Sales & Marketing Analytics

CRM Embedded Reports

JeffF — Wed, 12 Nov 2008 17:48:00 GMT

One of the great challenges with CRM is adoption. One way that I've found to help drive adoption is to constantly work to make the reporting as relevant and useful for individual contributors (i.e. sales reps and customer support reps) as is humanly possible. Dynamics CRM provides a great option to do this by creating tabs on key entities which can then render SQL Reporting Services reports that will display a rollup of key metrics associated with the specific entity. For example, it's often extremely useful for a sales rep to be on the account tab and have an at a glance view of not only the fields covered on the main form but also a summary of historical information (i.e. revenue over time) as well as a rollup of key issues that may be embedded below the main form (i.e. what service cases are currently open?).

Here are a couple of exmples of the type of embedded analytics that I'm talking about -

Account Embedded Reporting

For a larger image click here.

Contact Embedded Reporting

For a larger image click here.

Both of these examples possess a few key qualities that are helpful in using embedded reporting to drive adoption -

1. They leverage data that already exists in the CRM application and put it in front of the user in a way that is easy to understand and to apply in making decisions.

2. They pull data that would be multiple clicks deep in the application and put it in a nice summary format that is intended to save users time.

3. They work to not only summarize existing CRM data but to put it into a historical context.

Finally a couple of notes on the technologies deployed -

1. All data is being consumed from Dynamics CRM.

2. All reports were created with SQL reporting Services 2008. The key reason SQL 2008 is leveraged is to allow the reports to use the visually appealing gauges displayed in the contacts report.

CRM Online Custom Reporting

JeffF — Tue, 26 Aug 2008 21:50:00 GMT

CRM online represents a fantastic way to use Dynamics for customers who don't want an on premise installation. That said, for many organizations the reporting wizard and Excel are not enough to support the full scope of their needs.

One option is to replicate the data locally so that the same dashboards and reports displayed earlier on this blog can be used against the core online CRM data.

There are multiple approaches that can be used to solve this problem, but today I'll share one simple approach that only requires an organization use an existing standard SQL Server to both host the data retrieval components (SQL Server Integration Services packages) and to maintain the replicated data.

Here's a rough overview of how the process can work -

1. The SSIS job will contain a step within which a custom assembly will be called that will connect to the online crm webservices, and the resulting returned data will be an xml file for the relevant CRM entity.

2. The SSIS job will contain a setp that will format this data and load the extract into a replica of the CRM filtered view for the relevant entity.

3. The core CRM security will be applied to the filtered view so that users will have the same permission against the replica data that they maintain against the online application.

The result is a set of replica views and tables that are refreshed at a client scheduled interval, and then these views can be queried and reported off of just as on premise reporting is done today to deliver dashboards or OLAP reports. This approach is one that I've used in production today with CRM Online and has worked well for the past couple of months.

Benefits of OLAP Cubes for Sales Reporting

JeffF — Tue, 12 Aug 2008 20:19:00 GMT

Many organizations have sales or marketing analysts whose focus is to deliver summarized information for management that goes beyond the basics that come from dashboards or operational reports. These analysts typically are focused on extracting data from Dynamics CRM using Advanced Find queries and then developing their own pivot reports based on this data. In building these reports, they may slice the data based on Fiscal Quarters or based on the analysis of a specific sales rep or geography.

The primary benefit of a cube is that it can provide a free flowing environment in which an analyst or manager can quickly and easily slice CRM data using Excel without going through the hassle of running a set of Advanced Find queries and without developing their own custom logic to represent things like a fiscal calendar or management hierarchy.

The cube can significantly improve system performance when doing Excel based analysis of large data sets, and it can be developed to automatically provide analysts with drill down into hierarchies in all types of relevant business domains (i.e. management, product, calendar).

Here's a simple screen shot of the outcomes from a cube based analysis of CRM data.

This image shows a basic cube that's pulling data from Dynamics CRM where an analyst has created a simple dashboard based on having multiple pivot charts displaying from the cube.

That said, the real power is in the ability to dynamically adjust the date criteria and drill up and down from Fiscal Year to Quarter to Month. This type of drill through with the associated hierarchical nature of the dimensions are the key business benefits that a cube exposes that a user does not get with the out of the box CRM reports and Excel reports.

