Does Profile Management Require Predictive Scoring?

Convert data into actionable intelligence with predictive

 

This is the second post in our profile management blog series. Last month we talked about where the Infer Profile Management platform (PMP) fits into your sales and marketing stack. In this post, we’ll discuss whether or not you need to layer in predictive scoring to reap the full benefits of PMP, and identify where Infer’s platform can fit into your predictive journey.

A question we hear quite often is whether or not a predictive model is required in order to leverage the full capabilities of Profile Management. While predictive scoring models enhance PMP, you can still identify your ideal customer profile and get significant value from the platform without any type of scoring model:

  • PMP allows companies to combine all of of their prospects’ buying signals (i.e. firmographics, geography, personas, engagement, free trial usage, etc.) into data-rich, descriptive and actionable profiles, which can be sliced and diced in countless ways to get more granular. We call this hyper-segmentation.
  • These profiles can be shared across your CRM and other sales or marketing tools (like Marketo or Eloqua), instantly enabling better targeting, at-a-glance personalization for reps, and a clear understanding of the pipeline metrics surrounding each segment.
  • Access thousands of predictive signals to build profiles to help you expand into areas you might not currently have data sets for – new products, geographies, or going upmarket (ABM).

Infer’s View of the World

Before we go any further, here’s a quick breakdown of the difference between our Profile Builder and Profile Management.

Infer Profile Builder Icon

 

Profile Builder (PB)

  • Profiles
  • External Data

 


Infer Profile Management icon
Profile Management (PMP)

  • Profile Builder
  • Predictive Models
  • Recommendations
  • Actions
  • Analytics
  • Net-New

 

FINDING OUT WHERE YOU ARE ON THE JOURNEY TO PREDICTIVE

When choosing a predictive sales and marketing platform, a lot depends on your company’s business needs and where you are in your predictive journey. A great place to start is to first identify your goals and several use cases for infusing predictive intelligence into your business.

 

Scaling predictive sales and marketing

 

For illustrative purposes, I’ve broken these down into levels of predictive adoption.

Level 1 – Starting out on your path to predictive

I recently chatted with a small start-up that has a big growth initiative. They don’t currently use predictive models, and their database is ‘messy’ and relatively small, with only a few reps just starting to ramp up. As a younger company just hitting their stride, they don’t yet have a heavy inbound lead flow or a ton of historical customer data in their CRM tool. But, they do have a general idea of what attributes make up a ‘good’ customer, and they’d like to find more prospects who fit that profile. This is a business where Profile Builder (PB) can help to quickly scale their sales and marketing programs. By turning new and existing customer data into actionable intelligence, PB will allow them to quickly understand their ideal buyer and concentrate marketing and sales resources where the best opportunities for revenue are.

Profile Builder can be implemented quickly, and database ‘hygiene’ is irrelevant — it is designed to easily work with datasets that are small or large, clean or dirty. PB also allows companies to unearth new, deeper buyer signals from external sources (such as technographic signals and other public data like patent filings) and consolidate them along with information from internal sources like their CRM or Marketing Automation system. All of these signals can be aggregated into a data-rich profile of each prospect or customer, which can in turn be hyper-segmented in many ways. Leveraging their knowledge of ideal customer traits, sales teams can use very narrowly segmented profiles to quickly identify net-new ‘lookalike’ profiles.

Though this particular example highlighted a small business’ first steps to predictive, PB can also add tremendous value for larger, more established companies looking to branch into new market categories.

Infer Profile Builder in Action:

Let’s say we were hosting a webinar titled Marketing Automation Tactics & Strategies, airing in PST. For this campaign, we’re interested in both conversion rates (i.e.registrants and attendees) and campaign engagement metrics (i.e. open and click-through %).

Build your ideal customer profile with Infer Profile Builder

Using Profile Builder, we can quickly create a simple, custom profile that allows us to target a narrowly defined set of prospects and customize our outreach to them. In the left-hand side of the example below, I’ve named the profile “MAP Users in US and CA.” To optimize for the time zone, I first added a geographic signal (location). Then, I added in technographic signals to find people who use Pardot, Hubspot, Marketo or Eloqua. The right-hand side of the screen shows pipeline metrics that are calculated automatically as we build the profile.

Level 2 – Layering in predictive intelligence for maximum impact

Now from the perspective of companies that are farther along in their predictive journey, we occasionally hear that “a score is not enough.” Usually larger companies have databases overflowing with inbound leads and plenty of historical customer won/lost data to model on. While predictive scoring allows them to cleanly stack-rank accounts based on their fit for the product and their revenue potential, it can be hard to execute deeper personalization and engagement with a score alone. For folks like this, combining PMP with predictive scoring (whether from Infer or another vendor) will provide the most complete view of each prospect — including all the buyer signals mentioned above, along with statistically accurate fit and/or behavior scores. As a result, companies can execute better-informed marketing and sales’ outreach, and determine the next-best programs and tasks for each group of prospects.

The example in Level 1 above is a pretty simple profile, but we can continue to refine it by including a predictive score as well, and a few more custom signals. Here’s what we added in the example below to help us further hyper-segment for even more personalized outreach:

  • Website Technologies = focused only on Marketo
  • Has B2B keywords = True
  • Job Title = Marketing
  • Infer Rating (Grade) = A
See how easy it is to build the ideal customer profile with Infer Profile Builder

The audience for this webinar is B2B marketers, and in order to deliver a more targeted, high-value message, we’ll focus only on people in marketing roles whose companies are using Marketo. We’ll also add a predictive score in the form of a simple rating, to make sure that we only invite prospects that are a good fit for our business.

Closing Remarks

Companies who use Profile Management to laser-focus on finding the right customers for their business will produce better results across the funnel. By centralizing thousands of buyer signals into one descriptive profile, PMP helps go-to-market teams to increase operational efficiency, deepen personalization, and move on actionable intelligence. Through hyper-segmentation, marketing can easily define high-value segments and buyer personas, pinpointing the best prospects for sales to engage with. Sales reps can then create personalized messaging that communicates a clear value proposition and facilitates more meaningful engagement.

If you’d like move up the predictive maturity curve, contact us for a live demo of Profile Management and find out how predictive is benefitting real companies today.

 

Sean Zinsmeister

Sean Zinsmeister

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Senior Director of Product Marketing at Infer Sean crafts the positioning, messaging and overall go-to-market strategy for Infer’s trove of next-generation predictive sales & marketing products. Once a satisfied Infer customer himself, Sean joined Infer from Nitro, where he developed and led an award-winning global marketing team.