There is no shortage of marketing data. For marketers, all this data is a goldmine with the potential to drive business strategies and results. Yet, most teams can’t leverage the data they have today, much less what’s around the corner.
This struggle to navigate and manage vast sums of structured and unstructured data across too many systems and formats is a common problem across industries. Plus, many companies silo data based on organizational structure or job function. This means that there are too few people with the necessary access and experience to use some of the most valuable data sources that a company possesses. In fact, marketers named “centralizing data” and “dissolving silos” as the top two changes needed in order to derive value from data (source eMarketer via Lucy).
To combat this problem, companies have looked to enterprise knowledge management systems. Rather than users having to search through dozens of systems or just not taking advantage of their organizational data, enterprise knowledge management platforms store it centrally for them. The problems that many companies face when they want to implement a knowledge management system, however, are cost and time for setup. Data needs to be correctly tagged, and these systems struggle with too many documents and inconsistencies. In addition, these systems rarely have the ability to also take advantage of valuable third-party research (think of eMarketer for example). And, those that have gone through the resource-intensive work and user training to implement a knowledge management solution, often find themselves disappointed with the final results. Now what?
Forward-thinking companies are starting to evaluate AI as a way to manage their organizational data. Case in point: a Global 500 company had built a custom enterprise knowledge management system to handle the tens of thousands of documents used in research by their sales and marketing team. Yet they still couldn’t effectively find anything. It was based on keywords and taxonomy so when asked a question, users were delivered thousands of answers. Too many to be useful or efficient. They decided to evaluate Lucy—the AI powered marketing assistant—against their existing system. Based on their testing of accuracy and speed, they discovered a 283% improvement by using Lucy for research. By moving through the research process faster, the team is now able to spend their time on big-picture thinking and driving results
They aren’t alone. 79% of companies that are already implementing AI say they are gaining new insight and better data analysis (source eMarketer via Lucy).
With AI-powered platforms like Lucy, you end up with a single knowledge management portal for research across first-party and third-party data. Multiple document discovery challenges such as RFP responses, training information, technical documentation, manuals, or sales decks can be tackled. She can pinpoint a graph within a 100-page PDF or find a single stat in a 65-page PPT. Or she can take a complicated dataset and pull out the relevant information and return with an easy to understand chart. AI moves the needle past simply finding documents to finding answers.
Unlike traditional knowledge management systems, machine learning systems like Lucy exponentially improve through user interaction. Use cases can continually be added. Imagine being able to not only instantly find the information you need, but also automating recurring reporting and complicated data pulls.
Making the data accessible to every employee at every level, means better decision-making for all. Team members spend less time digging for gold and more time leveraging the right data, to your organization’s advantage.
Source: eMarketer via Lucy
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