Successful CX strategies go hand in hand with tech solutions that aid personalization and engagement. Such solutions heavily rely on customer relationship management (CRM) software in the business space.
But with great volumes of data, there appears a new issue: data accuracy. Recent studies have shown that CRM data accuracy diminishes yearly by approximately 30%. Remaining competitive and staying on top of customer needs becomes more complicated, with companies looking at inaccurate and incomplete data. Data quality impacts personalization efforts company-wide with sales, marketing, and service departments being subjected to data relevancy.
However, high-performing CRM platforms now feature AI technology and automated data acquisition to improve their capabilities, accelerate CX efforts, and eliminate manual data entry. Looking into the (near) future, CRM systems may feature data that users have never logged in.
How AI and ML Change Companies’ Data Strategy?
CRM software is the cornerstone of a business with implications across all departments: sales, marketing, and customer support alike. However, we spoke to 1,600 sales reps and marketers worldwide, and more than half (56%) of the companies surveyed feel their CRM system is missing essential data that could improve their marketing and sales efforts.
But as we mentioned, the value of a CRM tool is given by the data you feed it. Manual data entry increases the chances of inaccurate information while lowering your teams’ opportunities to handle valuable tasks within their departments. At the same time, the more data you feed your CRM systems, the more complete the view you have of your customers and their needs is.
This is one of the biggest challenges in CRM. While users need high data volumes to get a better view of their customers, manually entering that data lowers their chances of using it due to the time-consuming process. Also, manual data entry increases the chances of error and inaccuracy across your system, thus making it more difficult to properly use it.
AI-powered CRM tools are one of the most significant innovations that help solve this recurring issue, using company-generated and third-party data. Tapping into external data sources enriches internal information for a complete view of your audience base and customers. Besides, such solutions allow you to keep data fresh and relevant while eliminating manual labor.
The Analogy of Scale Conundrum
While AI-driven CRM tools offer many advantages, one of the biggest we can think of is reducing the time employees manually input data into systems.
Although many other systems (think weather forecasting tools) use AI to generate reliable predictions, we still heavily rely on manual data entry when it comes to CRM. This error-prone and biased path of feeding data to systems also steals valuable time off your employees’ table and quickly becomes taxing on your data quality.
AI models need vast data to generate valuable insights and fundamentally change cross-organizational operations. As a result, companies’ data strategies must go beyond internal data warehouses. One common solution is using data enrichment strategies to collect data from external sources (geographic, financial information, professional data, socio-demographic data, etc.) for building high-quality AI models that aid daily operations across sales, marketing, and customer support. But, modern data collection models also need to be acquired.
AI AutoML systems offer ways to decide what information and models to drive the highest quality results and business options. AI can select the most influential factors and models that drive the best results. In the analogy of weather forecasting, that would include aspects such as weather, equipment, equity pricing, etc., to influence purchase decisions. By looking into historical trends, AI can predict what information is relevant in business settings.
Making Sense of “Big Data” to Fuel CX
Once companies collect the data they need, making sense of it becomes their next big challenge. The average company holds about 162.9TB of data. In this context, AI is the key to understanding and filtering what data is relevant or not. Tools featuring this technology offer businesses a competitive advantage and deliver an unparalleled level of predictability across the organization.
In the past, such solutions were unavailable, leaving humans to make such decisions. However, these decisions are error and bias-prone, leaving them unreliable and inherently faulty. By nature, humans can only process and judge information and data only in small increments.
In this context, we can conclude that AI primarily predicts current and upcoming business scenarios across sales, marketing, and services.
The Future of CX is Data-Driven
Customer experience is and will remain at the top of companies’ priority list, as it is a crucial factor in boosting brand loyalty and increasing revenues. At the same time, customer retention will grow increasingly challenging in scenarios where CX standards are not met. Innovations in AI-driven CRM platforms are helping businesses automate anything, accelerate everything, and predict what’s next – exceeding customer expectations by bridging the data gaps today.
This blog post is based on an article initially published on Digital CxO
