AI for Marketing & Sales

Between Analytics and Augmented Analytics there is the same difference between words and deeds

  1. Investments in analytics and weaknesses
  2. Augmented Analytics: definition, advantages, workflow and examples

Investments in analytics and weaknesses

A years ago, Gartner published The Annual CMO Spend Survey and highlighted how 2020-2021 investments are concentrated in the Analytics area. 73% of the Chief Marketing Officers of large companies interviewed said they were inclined to increase their investments in technology and human resources, despite Covid-19 impact. Unfortunately, all that glisters is not gold.

In the Marketing Data and Analytics Survey 2020, published at the end of September by Gartner as well, it emerges that over half of the sample is not happy with the results obtained so far and only 54% of marketing decisions are based on what is indicated by analytics.

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Lizzy Foo Kune, senior director analyst at Gartner, continues: “Although CMOs understand the importance of applying analytics to the entire marketing organization, many of them struggles to quantify the relationship between the information collected and the economic results. Nearly half of the respondents in the survey say they are unable to measure ROI”.

Marketers complain of poor data quality, difficulty of self-interpretation, inability to understand the actions to be taken and consequently be proactive. In brief, Analytics insights can “say” something, but their words are not easily understandable and above all they do not start actions because they cannot be activated.

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Looking the chart above, we understand that today we need something more than just Analytics. We need advanced and enhanced analytics, capable of moving intelligently and possibly in real time from data to “doing”, to action, to “producing”. We need Augmented Analytics.

Augmented Analytics: definition, advantages, workflow and examples

Augmented Analytics take advantage of artificial intelligence techniques (machine learning, deep learning, NLP, …) to transform the way analytics are developed, shared and activated.

Augmented Analytics automates the search and display of information hidden in the data, trends and most important anomalies (in a different way depending on the operational context of each user). The goals are to predict, to optimize, to anticipate changes and to make decisions with significant final impact. Augmented Analytics can make these actions in a fraction of time, without required prior knowledge of data relationships and without in-depth knowledge of data science and AI. Augmented Analytics can be implemented with Natural Language Processing (NLP) and conversational interfaces, to allow a larger amount of people across the organization to interact easily “verbally”, get answers quickly, make predictions and get actionable actions starting from data and insights.

Augmented Analytics platforms transform and democratize the use of data and insights thanks to the automation, enabled by AI, of the data collection and preparation phases (including cleaning, data cataloging, management of metadata), of generating insights and predictions, of their explanation and of the consequent action proposal. Data Scientists – increasingly scarce resources – can consequently be freed up for activities with high added value.

Beware, however, that in order to grounding Augmented Analytics potential, companies need to promote data literacy at all levels and roles of the organization.

Augmented Analytics solutions must not be a black box, in reverse they need to be transparent, accessible, and modifiable. This is the only way to generate confidence.

Data analytics is a strategic component to generate value. However, as the quantity and complexity of structured and unstructured data increases, business functions are overwhelmed and struggle to identify what is most important and the best actions to take. Combinations of larger and more heterogeneous datasets mean a great amount and variables and relationships to analyze, explore and test. Many data-related activities are still largely manual and subject to mistakes generated by cognitive bias. We can refer to collection, preparation, analysis, creation of machine learning models, interpretation of results, data storytelling, creation of strategic information and activation. Using the traditional approach, users can not explore every possible combination and models and they need to determine alone which are the most relevant, meaningful, and actionable results. Potentially, this approach has negative consequences on decisions and results. By automating these time-consuming and bias-prone activities, Augmented Analytics exponentially expands the capabilities of companies. Not surprisingly, during the Gartner Data and Analytics Summit, more than 60% of poll respondents said they believe Augmented Analytics will have a high or transformative impact on their ability to scale the value of analytics in their organization (see figure below).

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Here is how, according to Gartner, Augmented Analytics changes the analytics and BI workflow.

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In fact, they are end-to-end solutions that automate and accelerate every step of the journey from data to value.

To understand better how data analysis and activation can respond to your business needs, you can learn more and request a demo of:

  • the predictive marketing and marketing automation platform DataLysm, which unifies customer / user data from multiple channels, identifies the clusters most likely to purchase, those with greater lifetime value or at risk of churn, and activates them to achieve greater conversions,
  • the Alternative Data Intelligence platform for investments,

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Between Analytics and Augmented Analytics there is the same difference between words and deeds

Marco Belmondo | 22 Oct 2020