By Mary McGuire (@Badgerpolo)
How prescriptive and predictive content recommendations approach the same problem very differently.
We all know that machine learning and artificial intelligence are everywhere these days. Whether it’s how it will influence the next car you buy, how you turn on your lights, or how you use technology at work – AI affects all aspects of our lives.
With this more recent technology shift, buzzy words like prescriptive and predictive are constantly thrown around. It’s also easy to confuse the two, as they are often used interchangeably. However as we learned from SAVO’s in house data scientist, predictive content recommendations is the primary use case for machine learning within Sales Enablement technology. So when it comes to Sales Enablement, what exactly is the difference between predictive and prescriptive technology? While they are both driving towards the same goal– the goal of getting the most relevant information in front of your sellers at the most relevant time–they do so by different means.
Let’s start with what they have in common.
- Both prescriptive and predictive make content recommendations using data.
- Both prescriptive and predictive can recommend sales information (e.g., content, coaching, subject matter experts, etc.) relevant to a specific selling situation (e.g., deal stage, industry, competitive landscape, etc.).
- An algorithm or methodology is used to determine when to use various content.
So how are they different?
- With prescriptive, a person analyzes data and decides when to use various content.
- With predictive, an algorithm uses data to determine when to use various content.
Can you expand on that a little bit?
- With prescriptive, when determining the optimal content for sellers, a Sales Enablement team will collect data, such as which content is resonating with buyers, which content is most relevant to a specific industry, and will prescribe relevant content based on selling situation criteria.
- With predictive, a machine collects data on user engagement patterns such as what content is used together, or what content is used often in certain situations, and use that data to recommend content when it finds situations that match those patterns.
So which one is better?
- They are both effective ways to ensure that your sellers are getting the right content at the right time – and they work best when used together. Think about it this way, there are certain qualities that a machine just cannot learn, in which the expertise of your sales enablement team is required. On the flip side, it’s impossible for a human to be able to process data and recognize complex data patterns as quickly as a machine can.
Your sellers would never know the difference between the two, and they shouldn’t – they should not be concerned with the technical reasons they are getting the information they need, only that they are getting what they need, when they need it, and that they have the context to know how to use it. So, educate yourself when evaluating technology by asking questions. You’ll want to know that the solution you select handles both prescriptive and predictive content recommendations to ensure that your system is fine tuned to deliver the coaching, messaging, and information that your sellers need when they need it.
Want to see predictive and prescriptive in action? Request a demo.
You may also be interested in: