How CRM Companies Can Borrow a Page from the AI Playbook

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In this special guest feature, Lisa Fairbanks, Director of Product Management and Product Marketing for Tata Consultancy Services’ (TCS) Salesforce Practice, discusses the success of and how other CRM leaders should look to copy a page from the Salesforce playbook – that is, identify AI approaches specifically tuned for B2B sales reps, including closing a deal and forecasting results. Lisa is responsible for managing the complete lifecycle of new products from ideation through to prototype and commercial release. With more than 12 years of experience working with products in high-tech, telecommunications and life sciences, Lisa’s core product competencies include analytics, customer insights, CRM, sales methodologies, customer experience and marketing automation.

Artificial Intelligence (AI) is taking B2B sales functions by storm – and for good reason. As both the abilities and availability of AI platforms continue to improve, the use cases for sales reps – including the ability to spend less time on administrative tasks and more time on building relationships and selling – are too compelling to ignore.

Realizing these tangible benefits of AI, while simultaneously overcoming big data challenges, the looming question for CRM providers is no longer “how do we get good data?,” but rather, “what do we do with it?”, the undisputed leader in customer relationship management, is one of the key companies beginning to tackle this challenge. Through the release of their Sales Cloud Einstein – the first comprehensive AI for CRM – they’re able to not only capture sales data, but derive targeted insights for customers.

Given the platform’s success, other CRM leaders should look to copy a page from the playbook – that is, identify AI approaches specifically tuned for B2B sales reps, including closing a deal and forecasting results.

Closing a Deal

It’s imperative that sales people can monitor (and then adjust) the likelihood that a sales opportunity will close to maximize profitability. One way to do this is through integrating natural language processing (NLP) techniques (a field of AI centered around interactions between computers and humans) into a cloud platform. An example of this is Einstein Opportunity Insights on the cloud, a feature which monitors for key words to help minimize risk and secure opportunities. However, any CRM leader can (and should) integrate NLP into their own cloud platforms, looking for items including:

  • The presence and/or absence of a positive/negative term via digital communications between the prospect and the sales rep. Identifying specific terms (or lack thereof) can help make clearer where the prospect is within the decision process.
  • The amount of time that passes between communications among the customer and the sales rep. Monitoring the cadence of interactions can help reps determine how often to contact a customer to generate the most favorable outcomes.
  • The presence and/or absence of competitor research by the prospect via monitoring of digital communication channels. Understanding who else the prospect is considering doing business with is another way for sales reps to better target and course correct their outreach.

As these functions demonstrate, AI has the potential to not only derive previously uncoverable insights from prospects, but obtain this information in real-time – affording sales reps unprecedented agility within the competitive sales landscape. CRM organizations would do well to integrate automated intelligence in way that affords their customers the opportunity to not only monitor their sales interactions, but offer tangible and easily-digestible feedback that promotes B2B sale success.

Forecasting Results

Although forecasting is one of the most critical tasks for any sales organization, it’s also one of the most challenging. One of the key data points used in building a forecast is the placement and movement of a deal’s sales stage, a categorization that oftentimes has a corresponding probability to close percentage. Unfortunately, the sales stage placement and movement is oftentimes at the discretion of the sales representative – meaning a significant amount of human subjectivity, and quite frequently, less than reliable predictions.

However, by leveraging NLP techniques via an integrated AI platform like Sales Cloud Einstein, organizations can layer objectivity onto human subjectivity by adding a systematic validation to sales stage categorization. In short: AI provides more science to the art of forecasting.

In practical terms, this concept would mean each sales stage has a corresponding list of potential terms that should have likely occurred – either in transcribed meetings or phone conversations, emails, notes, virtual assistance, chatbot transcripts, etc. Then, via an AI platform, terms can be captured and synthesized to determine real-time progress and future results. For example, if discussions around budgets, including amounts, timelines, competitors, etc. haven’t taken place, should it be permissible to set a deal at 50% probability? If procurement processes haven’t been identified and documented, should it be permissible to set a deal at 80% probability? These are the types of answers that augmented intelligence can help uncover.

Many CRM leaders have adopted, or are at least considering, AI applications for their respective platforms. However, an “AI everything” approach is no longer sustainable – now is the time to move to specific, tangible applications of the technology, including bettering the way in which sales reps conduct their business. CRM companies should look to platforms like Sales Cloud Einstein as an example of how automated intelligence services not only lead to more accurate and effective pipeline management and forecasting, but can help customers tailor their approaches to the needs of their specific prospects. Learn from the AI pioneers, and then adapt to specific customer needs – the augmented intelligence evolution in the B2B sales space is already underway.


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