Infer’s Automated Form Monitor for Marketing Operations

Infer’s Automated Form Monitor, which is a simple WordPress plugin, helps marketing operations teams periodically test their site forms to ensure they’re operating properly.

How to Use Infer’s Automated Form Monitor Plugin

If you’re unfamiliar with installing and activating WordPress plugins, please see this post in the WordPress Codex.

After installing and activating the automated form monitor plugin, you will see an Admin Menu option called Form Monitor (see left screenshot). To begin monitoring your site forms, click the Form Monitor option, then follow these next steps:

 

  1. Download this CSV template.
  2. Double click the .zip file, and open the CSV file in your preferred spreadsheet or text editor (Excel, Numbers, Google Sheets, TextEdit, etc.).
  3. You will see a number of columns in the CSV file. Create and fill out one row for each form you want to monitor.

How Can We Blend All of Our Customer Data into Actionable Profiles?

Infer Profile Builder Icon

This is the third post in our profile management blog series. Earlier this month, we talked about whether profile management requires predictive scoring, and in this post we’ll discuss how it can be easily incorporated into your existing sales and marketing workflows. Often we talk with companies that don’t yet have enough leads or conversions to build a statistically accurate predictive model, but that no longer needs to hold them back. The key to establishing impactful data-driven workflows – with our without predictive scores – is creating actionable profiles of your ideal customers, and infusing that insight into your day-to-day work.

What’s New at Infer?

Originally published on TrustRadius

Over the last year, $242 million in venture funding has flowed into B2B predictive marketing. That is great validation that we’re on the edge of a mega trend that could be every bit as big as cloud, social, or mobile.

At Infer we feel fortunate to have gotten out front and had time to build up tribal wisdom. By working with lots of forward-thinking companies, we’ve been able to learn the edge cases, improve the technology, and develop playbooks for success. Just as we saw with Salesforce, the community you attract and the success of your customers is the catalyst for growth.

About a year back we had a realization that scoring alone was not enough to win this market. From working with customers we knew that you can have a great predictive model, and that is critically important, but a score by itself is not enough. It is the applications of the scores that unlocks value.

What’s the Difference Between Traditional and Predictive Behavior Scoring?

This is a question we get asked a lot. 

Instead of manually adding points for a given action, a predictive behavioral score mines the full spectrum of activity-data being collected by your marketing automation platform including every email click, website visit, and social engagement. Machine learning is used to weigh each signal appropriately in order to predict the likelihood of an outcome (e.g. conversion) within a set time period (e.g. next three weeks).

Learn the difference betweem tradition and predictive behavior lead scoring
To help explain these concepts in more detail we’ve put together a new eBook. Inside you’ll learn:

  • The six most common use cases for behavior scoring
  • Best practices for operationalizing your fit and behavior scoring
  • Why predictive behavioral models are more powerful than the scoring you’ll find in traditional marketing automation platforms

Download your copy here >

 

Infer Lenses – Using your predictive model to score different segments of your business

Infer’s predictive models score and stack-rank prospective buyers on a 0-100 point scale based on their fit for a specific company’s product. For example, here is a typical distribution across an entire business:

All Prospects

Sometimes a model will score a certain subset of leads (e.g. leads from a specific geography, industry, product, etc.) lower than average because a company hasn’t historically focused on those types of leads. For example, prospects in EMEA might just score lower because you may have started selling to EMEA customers only recently. This can cause problems if left as-is because your sales reps (or marketers) who focus on EMEA would only see Infer C and D-Leads, giving them limited insight with which to prioritize those leads. 

Two approaches to scoring leads – Fit vs. Behavior

This post originally appeared on the Salesforce Blog

While some companies aggressively pursue every lead that is created, others are leveraging lead scoring to work smarter. If you can programmatically spot your good leads, chances are you’ll be able to increase win rates and conversion.

So what makes a good lead?

A good lead has two key ingredients

Fit Score (also referred to as an explicit score)

Intended to capture how much an incoming prospect resembles a likely buyer. For example you might look at the lead’s company size, geographic location, industry, and job title, to determine if it is a fit. A quick look at its employer’s website might give you other clues regarding their business model or online presence.

Behavioral Score (also referred to as an implicit score)

Intended to capture how much a prospect is engaged with your company. This could include the lead’s website visits, form completes, email clicks, and maybe even application usage data.

Examples of External Signals Infer Tracks

Sales and marketing folks have been talking about lead scoring for years, so we often get asked “what’s different” about Infer’s way of doing things. One of the reasons Infer’s models perform so much better than traditional lead scoring is that our system pulls in several thousands of external signals, going well beyond what most organizations track in Salesforce.com or other CRM and marketing automation tools.

Signals

Broadly speaking, Infer gets these signals from three sources: crawling the web, purchasing data, and inferring signals from raw data sources. The last of these is the most subtle, and it’s our equivalent of Google’s secret sauce for web search.