The Future of Sales: Part 4

July 24, 2018

Sales is getting harder and sales processes more complex. Only 40% of sellers achieved quota last year, solution sets have grown, and customer expectations have changed. Today, Augmented Intelligence (AI) is the only way sales organizations can eliminate guesswork, exceed quota, and free up sales to act as strategic advisors for their customers. 

In the last installment of our Future of Sales series, I chatted with Max Quinn, Bluewolf’s AI Research  & Engineering Lead to understand how AI works under the hood within sales, and the tangible steps sales leaders can take to make AI a reality. 

Let’s start by defining AI. At Bluewolf, we refer to AI as “Augmented Intelligence,” versus “Artificial Intelligence.” Why?

In 1956, researchers coined the term “artificial intelligence” to refer to a large umbrella term encompassing the effort to design or create intelligent agents that exhibit human cognitive abilities, including reasoning, environmental interaction, learning, vision, and speech. 

But as AI research has progressed, it has been met with fear. Partly inspired by sci-fi novels and movies, some envision a world where robots will take over and create an AI apocalypse. More realistically, many worry that AI will cause automation that drives unemployment, or that people will misuse AI for selfish reasons. 

“Augmented Intelligence” reframes the discussion, involving human decisions that are aided by technology, to unearth the positive opportunity AI provides--like empowering sellers and employees to focus on more challenging and fulfilling tasks while achieving higher levels of success. 

Where does Machine Learning, Natural Language Processing (NLP), and Computer Vision (CV) fit within AI?

When computers learn to perform tasks without being explicitly programmed to do so, machine learning is at work. For example, as a seller, you often respond to customer requests and questions. While we can’t predict exactly what customers will say, we can develop algorithms that teach the computer how to find answers to various questions, saving developers time from coding hundreds of explicit rules. Machine learning is really part of the umbrella term AI. 

NLP connects technology and “natural language,” like human speech. Speech recognition, sentiment analysis, and answering questions are a few features common to Chatbots for example, which are a very clear example of using AI for Guided Selling so sellers can improve customer engagement. A sales assistant Chatbot could understand an incoming customer request, and then direct the conversation so a rep offers an optimized price to a customer. 

While NLP focuses on words and language, CV relies on image recognition to extract critical customer data points for sellers. CV would detect customers who use your products, or similar products, in photos posted on social media for example, and feed that to sellers who could engage in highly personalized, timely, and relevant advertisements to acquire new customers. 

By modeling technology on the human brain, AI clearly enhances the selling experience. What value does a seller or employee bring to the tool?

The advancements in machine learning that have improved NLP and CV all focus on mirroring human cognition. This is what deep learning refers to--deep learning models and architectures are loosely based on how neurons transmit signals in your brain. 

But these models and the results they produce are only as good as the data provided as input. Chatbots for example, can suffer from poor speech patterns and typically only perform well when trained in a specific domain and they don’t always have intrinsically human characteristics like identity or memory. So, a seller is essential to interact with the tool and pick up where it leaves off. 

If a Chatbot offers a Next Best Action, recommending a new account to sell to and an optimal price, a rep still has to decide if that action makes sense. The art of selling requires that they still do the real work of building relationships and becoming strategic advisors for their customers. 

What are some “quick win” use cases for AI in sales that you see today?

Automated B2B Lead Generation

  • Listen to unstructured market news, reviews, and social channels to automatically create leads in Salesforce that are relevant for your business. Analyze leads to prevent duplication and score them so inside sales teams prioritize outreach to prospects most likely to turn into customers. 

Guided Selling

  • Improve seller responsiveness to customers, increase high-propensity offers, and maximize revenue through cross-sell and upsell recommendations. Guided Selling powered by Configure Price Quote (CPQ), answers 3 questions for sellers: “What do I sell, at what price, with which offer?”

Proactive Customer Retention

  • Reduce churn by predicting at-risk customers before it’s too late. Analyze tone and sentiment across customer touchpoints, alert employees of issues, guide next best-actions, and continuously learn from data to improve customer resolution and employee performance. 

Sales Acceleration Chatbot

  • Automate and expedite repetitive sales tasks like customer and prospect interactions. Understand purchase intent across all channels, freeing your sales teams to focus on deeper engagement with customers. 

How do you recommend Salesforce customers start integrating AI for their sales organizations?

I have 3 general guidelines: 

After completing those steps, I recommend investing in Salesforce Einstein, which is less a product and more an electrical current that runs through all of Salesforce’s products and infuses intelligence into all areas of the platform. Since it’s pre-baked into the platform, Einstein offers direct access to Salesforce data, while guaranteeing the security and innovation Salesforce customers are used to. 

Adding IBM Watson on top of Einstein takes advantage of Salesforce and IBM’s partnership to paint a more complete view of the customer--while Einstein knows your customer, Watson knows the person, by mining unstructured data like emails, social media, and news articles. Accelerated lead generation is a great example of Einstein and Watson working together. Einstein analyzes past leads that convert to won opportunities, and identifies attributes (demographics, industry, title, etc.) most likely to convert, and then Watson mines external data sources to bring those leads to Einstein. 

Have any technical questions about Salesforce Einstein? Connect with an expert at Bluewolf. 

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