Autonomous CRM For Modern Revenue Organizations
Understand the revenue organization through artificial intelligence and automation that captures data, derives insights, flags risks, and highlights opportunities.
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Autonomous CRM
Autonomous CRM
Every field of business is being transformed by new technology, and CRM is no different. Yesterday’s CRM systems started as “empty boxes” which were then manually filled with data: contacts typed in by hand, deals assessed manually, and sales forecasts built through numerous conversations.

This manual approach led to numerous challenges. Slipped deals, inaccurate forecasts, and missed opportunities were common. Lost and damaged relationships with prospects and customers were frequent due to forgetfulness. Not to mention, thousands of hours of wasted time led to frustrated teams and poor revenue productivity.

Today’s CEOs demand more of every function in their businesses. Today’s leaders are used to accurate, complete, and up-to-the-minute information across the entire organization, and the revenue organization can no longer be an exception.

Autonomous CRM is an approach to understanding the revenue organization that relies on artificial intelligence and automation to capture data, derive insights, flag risks, and highlight opportunities. Autonomous CRM enables leadership, sales, and customer success executives to operate at a higher level – focusing on strategy, relationships, and the customer experience, not on administration and searching for data.
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Why Is It Important?
We are in a highly competitive environment that is only becoming more competitive. Competitors move faster, customers are more demanding, prospects are harder to reach, and employees are quicker to seek new opportunities.

Organizations that are hampered with legacy approaches to revenue will struggle to survive. The experience their customers receive will be hampered by missing data and inadequate systems. They will find themselves outpaced by competitors with more modern approaches, and will fail to engage new prospects, and gain new sources of revenue. Simultaneously, their employees, frustrated by both a lack of success and legacy tooling, will leave, taking with them their knowledge of customers and prospects.
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Levels of Autonomous CRM
Autonomous CRM is not a replacement for existing CRM systems. Systems like have become industry standard for many reasons, and should not be replaced. Autonomous CRM is an approach that brings those systems into the modern era while preserving the investment that has been made in them, and the capabilities they provide.

The journey towards autonomous CRM is much like the move towards autonomous cars. Features like cruise control and lane assist are simple steps on the journey toward fully autonomous driving for cars. For CRM systems, there is a similar progression toward fully autonomous CRM, and each organization progresses at their own pace.

Level 1: Data
The first step towards autonomous CRM is at the level of data. Having intelligent systems, rather than humans, create data in your CRM platform ensures that the data is complete, accurate, and up-to-date.

Data that can, and should, be created automatically, includes a wide variety of facts about each account, deal, and person:
– Contacts: contact data and job title of everyone you are interacting with
– Activities: emails and meetings that have taken place
– Calls: records of phone conversations and web conferences
– Pricing: details of pricing proposed and discussed
– Contracts: terms and conditions that have been negotiated or proposed

Having these facts present in CRM, automatically, forms a valuable foundation for all subsequent efforts.

Level 2: Information
Raw data is certainly useful, but in many cases there’s a level of “translation” that needs to happen to turn that data into useful information. Historically, there needed to be a bit of “human intuition” to pull this meaning out of the raw data, but that can now be done with machine learning and automation. This leaves just the relevant information, such as:
– Relationships: who really knows whom at the target account, and how well
– Last interaction: how long has it been since anyone was in touch with each account
– Decision-makers: who are the people in the right roles and at the right levels of seniority
– Call dynamics: how long was the conversation, who spoke, and how much did they speak
– Topics of discussion: what was talked about in the conversation
– Discounting: how different is the proposed price from list or baseline pricing

It may seem like tiny steps to move from “data” to “information”, but it’s often the difference between noise and signal. Having information on each account and each deal allows insights and decisions to be made quickly and precisely.

Level 3: Management Insights
Once information is available, the next challenge is to turn it into action. This is the role of management in most organizations, looking through data, finding insights, and pushing the team to take the appropriate action to fix the problem or minimize the risk.

This type of management intuition can now be captured to look for many of the typical pipeline challenges automatically and raise them as issues and opportunities:

– Deals slipping: late stage opportunities that have gone “dark” with no communication, but are still forecast to close
– Single-threaded deals: deals that are anticipated to close, but with only one strong relationship
– Missing executive sponsors: no clear executive sponsor or even executive relationships within the buying committee
– Legal engaged too late: legal (or procurement) engaged only recently, while the deal is projected to close very soon – far sooner than the typical legal process.
– Deal review selection: the choice of deals to do a deeper dive on during the deal review process
– Forecast accuracy: which deals should be kept in forecast, removed, or heavily discounted, based on the deal dynamics
– Poor objection handling: which reps continually struggle with objection handling or competitive questions in calls

Identifying these risks and opportunities should not be left solely to management intuition. While intuition can be excellent at times, the fast pace of most sales organizations means that many opportunities will be missed and many risks will go unhandled if just left to management intuition.

