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Demystifying AI

The significant number of seller enablement tools developed in recent years have made for a range in sophistication and uses. Companies employ these tools for everything from straightforward tasks like pulling data from emails to complex algorithms that can analyze internal and external data to make sales predictions. 

How do you navigate through such an increasingly crowded market? 

Our tips to remember, artificial intelligence (AI) and machine learning (ML) are most effective when:

  1. They’re applied to well-defined, clear, and relevant challenges
  2. They integrate seamlessly with the tools salespeople use every day.

Keeping those two things in mind will help you as you research the available options.

How is AI applied in sales tech?

With so many sales tech options out there, we thought we’d highlight the five most common areas in which AI is being used, to help compare apples to apples. 

1. Machine learning for data capture.

The first and most fundamental capability of any seller enablement tool is ML for data capture. Typically, this is being used to pull data from email systems. This kind of data can help show users who corresponded with whom, and when, offering insight into sales activity and relationships. More sophisticated solutions can not only capture and upload contact records to CRM, they’ll also enrich them with latest email addresses, phone numbers and other rich data on an ongoing basis.

2. Pattern analysis of in-house data.

Sales tech can score opportunities, deals, accounts, and relationships using AI. The system will analyze data and identify the factors that point to winning a deal or landing a new account. Comparator data (which is data from competing sources) can include either an amalgamated dataset across organizations or historical data within one company. Some systems are limited to analyzing only CRM data and seller activities, while some have the capability to include more, like marketing interactions and online behaviors. The systems that extract relationship network data will also be able to interpret the strength of those relationships.

3. Incorporation of external data.

Typically external data included is publicly available data about companies, executives, industry trends, and regulations, but it may also use information from social media channels. While each system has its own algorithm for evaluating this external data, there is a common goal: find patterns and factors that are highly correlated with certain outcomes (like making a sale) to analyze the significance of an opportunity.

4. Natural-language processing (NLP) analysis and interfaces.

NLP does two important things: analyze call recordings and text interactions (including email and chat) to find trends and critical information; and be a kind of digital assistant to sales teams—using prompts and allowing them to ask questions to find information or perform basic tasks. This is referred to as “conversational AI” in seller enablement tools. NLP allows for the system to offer coaching capabilities that use the data from the conversation analyses. 

5. Prioritize actions.

Using AI to identify priorities and recommend next-best actions varies significantly from tool to tool. Some systems are not using AI in this way at all, but instead are basing priorities and recommendations on business rules and traditional sales methodology. Some are generating priorities and recommendations solely on analytics derived from AI. Given the limitations of existing B2B datasets and rapidly-evolving abilities to capture and evaluate external datasets, several tools use a combination of business rules and AI.

What to look for in your sales tech

When considering the options, first look at how and where your people work. What are our jobs to be done? You want the least amount of friction possible when choosing a new tool, so that means how it integrates and works with not only your existing CRM and other tech, but how it complements your sales team’s workflow. Introducing a new piece of tech that requires a separate login, for example, we would avoid, because people just won’t use it if it’s not convenient. 

Once you’ve identified a system that’s friendly to how your team works, it’s time to consider the amount of clean data that’s needed for the piece of tech to work properly. Is your CRM data ready for this system? 

The sales enablement tool must be trained on your own data. Machine learning models are trained on your organization’s own opportunity data, so the type of activities, the timing of the activities, the people involved in opportunities, when they’re involved, etc. Alternatively, if the ML model is trained on a more generic set of data, the insights that the model produces will also be more generic, and less applicable to your organization. 

Every company differs from another. The length of their sales cycles, the frequency of communications, all change from company to company, industry to industry. A machine learning model that is trained on your data means it will be learning from how your company operates and will adjust as you evolve too. 

The ML model can identify patterns of characteristics and actions that have historically led to closed won opportunities, and lost opportunities. From there, it can keep you on the right path, guiding you toward activities that yield the results you’re looking for, steering you back on course when you might be going down the wrong path. 

Want to learn more?

Watch our webinar about the shifting B2B sales landscape, the technologies that are supporting it, and leave with a few tips for navigating your purchase decision. Watch on-demand here.

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