Traditional CRM systems, at a data level, are about collecting, organising & analysing customer data. The data is collected in a structured manner through the various organised channels like PoS, phone, mail, fax, direct contact, etc. by the customer facing employees, belonging to sales, marketing or service. DW & BI tools helped in analysing these data & taking more “informed” decisions by the businesses.
Predominantly, in all this, the data collected are most often predetermined & structured. Complex CRM systems like Siebel provide verticalised solutions that already structure the data for you to a very large extent, since each industry has its own requirements.
OTOH, with the prolific growth of the web 2.0 technologies which have facilitated the growth of social media, we have the customer/consumer directly providing a plethora of information about themselves, their needs, wants, wishes, choices, their friends & networks, their opinions, their feedback, ratings, reviews, etc. – all in a very unstructured format.
Unfortunately, none of this information hoard is compatible with the traditional CRM systems and thus of no or little use to the BI tools & the resulting decision making process. There are a few Social Media Optimisation options available that enable the marketers & sales people to talk more effectively in the new media, but then again, its mostly bent as in the traditional approach towards talking to & not listen to the customers.
So we need ways to integrate the social data from the various social media with the traditional CRM systems as a first step towards implementing Social CRM systems. Since these are two disparate systems, with different data definitions & data types, ETL must come into the picture. We could aspire to Web 3.0 or Semantic Web to solve our data compatibility issues, but that day is still far away. Not because the technologies don’t exist, but because they have not many takers.
Moreover, social media contains lots of data in natural language, we need Natural Language Processing to help us extract meaningful data from them. One aspect that’s gaining prominence is Sentiment Analysis that detects the sentiment of the person towards a product/service and thus can provide the info to the CRM system.
Yet another aspect is a direct mapping of the social data & traditional CRM data and integrating them behind the scenes, most probably through web services. This can however function if either standard services are provided by the apps built on social platforms like Facebook, etc. or customised gated communities are built by the brands/organisations.
In the first approach, we go where the customers are already present, in the second approach, we need to bring in the customers to our community site. There is greater control on the data in a gated community, however, getting the customers on to it & thus starting a network effort is the tough job. So a good approach would be to build a convincing presence on popular social sites by actually listening to the customers & having conversations rather than providing rote replies as per the scripts, and then lure them into the custom built gated community.
However, an ideal system would contain a blend of all these three systems (semantic, sentiment analysis & other NLP techniques, direct data mapping) at all points of time, with only the percentages changing with the maturity of the Social CRM implementation.
In this post, I have only dwelt on the social data to CRM data integration and not the other way round. That way go stuff like Social Media Optimisation, Sentiment Correction, etc. which already have some precedents, albeit sans the traditional CRM connection.