AI enhances Business Intelligence
Marketing leaders often claim to lack the data needed to guide decision-making. BI vendors are using generative AI to give them the tools they need.
2023 may have been a breakthrough year for AI among the general public. However, data analysts have been using it to categorise and normalise data for far longer. There's been a lot of talk about using AI to build complex machine-learning models that spot data trends, which can be used to improve future marketing performance. However, that's not actually a new capability. The same technology has powered intent data, social media algorithms and predictive lead scoring for at least the past decade.
Extending those AI models to assist with data interpretation has long been an ambition of data scientists. Recent advances in generative AI have finally made that possible, democratising machine learning techniques by opening them up to a general business audience, beyond just highly-trained data analysts. Perhaps the real breakthrough for Generative AI is to aid marketers in navigating the typical marketing performance dashboard.
Easier Discovery
In recent months, many leading BI vendors have announced AI assisted discovery and integration tools for their platforms. For Salesforce and Microsoft, this was unveiled as part of their platform-wide CoPilot services. While standalone BI vendors such as Domo and Qlik have integrated ChatGPT into their products. Each of these tools allows marketers to query dashboards using natural language search terms, rather than just using pre-defined filters configured by the dashboard owner.
Most executive dashboards have a lot of data on them, far more than the best practice guidance of 6-10 reports. This makes it difficult for managers to see the specific piece of information they need, particularly for those not used to interacting with a BI dashboard. Generative AI allows marketers to pull out the specific reports they need in a search query, which is especially useful for more complex requests such as filtering down to an arbitrary group of campaign codes.
Better Interpretation
Then, once the right data is displayed on screen, AI can write a summary of the visualisation in natural language as well. Executives are typically concerned with trends and benchmarks rather than raw numbers. Yet, a lot of the graphs floating around the typical enterprise don't show such comparisons. Accurate reporting requires knowing the context.
Without a trend line or benchmark metric, few people understand whether the graph they're looking at shows good performance or bad performance. Due to the nature of their roles, senior executives are often divorced from the vital details needed to make sense of the raw numbers. To decision-makers, explaining the context behind a graph transforms it from a pretty visualisation into a meaningful business insight. That's why benchmarks are so important. They allow the dashboard viewer to judge overall performance in the context of the business.
After all, everyone wants their marketing strategy to be data driven. The real challenge is finding the data insights needed to drive investment priorities. In most organisations, the necessary data to guide planning already exists within the business. There may already be a dashboard which covers the relevant topic. It just hasn't been collated together and displayed in a format which makes sense to marketing managers. That's where AI can make an immediate impact.