Ad Tech: Before and After Online Behavioral Data

 In Academy, Behavioral Analysis, Consumer Behavior, Featured, Online Advertising, Popular, Trends

Ad tech has given rise to the data-driven marketer. This type of marketer not only knows all the marketing tactics aimed at converting consumers into buyers, but also knows how to use data to understand the customer as well as the competitive landscape, model target audiences, and optimize digital campaigns.

Online Behavioral data is a game changer for data-driven marketers as it empowers them to operate with deep insights into what drives purchases and influences market share. Armed with this data, marketers do better at reaching, converting, upselling, retaining, and engaging customers.

Customer journey analysis extends further upstream and downstream

Before online behavioral data, marketers could narrowly look at how people arrived at their websites or came to download their software. They could see the channels driving traffic, the top referring sites, and perhaps the keywords clicked. However, they couldn’t look further than one step prior to a user visiting their site.

With online behavioral data, marketers can analyze the activity patterns of customers hundreds of steps backward or forward in time. And, they can do this whether the customer buys from their own website or buys off-site from a reseller or marketplace. Marketers also gain access to all the search queries customers perform before they visited or converted. With this, marketers can compare their customer’s search, browsing, and shopping activity to that of a competitor or the general population. This gives marketers a robust picture of their customer and makes it easier for them to run successful acquisition and retention marketing programs.

Competitive analysis based on transactional information

Marketers tend to extract competitive insights from SEO and SEM tools, or by commissioning competitive insights from research firms. The tools reveal things like competitive keywords and rankings over time, while the commissioned research provides a one-time snapshot of the competitive landscape. However, marketers didn’t have a stream of competitive data, inclusive of market share data, flowing their way.

Jumpshot’s online behavioral data gives marketers unprecedented access to 2nd-party data, including transactional information, which marketers can use to reveal competitive insights on an ongoing basis. Marketers can analyze each competitor’s conversion funnel, click-by-click, to pinpoint where they lead and where they lag behind the competition, as well as track their optimization efforts and fluctuations in market share.

Marketers can also learn how people are getting to a competitor’s site, which keywords are bringing people there, which products, pages, and website sections generate the most interest, what visitors purchase on-site, where they go when they leave the site, how many of them return to the site, and more.

Look-alike audiences based on 2nd-party data

Marketers typically advertise to look-alike audiences that are modeled on general browsing activity captured by cookies. The advantage of this method is that general browsing data is easy to get, and therefore provides advertisers scale. The disadvantage, though, is that the resulting audience may not be as targeted as it could be, as this method lacks information about customer activity that takes place further down the funnel than on a site’s exterior pages, which contain the cookies.   

Marketers can scale customer acquisition by targeting audiences based on 2nd-party data with Jumpshot’s Audiences. For example, an audience can be defined using past conversion behavior, path-to-purchase activity, and even shopping cart abandonments. Since online behavioral data contains search, browsing, and shopping activity, all these aspects of online activity can be used to create look-alike audiences, and Jumpshot has already created hundreds of them across multiple verticals, including travel, e-commerce, and automotive.

Campaign optimization measuring all forms of incremental lift across the web

Many marketers measure the performance of their digital campaigns by the incremental activity on their websites resulting from ad campaigns. For digital marketers whose only online conversions happen on their own websites, this works fine. However, this doesn’t work for marketers with off-site conversions taking place on reseller or marketplace sites, like CPG marketers. This form of measurement also isn’t suited for brand marketers, as it does not cover relevant aspects to them, such as brand awareness.

With Jumpshot, marketers can measure incremental activity influenced by an online ad campaign across the web. Jumpshot’s Campaign Optimization solution enables marketers to measure incremental lifts in sales and conversions that take place on-site and off-site, as well as measure other types of incremental activities such as increased brand searches or online reviews. Marketers can essentially drill down into any online activity they want and analyze the impact their online campaigns have had on it.

Advertising technology is better with online behavioral data

Jumpshot’s online behavioral data helps marketers get more out of the advertising technology platforms they use. It expands customer journey analysis further upstream and downstream and facilitates competitive analysis that’s more powerful than what is currently available. It makes it possible to optimize targeting and scale customer acquisition with audiences based on 2nd-party conversion data, not just browsing activity. And, it allows marketers to understand the true value of their paid programs by attributing incremental lift of on-site or off-site activity to online campaigns.

Bottom line: Marketers who incorporate behavioral data into their processes will soon discover that it’s an indispensable data source. By working with Jumpshot in this endeavor, marketers will find that clickstream data is easier to use than they may realize. Jumpshot’s 100-million consumer panel pulls desktop, mobile-web, mobile location data and offline transactions into a 360 degree view of the customer that goes all the way back to January 2014.

Recommended Posts
Jumpshot Credit Card Path-to-Purchase dataJumpshot clickstream data