Everyday the world creates another 161,643,840,000,000,000,000 bytes of data. That’s a 4800% increase over the last decade.
The explosion of growth has lead to an unstructured data nightmare. Our ability to collect and create data far exceeds our throughput to analyze it. Most of the information we collect ends up as dark data.
Gartner defines dark data as “All data objects and types that have yet to be analyzed for any business or competitive intelligence or aid in business decision making.” The amount of data companies leave in the ‘dark’ is estimated to be 60-73%.
We are surrounded by data, but starved for insights ~ Jay Baer
Hoarding all that dark data may be costing your business. Beyond increasing storage costs, you could be missing revenue opportunities, lowering business productivity and efficiency, and increasing your data corruption risk.
CRM and Big Data
Businesses need the right tools to manage data sprawl and data security. CRM is designed to filter out the noise and focus your company on high-value data. To that end, a CRM system collects customer information across four strategic categories:
- Personal data: Name, company, job title, address, lifestyle, DOB, education
- Engagement data: Site visits, social media likes, click-throughs, email opens, bounce rates
- Attitudinal data: Reviews, customer feedback, survey responses
- Behavioral data: Purchase history, product views, wish lists, returned items
Though CRMs generate an enormous amount of data, none of it gets left in the dark. The CRM takes all of this cross-channel data and makes sense of it at both the micro and macro levels.
By creating a customer record across all touchpoints, you get a 360-degree view of each client. And by getting to know your customers as individuals, you can deliver a better customer experience.
By applying commonality analysis, the CRM can identify patterns across all customer data. These insights are displayed on the CRM dashboard in easily digested bites such as scores, percentages, and ratios.
For example, a CRM can segment leads according to a set of conditions you define (e.g., job seniority, location, number of touchpoints, and so on). From there, it would assign a lead score and display it on your dashboard. You can assess at-a-glance who’s ready for the sales team and who needs more nurturing from marketing.
What Is the Difference Between AI and CRM?
Artificial intelligence is a technology that displays abilities we once thought of as uniquely human–knowledge, perception, insight, and the ability to learn. AI serves many of the same goals as a CRM system, like automating business processes and gaining insights from data. But it differs in its execution and capabilities.
CRM’s analytics can be overwhelmed by too much data and too many variables. AI, on the other hand, typically requires thousands of data records to develop a good algorithm. The more information you throw at, the more enriched and powerful AI models become.
A CRM is primarily bound to the data you input and the code/conditions you write. Whereas AI can seek external data, combine it with internal data, and draw deductions without any human involvement.
Access to unlimited data coupled with deep learning allows AI to make predictions at a scale and depth impossible for a CRM system alone.
Better Together: CRM and AI
CRM and AI are at their best when they combine forces. AI can make CRM’s analytics more profound and more powerful. And CRM can break down data siloes, giving AI access to the entire sweep of organizational data. Here are six examples of the power couple at work:
Knowing when to offer a discount is always a sticky situation. You want to seal the deal, but you don’t want to leave money on the table. It’s a delicate balancing act. Instead of relying on instinct, an AI algorithm can calculate the optimum discount rate for your proposals by analyzing the won and lost deals in the past. It can deduce the factors that influence the outcome such as dollar amount, actual product or service, company size and location, industry, sales quarter, annual revenue, new or return client, and more.
Every quarter, managers are tasked with predicting where their sales team’s numbers will fall. CRM analytics can comb through the database and make a descriptive analysis (meaning it’s based on the past). AI can then enhance your forecast by incorporating external information, such as the economy’s overall health, and predictions for your industry.
We’ve all had a frustrating encounter with an automated phone system. And on average, it takes 9.5 minutes to escape the automated system and talk to a human. Thankfully, AI is getting increasingly skilled at determining a speaker’s mood from their tone of voice. Shortly, AI will accurately detect a negative manner (such as annoyance) and immediately transfer the caller to a human representative.
A record of this interaction will be exported to the CRM’s client record in real-time. The customer representative can pick up right where the automated system left off. There’s no need for an already aggravated customer to repeat themself.
Most CRMs already offer a chatbox that operates from a predictive script. Common issues and questions can be resolved using the automated tool, saving your employees precious time. Chat boxes can also engage prospects while they’re on your site and ask pertinent questions that will help you qualify them as leads. AI will make this process even smoother and more natural by learning how to communicate like humans versus repeating a preset dialogue.
Upselling and Cross-Selling
The most cost-efficient way to increase your sales is to sell more to your existing customers. Acquiring a new customer costs between 5 and 25 times more than keeping an existing one. But who among your client base is likely to buy more? You can draw your conclusions from the behavioral data your CRM collects. Or you turn that data over to AI and let it apply an association algorithm to determine who is most likely to upgrade what they already own (upselling) and who is most likely to buy a new product or service altogether (cross-selling). The net effect is more revenue and decreased marketing costs.
Cognitive Robotic Process Automation (RPA)
AI is also busy tackling dark matter with cognitive RPA. RPA can perform complex tasks like evaluating insurance claims and calculating credit scores by giving structure to the opaque data stored in documents, images, emails, and more.
Prince Kohli, who is the CTO of Automation Anywhere, is optimistic about the new technology’s potential, saying, “We believe that within five years, knowledge workers will be freed from the task of extracting information from unstructured content.”
In addition to doing the tedious work of data sifting, AI may discover dark data patterns and add new categories of meaningful data to CRM systems.
Give Your Business a Competitive Edge by Combing CRM and AI Technologies
Mindlessly hoarding data “just in case” is of no value. It can harm your business by taking up too much space, cluttering the data you do need, and exposing you to a higher risk of hacking. By combining the power of CRM and AI, you can put Big Data to fair use, giving your company a competitive edge.