Customer Segmentation: Data, Dreams and Execution
- Part 1: I feel your pain
- Part 2: I share your dreams
- Part 3: We can make it happen
I have been covering the Customer Segmentation space for much of my career now, and being a marketer for many years, I can feel the pain that marketing professionals today are suffering from when working to get an optimal list of customers to run a marketing, sales or service campaign.
In this series of blog posts, I would like to share and discuss with you the key pain points (Part1: I feel your pain), the ideal solution (Part2: I share your dreams) and what modern technology can provide today and in the near future (Part3: We can make it happen).
Based on hundreds of conversations with companies from all sizes and vertical industries across the globe, the key pain points of customer segmentation can be broken down into 4 areas:
- The data is all over the place
- The nature of data causes nightmares
- It simply takes too much time
- It is science – not art
For today, I want to focus on the first: 1) your marketing data resides in different data silos.
Many claim that they want to establish a 360 view of the customer and to be frank (lowering my voice): you don’t need it in each and every crevice and angle of your CRM system. But in segmentation or target audience definition as some refer to it, there is no way around it – and the homework of bringing all data into one place and providing good enough data quality – even if it’s painful – must be done. You simply can’t afford to build target group selections on CRM data only. That won’t match your desire to run highly personalized campaigns where you really need to leverage every bit of data that you have about your customer.
To illustrate by example, a large consumer home appliance manufacturer runs periodic campaigns to everyone who registered a product in the last week. The product registrations are stored in the Business Intelligence (BI) system, the contact information is in CRM and the product data is in ERP.
It gets even more complex when you start analyzing the behavior of your consumers to build scores for e.g. “likelihood to buy” or “churn” or to “identify clusters of common behavior” to refine target audiences. The data needed for this often resides in industry-specific systems such as billing systems (Telco), smart meter data management (Utilities), POS systems (Retail), subscriptions solutions (Media) accounts and policy data management systems (Financial Services) or Webanalytics (e-Commerce) – for analyzing consumption and behavior.
And of course, there are tons of legacy applications – for complaints handling, loyalty management, survey tools even though these could have been covered by your CRM base system of choice long time ago. And there is response data from your best of breed execution tools for SMS or Email marketing. And you are buying external data. One could go on and on and on with additional data silos that you need to leverage to build a proper segmentation practice.
If you don’t get this right, you are risking annoying customers, wasting budget and missing out on excellent up and cross-sell opportunities:
“Why do they keep sending me promotions on my new tablet device that I bought and registered 4 weeks back? They should have known, because I’ve shared this with them. A voucher to buy some media – that would have been cool.”
In the next blog post, we will take a closer look at pain point #2: Why it is so difficult to deal with the data when analyzing it and how social media adds another level of complexity to it.
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