the same period. And while this opens new avenues for marketers to reach consumers, there are several factors every marketer should consider when planning a native advertising strategy.
First, the continued FTC scrutiny of native advertising has led a number of platforms, Facebook for example, to proactively create tagging and labeling requirements that clearly alert users to the commercial nature of the content. It is unclear if the FTC will eventually standardize labeling requirements. But it looks as if any labeling may reduce consumer engagement levels and potentially, effectiveness.
Second, pricing models for native are changing. Where marketers once paid for creation and placement, now they are finding an increasing number of media outlets charging on a pay-per-view model. It’s a smart move by publishers who might otherwise be competing on price for creation and space alone. The downside for marketers is that unexpectedly successful content can drive up media costs quickly.
Third, and potentially the largest challenge for most marketers, competing in a growing native advertising environment requires a continuous flow of new, high quality editorial content. Content is less marketing than publishing and requires resources that can meet those needs.
Consumers are increasingly demanding personal relevance in the services they seek in exchange for anything resembling loyalty. It’s increasingly a similar situation for associations. Ask your members for access to their metadata from across their devices to build a more detailed universal profile of their GPS data, preferences and behaviors and they will respond, “no.” However, if you show members a truly customized and convenient experience fueled by that data, (e.g.: having your steaming hot pumpkin spice latte waiting for you the moment you enter a Starbucks) and they are likely to check the “allow” button when the app asks for access to just that kind of data.
Machine Learning/Artificial Intelligence
Big data was about processing voluminous data sets. AI is about divining the meaning and opportunities within it. And in the coming year, we will see an increasing number of marketing organizations turn to machine learning and artificial intelligence to improve product recommendation engines, clarify segmentation models, optimize pricing by segment and customer, analyze sentiment across social media outlets, create proactive and predictive customer service models using “chat” bots, improve ad targeting, optimize marketing messaging and design, and even create new marketing content from scratch.
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