How Augmented Analytics Can Give Marketers a Helping Hand

Augmented

In this article for ExchangeWire, Andreas Glänzer (pictured below), CCO of Adverity, discusses how augmented analytics platforms can be used to provide granular insight at pace, helping data scientists to bridge gaps between data and users.

Data is more than a ‘nice-to-have’ for marketers; it’s the force that guides their every move. When asked which areas they expected to jump up the priority list this year, over half (55%) cited better use of data for targeting and segmentation, and two-fifths (42%) named improving customer intelligence.

With data reliance, however, comes overwhelming volumes of information. The more data marketers collect, the more time they spend sorting, cleansing, and filtering it to extract insight. Little wonder 80% of UK companies are planning to hire a data scientist.

But before investing large sums in expensive and elusive specialists, there is another element to consider: augmented analytics. This next-generation of user-friendly technology gives marketers the helping hand they need to make sense of chaotic data.

What is augmented analytics?
Andreas Glänzer

Andreas Glänzer, CCO, Adverity

Gartner was ahead of the curve when it dubbed augmented analytics (AA) as the future of data in 2017. The last few years have seen AA become a business intelligence (BI) buzzword for the key reason that its automation of data management makes turning data into insight simple. By mixing advanced forms of artificial intelligence (AI) — such as machine learning (ML) — and natural language processing (NLP), AA platforms instantly organise, filter, and analyse massive data sets, and produce insight that’s both digestible and actionable.

What does it do for marketers?

So far, there has been a gulf between ‘mainstream users’ and data scientists. AA reduces this gap. Now the benefits of advanced tech can be shared across organisations for employees use in every department, including marketing, and there are plenty of them:

1. Lower costs and increased speed
Growing dependence on analytical professionals has created two key issues for marketers. Firstly, there is the strain on resources. Data scientists possess expertise that are both rare — with IBM estimating 28% more are needed to meet global demand by 2020 — and expensive, meaning hiring is lengthy and costly. By opening up smart analytics to all, AA platforms empower marketers to take charge of data themselves, and effectively allocate resources.

Plus, AA efficiency can also address the other core problem with data scientists: wasted talent. Conventional BI platforms require data scientists to put in considerable groundwork before using their abilities; spending hours unifying and entering data in spreadsheets ready for machines. By automating data handling, AA platforms take on this labour-intensive stage, allowing marketers to reduce their workload and optimise the value of any specialists they recruit.

2. Immediately accessible insight
Marketers don’t just require granular insight; they need it fast. Communications must be based on a real-time view of individual needs and behaviour to provide positive experiences: especially with 73% of consumers preferring personalised advertising. And this is exactly what AA tools can produce. As well as immediately organising data, platforms rapidly run deep analysis; the combination of ML and NLP allows tech to interact with huge quantities of data — quickly assessing myriad dimensions to uncover patterns, anomalies, and trends.

For example, analysis might identify which environments a specific audience segment finds most engaging, alongside the types of ads likely to achieve the greatest responses. Armed with this insight, marketers can consistently maximise in-the-moment impact; aligning messages with specific tastes, habits, and current requirements. Additionally, they can also minimise wastage by directing ad spend at media with the highest probability of capturing their ideal audience and matching promotions with what customers want right now.

3. Persistent performance measurement
Last but not least, AA platforms can measure the outcomes they drive. Not only can marketers use sophisticated tech to log efforts inspired by analysis across multiple channels and audiences, but they can also continually evaluate progress. This means they have a constantly refreshing picture of what is working and what isn’t that can be used to adjust campaigns in-flight, and reallocate investment for better results. Moreover, they can harness the visualisation abilities of AA tech to share findings in simple formats, allowing them to present the C-suite with tangible and engaging evidence of their success.

AA is poised to transform the way all businesses assess and implement data. For marketers in particular, it provides a long awaited helping hand in regaining control of unruly data and meeting consumer expectations of relevant, personalised experiences. That’s not to mention proving the contribution marketing makes to the business bottom line. By automating large-scale analysis and putting the power of insight generation into the hands of everyday data citizens, AA tech is paving the way for a better-informed and more productive future.