In the last two decades, competition has increased rapidly. This has led to more choices for customers which, in turn, has increased the pressure on businesses to become more agile and responsive.
Along with affordable prices and high-quality service, customers now look for personalized experiences. Relevance is the new currency in the digital economy. So personalization alone is no longer enough. The experiences have to be smarter, faster and in the right context.
Responding in an agile manner for a handful of customers is manageable for a business. But when every customer that a business caters to, or wants to cater to, has the exact same expectation though their needs are as different as chalk and cheese, this becomes a challenge.
Customer Relationship Management (CRM) addresses this need for most organizations to a large extent. They enable organizations to capture customer data, usage patterns and buying behaviour and study them to serve their customers better. But this increase in the fields of information to capture has led to an increase in a non-revenue generating task. This task is data entry. According to research, almost 21 Million salespeople globally are forced into data entry, which becomes time spent away from communicating with their customers.
How AI Is Filling This Chasm
To address this glaring challenge for businesses, CRM is already witnessing an evolution in form of Artificial Intelligence (AI) and Analytics becoming central to CRM of the future.
People already engage with AI somehow or the other today. From Amazon Prime suggesting videos to watch based on previous browsing history and patterns to Facebook recognizing locations and people’s faces in photos, AI has become a regular part of our lives.
This AI will now enable companies to improve their client experience through various steps. Three of them are:
Automated Data Capturing: CRM tools are now collecting customer data from various onboarding touch points like WhatsApp, social media, exhibitions, wearables and product usage with minimum or no human intervention. Thus, the sales team saves the time by omitting the duplicate work of manually entering data into the software. They can utilize this time to engage with customers and generate more revenue.
Product Usage: There’s no data like the data which customers readily share by themselves. Thanks to automation, data of this type – usage patterns, buying behaviour and more – get fed into CRM tools real-time without salespeople having to intrude in customers’ lives. Companies are successfully able to collect this data by gamifying their offerings for their customers. The more customers use a product or service, the more intelligent machine learning becomes.
Data Analytics: All the data collected in a CRM tool accounts for nothing if it’s not used. And this is where most CRM-using businesses struggle. Lack of time and resources means a goldmine of customer data lies unused. Data has to be cleaned, sorted and analyzed to help a business improve its customer’s experience.
AI will move from backwards-looking analytics to harnessing all technologies. It will use data to recognise patterns, recommend optimal steps, predict possible outcomes and automate the customer engagement process. It will make businesses smarter and allow them to present a 1-on-1 experience real-time to each customer, no matter how large or small the scale.
Summing Up, machine learning will adapt to changing customer landscapes and get smarter. When applied optimally in CRM tools, it will make a business provide predictable service to its customers. In turn, this will help the business generate predictable revenue. AI will be essential to deliver high-quality personalized experiences for customers. And it is the next step in the evolution of CRM.