• Artificial Intelligence For Sales Decks

    Game of Sales Intelligence

  • Smart documents for 5-star sales experiences.

    Your sales reps and content are the ambassadors for your brand, use AI to optimize your client's experience.

    Get a real-time recommendation engine for sales reps, sales trainers, and content creators, without a team of PhDs.

    The data you currently are not collecting is a large opportunity for automating away major sales & product marketing pain points and inefficiencies that are not going to go away and need to be solved.

    As content and teams grow, knowledge complexity increases exponentially, creating pain points between sales reps and product marketing. AI can collect and recommend knowledge automatically, including from audio from zoom conference calls (for example):

    • Data collection: What pain points do they have?
    • Content Recommendations: Real time recommend optimal content.
    • Learning Experiences: Sales Reps immediately learn what the appropriate content is in, improving sales training as part of the system itself.

     

    The Solution I Can Offer Your Organization:

    Translate business concepts you want humans to learn (sales reps) into data schemas you want machines to learn (content analytics), so that your sales reps are automatically recommended the right documents without ever having to search. The system just listens to your sales rep, and when your clients share their problems and questions, it automatically recommends them to the sales rep to show clients. The information is personalized to the client's industry, size, and technical maturity, for example. This will require the necessary team and resources to support this type of project. This is essentially a Human-Machine Learning System, your sales reps will learn as the system improves its recommendations and gives an anticipatory user experience, meaning it anticipates the knowledge your rep will need without ever having to search.

     

    * Andrew Ng Keynote MIT Emtech presentation:
    https://www.youtube.com/watch?v=NKpuX_yzdYs&t=1521s

    The challenge is in making it easy for domain experts to efficiently label concepts that data scientists can easily use for their ML models.

    Knowledge Mapping is costly, but far less expensive than these problems, and extremely effective:

    • Sales reps have specific needs for each client, and as you scale your sales team, product marketing is overwhelmed with support requests.
    • Sales reps are learning what works and what doesn't in each interaction with clients but have no way to record that information.
    • Sales reps use opinion, there is no centralized analytics store for content interactions.
    • Product Marketing is using opinion with no insight into what sales reps are using.
    • Human opinions are biased and perform poorly with no data to guide them. 
  • Existing Scalable Solutions & Immediate Application

    Highly sophisticated real-time transcription sales systems, like gong.io can capture the topics within live sales audio & video meetings, and can trigger events based on keywords. However, they are generalized and require a map to your business' specific domain of expertise and language, and knowledge models.

    Gong.io auto-transcribes sales meetings and provides topic-based analytics connected to Salesforce win/loss data.

    Low hanging fruit to implement with the high potential upside.

    Topics are automatically recorded, now how do we map these to your company's document content?

  • Simply put:

     

    The more sales reps you hire, the more diversity in content will be needed. Product Marketing can't know every client situation and optimize a deck for every sales rep's needs. Their necessary output quickly increases the complexity of documents and knowledge other sales reps need to sift through and/or learn, putting serious downward pressure on sales managers being able to keep up with their workload demands.

     

    Sales managers, from their perspective of being "teachers" of reps, have no formal educational expertise, and thus the sales reps below them are poorly served. Sales reps will feel completely inundated by all of the emails for their sales training, and sales managers will feel they are always short on time.

     

    The biggest tell that there is a large operational efficiency to gain by operationalizing your knowledge is that quit often in organizations below average performers are the majority. AI can monitor top performers in ways they are not even aware, just like your reaction to this paragraph might be recorded and optimized for and you were not aware of that until I explained that in this moment.

     

     

    Doing these will not solve your problems:

    • Rapidly hiring more sales people
      - diminishing ROI as content expenses explode from support request demands increase on product marketing.
      - reduced quality due to training complexities increasing.
    • Hiring more content consultants
      - paying for opinions.
      - challenging to define and measure success.
    • Buying something off-the-shelf
      - no content interaction analytics
      - no knowledge mapping

    Doing these will solve your problems:

    • Label knowledge within your documents
      - domain specific expertise within your organization.
    • Create a knowledge & operational map
      - connect content, learning, and operations to a single set of metrics.
    • Automate content authoring that is personalized
      - by collecting tribal knowledge into a single recommendation engine.

    How Would A Data Scientist Get the Data & What Would They Do With It?
    Your data scientist, let's call her Joanna, needs to understand your business problem. Her challenge, where she spends 80-90% of her time, is just getting and cleaning the data from all of the various stakeholders. Once she gets the data, she has Gandalf like powers which are beyond human capability.

     

    Joanna goes and talks to your sales reps, sales trainers, and content people and sees that your sales reps are taking too long train, and the avg is performing under quota.

    As the number of sales reps and number of clients increases, so does the demands on product marketing and the demands on the sales rep time to prepare for the client meeting. Joanna needs to give your sales reps the right document at the right time, and the best way to do that is by predicting it in advance.

    These inefficiencies create resistance in reaching sales goals, and even the best human-optimized systems cannot compete with AI.

    Rather than sales reps spending 50% of their time searching for documents and sending support requests to product marketing, Joanna can recommend exactly what the best performing content is specific to each individual client. Rather than putting constraints on Product Marketing, Sales will then free up Product Marketing to focus on strategy and optimizing insights around client interactions. Joanna needs to take operational real-time data like audio, text, and images, and map them to Salesforce data and the knowledge map that Product Marketing builds around the concepts that your organization has invested in your documents. AI can do with higher efficiency and precision in minutes what takes dozens of humans years, the challenge is labelling your tribal wisdom from your documents.

    Transforming a pitch deck to JSON-AI, an open source standard format for document knowledge extraction.

    jsonai.org is the open source knowledge labelling schema used in the example above.

  • My Recommendations

     

    Step #1: Define your Objective

    Accelerate sales velocity to surpass goals set out in IPO forecasts.

     

    Step #2: Define your key results

    • Reduce Sales Reps time to hit quota.
    • Increase Quota Goal.
    • Increase Avg. Performer performance.
       

    Step #3: Map the knowledge in your content to your OKRs

    • The subject matter expertise of your strategy team and product marketing team, is their tribal knowledge.
    • You must map their tribal knowledge to documents, and to their use in the field.
    • Then you can operationalize & automate this knowledge.

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