Proactive audience insights are topping marketers’ wish lists. Here’s why.
Augmented analytics, a machine learning approach for proactive automated insights, marks the next wave of disruption in the marketing analytics market
Have you ever looked at audience data and struggled to make sense of it?
As a marketer, you’re experiencing the increasing complexity of your audience data landscape on a daily basis. The sheer amount of data and data sources is on the rise. Channels, touch points and funnels are becoming more and more fragmented. The overall data landscape is becoming exponentially complex. Meanwhile, end-user expectations are rising, and the pace of business change is increasing daily.
If in the early days the big data revolution held the promise of more effective marketing — More channels to interact with! More user attributes! Deeper audience analysis! — ensuing developments have turned it into a data nightmare. The result is often analysis paralysis. The resources needed to successfully navigate this sea of data bite off substantial marketing budget chunks.
Consider the most basic marketing activity: identifying, testing and validating new audience segments. Granted, available tools offer today a lot more options for conducting audience discovery than ever before. Facebook Audience Insights, for one, allows you to cut audience segments according to hundreds of attributes. However, while the platform gives access to data, it’s still the marketer who needs to analyze it, build the segments, test and activate.
Given unlimited man hours and resources you could arguably create thousands of customer segment permutations, with every combination of geography, interests, age and gender, to pinpoint your brand’s optimal audiences. The thing is, you probably don’t. Your time and money are limited, and there is only so much audience analysis and targeting you can do with them. Which means that you’re leaving a lot of high potential brand audiences on the table.
Unless, of course, you’re using augmented analytics.
A next-generation data paradigm
As the need to wrangle and understand growing volumes of data exists, augmented analytics represents a major shift in how organizations and users interact with data, and how they consume and act on insights.
The term Augmented Analytics was coined by Gartner to describe the automation of insights using machine learning, NLP, and other AI strategies. Platforms which already feature augmented analytics offer users the ability to receive proactive insights, or to retrieve insights by querying data using natural language. Gartner predicts that by next year, 50% of data insights will be augmented. Additionally, the number of users of modern analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not.
Augmented audience analytics impact marketing teams in three main ways:
Acceleration of data discovery. Rather than sifting and modeling endless data manually, augmented insights use machine learning to automate the process and proactively deliver relevant insights. Marketers can thus deliver the same value while tremendously reducing the time required.
Democratization of insights. Augmented analytics are generated automatically, allowing marketers to extract complex insights independently of coding and / or data science skills. This paradigm effectively democratizes insights by enabling non-data-scientist marketers to plan and execute data-driven strategies within their domains.
Cross-team data-based cooperation. The availability and easy extraction of insights empower shared activities across the department, e.g. between the audience analysis team and creative team. Bridging the data-literacy gap through augmented analytics thus enables streamlining operations for improved agility and results.
Augmented analytics reduces time-consuming exploration and the identification of false or less relevant insights, and reduce the risk of missing important insights in the data. It also optimizes resulting decisions and actions, and crucial for delivering unbiased decisions and impartial contextual awareness. By independently correlating between vast sets of data, augmented analytics also expose “unknown unknowns”: not only insights users don’t have the time to extract, but also insights that sit outside users’ perception.
For marketing teams, the benefits of proactive audience insights are paramount, including, but not limited to:
- Agility: Faster insights are translated into faster execution and optimization, increasing teams’ agility by an order of magnitude. Often, adoption of augmented analytics platform also free marketing teams from their dependence on in-house data scientists or agencies.
- Easier, faster testing and validation cycles: Augmented audience discovery relies on machine learning to model and expose high-potential segments, which can be quickly tested and validated allowing marketers to dramatically accelerate this process.
- Ability to seize opportunities: Proactive intelligence is based on on-going real-time analysis, enabling marketers to seize opportunities on the fly and react to threats as they are happening.
- Ongoing audience, campaign & media strategy optimization: On-going insights also mean an on-going ability to optimize marketing activities in response to real-life events. Intelligence about a new competitor campaign may require you to adjust yours; a sudden drop in brand sentiment may necessitate immediate action. Not knowing about either is sure to have an adverse effect on the brand.
- Save time and resources. Across all marketing activities, augmented insights spell significant savings in both time and money, allowing marketers to invest in areas which are usually starved for both, such as creative and strategy.
- Improve ROI. It’s a simple equation: faster insights = faster time to market = stronger ROI.
Adoption predicted to grow
Although augmented analytics is still considered a next-generation data and analytics paradigm, it’s already implemented by innovative brands, with adoption forecasted to increase substantially over the next year.
Brands using augmented analytics platforms spend less time exploring data and more time acting on the most relevant insights, with less bias. Relying on the speed, democratization and broad adoption of augmented insights, organizations can meet the demands of today’s market head on. Instead of drowning in data chaos, they are well-equipped to anticipate and understand customer needs, adjust and optimize business processes, and position themselves for success in a marketing landscape that never sleeps.