For years, marketers are used to evaluating online marketing efforts with last click attribution, meaning they only care about the click that directly results in a conversion without thinking about other possible consumer “touchpoints” before or even after the conversion.
Cross channel reporting/attribution has become a hot topic among digital marketers as more advanced and comprehensive tracking technologies made available. In today’s market, cross channel measurement becomes more and more important for an organization to build a seamless integrated marketing mix that maximizes the impact on consumers.
What is Cross-channel analytics?
Cross-channel analytics is using data from various marketing programs across different channels to understand the impact of each channel as well as the interaction between them. For example, the Cross Channel report from Marin provides insight into your online marketing funnel by summarizing the click path visitors have taken prior to converting. The report can show data from clicks through all online marketing channels, including Paid Search, Organic Search, Display Ads, Comparison Shopping and more. Cross-channel data allows the marketer to measure the interactions among various channels, for example, a retail advertiser will be able to use the cross-channel data to determine the relationship of Paid search campaigns and Facebook ads on consumer’s purchasing behavior.
The Benefits of Cross-channel analytics
Cross-channel analytics can add value to your current marketing mix in various ways:
- Latency/Time impact. By analyzing user level data across all online advertising channels, marketer will be able to identify the “true” latency pattern for visitors that converted through particular online channels.
- Funnel Analysis and Attribution. True multi-channel attribution method splits the credit for a conversion and gives a portion of that credit to each and every advertising channel the user had interaction with. Marketer will be able to identify which channels combination is driving the highest return as well as understand the sequence of clicks that drove a conversion. All these insights will drive the optimization of your attribution model in order to optimize budget allocation to maximize the return.
- Measure referring URL/Domain/sites By analyzing referring URL data across online advertising channels, marketers will be able to identify high/low ROI placement and adjust campaign strategy accordingly (add negative placements or increase bids/spend on high ROI placement)
- Leverage user demographic/behavioral information Cross-channel analytics can capture a lot of useful information about the user, e.g. geographical information, mobile vs. desktop visits and etc. Cross channel data is compiled through tracking user behavior data (how individual user interact with you site), there’s also opportunities to optimize based on user level behavior data = behavioral targeting/retargeting
Challenges and thoughts on the Future
One of the very first challenges that a lot of advertisers face when it comes to how to leverage the cross-channel data is defining goals. Why do you need the cross-channel data? What are you trying to achieve? The goal has to be clearly defined so that you understand what to look for among the enormous amount of data and benchmark your success.
Analytics is about extracting actionable/measurable insights from data. The ultimate goal of analysis is to determine measurable next steps to achieve overall business goals. The typical data analysis flow looks like this – data, reporting, analysis and actions. A lot of analytics providers are still in the data/reporting stage, there’s a lot to explore in terms of converting the raw data into a meaningful format for advertisers to easily extract actionable recommendation.
In my opinion, more data should lead to eventually eliminate the inefficiency of advertisement, which creates a win-win-win situation for advertisers, users as well as publishers. As the cross-channel analytics capabilities continue to improve, marketer will be able to develop advanced statistic models that can predict traffic and revenue impact associated with changes in the marketing channel mix, which can be used to make real-time optimizations.