Effectively Drive Revenue With Data-based Programmatic Ad Targeting

What’s the best way to drive conversions and save yourself from the frustration of a promising lead turning cold? The answer is simple and it’s data-driven programmatic ad targeting.

The 2020 Attitudes to Programmatic Advertising survey reported a notable spur in data-driven programmatic investments with a rate of 80% revolving around programmatic ad targeting efficiencies. Data-driven programmatic ad targeting optimizes ad campaigns and reduces budget wastage. With the help of programmatic ad targeting, brands can eliminate irrelevant segments and focus only on targets based on predefined traits like demographics, age, gender, geolocations, browsing history, and online activities. 

What Are the Best Programmatic Ad Targeting Methods For You?

In addition, based on different campaign requirements and business goals many B2B brands have in place, oftentimes, they can choose from various data-driven programmatic ad targeting strategies where each method has their own specific roles to play.

  • For starters, behavioral targeting dives deep into the personal behavior of a target audience such as website communications, product interests, preferences, and lifestyle choices
  • Contextual targeting focuses on looking into content on a website and understanding  which ads will appear and where. If brands display ads that are relevant to their website, viewers are more likely to click them
  • Then there’s audience targeting, which segments user groups based on geographical locations, genders, age groups, and household incomes
  • Whereas, geotargeting uses location tracking to target audiences based on specific regions, zip codes, and IP addresses 
  • Next is cross-device targeting, which helps marketers reach users across different devices. If a user watches a particular ad on their phone, the individual will likely see a different ad from the same brand on their laptop. In turn, this engages an individual across all connected devices
  • Last but not least is retargeting, which brings dead-end or stalled-out leads to life by displaying relevant ads across various websites, sending relevant emails, and delivering personalized messaging. Retargeting techniques help re-engage leads, drive buyer retention, and increase brand loyalty

How Intent Data Boosts Ad Targeting and Revenue Growth

With the right use of B2B intent data, you can take the following actions:

  1. Identify new traffic and opportunities: Data offers a direction and insights regarding  what  users are looking for, who they are, and where to find them

    Data-as-a-service (DaaS) harvests unique and hard-to-find data (HTFD) to deliver qualified leads. They are highly customized and targeted marketing assets that enrich data-driven programmatic targeting. DaaS is known to provide accurate knowledge to marketers so they can achieve sustainable revenue growth.

    Furthermore, the prospecting and lead scoring process generates transactional data, which pumps cross-selling and upselling opportunities. Global big-data-as-a-service (BDaaS) is projected to reach $46.82 billion by 2025, which hints at the importance of data within this competitive B2B landscape.
  1. Drive buyer retention and engagement: In recent months, the Covid-19 pandemic has escalated the stakes within the B2B landscape in many ways, and it has become more expensive to find a new buyer rather than retaining existing ones. Reducing the churn rate and maintaining buyer satisfaction is of utmost priority within volatile economic conditions. Buyer intent insights are helpful in identifying the actions of an existing buyer before they exit.

    Once you know a buyer’s  actions, you can roll out your retargeting and communication strategies to guide their purchase decisions. The next step is to prioritize buyer intent analysis, which provides ideas of what a business might be lacking so that the organization can develop essential features to stay ahead of their competition.
  1. Reduce additional costs: Data responsive creative techniques use machine-learning to determine which ad types will drive the maximum conversions for specific audience segments. Then there’s intent-driven audience segmentation, which reduces additional costs since brands only serve ads to the specific segments that bear a chance of conversions.

    In addition, data-driven programmatic ad targeting thrives on delivering creative and personalized content to a targeted audience. Whereas, historical and current data insights can supercharge marketing analytics such as descriptive and predictive analytics, and as a result, this can give brands a better understanding of their campaign’s performance.

What’s the Next Step?

Overall, data signals garner audience segmentation, lead generation, programmatic ad targeting, and even accelerate revenue. Marketers might have the best messaging included in their content, but if they  can’t convert, they’re essentially waving revenue goodbye. What needs to happen is for a  potential buyer to click on a brand’s  ad. But first, it’s essential to have a strong landing page with ads that tantalize and draw potential buyers in. You may wonder where the special one is, who will click on your ad, and in order to find out, it’s ideal to focus on data to get the answers you’re looking for. Get started by checking out this helpful data-driven article!

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