Enabling Brands and Agencies with Insights, Powered by AI

As more and more companies transform themselves into insights-driven businesses, they’ll look to bring their obsession with customers to their marketing operations. Consumer insight is the intersection between the interests of the consumer and the features of a brand. Brands that want to engage with consumers need actionable insights that help them reach those existing and new customers.

Machine learning in conjunction with big-data analytical frameworks makes this learning happen at scale in an almost real-time basis, which translates to actionable insights and learnings on a daily basis.

While it is true that more data translates to better information, it does not automatically result in better insights. Granted, insights-driven businesses are powered by data. But there remain three immediate hurdles that insights-driven businesses need to tackle when it comes to consuming more data for better insights:

Expense

More data can easily increase a company’s expenses very quickly. From procuring the data to consuming, storing, analyzing and aggregating it, every step requires spending more on data partners, infrastructure, and improved, scalable technology solutions to deal with the data. On top of that, investing in qualified industry experts to analyze petabytes of data is a must and the most expensive component of all. This is a tall order and requires a strong top-down commitment as well.

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Data Hygiene

In the current digital world that we live in, procuring data is becoming easier. The real challenge lies in finding channels or partners that provide clean data and building algorithms that auto-detect and filter out anomalies and fraudulent traffic. To better mine data, most companies must invest in both identifying clean data providers and in building in-house data-cleansing algorithms. Given that what goes in comes out, your insights are only as good as the data you equip them with.

Actionable Insights

It is one thing to be able to slice and dice billions of data points, and it is a completely different ballgame to derive meaningful insights that are actionable and present, along with explanations on the reasoning behind those insights. This is where an experienced data scientist and a high-scale technology framework and infrastructure become must-haves.

Two things help here. Analytics powered by Machine Learning can come to the rescue. At its core, machine learning relies on pattern recognition that helps make data-driven predictions or decisions.

As more and more companies transform themselves into insights-driven businesses, they’ll look to bring their obsession with customers to their marketing operations.

Machine learning in conjunction with big-data analytical frameworks makes this learning happen at scale in an almost real-time basis, which translates to actionable insights and learnings on a daily basis.

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Secondly, iteration is the key. The beauty of an insights practice is that it fosters learning from the ground up. While getting the right answers to our questions is important, the real power of insights is realized when they equip us to ask the right questions. Every insight should have an actionable next step, which can then lead to more data to analyze. This is a very iterative learning process, where each iteration leads to better and refined actionable insights.

In essence, what comes between data and insights are the things that really matter the most and differentiate successful insights-driven companies from the rest.

With all the advancements in the field of data streaming and analysis, the industry is slowly evolving. Sadly, there is still an overwhelming number of bad players in the market who use AI and insights as buzzwords to attract marketers just looking for these keywords. It is not easy for marketers to weed out the good players from the bad ones. In an effort to clean up our ecosystem and help marketers identify the good players, here are three key signs that brands and agencies should pay attention to while talking to insights-enabled companies:

Data hygiene:

The team feels data hygiene is important and highlights the ways in which they clean their data and remove fraudulent signals.

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ML and AI frameworks:

Insights-driven companies cannot come up with effective and actionable insights without employing iterative and efficient machine learning that leverages the market’s leading AI technology and frameworks.

Data Aggregation vs. Insights:

The team knows to differentiate insights from data aggregation.

Aggregation of data is easy to produce (like, 30% of your customers are high-income males) but they don’t provide meaningful feedback on the whys, nor do they provide actionable next steps. This is where insights take the lead.

Insights provide feedback into why your brand signals look a certain way and compare with the industry norm. These provide actionable next steps that will help in expanding your customer base, like targeting a different audience, changing the branding message, building a new product line, or identifying new channels to reach out to your existing audience.

It is important for all players in this ecosystem to realize that prescriptive and actionable insights result from employing machine learning for iterative large-scale analysis of clean and reliable data.

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