To alleviate the boredom of a long flight, I recently found myself flipping through the pages of the inflight shopping catalog, where I came across an intriguing item with a QR code next to it. I scanned the code with my tablet, and (thanks to the in-flight WiFi) was taken to a mobile microsite that featured an interactive product demonstration. After watching the demo, I visited the manufacturer’s website to check out more details about the product. Then, shortly after landing, I received an email from the retailer, thanking me for inquiring about the product and offering me a 10% off digital coupon, which I later used to purchase the item at my local retail store. To start using the product, I was asked to go to the manufacturer’s website to register the item and activate it, which I happily completed within a day of purchase.

This experience got me thinking about a common misconception among brand marketers that conversions that take place offline are inherently “untrackable.” It’s true that marketers have historically been challenged in their attempts to correlate online activity with offline purchases. However, “digital onboarding” techniques enable marketers to connect offline transaction data with different marketing tactics, providing a more accurate and holistic picture of the customer journey and a far better understanding of the influence each marketing touchpoint has on an offline conversion.

What Digital Onboarding Does

To understand the basic premise of digital onboarding, let’s return to the inflight shopping example above. Though I ultimately purchased the item in-store (where I’m a regular customer), I was influenced by multiple online and offline touchpoints along my path to conversion. So how can retailers measure the true impact of these touchpoints on in-store sales?

Without using any personally identifiable information (PII), digital onboarding would enable this retailer to connect my in-store purchase with all the previous marketing touches I was exposed to, including the inflight catalog I was reading (including which issue and potentially even the airline and class of travel), the microsite I visited on my tablet, the email I read on my work computer, the smartphone I used to show the digital coupon to the store clerk, and my home computer, which I used to register and configure the product for activation. Moreover, the retailer would also be able to link all the marketing touchpoints I was exposed to before my trip — potentially adding up to dozens or even hundreds of online and offline interactions.

Since I am an opt-in customer, the retailer could also use digital onboarding to connect my customer ID — including my non-PII based demographic and geographic information — to all the direct mail, catalogs and emails I’d received over the years and integrate them into my journey to an offline purchase. By digitally onboarding my information, along with all of their other offline customers, the retailer gets a goldmine of data that can be used to accurately measure the impact of each marketing channel and tactic, and optimize its media investments for the best returns.

Though I happened to be an existing customer of this particular retail store, a similar “post-conversion” onboarding process can be applied to newly converting customers as well, even when no digital activation is required. For example, after making an in-store purchase, a customer could be offered a discount in return for going online and registering a product. Such an approach would enable the retailer to see the entire past history of media consumption (both online and offline) and how those touchpoints actually influenced conversion activity—all in a privacy-conscious way.

Look-Alike Modeling

By leveraging digital onboarding to tie offline activity and purchase data with online marketing efforts, marketers gain a more complete picture of media effectiveness. Marketers can also benefit from the ability to target prospective customers in the right place, at the right time, and with the right message by combining digital onboarding with another technique called look-alike modeling.

Look-alike models are a sub-group of individuals that behave in a certain way, created by marrying transactional data that’s associated with customers, with media performance data and customer demographic information (which is collected through the transaction process, surveys, or via any number of third-party data providers).

Using look-alike modeling techniques, marketers can determine which segments have the highest lifetime value (LTV), which have the highest propensity to convert, and which online tactics produce those segments with the greatest efficiency and speed at each stage of the conversion funnel. Which channel, publisher and offer serves as the first touch (“introducer”) for the customer segment with the highest LTV? Which combination of tactics serves as the last touch for the segment with the highest propensity to be repeat buyers? With answers to questions like these, marketers can not only determine which tactics produce the highest results by customer segment, but can also prescribe at which funnel stage and in which sequence those tactics should be executed.

A Note of Caution

The last thing you want to do is break privacy laws. Therefore, it’s critical to ensure that the integration of customer and advertising data does not exchange or use any PII data, such as name, home address, IP address or email address. If you use a marketing partner for digital onboarding and/or look-alike modeling, be sure to check their privacy credentials.

The Best of Both Worlds

Digital onboarding and look-alike modeling are providing marketers with great insight into the customer journey — from initial awareness through to the final sale. By utilizing both practices in combination, marketers are able to draw a more complete and accurate picture of the tactics that produce the most valuable audience segments and the most return on their marketing investments.

(This content was originally posted at MediaPost.)

About Anto Chittilappilly

Anto Chittilappilly is the co-founder, president and CTO of Visual IQ. He is responsible for the company’s overall engineering and technology strategy and is the visionary behind the company's IQ Intelligence Suite.  Anto is a recognized expert in marketing mix modeling, marketing attribution and cross channel marketing analytics. He has spoken at industry events such as ad:tech and the DMA Annual Conference; authors a monthly column for Online Metrics Insider, and has written articles for iMedia Connection, Target Marketing and BtoB Magazine. He is a member of the American Statistical Association and sits on the Research Council of the IAB.



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