You’ll be a better parent for buying this bread
You’re going to be sexier if you use this deodorant
You’ll definitely have more fun at the game if you drink this beer
But over the past 15 years, brands have found themselves competing with other voices. Reviews hosted on external platforms such as Google and Amazon allow consumers to rely less on the brand voice for purchasing decisions. We’re seeing this play out with brands that previously thrived on the old way of marketing.
Top CPG brands are losing market share year-on-year to influencer-driven brands that spend little to nothing on traditional advertising. These declines have appeared at the maturation of search and social activity en masse. And when we consider millennial consumers, we see they proactively use technology to bypass advertising, embrace digital assistants, often think of a brands’ purpose as a supporting resource and have less brand loyalty. To win in this environment, brand marketers need to rethink the consumer/brand relationship.
A New Value Paradigm
Rising interstitials between brands and consumers have given way for artificial intelligence to take on a HUGE role. Digital leaders like Amazon CEO Jeff Bezos are already referring to this period as the golden age of AI. The intelligence is being used to rank search content and it’s the fundamental technology behind the language recognition driving voice assistants like Siri, Alexa and Cortana. It’s projected that entities like these will execute more than half of all searches by 2020 — and there’s the rub — the web is no longer a destination but a data source.
The web is no longer a destination. It’s a data source.
As such, marketers should begin optimising their content for AI. The good news is that this is possible for any company to jump on today.
Reduce It All to Data
In this new paradigm, micro-moments are the new engagement currency. To facilitate these moments, there are certain rails content and data must ride on. And since neither Google, Amazon or Microsoft has released an ad format for voice yet, it’s up to marketers to think about their content strategy and structure to be present in these moments and deliverable to AI-driven tech. In other words, you’ll need to gather intelligence. Fortunately, this data is available in the form of search behaviour. This data is a great resource to see where AI and voice driven entities are engaged. At Resolution, we synthesise these datasets to build a landscape that shows where brands need to focus content and optimisation.
Marketers should also utilise internal web analytics. These will help pinpoint where leads generate and where intelligent experiences can be deployed.
Lastly, paying attention to the number of reviews for skills in relevant categories is important. They’ll signal what is being embraced and how brands can participate.
Prepare for the Future
After reducing the content to data, marketers should power it for AI with a plan of proactive attack. Below are four steps that can be used to get started:
1. Micro-Data Markup
Schema.org is a universally accepted language used by search platforms. The code helps bots understand the context of content, such as denoting that the five characters in $2.99 represent a price. These tags can be used by AI entities in all sorts of ways, but the most common way is with enhanced listings in search results.
In the picture below, Schema tells Google that the stars are reviews, ’35 min’ is a prep time and ‘293.97’ is the number of calories in the recipe. AI powered entities can access and organise.
It’s the marketer’s job to ensure published content has a tag relevant enough to be delivered at the most appropriate moment.
2. Assistant Skills and Actions
It’s clear skills and actions will become more important with the rising success of voice assistants. Like hyped platforms in the past, there is a lot of quantity over quality. Currently, there are over 10,000 Alexa skills. Many are never downloaded and those that are have a less than 3% chance of being used the following week. To rise above the din, marketers must understand what the audience is looking for in a skill or action-set as well as build a marketing content strategy to build interest in the skill.
3. Feeds and Structured Datasets
In AI, the ability to look at structured sets of location data makes parsing the local relevance much easier. U.S. platforms such as SIM’s Velocity or Yext’s platform allow marketers to manage location data at scale across hundreds of local engines while optimising the key aspects like hours, availability and local colloquialisms.
This feed-based dataset approach is eminently deployable in e-retail. Resources such as Salsify help marketers deploy product feeds to retail partners at scale while optimising them for the needs and behaviours in those resources. This data ensures a brand’s products appear when people order items. A brand with multiple locations or products not centralising how they manage digital content is missing out on a critical content-to-data opportunity.
Proper intelligence gathering can help engage your audience at scale using automation. Resources such as the Google-acquired API AI lets marketers build machine-learning entities to be deployed on conversation platforms such as Facebook Messenger. Understanding where bots can embrace moments is all about reading consumers’ needs and frustrations in the purchase journey. Using search behaviour and internal search data from analytics to power these entities not only ensures that a bot will work effectively, but it will also help develop a marketing strategy to help target users.
Overall, the disruption and evolution of brand marketing continues and will continue to be a complex issue to be unpacked over the months and years to come. But by applying a proactive content-to-data approach, brands will position themselves well for embracing this emerging space.