How many times have we read about Social CRM stuff and on the importance of creating an engagement environment correctly supported by technology along the main customer-facing perspectives (Marketing, Sales, Service and – last added – Innovation)?
Maybe too much and also this blog, I admit, committed this sin. Now we have more case studies to show and tell to enhance the intrinsic benefits of a Social CRM approach in business but we have also noticed the potential pitfalls of a new CRM “failure” due to tech stuff predomination on business strategy and analysis.
Maybe there is a lack of structured framework to support a social business model or maybe it won’t ever be and we’ll see an everlasting battle between strategy, organization, process and technology business components.
In this situation what I think is becoming more and more important for organizations, to understand the real drivers for the change, is a real understanding of their customers. And what you need to understand them? Tipically:
- quantitative analysis of customer behaviour through “transactional” measurements that track the way they interact with your business landscape
- qualitative analysis of customer thinking through a wise mix of human and automatic analysis of content produced during formal (i.e. VoC, speech analysis or survey outputs) and informal (free opinions expressed on social media) interactions
While the quantification of customer behaviours is quite a normal output for structured organizations which use descriptive and predictive analysis on data coming from customer/prospect databases where all their lifecycles’ information – produced by legacy and CRM platforms – are archived, the second task is just at the beginning of a hard path to walk even if it’s the “dark side of the customer moon” which we need to know to have a complete and 360° comprehension of the people that exchange values with your companies. And it’s a hard task first of all because we are just beginning to handle big data from social communication platforms (private or public) but, mainly, cause we’re not well prepared to extract sense and meanings from them.
The biggest challenge, in fact, is to map coherently your customers journey in order to formalize collected data and help you to get through the information-insight-knowledge funnel where:
- information –> data are re-organized in order to be intelligible
- insight –> pieces of information are correlated to specific entities (the analysis linchpins) gaining meaning
- knowledge –> meaning elements become the leverages which support action and change
So, typical output from this kind of activities can support your organizations to decline operationally your social business strategy identifying:
Process –> how are you serving your customers? where do you excel? where do you need to re-design processes with a more customer-centric perspective?
Organization –> where do you have to create osmosis between silos to make easier and fluent the knowledge exchange? do you need to re-think you internal structure to help your resources with their job-to-be-done?
Technology –> which are the essential features mandatory to support the organization and its ecosystem to reach their objectives of mutual benefits?
Not easy at all, but now it’s really time to help analysts with their “comprehension” task and the only way you can support them with a scalable solution in a world of big data (big volumes, high velocity and extreme variety) is investing in semantic tool capabilities (I’m not talking about sentiment with it’s accuracy problems) which can filter and categorize entities, concepts and their correlations in order to give more “sense” to your informative assets.
What do you think? Must these new features be one of the foremost area where you need to invest?