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Best practices for year-end retail ad spend

This week’s guest blog post is contributed by Sara Viard, Client Partner, Retail, Oracle Data Cloud.

There’s a lot of pressure on Q4 end-of-holiday campaigns—especially for retail marketers.

According to eMarketer, Q4 2016 spending was more than $650 billion, and ad spending grew +64 percent versus the same period in 2016 (note the difference from the 40-50 percent growth rates in other quarters).

With so much at stake, how can retail marketers determine the most effective way to allocate their limited digital advertising dollars at the end of the all-important holiday season?



Consider measurement and audience type now so you’re ahead of the game heading into the new year.

1. Measurement

Don’t just hope for the best—for results that will positively impact your bottom line, optimize your media budget by applying a measurement strategy to your holiday campaigns. 

This strategy will garner success and provide additional insight into future campaigns. Measurement can be applied to all campaign windows, from a 24-hour blitz to a pre-Christmas holiday push

What do you need to do?

It all starts with creating a realistic benchmark. Then determine the campaign window and budget. Utilizing a measurement strategy also allows marketers to assess media partner performance, measure revenue generated from in-store/offline sales, and provide the response rate by channel.

How does this work?

The actual measurement stems from tracking the impressions served, during a campaign window, to the in-store or offline conversions made by served customers in that timeframe.

By matching impressions to sales, the marketer can evaluate campaign success through a variety of information, including:

  • Total sales and revenue generated

  • Campaign response rate

  • Acquisition cost for a new customer

  • Overall return on ad spend (ROAS)

These insights will allow the marketer to effectively analyze campaign performance, scale accordingly, and determine if the pre-set benchmarks were met. Furthermore, it provides great insights going into the new year.   
 

2. Audience type  

An integral part of all campaign development is determining and targeting the correct audience, then utilizing the appropriate creative to engage that audience. Why is that important?

Audience creation can make or break a campaign’s success. Marketers have become more sophisticated at building successful, semi-custom audiences made up of a variety of demographics or syndicated audiences combined to reach the appropriate group.

However, there is a better, more effective way to build your audience.

Our Oracle Data Cloud team can utilize the company’s CRM file to build a highly customized spend-alike audience.

This custom audience is built by analyzing the purchase behavior of the existing customers, then developing a prospect audience that offers similar characteristics and purchase behavior to your existing customer base. And this spend-alike audience can outperform a semi-custom audience 3.5x to 1*.

Depending on the campaign objectives, prospecting or retention, the option also exists to utilize the CRM file as a seed for building a custom spend-alike audience, but then the CRM file is suppressed. 

This possibility creates a truly incremental prospect campaign that targets only new customers and provides additional opportunity from a revenue generation and branding perspective.

Bottom line for retailers at year end

To get ahead of the curve, don’t wait until 2018 to implement these strategies.

During this year’s holiday season, maximize your opportunities with campaign measurement or a custom audience to help drive campaign performance.

If you have questions about this process, we’re here to help! Contact The Data Hotline today with your retail Q4 questions. (What's The Data Hotline?)

About Sara Viard

Sara is a Client Partner for Oracle Data Cloud within the Specialty Retail vertical. She enjoys working with retail partners to develop successful offline and online campaign strategies. 

Image: Shutterstock

*Sourced data from Oracle Data Cloud July 2017 retail case study

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