Analytics & Insights
Leverage data science and augmented intelligence for optimal profitabilityRead More
Author: Jesse Henson
With all the sci-fi movies, books, and computer games in the world, the term Artificial Intelligence (AI) has taken on a mythical quality. While everyone knows of AI, few people understand that AI is less about murderous robots and more about integrating technology into day-to-day life. And while we’ve all gotten comfortable asking Siri for directions, even fewer people understand the value AI can bring to their business.
In short, AI finds patterns by looking at data from a bird’s eye perspective. AI can take in an amount of data that a single individual — or even a team — could not possibly analyze and find more patterns than a human could possibly observe. Whether it’s a traditional machine learning solution or a deep-learning architecture under the hood, the same fact remains: AI solutions devour an organization’s data and spit out extremely accurate patterns to help top stakeholders make great decisions.
AI Solutions can:
Help with Decision Making:
In a world full of data, being able to use it properly is critical. Few people need to be convinced of the power of data. These days it is a company’s fuel. Without AI, though, there is a limit to the amount of data that can be analyzed.
AI can consume petabytes of data, which it then uses to find patterns specific to an organization’s problems. These can include customer churn analysis, market segmentation, staff performance issues, and so much more. AI, along with a data scientist, can transform data into insights that drive critical decisions.
Insights from Unusual Data Sources:
Beyond its ability to consume obscene amounts of data, the type of data AI can process is much broader. A data analyst will primarily use structured data, that is data from a spreadsheet or database.
An artificial intelligence solution has the ability to consume semi-structured or unstructured data. This opens a world of data available for insights. These solutions include analyzing feedback to understand the sentiment of the text, using a video of an assembly line to classify defects in the manufacturing process, and even personalizing customer experiences.
A Holistic View:
While data maturity strategies and data analysis are extremely valuable in making data more visible for decisions and monitoring, this still only paints a small piece of the full picture. An AI model doesn’t have the same constraints as human analysis. AI can absorb every piece of the data — even hundreds of billions of data points — to produce outputs that can inform decisions.
More Value and Less Risk:
A business’ success relies heavily on its ability to stay ahead of risks while increasing its value. AI modeling can predict risks based on all available data and predict and segment potential gains into relevant categories.
For example, a key metric in retaining customers is quantity of troubleshooting calls within a two-week period. However, that’s only one metric. An AI model can look at every piece of data on a customer at once, such as the number of complaints they have made or the times they’ve accessed the customer portal. Then, the model can recommend an action that is potentially more helpful and accurate than traditional retention analysis. This helps decrease risk of customer turnover and maximizes that customer’s value.
Extract Hidden Patterns:
While a human might be able to find patterns based on the combination of one or two metrics, AI can look at patterns between all the dimensions it has been fed.
Continuing with the customer retention example, AI can predict that because the customer has three specific services on their account, has been a customer for 3.4 years and has accessed the portal one time in the last three years, this customer could have a very high chance of leaving.
Building a Model for Optimal Impact:
A data scientist does a few things. First, they pre-process and clean up data for better predictions. Second, they optimize the model and architecture for the right metric. Third, they produce meaningful insights from the outputs and parameters. Finally, they help your company implement insights into the real-world workflows, decisions, and applications running your business.
They are also key to guiding the design and implementation of an AI model so that it has the greatest impact to a business. There are just some judgement calls a computer isn’t qualified to make. For instance, the accuracy of an AI model is important. However, in some circumstances, being less accurate and more conservative — erring on the side of false positives — is actually more helpful.
Let’s return to the example of customer retention. In predicting whether a customer will leave, is it better to overpredict and assume the customer will definitely leave, increasing the cost of customer retention strategies or would it be better to save the money on retention costs on a customer who is predicted to stay but ultimately ends up leaving?
A data scientist can find the right metric to base the model on, as well as the loss or gain from each option. They would tilt the model in the best direction for the business, which may not be the direction the data would point on its own. They could also inform stakeholders of the tradeoff between customer retention costs and the loss of a customer.
Data Science without AI:
Even without the help of AI, a data scientist can analyze the data and produce several scenarios that might impact a business. For instance, they can identify the likelihood our flighty customer will actually switch products by comparing the customer’s age range, technical problem calls, and portal usage.
However, this analysis cannot consider all potential scenarios. Perhaps this customer has three expensive services and has visited the portal three times in the last week looking for a cheaper package deal. AI could recognize that pattern of behavior from previous customers and identify that the company needs to retain that well-paying customer before they leave for a competitor. Without AI, that pattern would be difficult for the data scientist to identify.
Together, AI and data scientists inform top-level decision making by analyzing a superhuman amount of data and predicting patterns at state-of-the-art accuracy levels. While we’ve illustrated this with customer retention, there is no limit to the number of applications AI can have for a business.
Every company needs AI to do the grunt work of consuming data and finding patterns. However, AI with no data scientist is like being in a boat without a paddle. This is where RevGen can help. We have data scientists trained in the industry’s cutting-edge of the data and analytics space.
Contact us to schedule a chat about how your organization can turn data science into valuable, actionable solutions.
Jesse Henson has a master’s degree in AI and machine learning, and has several years of experience in the data industry. He is passionate about shaping the future of data and AI technologies.