How AI Transforms CRM Software


  • Artificial intelligence (AI) can decipher data to understand customer preferences, behaviors and buy criteria and thereby be used to improve products and services, deliver more relevant communications and grow customer relationships.
  • When combined with artificial intelligence, CRM software shifts from a customer data repository to a predictor of customer behaviors, creator of customer insights and facilitator of customer and company objectives.
  • Cloud computing, cost-effective data storage, integration tools and AI software apps designed for business analysts and power users have morphed with CRM software to make AI available to far more companies.
Johnny Grow Revenue Growth Consulting

Based on our experience we have found CRM with AI delivers the biggest revenue impact in three areas.

  1. Resolving revenue leakage. AI apps within CRM software can surface problems or opportunities and notify the right resources in real-time. These alerts may include business process breakdowns such as a quoting or order entry problem or that a high value customer is dissatisfied and at risk of churn. Swift course corrections prevent the loss of short-term and long-term revenue.
  2. Keeping customers satisfied. AI can detect customer behaviors in real-time. CRM AI software can identify buyers who want more information, customers who are frustrated or customers who are struggling with your product or service and need some help. Or on the flip side, it can identify customers who are likely to deliver referrals if asked or become brand ambassadors if engaged.
  3. Delivering actionable predictions. CRM AI sifts through large volumes of data, identifies patterns and correlations, forecasts the trajectory of data and makes customer predictions. It advises which offers will achieve high conversions with specified target audiences, products that increase sales and margins when bundled, prospects that are most likely to buy, or customers most apt to respond to cross-sell or otherwise increase customer share.

See the three biggest revenue growth opportunities available from CRM software with Artificial Intelligence (AI).

Click to Tweet

CRM software vendors such as Microsoft Dynamics and Salesforce, marketing software vendors such as Adobe and IBM, and ERP software vendors such as SAP and Oracle are embedding AI technologies into their applications because these intelligent apps can resolve some long-standing challenges and deliver substantial payback.

AI for CRM

How AI Makes CRM Software Smart

Below are some examples of how AI is shifting CRM software from a customer data depository to a predictor of customer behaviors, a provider of customer insights and a facilitator of improved customer experiences.

  • From what to why. CRM software quantifies customer data and can report on how that data changes over time. It essentially advises what has happened with customer data. CRM with AI can take it a step further and advise why things happened. Marketing AI can determine why offers were not accepted by a customer segment. Sales AI can advise why salespeople are losing sale opportunities in a certain territory. Customer service AI will share why the call center is receiving lower customer satisfaction scores. The answers to these questions are essential to implement swift course corrections.
  • From mass to individual. CRM systems have historically grouped customer data to understand customers in mass or by segment. Machine learning brings automation and scale to view and engage customers in a one to one fashion. It can specify data by persona and deliver the right message to the right person at the right time. It can streamline, accelerate and optimize processes to serve individual customers, such as delivering personalized customer communications, highly relevant collaterals or contextual offers.
  • From static to real-time. CRM software data is almost entirely static. However, customers and products are much more fluid. Machine learning can be used to automatically update customer data or CRM records, so analysis and processing is far less stationary and more dynamic.
  • From structured to unstructured data. CRM software manages structured data well but struggles with unstructured data. Machine learning and deep learning accommodate qualitative data such as email, SMS texts and customer service voice recordings.
  • From notes to intelligence. This is complimentary to the prior point. A machine learning example is extracting insights from CRM software notes fields. Website landing pages, customer service incident submissions and CRM software all use plenty of text fields. Deep learning can analyze terms that imply certain behaviors or sentiment and identify engagement opportunities with individual customers or link data to show customer trends by customer segment or another variable. These patterns may reveal breakdowns in business processes that need fixing or identify renewal or upsell opportunities. A similar approach can be applied to images, audio, video and other unstructured data.
  • From passive to active. CRM software by itself provides a unified customer view, possibly even a 360-degree customer view, but does not deliver insights or make that data actionable without some form of AI. Some examples of machine learning that make data actionable include real-time and contextual delivery of Next Best Offer or Next Best Action recommendations.
  • From stagnate to adaptive. Machine learning is continuously improving through real-time feedback. When offers, services, engagement or actions fail to achieve the desired result, the logic and recommendations are automatically updated. Machine learning gets smarter with experience and larger data pools.
  • From hindsight to foresight. Machine learning can change the executive view from the rearview mirror to the front windshield. Examples of AI and machine learning include pro forma decision making such as predictive analytics which answer the question, "What could happen?" and prescriptive analytics which advise what action should be taken. These are the enabling technologies to advance along the Business Intelligence continuum from hindsight to foresight.