True to the core principle of Kano framework, salesforce product has evolved over the period to meet the basic needs, performance features and delighters for customers. Once delighters turn into basic needs, something new and interesting comes up again to keep the arena exciting. With increasing product portfolio with both organic and inorganic growth Salesforce seem to have identified the trick to select feature that can greatly enhance user experience. As per recent releases salesforce came up with few productivity features related to Service cloud to delight the users and customers alike. Some of it listed below that can help to cut down the customization and make application/product feature rich with little effort (read low code / no code).
- Identifying Knowledge article relevant to the case to give faster case resolution – In many of the engagement you may have used once a delighter feature of knowledge sidebar which gave a quick option to agent to select suggested article (unordered set) or search for knowledge article and attach it to case or email response to take forward the case for closure. Any requirement around auto association of article based on certain set criteria ended up getting into customization route. And then came then Einstein knowledge article recommendation that gives an AI based model which continuously gets trained with each agent actions on article attachment or downvoting. Advantage it gives over standard suggested article is that outcome of recommendation is an ordered set with highest match on top of recommendation bar and will potentially saves the costly real-estate.
- Recommending probable picklist field value for case classification – Each second saved in resolving a case in service/call center industry counts a lot as each of those seconds by giving agents prefilled suggestion translates into millions of seconds for millions of cases addressed by the agents over a period. One such mundane and repetitive task is to fill some of the key fields on case which are useful for reporting purpose. Case Type, Sub-type, Reason, Resolution Type etc. which quite a few times left blank and hits on data quality. Only caveat to use this feature is a significant count of closed cases with relevant fields filled in (~4000) to train the model. But at same time model will get trained and start functioning with organic growth in data.
- Identifying likelihood of case escalation or reopening – Nothing beats the customer experience and enhanced loyalty than timely response to cases without a need to follow-up. Knowing in advance which customer can potentially reopen cases or identifying in advance the complex cases which potentially escalate in future help in routing the cases to expert agent to give attention case deserves as well win the customer loyalty. In service cloud context, prediction builder comes handy which again gets trained organically but having a cleaned data set to train it greatly improve the outcome.
- Einstein Reply Recommendation – Once a delighter feature called quick text for chat, email, etc. are slowly becoming a basic requirement. To up the game further salesforce has come up with reply recommendation primarily for chats. This gives an option to agent via side bar on possible response that can go for upcoming message from customer. To enhance the experience further sidebar gives opportunity to agent to edit the message before posting it. Unlike other features, here there is a mandate to have minimum number of chats replies available in system. It makes sense to include this once ample number of entries are available in the org and bring it in delta release to continuously wow the agents/customers.
While all these features greatly improve the experience it majorly feed on data. And, major issue with all data led models is – the models are as good as the input/training data. It always helps to have cleaned data to train the system and utilize the feature to full. If that is not the option, keeping those fields as mandatory and monitoring the data correctness in initial stage will serve as a good investment.
All these features typically come along with Unlimited/Enterprise edition having Einstein service cloud feature license enabled. Bundled licenses typically comes at heavy discounts and certainly a good investment.
Details for implementing these features and associated prerequisite are available in reference links.
References –
- Kano Framework - https://en.wikipedia.org/wiki/Kano_model
- Licenses - https://www.salesforce.com/in/editions-pricing/service-cloud/einstein/
- Einstein Article Recommendation –
- Trailhead Link - https://trailhead.salesforce.com/content/learn/modules/einstein-article-recommendations-for-service
- Model Improvement - https://help.salesforce.com/articleView?id=sf.einstein_article_recommendations_improve.htm&type=5
- Challenges - https://trailblazer.salesforce.com/issues_view?id=a1p4V000001JilCQAS
- Einstein Case Classification –
- Einstein Prediction Builder –
- Einstein Reply Recommendation