Early adopters of Artificial Intelligence sales tools are starting to see the revenue needle move in a big way. What’s their secret? In this article, we’ll look at one major factor: the difference between an AI-enabled dynamic model and the more prevalent static predictive model.

Applying a dynamic model to these two essential elements is the key to maximum results:

  1. The data that feeds into the playbook model (static vs. dynamic)
  2. The playbook model itself (the static predictive model vs. the dynamic AI model)

Let’s take a closer look at how this translates to more revenue.


Static Predictive Playbooks vs Dynamic ‘Living’ Playbooks

The most recent sales playbook advancement is based on predictive selling. Organizations use it to derive deeper customer and market insights, particularly in identifying and understanding the many different buying journeys out there. This knowledge is leveraged into multiple pre-programmed playbooks, which help sales professionals match their approaches to specific opportunities. An improvement over the old methods, to be sure! However, these playbooks are still static; they are fixed pathways for teams to follow.

AI takes the playbook to the next level, from set and static to dynamic and living. Living playbooks adjust over time. They learn from market shifts and from sales teams’ actions, successes, and losses. These smarter playbooks absorb info from incoming data and use it to get better. This is the deep learning that everyone is talking about. Deep learning uses outcomes and new data to adjust algorithms over time. A deep learning system continuously learns, improves, and gets more accurate.

So what does this mean for the sales professional? With AI, they get adaptive guidance throughout each buying journey. A rep can be prompted to adjust their message, timing, product offering, and channel of communication on the fly thus getting a competitive edge even in a tough sales situation.


Static vs Dynamic Data

The static data CRM data, past sales info, firmographics, etc. that drives most predictive sales tools is an improvement over solutions that don’t have predictive capabilities. However, this is still prediction based on the past. It is backward looking.

Dynamic data social media, news, trending topics, etc that is continuously fed into your playbook model is the golden key. Real time data takes a bit of data harmonization, but today’s technologies can automate that process. Moving from static data to near-real-time data ingestion, harmonization, and availability will further boost deep learning models.

For example, a deep learning tool can layer in real time information from news sources and social sites. This can identify shifts in buying behaviors, market trends, and even in competitors’ actions. It’s an emerging capability that goes beyond delivering insight; it highlights specific steps that salespeople can take to serve clients and prospects better.

Real time  is a hotly debated term. Even near real time data is not required for AI to be effective. Weekly or monthly updates still make an impact on models. But for organizations building on their AI capabilities, moving from static to dynamic data will make machine learning models smarter and allow deep learning to achieve a new level of intelligence.


The Dynamic Model in Action Finding New Opportunities

Let’s put this in a real-world scenario. Take a large sports equipment distributor that offers several lines of workout equipment to large fitness centers, universities, and hotel chains (cardio equipment, cycle rooms, yoga studio supplies, free weight systems, etc. ).

Emerging market trends create patterns that AI can recognize quickly; in this instance, a new line of yoga supplies is gaining momentum in the press and in social media. The AI-driven systems’ early recognition of this signal makes lead scores spike for yoga studios and fitness centers with yoga programs.

On Monday morning, sales reps get a fresh batch of yoga-themed leads. They are given precise details on which yoga-related products to present and why each contact should consider the product(s). The reps also receive links to relevant articles they can share with the contacts. AI has enabled them to ride the trend, not come in behind it.

This is a much more powerful approach than waiting for the trend to become obvious and then responding to inquiries. A dynamic AI model gets a salesperson in front of a prospect before the prospect starts looking for new equipment. And it puts the equipment distributor way ahead of their less-savvy competitors.


How Real Time Can Save a Deal

Now let’s move to another common scenario. Every sales rep has had an apparently “done deal” disappear unexpectedly. Real time dynamic applications of AI can make or break a sale in process even in the middle of a purchasing decision.

Suppose a large mid-priced hotel chain is upgrading their cardio workout rooms. Their motivation is to stay competitive. The chain’s buyer has already selected a new cycle model and is negotiating price and delivery a near-done deal. But a competing equipment company releases a cycle model with new heart monitor technology and the prospect goes quiet. A human rep might not know of this new threat; they might just assume the buyer is busy. AI because it’s tuned into competitor activity and market conditions can alert the rep that the deal may be at risk. The rep is prompted to suggest the buyer consider the next model up, which comes with heart monitors. The rep can even provide a very compelling rationale: they understand that the hotel chain needs the latest and greatest workout rooms to compete with other hotels.

What ensues? The buyer feels understood, trusts the salesperson to know their needs, and may even spend more on the upgrade. Loyalty and increased revenue is the natural sequel, and the competitor is cut out of the picture.


Making the Leap from Static to Dynamic

Transformative AI-powered tools are helping sales professionals build stronger relationships and serve their customers better; organizations utilizing these tools reap the rewards. When evaluating new sales technologies, make sure you understand the subtle but important difference between the dynamic data powering a “living” playbook and the traditional predefined playbook based on static data. By making the leap from a static to a real time approach, sales teams can gain a bulletproof competitive advantage.


Check out my follow-on post which combines this dynamic model with AI-based strategic actions (not just insights) to give sales leaders criteria for evaluating AI software for sales.

Authored by –Sandra Peterson, VP of Marketing at Absolutdata


Related Absolutdata products and services: NAVIK SalesAI, NAVIK AI Platform, AI & Data Science Services, Data Integration Services, Tech

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