Closing More at Lower Cost With Accessible AI Predictive Power

Business leaders aren’t looking for more raw information. They’re drowning in data.  Between 60% and 73% of all data within an enterprise goes unused for analytics according to Forrester.  Answers are what is in short supply. Those responsible for revenue growth have to balance the value of insights within that data, from the cost of extraction.  Here are the options …

• Reports, Charts & Graphs
• Purpose-built Tools 
• Custom Data Science (AI & Machine Learning)  
• AutoML (Automated Machine Learning)

Reports, Charts & Graphs are the meat & potatoes for most sales organizations.  While the attribution for the famous quote about managing only what you can measure is up for debate, the maxim is not. Identify a few key inputs, graph it over time to show you where you’ve been and how some stats relate to one another. This may be more or less automated by BI systems and dashboards, but the result is a clear look in the rear-view mirror. You bump into unexpected things driving that way.

Purpose-built Tools were quick to identify the biggest problem with AI & machine learning. Even though most users knew of AI’s power, few knew how to apply it to grow top-line revenue. These solutions were quick to grab an algorithm and apply it to a specific use-case like lead scoring, sales coaching, or identifying churn risk in your existing customer base. While these point products are often worth the investment, sales organizations are wary of yet another subscription to solve a problem that is rooted in data analysis. With AI becoming essential across the spectrum, the idea of buying multiple, AI-embedded layers in the sales and marketing tech stack is a frightening for several reasons:

• Up to half of AI startups are actually not using any AI at all according to a study that evaluated 2,830 startups.
• AI embedded in apps is often opaque. “Take our word for it, these are your hottest opportunities” is a big leap if you can’t see why they were ranked that way.
• Layers of AI may produce conflicting answers, with no way to vet or normalize them.

Custom Artificial Intelligence & Machine Learning have dominated the conversation lately. Unfortunately, many tools built by and for data scientists and programmers are impossible for business people—even data-savvy analysts—to use. Coders gonna’ code. While large businesses can afford to throw massive resources at this, CXOs and sales leaders should bear in mind the prohibitive expense of staffing the “sexiest job in the 21st century”. Peek under the veneer of Google, Amazon, and Microsoft Ai and machine learning offerings and you’ll quickly discover eye-glazing references to GitHub and Jupyter notebooks and Python code.

AutoML Fortunately, there is an alternative for applying machine learning without using data scientists or their complicated tools of the trade. A new type of technology (AutoML) allows organizations to forgo much of the process by automating data preparation, feature engineering, and model creation. CROs, analysts, operations and sales enablement leaders can arrive at insights faster and provide their teams huge advantages.

Squark offer AutoML that can be applied across the entire sales operations and marketing decision making processes.