Multi-channel Analytics-why every Web Analyst should embrace it?

As the name suggests, multi-channel analytics is a business process in which data is aggregated from multiple channels and analyzed together, in order to derive effective business decisions. Multiple channels imply all the channels that a user would have traversed before reaching the point of making a conversion. A conversion is when a user takes a desirable action on your site. For an e-commerce store conversion would be a purchase.

Some of the common channels that drive traffic are Online marketing (social media, paid search, email marketing, banner ads) and offline marketing (newspaper, TV, banners). Online marketing and offline marketing are not two distinct realms any more. Usually a mix of online and offline campaigns drive actions that finally leads to conversion on a site. A new term has been coined by David Hughes to represent this heterogeneous marketing: nonline marketing.  Not being able to track the nonline marketing channels, would lead to a huge miss out on the actual sources for the profit or loss.

Why is multi-channel analytics crucial?

Campaigns for our products are not always configured on a single channel, as with a single click from an ad. The customer might have hopped through different channels, to arrive at the destination. This video on Google’s multi-channel funnel tool clearly depicts that.

Offline campaign impacting online conversion:

Consider offline sources that drive traffic to your site. For example, a TV or newspaper ad for your product, which might have spurred some people to search for it online and make a purchase. If there is no way to associate this, you might assume the visitor to have come from a basic search or by directly typing in the URL (if a link to your site was provided). This keeps us from judging the actual value of the ad, or if it is working at all. However, if we have a way to track the conversion, then we can wisely invest our funds.

 In the above example, if we added a URL specific to the ad campaign (www.xyz.com/tv and www.xyz.com/news), then we can measure the traffic coming from the TV and the newspaper individually and correctly.  Now, we know how much traffic we are receiving from the ads in newspaper and TV, and then we can decide where the next ads should go to, based on which was more profitable.

Online campaign impacting offline conversion:

Yet another case is online campaign affecting in-store sales. Example, consider a promotional mail from Hollister, we follow the link and browse through the site, but decide to make an in-store purchase. How can they measure the success of the campaign, if they cannot account for the total number of people who made purchases, online as well as offline, after viewing the promotion? Online purchases can be tracked using the link provided in the mail. But offline purchase?

In this case the simplest thing to do would be to ask the customers, to whom the promotion was sent, if they were there because of the mail they received. This is similar to the onexit surveys that we might have taken many a times. Have you filled up a survey online or offline where a question was asked on how we reached the place or how we heard about them? This would be a similar tactic.

Hence, multi-channel analytics calls for a single dashboard that can track the online and offline analytics which enables a holistic impact measurement. Without it we only get a partial view of the status of the campaigns or our activities and hence we are not taking the right decision.   

Challenges of implementing multi-channel analytics:

The challenge here is finding a common key that can be used to link the online and offline data. In other words, the key aids in identifying the person when he is online as well as offline. The solutions suggested for the earlier examples, unfortunately, do not hold for every scenario or situation. Consider all the folks who browse online for products and their reviews and make the purchase in store.

 I am a person who does massive research, for product prices available at different stores and their reviews. And later pin down on a product from a particular store. But finally when I make this purchase, my research online on this store’s site is in no way associated. My browsing or research pattern or customer behavior cannot be finally associated with success or failure, as my final offline action cannot be associated with the online data. Ex: My browsing behavior online can be associated with a unique cookie, but there is no way I am going to produce that at a store checkout.

However, this is not a no solution situation, though it is hard to implement. Different firms have to identify a primary key, which they could use to link their online and offline data to get a holistic view. Nonline marketing is prevalent today, and hence multi-channel analytics is important for any organization that wishes to stay ahead. I would be going over the 8 simplest ways to track multi-channel marketing in the next post.