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The move by Google to secure 100% of its users searches and thus remove keyword data entirely from Google Analytics caused headaches for the digital marketing world. The writing had been on the wall for some time though. But rather than join in the vitriol against Google for taking this crutch/drug away (unless you want to pay them for it), it’s been an ideal time for digital marketers to reassess what they measure and how they measure. Despite the outpouring of grief, there is still plenty of data left….
I wrote last month about the benefits of incorporating conversion attribution modelling into your marketing analysis. This is just one approach analysts or marketers can take towards understanding a site’s audience. Eric Fettman wrote an eye opening piece on his excellent gatipoftheday.com blog that introduced the concept of ‘Customer Modality’ and its place in Conversion Rate Optimisation.
Customer Modality essentially segregates audiences into four overarching customer segments using the approach outlined by a book cited by Eric written by Bryan & Jeffrey Eisenberg and Lisa Davis. The four groups are defined by the intersection of decision speed (fast vs. slow) and decision driver (logic vs. emotion).
The groups break down as follows:
This gives us a highly effective way to profile our target customers.
Looking closely at these definitions reveals some commonalities. Do these groups split neatly into two based on their speed characteristics or decision drivers? i.e Competitive and Methodical vs. Spontaneous and Humanistic. Potentially, however as with most posts I’ll ever write, you need to make this decision based on the data set you have.
The insights this type of analysis can provide will inform not only a web strategy (content type, site architecture, design and messaging etc) but the rest of the marketing plan, on and offline.
In order to set ourselves up to analyse these audience sub sets, we should define some typical on site behaviours. We can then assign metrics to better track how a site meets or doesn’t meet the needs of these customer segments.
The first two groups are pretty straight forward to profile.
Typical behaviour of a ‘Competitive’ customer sees them looking to get in, find what they’re after and get out again. Marketing priorities for these people need to be landing pages with clear call to actions that are optimised for keyword themes (SEO & PPC). They need to be geared up with the most telling information and a means to convert these people.
Almost the inverse is true for the more methodical internet users:
Content Marketing comes into play when converting the more Methodical customer type. On top of needing to be found in the SERPs with organic and paid listings, these people are likely to visit several sites prior to transacting so being referenced on authoritative domains becomes essential.
On site features may need to include; lifestyle imagery and trust signals such as live chat, reviews and industry accreditation.
You will need further or expanded content such as related posts or a string of content that keeps their interest other time. Social proof can also play a key part in convincing them to commit to your website.
With this profiling you’ll now be able to apply further segmentation:
Soon Google Analytics will roll out further demographic segmentation to give you further insight:
This will provide a pretty powerful basis to inform site structure and content in order to maximise site conversions and make you more money. For now I’d recommend you consider the best ways to use conversion rate optimisation on your website to reach each of the four types of customers as they are all good markets to consider.
For more segmentation goodness check out Anna’s #measurefest presentation “The Power Of Segmentation In Web Analytics”.
In today’s multichannel world, there are mountains of data which provide insights into how users have interacted with your business and their path to conversion (or non-conversion). It is important to understand performance with multichannel marketing, which can be achieved through attribution modelling. Attribution refers to assigning credit to something (a channel, touchpoint, etc.) for the role it played in the final conversion. An attribution model is a rule, or set of rules, that assigns this credit correctly to the right channel or touchpoint.
For a long time, Bing, the UK’s second-largest search engine, has been underappreciated and, in some instances, even ignored. Often regarded as the inferior search engine to market leader Google, Bing has historically struggled to appeal to many in the digital world. Most PPC analysts would give justified reasons for neglecting Bing for so long; these include the volume of traffic and the user experience just not matching up to Google. However, the validity of these assessments is now diminishing. Bing has grown and improved rapidly in the last couple of years; if you are not integrating it into your comprehensive digital marketing plan, you run the risk of missing out on a large portion of your chosen market and significant revenue.