Forrester Research defines digital intelligence as, “The capture, management and analysis of customer data to deliver a holistic view of the digital customer experience that drives the measurement, optimization and execution of digital customer interactions.” The key word here is customer. Customer data answers the “who”—who is doing what on my site—down to the individual customer level. Answering the “who” then leads to the answering the “why”—why is customer X exhibiting behavior Y on my web properties?
But the fact is that most brands are still only answering the “what,”if answering anything at all. The “what” mainly looks at channel and not customer level metrics—what are my page view counts, what are my times on page/site, and what my total visit numbers are? As you can see, those questions have nothing to do with the individual customer.
So what is the big deal if we are only asking channel level questions to answer the “what”? The big deal is that it’s hard to deliver personalized digital interactions that enhance the customer experience if you don’t collect the correct data. It’s kind of like a fighter jet launching a missile at a target without plotting the coordinates first—highly unlikely it will hit the mark. The same thing occurs with digital interactions—if you don’t know how an individual customer has behaved across your brand’s digital properties, it’s very hard to deliver a contextualized offer to them across digital channels that will hit the mark.
This applies to all industries—from retailers to hospitality and service providers to airlines and telecom companies. Isn’t it about time that a cable provider could use network behavior combined with web navigation behavior on branded sites to improve the individual customer experience? Isn’t it time that hotels use the information guests give them while browsing their sites looking for a vacation getaway to contextualize the room offer and amenities they provide? Isn’t it time that airlines provide an occasional upgrade to someone that has attempted to change their seat online but has ridden in the middle seat the past five legs with the airline? I think it is.
Ok, great. So now you’re thinking “I want to answer the “who” and “why” but how do I get there?” Let me provide three steps to starting down the digital intelligence path—namely digital data collection, normalization and end use.
- Digital Data Collection. This involves the collection of digital data across a brand’s web properties—websites, social platforms and forums, or mobile applications. Data must be collected at a very granular level – and can even be collected down to the individual keystroke. This data collection can take place for both anonymous and authenticated visits. After authenticating, a customer’s session data can then be stitched together over time and across browsers, devices, and channels. It can be integrated with offline or traditional data that exists for a customer to augment or round out a customer profile. Because this data is not aggregated web data or collected via tag management technology it is much more granular, and thus much more valuable for further use.
- Normalization. The next step after data collection is data preparation, or what is referred to as normalization. This is the process of applying business rules and insights to this raw digital data to make sense of what has been collected. For instance, after normalization, a brand can see what an individual did across properties and if they achieved what are called “online goals” – which could be things like the downloading of content, the viewing of a video, or the completion of a web application form. This will inform the brand what subsequent actions make sense to take with the customer from a communication and interaction perspective.
- End Use. This is my favorite section, because this is where all the hard works pays off – where the dream comes to life! Digital data can be used as intelligence not only for marketing, but by other departments within the organization as well.
Let’s highlight a few use cases:
- Augmentation. Digital data obviously builds out not only analytics and reporting, but helps departments augment existing data sources to understand efficiencies and gaps as well as performance attribution. And for brands whose primary channel is the web—think online retailers, travel, and financial services—this level of digital data can literally mean the difference between life and death of the brand.
- Remarketing. Marketers can use digital data to perform retargeting. Classic example is when a customer does or does not achieve on online goal—a follow up message can be sent to attempt to complete the desired behavior or action.
- Dynamic Content Personalization. Based on behavior in-session or even over a series of web sessions, content and offers can be personalized at the individual consumer level. This can’t be done if customer level data isn’t collected. Imagine the joy of offers and messages coming to you on digital properties that are contextual and relevant versus you having to click around for them.
- Fraudulent Activity. This is a use case not commonly considered – but if a brand is collecting information at the individual customer level and down to the keystroke – they can monitor behaviors on digital properties. This may include the misspelling of a customer’s last name or attempts to match zip code to credit card numbers – a tell-tale sign of fraudulent activity and the copying of information from another data source in order to attempt to defraud a brand.