Gary Comer, founder of mail order clothing retailer Lands’ End, once said “Think one customer at a time and take care of each one the best way you can”. The only way to implement this in the early 1960s, in the days of limited data and computing power, was to segment consumers into subsets with common needs, interests or priorities and then target them appropriately.
Fifty years on, there is an abundance of behavioral data about each customer: both transactional data indicating past responses to campaigns as well as interactions. Customer values and opinions are also shared on social media networks. Most importantly, there are now scalable supervised learning technologies that can link and analyse all of these granular data to create accurate predictive models. These changes have given marketers the ability to understand each customer’s unique needs and priorities enabling accurate targeting of a single individual rather than segments. Yet, as Rexter’s 2007 data miner survey shows, 4 out of 5 data miners still conduct segmentation analyses, i.e., unsupervised learning on data with sufficient information to perform supervised learning. And this is more frequently used by those working with CRM/Marketing data, in other words, for targeting customers.
So why are most marketers still persisting with targeting segments rather than an individual?
There are a number of reasons for this including: 1. Choice of analytic platforms, 2. Campaign funding structures and 3. The fact that simplistic segmentation models are easier to sell to the senior management.
The Figure below shows the results of the 2015 KDnuggets poll on computing resources for analytics and data mining. A whopping 85% of all data miners still use PC/laptop for their analysis (even though they may also use other platforms).
Now, treating each person as a unique individual requires understanding their preferences, needs and current priorities. A person is targeted if and only if all of their behavior, social conversations and current events of relevance indicate that the campaign would be of interest. Such focused targeting needs building specific predictive models for each marketing campaign taking into account all of the customer’s transactions and interactions in all channels as well as any events of relevance.
Clearly, analysing such vast amounts of data in a timely manner is difficult or impossible with the PC/laptop, the analytic platforms used by most CRM analysts.
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