There’s no doubt about it, the predictive space is heating up. And with all the noise out there, it can be difficult to understand what separates one predictive vendor from another. To help navigate the space and see where the technology is headed, it is helpful to draw an analogy with the evolution of maps.

Pinpoint Where You ArePinpoint Where You Are

Once upon a time people drove around with AAA maps in the car. We found our way, but it took a lot of human intuition and manual work just to figure out exactly where we were. Then along came mobile phones and GPS – with Google Maps we were able to tell exactly where we were with precision down to the city block.

What does this mean for B2B Companies? This level of intelligence is very similar to where most companies are with Predictive 1.0 today. In the same way as connected maps went mainstream, predictive technology is now available to companies of all sizes.

How will vendors differentiate themselves? With statistically proven models, sales and marketing teams can now pinpoint their exact coordinates – i.e. how many ‘good leads’ they have, which ones are showing real buying intent, and what attributes make up a good customer. Just like connected maps, plotting location on a map will become more of a commodity service over time. Yet within the foreseeable future though, there are material differences in model performance from one predictive vendor to the next. Some vendors are able to plot your “longitude” and “latitude” down to the centimeter, while others might place you in the wrong location all together.


Map the Fastest Route from A to B Map the Fastest Route from A to B

Mapping technology entered a whole new frontier when Google introduced free turn-by-turn directions for mobile devices in 2009. This was maps first killer app. Finally, people who were sick of getting lost could easily plot the fastest route from their current location to the destination of their choice. Like location coordinates on a map, a predictive score by itself is nothing more than a number. It’s the application of the score that really starts to unlock value.

What does this mean for B2B Companies? But to build a killer app equivalent to turn-by-turn directions you’ve got to tailor it to your business. You need to map out your roads, your objectives, and your integration points. For example, you might identify a process that can be optimized with predictive scoring. The simplest one to think about is your inbound lead flow. To increase conversion rate you need to configure your routing rules in Marketo and train your sales team so they trust the scores. Depending on the size of your company that can be a trivial or nontrivial amount of work. But if it’s done right you should be able to demonstrate real ROI measurement, otherwise known as a step-function in growth.

How will vendors differentiate themselves? Best-in-class companies have not one but several proven playbooks for predictive scoring. For example, filtering out bad leads, prioritizing prospects, sourcing net-new leads, nurture campaigns, executive dashboards, etc. When reviewing vendors, it can be very telling to observe whether they have proven customer success stories and use cases that demonstrate real ROI measurements and a variety of effective applications, aka turn-by-turn directions. Otherwise, you risk ending up on roads that don’t exist – like all those folks who unfortunately trusted Apple Maps too soon.


Receive Intelligent Recommendations Receive Intelligent Recommendations

The next evolution of mapping solutions which is beginning to achieve wide adoption is real-time recommendations. Companies like Waze are monitoring traffic patterns, alerting us to issues, and routing us around traffic – all in real-time. Similarly, Uber is developing new applications for location data, such as determining where the nearest driver is for each ride request, predicting where that rider is likely going based on the time of day, and calculating how much the fare is likely to cost.

What does this mean for B2B Companies? Next generation predictive apps for B2B are following a similar path with alerts, intelligent recommendations, and actions. They are distilling specific segments based on buying stage, firmographic characteristics, potential revenue contribution, and sales effort. By snapshotting these cohorts, predictive models will be able to measure the impact of one action over another and feed that insight back into sales and marketing intelligence engines.

How will vendors differentiate themselves? Just like Waze, the future of marketing automation will be all about alerting the person in the driver's seat (the marketer or the sales rep), highlighting the opportunity, and recommending their next best actions. And, like Uber, these systems will be plugged into other execution engines in order to automate key workflows.


Autonomous Vehicles Autonomous Marketing

We’re seeing more and more signs of the next major advancement in maps. Self-driving cars are already roaming the street where I live, and they’ll have free reign across the country within five years. These cars will know enough about you to understand where you’re going and how best to get there, making all the stress of driving fade into the background. In this new utopia, traffic congestion will be reduced and people may be more willing to commute further distances.

Interestingly, Marketing Automation has been promising something similar to B2B companies for years. The promise was that you’d set up customer journeys and just let them run, but that vision is leaps and bounds from the spaghetti code and suspect data marketers slog through today. However, the vision of true automation is found in predictive. We believe that over the next 5 years, the predictive industry will deliver intelligent, prescriptive navigation and recommendations that are effective and accurate enough to finally make it a reality.


"The future is already here — it's just
not evenly distributed."
— William Gibson

Level 1

Predictive Scoring

Behavior Score

How it Works

  1. Connect to your existing data
  2. Add thousands of signals
  3. Generate a predictive score

Comparing Vendors

  • Competive bake-off
  • Breadth of models and expertise
  • Customer Success

Level 2

Proven Playbooks

Predictive Playbook

Comparing Vendors

  • Proven ROI Stories
  • Well Documented Playbooks
  • Breadth of Product Usage

Key Use Cases for Sales & Marketing

  1. Filtering
  2. Prioritization
  3. Net-New Leads
  4. Campaigns
  5. Nurture

Level 3

Intelligent Recommendations

Similar to Waze, next generation predictive apps will offer real-time alerts, recommendations and automated actions to help you reach your revenue targets even faster.
Level 3 details

How it Works

  1. Pull insight from even more sources
  2. Build ephemeral segments
  3. Trigger user alerts
  4. Recommend the next best action
  5. Measure cohort performance

Key Use Cases for Sales & Marketing

  1. Building Segments (Ops Team)
  2. Operationalizing Personas (PMM)
  3. Defining Narrow Campaigns (Demand Gen)

Level 4

Autonomous Marketing

We may be some years off from fully autonomous marketing, but there is little doubt we're headed in this direction.

The Future of Predictive Platforms

Companies who apply the power of AI to improve the customer experience and create distance between themselves and the competition.
  1. Put safety measures in place
  2. Test programs extensively
  3. Let AI steer the ship