Analytics is the Future of CRM
Customer relationships are constantly evolving. Today’s buyers are tech-savvy, independent, and demand instant gratification. The ever-changing client-business dynamics has ushered in an era of integrated and intelligent customer relationship management.
CRM is evolving to capitalize on big data sets by relying on insights, statistics, signals about prospects, and artificial intelligence, to create actionable roadmaps to service clients
Leveraging Analytics
Deploying the three types of analytical tools, namely descriptive, predictive, and prescriptive analytics, can help create strategies that will lead to more satisfied and hence, more loyal customers.
Descriptive analytics are backward-looking stories that help understand the pulse of consumers. In CRMs, they can be used to determine the strength of a bond with existing customers. Predictive analytics will organize such information to forecast what is to come. Technological advances like machine learning can help foresee, for instance, if a consumer will be satisfied with a particular kind of support provided, based on past data. Prescriptive analytics can help fill in the gaps, assisting with alternative solutions and how they will be perceived.
CRM and AI
Combining artificial intelligence with CRM is a powerful technique to improve customer engagement. Relying on AI can make do-it-yourself services much more accessible, thereby enabling clients’ needs for independence as well as speed.
Instead of employees doing the grunt work, AI can chomp through large amounts of data, handle analysis and make smart recommendations to simplify the process. Embedding CRM with AI can help an organization by:
- Automating routine queries: machine learning and predictive capabilities will allow AI to quickly handle repetitive issues while leveraging past data available to provide the most acceptable solutions. Chatbots are one such example.
- Providing greater personalization: AI can assist with combining isolated data sets from various knowledge logs to come up with a personalized solution for a customer on the basis of existing descriptions like their profile, historical usage and recommendations, and prior conversations without needing a refresher.
- Adapting Natural Language Processing: human interaction can be quite ambiguous. A self-service tool needs to be equipped to wade through vague speech, process it into actionable language, and infer customer requirements. AI-driven natural language processing makes it possible to train software to overcome this constraint.
Should You Outsource Customer Service?
Maintaining an in-house CRM system that is technologically updated can come with a heavy cost. This has given rise to the trend of customer service outsourcing to partners who are especially outfitted with the latest analytical tools to optimize relationships with clients.
A number of larger institutions are turning to automated record-keeping and cloud computing of data. This information is then passed on to outsourced customer service vendors who transform them into meaningful output.
The global customer service outsourcing market is developing to meet these needs, offering integrated services including social media outreach, video chatting, app-based support, AI solutions as well as traditional CRM services.
Relying solely on outdated CRM methods can prove to be detrimental in client interactions. Adapting to the changing times will yield better results – and analytics, especially driven by AI, is the new weapon to yield.