Level 4: Self-Management
If management insights can be pulled from the data directly, they no longer need to be rare. They can be shared and publicized to the team, allowing every team member to understand their own performance comprehensively.

This allows most of the basic challenges of management within sales and customer success to be self-managed.

– Alerts for any deals that are at risk based on their stage
– Notifications of any customer accounts without executive relationships
– Team leaderboards showing performance on key metrics
– Common, objective viewpoint in any deal reviews with management

Comparisons amongst accounts and across team members allows quick and accurate benchmarking. Knowing exactly what should be achieved at a given stage, for an account in a certain segment, allows a rep to make their own adjustments without waiting for management direction.
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Benefits of the Autonomous CRM Journey
The journey toward fully autonomous CRM is not a quick one. However, every small step adds tremendous value as it replaces time-consuming and error-prone human work with efficient, accurate, and comprehensive AI-based automation.

At each stage of the journey, organizations see their insights become more strategic, and their actions become more high-value.

Level 1: Data

Complete, accurate, and up-to-date data on all prospects and customers is an immensely valuable foundation to build upon within CRM. Our analysis has shown that for the average B2B sales team, 74% of the contacts the team is interacting with are not present in their CRM system.

Efforts to force a team to do this manually waste at least an hour per week for each team member, as well as significant management effort to cajole and coerce front-line staff to comply. The frustration that this manual administrative work causes is often cited as a reason for rep turnover.

Missing data is especially problematic when there is rep turnover. If the data is not in CRM, the data will be lost forever once that person leave the organization. Customer accounts can be jeopardized and prospects lost because there is no knowledge of who the key contacts were.

Autonomous CRM instantly solves these data challenges and provides a strong data foundation for your revenue organization.

Level 2: Information

Providing information, rather than just data, on the revenue process increases the value further. The dynamics of each deal and each customer becomes clear at a glance, and your team can quickly see if a relationship is needed with an executive, or a key role on the buying committee is missing.

Access to this information within CRM, automatically, allows a transition in sales rep coaching from tactical to strategic. Time no longer needs to be spent on understanding the basic mechanics of each deal, it can be spent on strategy and insights that truly help the rep become a better sales professional.

Not only is the information more readily available, but it is free from the inherent bias of any self-reported information. Even questions as simple as “when did you last talk to this account” have shown to be wildly inaccurate in human memory. Having accurate information on all aspects of the revenue funnel makes any analysis or management effort far more effective at moving the business forward.

Level 3: Management Insights

The art of management is being able to identify and focus on the right things. This means finding the risky deal amongst ten other safer ones, and flagging the growth opportunity amongst twenty accounts that will likely keep to the status quo.

Instantly highlighting management insights through autonomous CRM allows your front-line leaders to focus on the right set of challenges and opportunities, while allowing the “safe” deals and customers to be managed by the team without management involvement.

The ability to focus in on just the interesting and unique challenges makes team coaching opportunities far more frequent, as every discussion is about how to avoid a certain risk, engage a specific exec, or capture an interesting opportunity. Sales teams thrive under this leadership, and revenue grows.

Similarly, with accurate identification of risks throughout the funnel, high-risk deals can be addressed or removed from forecast, leading to much more accurate sales forecasts and a more predictable business.

Level 4: Self-Management

With the data and information flowing into place automatically, and management insights layered on top, the next step of autonomous CRM is to add the visibility that allows team to self-manage. With a strong, competitive, culture, and the right visibility into alerts, insights, and leaderboards, this tends to happen naturally.

When it does, the advantages to the organization are tremendous. The front-line management layer of the organization is freed up to think strategically about the business. Efforts can be focused on competitive win rates, market share growth, or expanding to new markets, rather than digging in on individual deals to search for problems.

At the same time, the clear, objective benchmarking highlights any challenges in the team at the earliest possible moment. New team members can see how their performance compares to others, and bottom performers will often self-select out of the organization.

A team with the data and insights to know how it is performing, is a team that is set up to greatly improve its performance. Autonomous CRM gets teams to that level of effectiveness quickly, with value created at each stage of the journey.
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Autonomous CRM: An Urgent Priority
The ability of artificial intelligence to transform markets is unparalleled. Examples are everywhere, from Uber and Google to Netflix and Amazon. When the capabilities become possible, and the transition begins, the leaders are propelled forward while the laggards are left behind and often disappear.

We are in that transition now. It is possible to have AI-enabled technology automate the process of capturing data, analyzing it, and drawing insights from it, throughout your entire revenue process. The companies embracing the transition are pulling ahead, while the laggards whose teams are manually entering and interpreting data are beginning to fall behind.
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