How artificial intelligence optimizes the use of customer data

A couple of decades ago, businesses faced the challenge of managing disparate data sources about customers, the company, products and services, etc. That was a problem solved through the use of Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) software.

These tools enabled businesses to access and manage all their data in one place, including accounting, inventory management, sales and purchases, etc. The better visibility into company data enabled executives and employees to be more efficient in responding to customers’ needs, aka drive more sales.

Now companies have another problem: too much data, a faster paced economy, and not enough time and human resources. Information is everywhere and the rate at which we generate data is growing chaotically. By some estimates, data will be 4,300 percent increase in annual data production by 2020.

This explosion in data hides endless opportunities in better managing customer data and the sales pipeline. But making sense of it and exploiting it to its fullest is becoming increasingly challenging for enterprises and organizations. Legacy CRM solutions provide ways to navigate and search personal and contact information about customers, as well as data such as the history of correspondence. But they offer little help in gleaning actionable insights and finding the people with the highest propensity to convert.

This is why numerous CRM and sales software vendors are exploring the use artificial intelligence and machine learning algorithms to help make optimal use of the deluge of data that is coming their way. AI algorithms are able to ingest and analyze data at super-fast rates and find patterns that would go unnoticed to human observers. A more accurate analogy would be to separate wheat from the chaff and present sales people with a concentrated dataset that is easier to examine and make decisions upon. All major vendors including SalesForce, SAP and Microsoft have deployed AI engines on their business platforms.

For instance, if you’re selling a software solution, your AI assistant can search through your data library to find potential clients who are using tools that can be complemented with your software. It’ll further investigate to see how much value your software can add to that company’s business by analyzing their past purchase,s reviews they might have left online about the software they’re using and pain points they’re facing with their current solution. This can help formulate the right pitch for the customer.

AI can further help you find the right person to approach in the company. It can go through social media profiles and find executives who meet the characteristics of early adopters or who are experiencing a rise in their career and are more likely to show interest in new solutions.

AI agents can study interactions between executives and customers and learn patterns of reaching out to potential customers. Some vendors are using this schemes to automate the outbound sales pipeline, doing everything from finding prospects to launching email campaigns and sending follow up emails. Natural Language Processing and Generation (NLP/NLG) to send personalized messages and to parse the meaning, intent and sentiment of clients’ responses.

Other possible uses of NLG/NLP include smart customer service chatbots that can interact with customers through messaging platforms and answer general questions and refer to executives when it can’t answer questions. The practice is already being used in other industries such as recruitment.

This means that instead of looking in the dark for prospects, the sales team will be able to make better use of its time tending to quality leads found by the AI agents. This is especially crucial as most sales teams fail to meet their quotas.

One of the benefits of machine learning algorithms is that they become better and smarter as they gather and analyze more data, and they’ll be able to perform even more functions autonomously.

One of these important functions is prescriptive analytics, the science of using AI to analyze data and suggest courses of action that will yield better results. By analyzing millions of data points available publicly and in the records of your CRM tools, AI algorithms will be able to find correlations and patterns and make suggestions on when and to whom to sell what product.

For the moment, we’re seeing some exciting developments. Down the road, small and medium business will find the power to scale their customer management and sales operations without the need to hire new crew. AI will become a decisive factor for success and staying ahead of the competition, both for CRM vendors and for technology adopters.

Further than that, you might someday see AI fully automating the entire sales process, not something that salespeople will likely enjoy.

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