PenPath Blog

PenPath's Insights: Marketing Analytics, Business Intelligence, & Data Visualization
lead scoring
Apr 06 2020

Improve Lead Scoring and Customer Journey Mapping With Blended Sales & Web Data

Imagine what would happen if sales pipeline data (Salesforce) and website data (Google Analytics) could work together. 

You’d get a clear image of the full end-to-end process, from the moment a person gets to your website to the moment that person decides to pay for your products or services, both the first time and as a repeat customer.

From that full customer journey map, you could then calculate lead scoring for customers and discover who and where your most valuable customers are. 

Today we’re going to be talking about how to do that. In our last article, we showed where to find website data and sales pipeline data. This time, we’ll show you exactly what you get out of blending that data. 


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blending sales and web data part 1
Feb 26 2020

Collecting Web + Sales Data For B2B Marketing Insights!

Sales and Website data are two of the most important pieces in the Digital Marketing puzzle. Although most B2B marketers have this data siloed, we’re going to see how we can blend it for a fuller picture of your marketing efforts!

Let’s first take a look at common definitions in Sales/Website Data and where you can find them in your marketing platforms.

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Feb 16 2020

CLV:CAC – An Analytics Refresher For Data-Driven Marketers

At PenPath, we want marketers to understand important metric definitions, where to find common metrics, as well as how they can be combined to yield powerful insights. 

In our last article, we talked about where to find Ad Cost & Website Session Data on platforms such as Paid Search and Youtube Ads. Now, we’re going to see how we can bring it together on our dashboard with unique calculated metrics. 

First though, let’s take a look at the calculated metrics we’ve done and how far we’ve come in our analytics journey. 

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Jan 27 2020

Combining Return On Ad Spend (ROAS) In Digital Marketing

In the “Measuring Metrics That Matter” series, we’ve learned where to find important metrics in marketing platforms. 

We also learned that it’s possible to calculate new metrics and visualizations, such as CAC (Customer Acquisition Cost) from these previous metrics.

In this article, we’re going to be exploring how we can visualize another important formula – Return On Ad Spend – which is possible across all platforms through holistic data. Take a look at our last article to see which metrics make up this calculated metric. 

Here are the topics to cover today:

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spend metric
Dec 15 2019

Learn How To Measure The “Spend” Marketing Metric – The One The Boss Cares About

Ah, the spend metric, or cost metric in digital marketing. One of the biggest metrics.

The bane of every digital marketer, yet also the greatest potential blessing. 

Today, we’ll be taking a look at what the exact meaning of this metric is, where you can find it in all your social platforms, what it would look like aggregated and why we would do that. 

Ultimately, this will give you a broader understanding of marketing metrics and take your analysis to the next level.

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marketing data blending
Dec 03 2019

Data Blending In Digital Marketing: What You Need To Know

As a data-driven digital marketer, you spend your time on making campaigns and ultimately getting leads for the company. But you’re also interested in knowing how digital marketing analytics can help what you do each day. 

Today we’re going to be talking about how data blending in marketing can enhance results. We’re going to show you examples of exactly what we mean and how those results can directly impact your business. 

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marketing data visualization
Nov 19 2019

Data Visualization Championships: Tableau Vs. Current Marketing Analysis Software

Welcome to the Data Visualization Championships!

Let’s take a look at some of the most commonly used ones in marketing. Data Visualization tools are used to see data in a meaningful way that makes it easier for robust decision-making. We’ll discuss their pros and cons, and judge them from a data visualization and analysis standpoint.

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