Ad Hoc Reporting for Dynamics CRM

JeffF — Tue, 22 Jul 2008 16:36:00 GMT

For most organizations, successful analytics requires a set of management dashboards coupled with the ability to dive deeply into the data with Ad Hoc analysis. With Dynamics CRM an organization has a number of options for generating Ad Hoc sets of data and analyzing that data. This post will focus on outlining some of the key options, and future posts will follow up with the specifics of implemention. In order to provide some context, I'll define some basic Ad Hoc data interactivity up through advanced with each step allowing for more flexibility, but requiring additional technical accumen to implement -

Basic -

1. Advanced Find - Dynamics provides the Advanced Find tool out of the box, and this user driven query tool allows you to search on and return sets of records based on a wide variety of user input parameters. This can be a very powerful engine for gathering records that can then be reported on using the report wizard or exported to Excel and turned into Pivot Charts. This option is great for getting started with Ad Hoc analysis, but it is limited in its usefullness as data volumes grow and query times slow down the workflow of power users.

2. Excel Connectivity - Dynamics provides the ability to directly connect Excel to CRM and pull back streams of data based on user selections or advanced SQL queries. With the ability to directly query the filtered views via SQL this provides a solid environment for technical users to generate both streams of data for analysis as well as Pivot Charts for management visualizations. This option is great for doing true one off analysis and prototyping reports, but it is limited in its ability to scale up and it requires SQL adept users to really get good performance as data volumes grow.

Intermediate -

1. OLAP Cube - SQL Server Analysis Services is an available option with the core data platform leveraged by Dynamics CRM. As a result, organizations can quickly implement an OLAP cube that users can connect to to perform Ad Hoc analysis via Excel or a web page. The power of this solution is that the OLAP storage format speeds up user performance significantly as compared to running queries through Advanced Find or via Excel directly against the database. In addition to the performance gains, users get better control over filtering and drag and drop interactivity when setting up pivot tables and charts. Future posts will focus on this approach as the best option for most organizations looking to move beyond the basics, and will provide implementation tips and some practical examples.

Advanced -

1. PerformancePoint - Microsoft PerformancePoint is a full fledged planning, analysis, and business monitoring system that can pull data directly from CRM or from an OLAP cube. This advanced tool allows for businesses to not only identified structured KPIs and their boundary conditions, but also provides the capability to move from structured KPIs into free form Ad Hoc analysis. The power of PerformancePoint represents a complete platform for managing the business, but my experience has been that most Dynamics customers are currently in a transitional phase from basic to intermediate and it's most important for them to successfully get to the intermediate stage before jumping to an advanced platform that requires both increased technical sophistication and clearly defined KPIs against which to measure the business.

My next post will focus on building an OLAP cube against Dynamics and then we'll dive into how an organization knows it's ready to move from the Intermediate stage to Advanced tools.

Using Dashboards to Manage Adoption

JeffF — Tue, 01 Jul 2008 04:48:00 GMT

One key challenge for almost all CRM implementations is driving adoption by sales representatives. In the past, I've found that having user dashboards that provide insight into adoption metrics can be an effective way to highlight patterns of usage within an organization or a team, and usage dashboards provide a vehicle for managers to drive adoption.

It's somewhat difficult to say how a user adoption dashboard should function for any given organization, but there are a handful of principles that I've used in the past -

1. Specifically identify the most meaningful activities that sales representatives should be engaging in and measure them. For many organizations this will be a combination of the following -

New Contacts Created - Are sales representatives actively adding contacts to the system?
New Opportunities - Are sales representatives actively creating new deals?
Activities - Are sales representatives tracking and managing their emails and appointments in CRM?

2. Track trends - A current measure can be useful, but managers will be much more capable of driving adoption if they can see the patter of usage and whether it is moving up or down or staying flat for any given team or user.

3. Provide a rollup and drill down capability - In order for the metrics to be meaningful, managers and users must have the ability to look at their teams as a whole, or individuals from within that team. This provides the ability to dynamically compare and contrast how users and groups are performing.

4. Provide the ability to extract the raw data - Credibility is derived from transparency. Managers are much more willing to trust the data if they have access to the raw data, and they are much more likely to actively drive adoption if they know the hard numbers are there to back them up.

