Let's talk about touch points. In marketing we have this understanding that customers and potential customers interact with the brand and it's assets in a lot of different ways. We create ads and content to go in different places all with the intent of inspiring interest. We imagine that these are cohesive and connected, but in reality that is not the case most of the time.
Now let's talk about analytics. The need for good analytics is critical for all businesses and marketing teams. In fact, analytics has been evolving rapidly over the past decade. The 2018 Gartner CMO study points to analytics as the #1 investment for marketing teams. So what is going on?
A lot of it has to do with the explosion of marketing technology that is centered on analytics capabilities which enable more complete personalization. This goes well beyond how most companies use analytics. For some context, let's quickly review the various "levels" of analytics.
While it is possible to collect data and build a report on just about anything, there does seem to be some stratification of reporting in the marketing world. Here is a simple overview of how I group the main layers of reporting for marketing:
Simple Response Metrics. These are the basic metrics you get from media sources. Things like "Impressions", "Clicks", "Click Through Rate", "Cost per Click". This is single source data.
Single Channel Performance Analytics. These are media metrics combined with site performance. This requires some data merging and extra tagging, but it is worth it. Here we can combine cost data with conversion data to get "Cost per Acquisition" metrics. This is usually limited to two data sources.
Cross Channel Performance Analytics. Cross channel analytics brings all the media data together and merges it with the site data to get a comprehensive view of media and site performance. Mixing things in this way makes it easier to optimize media mix because you have a way to directly compare performance and evaluate what is providing the best value. This is the type of reporting that most marketing managers use.
Account Based Analytics. Account based analytics look at the performance of an account over time. This aggregates many data sources to give a view of how productive the account is. The most valuable metrics here are related to "Lifetime Value". A CRM solution is required, i.e. Salesforce, and a lot of work is put into collecting data from websites, media, email, sales teams, call centers, etc. to give as full a picture as possible. This data is usually owned by the Sales team. However, the push toward more holistic CX is making this data more usable for marketing.
Customer Journey Analytics. Like account based analytics but more detailed because it is fully integrated at the individual level. It captures all interactions with touchpoints across the customer experience, across time. With the help of AI, it can identify the most effective paths to the best outcomes. With the proper integrations, the AI can even act on that data to offer real-time personalization (offers, messages, creative). It can also be rolled up to the account level.
If you think Journey Analytics sounds complicated, you're right! It involves layers of technology and capabilities that most marketing teams simply don't have. BUT, it's still important to understand because more and more investments are being poured into this technology and it is literally the future of marketing. This kind of marketing technology has been a big focus of venture capital for the last couple years. Even the Wall Street Journal has covered this.
In order to help you understand more about Customer Journey Analytics, I went looking for a good white paper on the subject. What I found was the Pointillist eBook on Journey Analytics. It's the best explanation I could find.
Clocking in at 58 pages, it is not a quick read, so I've summarized what I think are the four key takeaways for you:
Journey analytics is different than traditional analytics because it identifies people rather than groups. This is huge because it enables personalization in a much deeper way. A cohesive, personalized view of the customer experience is one of the holy grails of marketing and sales.
The Journey is the entire customer experience, not just marketing acquisition. The amount and complexity of integrations and touches across channels requires that this be more than just a marketing exercise. It is important that the entire organization is in alignment and decision making is de-siloed. The sales, IT, customer support and marketing need to cooperate in deeper ways than ever before if this is going to work.
Analytics can take action, not just enable decision making. By integrating marketing automation triggers across touch points, you can us AI to make personalized offers in real time. The introduction of AI as an active agent is a revolution for analytics. It redefines what analytics is. Whereas the promise of analytics was always actionable insights, now the definition becomes insights + initiative.
Automated messaging coordination across channels and data driven personalization positively impacts all aspects of the marketing-sales-support life cycle. Automated marketing becomes more pervasive. When we think of marketing automation, it is usually understood as triggered emails or drip campaigns. However, the integrations of a wide range of APIs, centralized data, organizational alignment, and real-time optimization across channels means that "marketing automation" is far more than just drip emails.
That's some pretty powerful stuff. The Pointillist eBook is a great way to get a solid overview of the features and potential of Customer Journey Analytics.
Was there anything missing from the eBook? I think so. The practical implications could have been explored more. For instance, although there may be "millions" of unique paths that customers can take, they will ultimately be distilled down to the X most effective paths. Then messaging will be developed specifically for those. The number of paths may well be limited to simply the amount of creative that can be supported. While the largest enterprises might be able to create massive amounts of creative to address a wide range of personalization, most organization will settle on a handful of paths that provide the greatest percent of success. This practical reality will have to be set against the cost of the technology and new capabilities required to execute. For many organizations, the cost-benefit analysis will favor a simpler, less personalized approach. This reality is not discussed at all in the eBook.
Data gathering also is not discussed at all. While the ETL process is described as a painful and slow way to integrate marketing data, the alternative is not clearly described. Other services like Datorama and Funnel.io do a good job of explaining how their special sauce of APIs and unstructured data processing that make data integration easier, but this particular eBook does not describe this.
There is more to the eBook than I've summarized. For more comments and insights, download my annotated version of the eBook.
Otherwise the original can be found at the Pointilist website.