2019 has already unfolded as a banner year in terms of the union between artificial intelligence and sales. While 2018 saw the artificial intelligence sales revolution beginning to gain momentum, the applications were limited. In 2017, 51% of enterprises leveraged some form of artificial intelligence. In 2018, the percentage increased by a mere 2% to 53% adoption.
This year, artificial intelligence will increasingly play a vital role in sales organizations. One of the most profound implications will be in the context of CRMs. CRMs have long struggled to gain the favor of sales professionals. Less than 40% of businesses report a CRM adoption rate in excess of 90%. This year and beyond, we’re sure to witness a marriage between artificial intelligence and CRM systems, a transformation that amplifies the capabilities and effectiveness of antiquated CRMs.
1. Data ingestion and retrieval
Many individuals have predicted that artificial intelligence’s foray into the sales landscape poses a threat to the human sales profession. Yet the belief that artificial intelligence signals the demise and replacement of the human sales function entirely, is tremendously short-sighted.
Artificial intelligence promises to enhance, not replace, the human component of sales. The sales professionals of the future will use artificial intelligence to complement their efforts and skillsets. When it comes to CRMs, this starts with data ingestion and retrieval. As it stands, sales professionals spend 17% of their time entering data—the equivalent of nearly one work day per week. Indeed, manual data entry is the primary obstacle that inhibits CRM adoption.
Artificial intelligence not only empowers sales professionals to eliminate manual data entry, it also bolsters their ability to centralize disparate customer databases and, in turn, capture the complete customer lifecycle—whether it has transpired via email, phone conversations, chatbots, etc. CallMiner Eureka, for example, uses artificial intelligence and machine language to capture and transcribe customer interactions. Transcriptions are tagged according to key topics and a rich categorization schema. When this data is ingested into a CRM, it can surface key insights, including objections, specific data with respect to competitors, and ideal use cases. Salespeople can search transcript metadata for keywords, phrases, or even acoustics such as increased voice intonation that may signal excitement and increased interests. With the air of topic clusters and frequency maps, salespeople are equipped to detect vital customer trends.
2. Sentiment analysis
It’s critical that salespeople develop high levels of trust and rapport with their customers. According to research by Salesforce, 79% of business buyers state that it’s absolutely critical or very important to interact with a salesperson who is a trusted advisor. We have a long way to come. A mere 3% of buyers trust sales reps.
With the vast majority of customer interactions occurring virtually, via mediums that conceal revealing body language and facial expressions, it’s become more and more difficult for salespeople to develop trust and a strong rapport with their customers. Fortunately, artificial intelligence offers a powerful antidote. Using sentiment analysis, AI-powered tools can analyze conversations and assess customers’ emotional states. Cogito, for example, provides in-call voice analyses that help salespeople understand customers’ emotional states and how best to respond. A color-coded meter serves as a gauge of how effective a specific conversation is. If a customer—or salesperson—reacts too abruptly, the color changes from green to yellow or red. Cogita assesses several key aspects of any given conversation, including energy, interruption, empathy, participation, tone, and pace. Analyses as sophisticated as this empowers salespeople to proactively redirect conversations.
When all this historical in-call data is integrated with CRMs, the benefits are far-reaching. Salespeople and managers, for example, can leverage the output for training purposes to improve conversations and relationships with customers. As CEO and Co-Founder Joshua Feast has explained, “Conversations are like a dance…You can be in sync or out of sync.”
3. Data integrity
CRMs are chock-full of dirty data. According to research by Dun & Bradstreet, 91% of data in CRM systems is incomplete, 18% is duplicated, and 70% is rendered stale each year. The fallout of dirty data is devastating. 8 out of 10 companies believe that dirty data disrupts their sales pipelines and 25% experience reputational damage due to bad data.
The effectiveness of artificial intelligence is directly proportional to the accuracy of the data it is fed. Garbage in, garbage out. Artificial intelligence tools are integral to data cleanliness. By 2025, we will create 180 zettabytes of data each year. Gone are the days when humans can ensure optimal data quality. Artificial intelligence is able to detect irregularities, anomalies, duplicates, and other errors that compromise CRM data and, in turn, customer relationships. By integrating with third-party databases, artificial intelligence can also interpolate missing records and update records in real-time as contact and other data changes. It can automatically detect duplicates. There’s no disputing the fact that data cleansing has been a big headache deterring salespeople from embracing CRMs. AI is the Tylenol.
4. Predictive lead scoring
Artificial intelligence primes salespeople to supercharge their lead scoring abilities with predictive analytics and algorithms. 74% of companies state that converting leads into customers is their top priority. It’s no wonder why. 96% of visitors who arrive at a company’s website aren’t ready to buy.
Historically, sales professionals have relied on “rules-based” lead scoring. That is, they’ve scored and ranked leads manually, according to a set of rules—”If this, then that”. This approach is outdated and suboptimal. Artificial intelligence is key in terms of motivating sales organizations to shift from rules-based lead scoring to predictive lead scoring. Artificial intelligence can analyze millions of different historical and real-time attributes, including demographic data, firmographic data, geographic data, activity data, and web behavior, to determine customers’ buying readiness. When integrated with CRM systems, artificial intelligence can analyze won versus lost deals to detect trends that can inform predictive lead scoring methods.
Perhaps most exciting is the fact that predictive lead scoring tools powered by artificial intelligence rely on a “champion-challenger” model. Different predictive models are tested and the most accurate one is selected. Each time a more accurate model is identified, it becomes the default.
5. Prescriptive account-specific recommendations
CRMs have traditionally been data repositories. When artificial intelligence is powering CRM systems, they assume a new and more useful role as a trusted advisor. Based on the relevant data housed in a CRM system, artificial intelligence has the capability to generate targeted recommendations for salespeople, including personalized sales and marketing collateral to be delivered at specified times. The most effective artificial intelligence-powered CRMs will also provide the “why”, informing salespeople as to the rationale behind certain prescribed courses of action.
CRMs are in desperate need of a makeover. The time is now. Sales reps can no longer survive without garnering the trust of the customer. Artificial intelligence promises to equip salespeople with a heightened reputation. With targeted and prescriptive recommendations, artificial intelligence empowers them to become a thought leader and transform customer relationships from salesperson/customer to doctor/patient, providing effective treatments for potentially fatal business issues. The implications are game-changing. Whereas only 3% of salespeople are trusted, 49% of doctors are trusted.