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What if we all had more intelligent assistants at work?
Intelligence begins with information. Data. But not just data — an understanding of the data. Which takes analysis. Which yields information and insights. Which coalesce as knowledge, with intelligence being the ability to apply such knowledge.
We know intelligence enables better decision making, so we collect lots of data and information to assist. Lots of data. What is most often missing is the analysis, and the time to analyze.
While it’s simple enough to collect and analyze empirical data structured into rows and columns, such as a survey, it takes more effort to apply structural frameworks to the tremendous volume of generally unstructured data generated by posting, sending, reading and viewing communications material on websites and in email. This communications interaction data can be distilled into reports and key metrics for messages, campaigns, publications and audiences.
Reviewing those metrics, you might notice a pattern, such as longer, more content heavy newsletters underperforming shorter, more visual newsletters. With that information, you may decide to reduce your content volume per newsletter while increasing your newsletter frequency. When that action results in improvements in your readership and engagement metrics, that’s intelligence at work.
Without the data, there would have been no opportunity to discover a way to improve. Yet without the analysis, such insights and knowledge would remain lost in that deep sea of data.
Manually monitoring key metrics from time to time and making thoughtful adjustments is good, but not the same as taking (never mind having) the time to analyze all the data and metrics for the quarter or year over year for all teams across the enterprise. Such comparative analysis, as more routinely done with financial metrics, holds the promise of organization-wide improvements with efficiency and productivity gains, but who has the time and inclination to do that work?
Fortunately, with the advent of artificial intelligence (AI) and machine learning tools, we will soon have bright, eager, virtual data analysts working alongside us, more readily surfacing those valuable insights.
Machine learning and natural language processing technologies will do the tedious work of analyzing your history of communications data: the hundreds of thousands or millions of individual interactions with different messages over time. Having uniquely labeled datasets will enable models to be trained, patterns to be recognized and insights to emerge — some a human analyst might never discover.
Before long, your trained models, leveraging automated tools like regression analysis, will predict the outcomes of messages and campaigns as you create them.
Soon you will be able to try different subject lines and calls to action and see accurate predictions of results based on the audience and words you select. As you compose messages and campaigns, you will receive real-time advice regarding content length, timing, layouts, image use, reading ease and other elements of your messaging content, helping you make improvements even before you hit the send or post button.
Today, list targeting, segmentation and follow-up tends to be a manual, time-consuming process. With the right data preparation, data models can be self-organizing using nearest-neighbor mapping, clustering and decomposition. Applying these models will recommend topics to audiences and vice-versa. When your corporate communications objective is getting an employee to take an action, these algorithmic models will automatically provide the follow-up and nudges for you, differently for different audience groups, discovering how to accomplish the objective most efficiently by learning the most effective policy.
Employee Experience Intelligence
Employees sometimes view corporate communications as just too much noise. Most often this is a relevancy problem related to lack of targeting, and the wheat is lost in the chaff. Reducing communications overload and increasing message effectiveness requires understanding the behavior of employee groups and individuals, which, in large organizations, is virtually impossible for an individual communicator but is completely reasonable for an AI. Determining what email arrives in the inbox when and ensuring employees stay informed will no longer be dependent on the send button.
April 3, 2019 at 10:19AM
Forbes – Entrepreneurs