Google Case Studies Display leads increase 10% while cost per lead remains flat thanks to Data-Driven Attribution

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	Display leads increase 10% while cost per lead remains flat thanks to Data-Driven Attribution Google

Display leads increase 10% while cost per lead remains flat thanks to Data-Driven Attribution

Google

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Telecommunications
Applicable Functions	Sales & Marketing
Use Cases	Demand Planning & Forecasting
Services	Data Science Services
Challenge	A large telecommunications firm that uses digital advertising to increase brand awareness and drive sales sought to understand how display advertising, in combination with other channels, was helping to drive leads among small and medium-sized business customers. A heavy internet advertiser, the firm turned to Google Analytics Premium and MaassMedia to leverage advanced Data-Driven Attribution modeling. In taking this approach, it aimed to measure the impact of display touch points on lead generation and to make better decisions around budget allocation and optimization. The marketing division wanted to expand its reach into new customer bases. Display offered a significant new source of inventory, but it had traditionally been difficult to measure its impact on lead generation. Purely click-based metrics suggested that display was not providing enough return on ad spend. At the same time, view-through metrics did not take into account how display worked with other channels, such as paid search, affiliates and email. The team wanted an approach that would properly credit display touch points throughout the customer journey. Read More
About Customer	The customer is a large telecommunications company that uses digital advertising to increase brand awareness and drive sales. The company's B2B marketing division is responsible for driving leads among small and medium-sized business customers. The company is a heavy internet advertiser and uses various marketing channels including paid search, email, and display advertising. The company wanted to expand its reach into new customer bases and saw display advertising as a significant new source of inventory. However, it had traditionally been difficult for the company to measure the impact of display advertising on lead generation. Read More
Solution	Once the data was all in one place, the team worked with Google to leverage a custom Data-Driven Attribution model. The model, built and refreshed on an ongoing basis, calculated the impact of each touch point on the probability of conversion. The model distinguishes how the presence of display impressions and other touch points in the path impact the likelihood of a customer filling out a lead form expressing interest in services. With this new basis for attribution, revised costper-lead (CPL) metrics were calculated for every aspect of the display campaigns. The marketing team compared the CPLs based on existing last-clickthrough models to the new attributed CPL numbers, and uncovered specific networks, placements, and creatives that showed dramatic differences in valuation. In some cases, certain placements were credited with 58% more leads than under the previous last-click model. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Telecommunications

Applicable Functions

Sales & Marketing

Use Cases

Demand Planning & Forecasting

Services

Data Science Services

Challenge

A large telecommunications firm that uses digital advertising to increase brand awareness and drive sales sought to understand how display advertising, in combination with other channels, was helping to drive leads among small and medium-sized business customers. A heavy internet advertiser, the firm turned to Google Analytics Premium and MaassMedia to leverage advanced Data-Driven Attribution modeling. In taking this approach, it aimed to measure the impact of display touch points on lead generation and to make better decisions around budget allocation and optimization. The marketing division wanted to expand its reach into new customer bases. Display offered a significant new source of inventory, but it had traditionally been difficult to measure its impact on lead generation. Purely click-based metrics suggested that display was not providing enough return on ad spend. At the same time, view-through metrics did not take into account how display worked with other channels, such as paid search, affiliates and email. The team wanted an approach that would properly credit display touch points throughout the customer journey.

About Customer

The customer is a large telecommunications company that uses digital advertising to increase brand awareness and drive sales. The company's B2B marketing division is responsible for driving leads among small and medium-sized business customers. The company is a heavy internet advertiser and uses various marketing channels including paid search, email, and display advertising. The company wanted to expand its reach into new customer bases and saw display advertising as a significant new source of inventory. However, it had traditionally been difficult for the company to measure the impact of display advertising on lead generation.

Solution

Once the data was all in one place, the team worked with Google to leverage a custom Data-Driven Attribution model. The model, built and refreshed on an ongoing basis, calculated the impact of each touch point on the probability of conversion. The model distinguishes how the presence of display impressions and other touch points in the path impact the likelihood of a customer filling out a lead form expressing interest in services. With this new basis for attribution, revised costper-lead (CPL) metrics were calculated for every aspect of the display campaigns. The marketing team compared the CPLs based on existing last-clickthrough models to the new attributed CPL numbers, and uncovered specific networks, placements, and creatives that showed dramatic differences in valuation. In some cases, certain placements were credited with 58% more leads than under the previous last-click model.

Impact #1	After implementing the changes, the team saw leads from display campaigns increase 10% above projections, while the cost per lead remained flat.
Impact #2	Optimized display placements saw a doubling of conversion rates.
Impact #3	The results of the model provided a framework for ongoing optimization, giving the team confidence that it could make truly data-driven decisions about display advertising.

Impact #1

After implementing the changes, the team saw leads from display campaigns increase 10% above projections, while the cost per lead remained flat.

Impact #2

Optimized display placements saw a doubling of conversion rates.

Impact #3

The results of the model provided a framework for ongoing optimization, giving the team confidence that it could make truly data-driven decisions about display advertising.

Benefit #1	Leads from display campaigns increased 10% above projections.
Benefit #2	Cost per lead remained flat despite the increase in leads.
Benefit #3	Optimized display placements saw a doubling of conversion rates.

Benefit #1

Leads from display campaigns increased 10% above projections.

Benefit #2

Cost per lead remained flat despite the increase in leads.

Benefit #3

Optimized display placements saw a doubling of conversion rates.

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Overview

Display leads increase 10% while cost per lead remains flat thanks to Data-Driven Attribution

Operational Impact

Quantitative Benefit