Science Behind Cross-device Conversion Tracking

Cross – device behavior of connected consumers A marketing definition can be put up as – “Connected customers are the perpetually connected individuals to each other, to internet and to brands through the adoption of one or more internet-connected devices” Connected consumers by definition are - Consumers who are perpetually connected to each other, to internet and to brands through the adoption of one or more internet-connected devices. A significant behavior of these connected consumers is traversing through multiple devices in their path to purchase a product specifically called the omni-channel buyer’s journey. Here are some statistics - 70% of the connected customers are the personal owners of three or more than three devices  98% of these device owners go online at least once a day  85% are online more than once a day 65% smartphone users go online several times a day followed by laptop where 60% of laptop owners explore internet several times in a day Source: Whitepaper on The Science Behind Cross-Device Conversion Tracking

Multi-device purchase path Retail customer who undergoes a 4 step journey starting at identifying a product, followed by exploring more options, making the purchase decision and ending at buying the product This omni channel buyer’s journey can be portrayed most likely in the following manner – The connected consumers are choosing different devices in different phases for their research and further traversing on a different channel to make the decision to buy and some more devices to make the final purchase. Source: Whitepaper on The Science Behind Cross-Device Conversion Tracking

Is trackable cookies the solution? The marketer’s nightmare Is trackable cookies the solution? The answer is NO Mainly because of these 2 factors – In the WAP (or mobile browser) environment, cookies are reset every time the user closes the browser In-app cookies are not sharable in between apps or across devices This is where marketers face a challenge to track the actual phase of the buyer’s journey the consumer is in and what should be the right communication? Now, the instant solution to this problem could be trackable cookies but, the story is a bit different when we talk about cookies: In the WAP (or mobile browser) environment, cookies are reset every time the user closes the browser. So, your responsive site will not give you information on what the consumer did on various devices he accessed the site from. In-app cookies are not sharable in between apps or across devices, which makes it a localized information relevant to activities of the consumer on that device. So, an app-first approach will also be more of an app-only approach in this case.

Analytics – driven attribution Analytics-driven attribution can be defined as a practice of tracking and analyzing the marketing touch points of a multi-device purchase path and credit their contribution to conversion At this point in time we introduce a new term – “analytics-driven attribution” which can be defined as a practice of tracking and analyzing the marketing touch points of a multi-device purchase path and credit their contribution to conversion.

What is the role of analytics? The analytics-driven attribution will benefit brands in the following 5 ways – The analytics-driven attribution will benefit brands in the following ways – It helps in cross-channel performance evaluation It provide actionable insights to improve user experience It helps design the campaign communication strategy We are able to effectively plan our marketing budgets Finally,  it helps in sales pipeline optimization Source: Whitepaper on The Science Behind Cross-Device Conversion Tracking

Analytics – driven attribution in multi-device path The multi-device purchase path acts as a blueprint for us to perform analysis on a set of parameters that define the time frame of every stage of the purchase path and the consumer behavior at every stage The multi-device purchase path acts as a blueprint for us to perform analysis on a set of parameters that define the time frame of every stage of the purchase path and the consumer behavior at every stage. So here are key metrics that can be mapped to each stage – CTR, Unique page views and leads in the Identify stage Impressions, page-views and time-spent in Exploration stage. Clicks and the time-spent by consumers in the Decision making stage. Final conversions in the purchase stage of the multi-device buyer’s journey. A very important parameter in the set is the device used by the connected customer to touch base with the brand. Source: Whitepaper on The Science Behind Cross-Device Conversion Tracking

How to analyze the multi-device purchase path? There are precisely 2 approaches in the industry today -  In order, to map this key parameter, there are precisely 2 approaches in the industry today with their own pros and cons which can be mapped to the business prospective and applied to crack the ultimate challenge, let’s talk about them – In predictive modeling, there are huge data samples to work upon while in login-based analytics it will be small and concentrated sample of data. In predictive modeling, we derive more sensible insights that are an average of the behavior of large data sets, while login based analytics will give you the most accurate insights. Predictive modeling doesn’t hold the risk of harming the user privacy as compared to login-based analytics. In predictive modeling, there is a huge dependency on the marketing logics while in login-based analytics the dependency is more on the data provided by big players like Facebook and Google.   Source: Whitepaper on The Science Behind Cross-Device Conversion Tracking

Read more on Cross-Device Analytics Here’s a whitepaper that talks in detail about each topic discussed in the presentation including: The cross-device behavior of the connected consumers Why cross-device conversion tracking is the need of the hour? Importance of analytics in multi-channel purchase path Solutions suggested by Facebook and Google 2 key analytics approaches to analyzing the multi-channel purchase path

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