Salesforce started off with CRM – a product that seeks to manage a companies sales processes. Sales processes are just one form of knowledge management so it’s not hard to see the fit with Salesforce’s latest acquisition, InStranet.
Instranet sell a tool called Dimensions that has a whole lot of potential to make sense of the rapidly growing amount of information that is at hand in business. Dimensions manages and sorts the knowledge base of a company so that when a customer makes an information request, Instranet can sort through the screed of information that is irrelevant to the query, but can serve up the information that is of relevance.
Dimensions allows a business to accumulate masses of data, secure in the knowledge that the information is readily accessible.
Salesforce are pushing Dimensions hard, saying;
Finally: A Knowledge Base that Gives You Just the Answer You Need – and Nothing You Don’t
Traditional knowledge bases rely on matching keywords against every answer in the system. The result: many pages of possible matches that leave your agents and customers lost in a sea of irrelevant information.
InStranet’s patented Dimensions technology organizes your knowledge base in multiple ways to pinpoint only those answers that are relevant to a particular customer. Add the Dimensions technology to the rich customer history in Salesforce CRM and you get just the answer you need—and none of the "noise."
Everything You Need for Happy Agents and Customers
Salesforce CRM Customer Service & Support is the fastest-growing application among enterprise call centers and support teams worldwide. This award-winning product delivers a:
- Call Center application that makes every agent successful
- Customer Portal that makes self-service the preferred destination for your customers
- Proven Knowledge Base used by 350,000 global call center agents and millions of self-service customers
Deliver Success in Weeks, not Months – Even for Large B2C Call Centers
Our customers love that they can be up and running with Salesforce CRM Customer Service & Support in weeks, not months. The new knowledge base is no different.
Salesforcetimes – ever keen to get a dig in at Microsoft, uses this as an example of the promise for Dimensions;
Head over to this travesty, and type in “office won’t open”. If they were using InStranet combining all of Microsoft’s data about my copy of windows, what updates I’ve taken from Windows Update, perhaps asked for some machine specs, when was the last time I performed maintenance, as well as my Dell support history… well, it might find something of actual use for me, maybe even, the right answer!
It’ll be fascinating to see how Salesforce integrates Instranet/Dimensions into their existing offerings.
quote:
Dimensions manages and sorts the knowledge base of a company so that when a customer makes an information request, Instranet can sort through the screed of information that is irrelevant to the query, but can serve up the information that is of relevance.
The killer app today in the field of knowledge management comes from using a combination of Ontology, Expert System, Natural Language Processing , machine learning and data-mining.
An ontology (knowledge-base) is populated with discovered nuggets of knowledge via machine learning & data-mining or otherwise populated with knowledge from human experts, and then inference is used to match the users query to the nuggets of knowledge already stored (ie, via automated data-mining discovery or knowledge acquisition from human experts in that specific domain of knowledge such as banking knowledge, sales knowledge, diagnosis knowledge, etc…). These types of knowledge management web apps is already starting to appear in the market or otherwise , they will become available in the near future. A brief description of such system is described in the following paper.
User-Centred Ontology Learning for Knowledge Management
quote:
Traditional knowledge bases rely on matching keywords against every answer in the system.
Yes, this is where machine learning/data-mining are coming to play, since usually, some of the nuggets of knowledge that is buried in the data have not yet acquired by the ontology (ie, not known as a prior), those nuggets of knowledge are mined/discovered and put these into the (populating) ontology on the fly. Queries are then matched into the ontology via the inference engine.
I haven’t heard of InStranet before, but this is a domain that traditional vendors such as Oracle, IBM, Hyperion, Fair Isaac, SAP, etc, will try and move into (online knowledge management) the near future, if it isn’t taking place right now, so the competition will be fierce in that market.