KPI enhancements with Customer Insights July 2.Microsoft Dynamics 3.Team blog.Applies to Dynamics 3.Customer Insights July 2.Dynamics 3.Customer Insights DCI July 2.How To Update Microsoft Expression Web 4 User' title='How To Update Microsoft Expression Web 4 User' />KPIs form the core of analytics capabilities of DCI.We continue to improve the KPI engine to define various metrics, from simple to complex, to model typical KPIs needed to capture any behavioral, engagement or transactional patterns seen with customers, partners, or employees or any entity modeled in DCI.In this blog post, I will highlight some of the notable new enhancements done with the KPI engine along with some real world examples on how to define the corresponding KPIs to help you model your scenarios.KPI engine enhancements.The list below provides an overview of features added or enhanced with the July 2.Expression support in aggregate and filter conditions Aggregate and filter condition definition now supports expressions using arithmetic and logical operators.KPIs over related entity properties using graph traversal with extracts DCI underneath gets a significant update with a new graph engine.How To Update Microsoft Expression Web 4 User' title='How To Update Microsoft Expression Web 4 User' />This graph engine now enables the possibility to define KPIs over relationship graph modeled with relationships and links in DCI.Extract provides the ability to extract properties from related entities and use them in KPI calculation.For example, City property from Customer profile can be modeled with extract for use in group by dimension of the KPI to compute chat interactions related with Customer.This update will see the death of the old email program Outlook Express, as well as the depreciation of the popular Paint application.As Microsoft told Gizmodo back.This article explains how to update existing data.Microsoft Office Access 2007 provides a number of tools for updating existing records, including datasheets, forms.Extracts can be used in aggregate, filter condition, or group by.Extracts can be defined over multiple hops of the relationships graph.Extracts can be defined over multiple cardinality relationships 1 to N or M to N as well.Such extracted fields are treated as Array value type.Advanced expressions with aliases for use in aggregate, filter condition or group by Aliases allow the possibility to define expressions over direct profile or interaction properties as well as extract based properties from related entities.Aliases in addition allow set operations such as Union, Intersect or Cross.Apply if the extracted property is an Array value type.Time window support for Profile KPI with mapping to a timestamp field A timestamp field can now be used to compute profile KPIs allowing grouping of data in time windows as well for insights.For example, the number of cases resolved by resolution day can be computed using the resolution timestamp of case entity to analyze the case resolution trend.KPI over KPI calculation for advanced calculation scenarios Sum, difference or division of several KPIs can be defined with expressions using KPI over KPIs.String and Date functions for use in alias expressions to transform data Simple transformations functions such as existence of a sub string or diff between two timestamps or daymonthyear type transformations can be used in expressions to build powerful KPIs.Text analytics function for sentiment extraction Sentiment function to compute the score for text properties in the range of 1 to 1.Amar Ujala Hindi News Paper Bareilly Editions .Session support for KPI calculations Users often engage with businesses in a series of interactions for a specific purpose such as buying a product or logging a support case.Often the interactions are grouped logically into a customer session and KPIs needs to be computed over the session to understand customer behavior.For example the average duration of a session, the average number of pages clicked in a session, the average number of support agents involved in a session, the average number of chat messages exchanged in a session, etc.A new session function is provided to group a sequence of interactions based on a timestamp into a session and then use session specific analytic functions to compute metrics such as session duration, first response time, or length of session.KPI examples.Its best to understand the power of the KPI engine with some real world examples to highlight the value of the new features.At Microsoft our mission and values are to help people and businesses throughout the world realize their full potential.The following examples will demonstrate setup of some typical KPIs used in sales, service, or marketing scenarios to get insights about customer behavior.Customer Engagement Score This KPI computes an engagement score for contacts and accounts based on the time decay of the various engagement activities done with the contacts of an account.A contact connects with the company doing various engagement activities such as Phone Call, Email, Chat, or Web visit.The more they engage with the company, the higher the strength of the relationship.The engagement activities are typically valued based on the recency of the activity.In this example, activity contribution to the score decays linearly from 1 to 0 over a period of 9.Each activity in turn weights differently based on activity type on a scale of 1 1.Web visit is worth 2 points.Email is worth 3 points.Chat is worth 5 points.Phone call is worth 8 points.Appointment is worth 1.Each Activity is related to Primary Contact which is related to Account per the following relationship diagram Lets go through the steps to model the various KPIs to compute the contact and account engagement score.This KPI is defined for Activity interaction entity grouped by contact.Id and account.Id to provide the ability to rollup engagement score to account or contact level per the following KPI Definition.Basic information.Name Activity.Engagement.Score.Source Interaction.Type Activity.Calculation Window Lifetime.Extracts.Name Account.Ids.Account traversal Activity to Activity.Primary.ContactContact Entity Name Contact.Property Account.Id.Aliases. 1. Days.Since.Activity Date.Diffday, Activity.Timestamp, Date.Time.Utc. Now2. Decay.Factor 9.Days.Since. Activity9.Appointment.Activity.Points Activity Appointment Phone.Activity.Points Activity Phone Chat.Activity.Points Activity Chat Email.Activity.Points Activity Email Web.Activity.Points Activity Web Account.Id Cross.ApplyAccount.Ids.Account. IdAggregate Function.Function SUMExpression Decay.Factor Appointment.Activity.Points Phone.Activity.Points Chat.Activity.Points Email.Activity.Points Web.Activity.PointsFilter Conditions.Days.Since. Activity lt 9.Group By.Contact.Id, Account. Adobe Photoshop Elements 10 Serial Cracks . Id. KPI definition JSON.Aliases.Alias. Name Days.Since.Activity. Expression Date.Diffday, Activity.Timestamp, Date.Time.Utc. Now. Alias.Name Decay.Factor.Expression 9.Days.Since. Activity9.Alias.Name Appointment.Activity.Points.Expression Activity Appointment Alias.Name Phone.Activity.Points. Expression Activity Phone Alias.Name Chat.Activity.Points. Expression Activity Chat Alias.Name Email.Activity.Points. Expression Activity Email Alias.Name Web.Activity.Points. Expression Activity Web Alias.Name Account.Id.Expression Cross.ApplyAccount.Ids.Account. Id. Calculation.Window Lifetime.Entity.Type Interaction.Entity.Type. Name Contact.Expression Decay.FactorAppointment.Activity.PointsPhone.Activity.PointsChat.Activity.PointsEmail.Activity.PointsEmail.Activity.Points.Extracts. Extract.Name Account.Ids.Expression TRAVERSEActivity.Primary.Contact.TRAVERSEContact.Account.SELECTAccount.Id.Filter Days. Since.Activity lt 9.Function Sum.Group.By Account.Id,Contact.Id,First response time of chat session This KPI computes the average time taken to provide the first response to a chat session in minutes in a typical customer support scenario.Imagine customers are engaging in chat sessions.You want to determine the average first response time that happens in a day by support center for a specific support category.You do this to plan for support resources and compare performance of support centers.Sessionization is a very powerful concept and it applies to any sequence of interaction that happen in time ordered fashion to capture an engagement session such as a chat session, web session or IVR session.Lets assume the customer is collecting Chat interactions to capture the chat sessions and property Chat.Session.Id is used to reflect chat messages exchanged in a single session.Lets go through the steps to define the KPI.This KPI is defined for Chat interaction entity grouped by Support.Center.
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