You might want to sit down for this: Gartner recently released its report looking ahead at 2020, and in it, they offer some surprising findings. Most notably, the firm predicts that “80% of marketers who have invested in personalization will abandon their efforts due to lack of ROI, the perils of customer data management or both” by 2025.
Yet consumers love personalization. According to Adobe’s 2018 Consumer Content Survey, 67% of respondents think brands should automatically adjust content based on context, and 42% are annoyed by content that isn’t personalized. Personalization isn’t something that gives a brand an edge over competition; it’s an expectation from consumers who crave relevance among an abundance of content. But when personalization seems tough for many marketers, what can be done?
These challenges identified by Gartner exemplify how important it is that marketers set themselves up for success when investing in personalization. Because personalization isn’t the problem—it’s whether marketers have built an attribution model, have enabled it to surface up insights or drive action, and are revising that approach based on the results they receive. Those who don’t will ultimately fail, leading to the frustrations raised by Gartner. While brands shouldn’t abandon personalization, they could do without unwieldy investments and initiatives that take years before their value can be adequately measured, perhaps even locking them into a setup that doesn’t actually work. Here’s what to do instead.
Strategic Planning is Key to Effective Personalization
In light of recent privacy concerns, some brands are completely rethinking the way they target audiences. Google and UK newspaper The Guardian, for example, teamed up to offer Google Home ads that are relevant to the types of recipes next to which they were placed. To achieve this, they taught a machine learning model to identify qualities about each recipe (like sweet versus savory or ingredients), which was then used to dynamically build relevant ads—basically, targeting data about the actual recipes rather than the readers that are interested in them.
67% of consumers think brands should automatically adjust content based on context.
There are two takeaways when it comes to initiatives like this. First, it signals the growing importance of contextual triggers and how they relate to the consumer’s mindset—consider, for example, programmatically delivering a piece of content in response to a playlist based on mood (“Songs for Relaxing”) or activity (“Background Music for Cooking”). Second, the strategy demonstrates the importance of having a backend taxonomy of content that can plug into the systems needed to deliver such a personalized experience—and that’s precisely where many are having trouble.
Data isn’t really the primary inhibitor to personalization, nor is it technology; it’s often people, and this can range from digital literacy to operational structure. According to data from eMarketer, only about a third of US marketers are confident in their ability to create or deliver personalized advertising to customers. A whopping 44% say that they have no real CX strategy or tech capability.
“Even digital professionals who have customer data often say that their teams are disconnected from other groups and lack the resources to find insights in the data to improve CX,” writes Forrester Senior Analyst Nick Barber and VP Principal Analyst Brendan Witcher in their report, “There’s No Personalization Without Content Intelligence.” “Failure to find the right size and structure for the organization is a common problem; in fact, digital execs cite it as the top barrier to the successful delivery of digital experiences.”
Look at other investments across the journey, across functions, that are going to have immediate payoffs and that are actually smaller in their efforts.
Brands need confidence in their data and ownership in orchestrating the digital experience, though the size and scale of digital transformation required have made this cumbersome for many. To avoid becoming stuck in lengthy implementation phases, brands should seek out agile partners that can help them build momentum and quickly and achieve faster results.
In an interview with LinkedIn, Digital Analyst Brian Solis describes the process thus: “While you’re migrating things to the cloud, while you’re doing bigger, more infrastructure-focused investments, we can also look at other investments across the journey, across functions, that are going to have immediate payoffs and that are actually smaller in their efforts.”
We call this zero-to-one: rather than boil the ocean by going immediately to a level-ten experience, we prioritize initiatives with the smallest investment but highest return. An example of this is when we developed a quiz for supermarket brand Jumbo, which helps customers find a wine or beer that best fits their tastes.
The first step was to build a basic questionnaire that could provide value to any customer; after the simple iteration went live, we expanded it to include a more advanced and diverse line of questioning to accommodate those with more nuanced preferences and taste. This shows how brands can iteratively implement more personalized solutions that drive meaningful value to consumers through an agile process.
Personalization Fails When It Doesn’t Add Value
“Today’s landscape has an amazing amount of engineering, but it’s used with little to no empathy: this idea that just because the technology’s there, we need to relentlessly retarget and stalk them across the web,” says MediaMonks Founder and Board Member Wesley ter Haar. “When you start thinking about the user, you start thinking about what we call personal inflection points. Where is the value for the user in how we communicate? How can we be assistive?”
Experimentation is key to adopting a more customer-driven approach to data—in a way, it’s about thinking of data as a two-way street, through which user feedback can be applied to further the relevance and reach of your message. This again ties back to the need for an agile production process, in which teams can implement this feedback with speed and iterate from there.
For skincare brand Gladskin, we continually tested elements--like while models or copy were used per asset--in an agile approach to content optimization.
For example, we took an interests-based approach to raise awareness of the research behind skincare brand Gladskin’s award-winning formula. The campaign centered on boosting reach while targeting its most relevant audiences based on interests, driving down CPM (cost per impression) to stretch budgets further and increase ROI in the process.
Through weekly split testing and reportage, we could determine which combination of assets made the most impact at both awareness, consideration and purchase stages across the funnel, per channel. Instead of being followed by the same ad throughout the social media experience, users ultimately found content tailored more toward their needs at each stage of the funnel.
Data can be powerful, but hoarding it away without building in the channels or workflows needed to activate it does little to help you build meaningful relationships with your audience. As consumer demand for relevant content grows, brands must be strategic in their investment with data and the architecture that powers their ability to derive insights.
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