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September 21, 2017

Artificial Intelligence (AI) is taking the B2B sales function by storm— and for good reason. With the constant maturation of AI algorithms across different branches of computer science and mathematics, the use cases are too compelling to ignore.

Consider, for example, the automation capabilities made available by AI. According to Harvard Business Review, up to 85 percent of a sales reps tasks, such as entering meeting notes into a CRM application, setting follow-up tasks and logging activities, can all be automated using machine learning. What Sales Manager wouldnt want their team members spending less time on administrative tasks and more time on selling? Or consider the power of predictive analytics, where a consistent flow of new data is constantly being monitored and refined by machine, rather than going stale waiting for a data scientist to tune manually.

Realizing the tangible benefits from AI has become possible because organizations are finally tackling their big data challenges. As a result, the looming question, as Research by Gartner suggests, has changed from how do we get data? to what do we do with it?

The automation and predictive analytics examples provided above are well documented what do we do with it in the AI-sphere for B2B Sales. In fact, many packaged application providers, including Salesforce, are now embedding these specific features into their solutions. But, for those of you who still arent convinced of the potential value AI can bring to your sales organization, below are several additional use cases that TCS has identified for machine learning and natural language processing (NLP), two specific techniques that fall under the AI umbrella.

AI for Improvement in Sales Forecasting

Forecasting is one of the most critical tasks of any sales organization. Unfortunately, its also one of the most challenging. One of the key data points used in building a forecast is the placement and movement of a deals sales stage, a categorization that often times has a corresponding probability to close percentage.

The reality is that sales stage placement and movement is oftentimes at the discretion of the sales representative – meaning a significant amount of human subjectivity. By leveraging NLP techniques, organizations can layer objectivity onto that human subjectivity by adding a systematic validation to sales stage categorization. In other words, AI provides more science to the art of forecasting.

The diagram below offers an illustration of this concept wherein each Sales Stage has a corresponding list of potential terms that should have likely occurred – either in transcribed meetings/phone conversations, emails, notes, virtual assistance, chatbot transcripts, etc. by sales stage. For example, if discussions around budgets, including amounts, timelines, competitors, etc. havent taken place, should it be permissible to set a deal at 50% probability? If procurement processes havent been identified and documented, should it be permissibleto set a deal at 80% probability?

Organizations may consider creating their own lexicon of keywords that serve as a pre-requisite to Sales Stage categorization and movement.

AI for Qualifying a Deal

There are many methodologies that can be used for deal qualification; BANT (Budget, Authority, Need, Timeline), ANUM (Authority, Need, Urgency, Money), etc. And from a reporting perspective, sales reps are often just required to answer Yes/No to the presence or absence of these qualification criteria. By taking rich qualitative data and condensing them into Yes/No questions, sales managers are losing valuable insights that can be used in qualifying deals.

Lets take a Wealth Management example, where the sales person has confirmed that the prospect has budget, authority, need, and timeline. But, after 2 months and over 200 hours investing in the pursuit, the deal is lost to a competitor. What lessons learned can be applied during the Deal Review?

By applying NLP techniques, similar to those mentioned in the Sales Forecasting use case above, to the large amount of data captured throughout the pursuit, sales managers can gain additional insights. If we continue to follow the example above, perhaps the AI-drive insights reveal that the customer requested several products and services that the Wealth Management firm didnt offer. This may indicate that the sale person should not have pursued the deal or, at a minimum, should not have invested so many resources into an opportunity where the firm knowingly couldnt be meeting customer expectations.

Now is the time to start exploring how AI can revolutionize your sales efforts. If your organization is leveraging the Salesforce platform for your sales processes and would like to better understand the AI capabilities currently available and/or discuss additional use cases, we encourage you to meet with TCS while attending Dreamforce 2017,November 6-9 at our exclusive customer venue at theTrace Restaurant at the W Hotel, San Francisco, California.


Lisa Fairbanks is an ex TCSer and was the Director of Product Management and Product Marketing for TCS’ Salesforce Practice. Lisa was 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. Lisa has been the lead data scientist responsible for bringing new TCS Customer Experience Insight solutions to market.


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