Bridging the Gap: Automated Business Analysis Uncovers Unseen Business Opportunities

In an effort to be more precise and data-driven in decision-making, companies are collecting huge volumes of business data. And while the sheer volume of data collected can be a blessing, it can be hard to store, process and analyze data from multiple sources quickly and efficiently – leading to a number of challenges. For example, the more data an organization collects, the more difficult it can be to uncover the most critical data points needed to help solve a particular problem, identify a new opportunity, or confidently plan for what’s next.

While solutions like data visualization and business intelligence tools have been tried and tested, businesses today need more. They need to be able to make nimble changes to strategy, capture an unexpected opportunity, or quickly discover things that aren’t working as expected. These needs are addressed by AI-driven tools such as automated business analysis (ABA) – an approach that can help businesses stay on top of data or behavioral changes in real time and surface the most valuable insights, providing the context that changes data into opportunity.

By implementing AI-driven tools like ABA, marketers, data scientists and business managers can understand their business in a way that’s not been previously possible. Organizations can automatically analyze large volumes of data from a variety of sources to help spot the next big growth opportunity or operational advantage. In short, ABA puts the right data in front of the right people to help organizations make better business decisions in real time.

ABA uses machine learning and AI-driven tools to discover and share unexpected changes in consumer behavior, customer demographics, buying patterns and more. For marketers and business strategists, this means unexpected changes tied to any campaign, program or customer behavior can be quickly flagged, reviewed and used to adjust the business strategy accordingly. Marketers can also uncover the root cause of evolving trends and customer behaviors – saving countless hours of work sifting through data points and quickly translating these insights to optimize business strategy.

Finding a Needle in a Haystack

Data changes constantly, often unexpectedly, and many organizations struggle to identify and act on these changes. However, some marketers, data analysts and business managers realize that they have “needles” hiding in their data, but they do not have the bandwidth or direction to always know what to look for or where. With the help of AI-driven ABA, companies can analyze billions of data points at once to identify unexpected changes or uncover the most useful trends in their data.

In one example, marketers at a leading bath and beauty brand were alerted to a trend that showed an opportunity to grow sales when revenue was generally falling. This “green shoot” of opportunity is a great example of how the next great marketing strategy can be hiding in business data in plain sight, but impossible to find without help.

With an ABA platform in place, the bath and beauty marketing team was automatically notified when candle sales exceeded the expected sales volume. The marketing team wasn’t analyzing each of their thousands of SKUs against their expected sales performance, since there is no way to do that manually, so this metric was never even previously considered. But the AI platform automatically found this insight, and in doing so, helped direct the marketing team toward a specific category of offerings that could bring in new revenue. As a result, the brand quickly launched marketing campaigns to promote candles and leverage this positive change in customer buying behavior. This unexpected insight also helped the team ensure that inventory levels could be aligned with the new expected sales. Simply by uncovering a trend, the brand was able to capture more sales by capitalizing on an otherwise unseen potential revenue stream.

Beyond Business Intelligence

While business intelligence (BI) tools can easily serve up answers based on what the tools are asked, these tools lack important context and only reveal part of the story. In fact, some of the most important insights for business leaders are missed, simply because no one asked the BI tool the right question. For example, a marketing leader at a retail brand might want to understand sales of a certain SKU by region over a three-month period. Traditional business intelligence tools might show a downward trend in regional sales, which might prompt big changes to the organization’s planned regional marketing campaign strategy. But this insight won’t provide additional context as to why it was showing a downward trend.

With an ABA platform in place, the marketing team could analyze this metric to find the cause and effect – finding, for example, that this downward trend was due to changes in buying behaviors in one specific region impacted by weather. For retailers with hundreds or thousands of stores, this kind of deep investigation doesn’t happen. Even with the help of data scientists, identification of unexpected changes and a search for data-driven insights are not only time-consuming but nearly impossible given the volume of data.

Unless the marketing team was specifically looking at the regional run down, and then identified the one city, store and modified purchasing behaviors, this context might have been overlooked. The regional marketing campaign might have been changed as well, when in reality, the root cause was temporary and limited in its impact. When looking at sales trends, using data visualization alone is not enough because it lacks context or expectation as to what the standard model or behavior should be. Understanding the expected model and the context around the data is what enables retail marketing teams to effectively shift or optimize promotions or make changes in go-to-market strategies and campaigns.

In another example, a large financial services company applied AI-driven ABA to its data to identify in real time unusual trends in customer transactions. The challenge the company faced was to identify and uncover unexpected changes quickly in order to stop fraudulent activity before it was too late.  

With the help of ABA, the company analyzed its customer and business data to flag changes in customer behavior that the brand would have otherwise missed. By leveraging AI-driven ABA, the company was able to quickly deploy its fraud prevention team to research these insights and put the appropriate fraud prevention mechanisms in place. As a result, the company prevented hundreds of thousands of dollars in losses and has become more proactive in its data strategy to quickly prioritize the most crucial insights, helping protect its customers’ investments.

Understanding your customers’ behavior is what helps organizations build effective business strategies or flag unexpected changes so a business can take action. As part of ABA, organizations receive a root cause analysis of their data. This is helpful because if data analysts, marketers or a fraud prevention team understand the potential root causes, the teams can efficiently act on unexpected changes to prevent a problem or effectively seize an opportunity. And knowing about these changes and developing a solution is crucial to an organization’s success.   

Deriving Value for All

When faced with huge amounts of data, unlocking true value requires a solution that helps companies get the results they need from their data faster and more efficiently. While ABA doesn’t alleviate the need for analysts and data scientists, it does provide the tools and solutions to streamline productivity and guide companies in the right direction This is especially important in organizations with limited or no analysts and data scientists. Making insights accessible to all, ABA automatically elevates areas that require more attention and provides daily “stories” that are simple enough for marketing, supply chain, fraud prevention, retail and other business managers to understand, leverage and take action on these insights daily.

Trusting the data and acting on it quickly have an immediate impact on a company’s strategy. But knowing if the data is accurate, properly collected and ensuring there are zero missed opportunities are insights where traditional BI tools fall short. ABA encourages the democratizing of business intelligence tools by placing data in the hands of people who need it most. It can be integrated with existing sources of data within minutes, allowing leaders to gather business insights quickly, identify potential opportunities and address the unexpected. ABA provides the context, direction and tools to navigate toward getting results from data faster and more efficiently, which makes it a useful tool within the analytics value chain.