Imagine the disastrous results if you skipped to the end of a complex recipe. Even when the finished product looks attractive, the consequences of poor preparation become clear the more you dig below the surface. Companies that rush to use artificial intelligence without a solid grasp of data analysis hit the same roadblock — continually compensating for previous errors. To effectively implement AI technologies, start small and identify areas where automation has the most impact.
Manage One Problem at a Time
Gathering too much data becomes overwhelming when you lack a plan to organize and apply information. To refine AI technologies, your team has to decide which data relationships to focus on and why they're essential to delivering quality services. Talk to your workforce and customers about the most pressing problems slowing down your operations. Use this insight to pinpoint one question or a few closely related questions that can be answered with basic data.
Instead of casting a wide net right away, experiment with data analysis by building a model to solve simple problems. Let's say you want customers to buy refill products after initial purchases, but they think it's easier to shop around for deals. A sample model might track competitor prices and the average frequency of refill purchases. Through data analysis, the company might realize that offering an automated reordering process with time-dependent discounts increases customer loyalty and profit. Creating simpler data models in the early stages of automation helps your company build a vast data warehouse that enriches machine learning down the road.
Fine-Tune Data Collection Processes
Data analysis is only useful when you have timely and accurate sources. AI can't add value to your business operations if your data collection methods are too slow to compete with more up-to-date systems. Develop standardized techniques to collect and organize data, so employees aren't constantly backpedaling or entering the same data in different places.
Measure the Importance of Human Interaction
The core goal of automation is freeing up human workers to handle more important tasks, so it's essential to determine when human involvement is necessary. Most companies already use basic customer satisfaction metrics, but they should also compare the outcomes of human- and computer-directed interactions going forward. Machine learning advances the more you supply data about customers, so AI systems get better at anticipating needs and providing relevant solutions. The company is able to devote more manpower to complex problems with a higher churn rate, cutting costs and leading to highly productive teams.
Use Unstructured Data to Understand Patterns
Structured data can't tell you everything about the motivations behind customer behavior. However, unstructured data, such as social media posts, forums, phone calls and open-ended surveys, provide context that should influence how you interpret patterns. Structured data may identify customer segments with low retention, but customer feedback might tell a deeper story about why they leave. Smart companies understand the limits of data analysis and develop strategies to fill the gaps, rather than acting on fragmented data.
Data analysis is equal parts art and science, requiring logical assessment and emotional comprehension of what customers need. Humans and computers run into problems trying to understand these intricate data relationships, so it takes time and ongoing experimentation to make AI technologies successful.
Photo courtesy of pannawat at FreeDigitalPhotos.net
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