With these examples in mind, I've provided a simple sample below. This is by no means the best approach for any given organization, but it's an attempt to bring some of these ideas to life in a simple interface that allows a user to see both a rollup and drill down view of adoption.

Click here for a full page example.

One final note on this example. The upper right chart is a composite metric that sums up an overall score based on a user's inserted contacts, appointments, and the fact that they are not neglecting existing opportunities and accounts. Composite metrics can be a simple way to rollup an overall score, but they should be used with caution because it's easy to get carried away and leave a meaningless measure that users don't understand.

Pipeline Trending with Dynamics CRM

JeffF — Tue, 10 Jun 2008 20:20:00 GMT

In our earlier post on Sales Manager dashboards, I brought up the topic of trend reporting for the sales pipeline. For many sales managers they feel a strong need to have visibility into whether the pipeline is growing or shrinking over time, and to get a more granular view on how that trend is playing out for each sales rep and/or product.

Dynamics CRM out of the box only maintains a current copy of the data so trending is not possible without some additional configuration, or through the use of core SQL Server tools like SQL Integration Services. Let's take a look at the alternatives -

1. CRM Workflow - There are a range of options available here. With Dynamics 4.0 workflow can be used to trap key events and write out the changes that occurred. These events could then be reported on with standard Dynamics CRM rpeorting tools. I believe this option is likely the easiest to setup, but it can be quite limiting in terms of trapping all of the relevant data attributes, and likely will become unwieldy as the system grows and changes.

2. CRM Development - The CRM application itself can be enhanced by developing code that will write out to an audit table or audit entity on all key event changes to an opportunity record. This approach is the most all encompasing because it will allow for users to have visibility into the audit history, but it is also likely the most expensive and time consuming to implement.

3. Integration Services Trending - A simple solution that can allow sales managers visiblity into pipeline trends is to create an Integration Services package that will extract all key data from the CRM opportunity table in an automated fashion at a pre-defined interval. This will allow for sales managers to look at the progression in the pipeline over time, and to pivot or drill down to view these changes by product, rep, territory, or any other key attribute. Setting up this type of trending requires answering the following key questions about the business -

How frequently should the trending extract run? Many organizations assume that the more frequently this is run the better. That may be the case for a subset of organizations, but I've found that most can work very effectively with weekly snapshots of the pipeline. The benefit of a weekly snapshot is that data volumes remain reasonable and managers can still look at trending from a quarterly, monthly, weekly perspective without getting consumed by the potential noise captured in day to day changes.
What data attributes do you care about trending on? Perhaps the most important question to answer is which columns of data related to the opportunity should you be storing in the trending table. A personal opinion is that organizations should think about this question broadly and pull in all attributes that have relevance today as well as those that may be relevant tomorrow. Some key fields include - sales stage, estimated amount, deal owner, territory, country, state, city, postal code, and currency.
How granularly should the data be stored? I believe it is absolutely critical to store the data at the transactional level so that the trending table has one row of data for each opportunity extracted on a given date. This will allow the end users to track directly back to the record involved if there are ever questions about the validity of the data and it allows for easier pivoting and slicing of the data as users drill into reports.

If you're interested in starter code that creates a trending table, and populates that table with CRM data, feel free to email jefff@madronasg.com

Delivering a CRM Sales Representative Dashboard

JeffF — Wed, 21 May 2008 21:36:00 GMT

Last week's post focused on a sales manager dashboard. This week, I wanted to follow up with a concrete example for sales representatives. Future posts will focus on some alternative views to this display as well as views that are critical for marketing managers.This is intended to represent the type of outcome you can get with an initial release of a sales representative dashboard when working with the standard Dynamics CRM application and reporting tools. Let's take a look -

Note: Feel free to click this link for a full sized image.

A dashboard like this can be a good start for sales representatives for a number of reasons. Let's breakdown why this format can work, and who it is primarily geared for -

1. The display is fairly data dense and conveys quite a bit of information in one visual layout.

2. The specific content displayed tells a story that is critical for sales representatives. Sales people are heavily incented to hit their quota, and this type of display prominently charts how they are performing with respect to won deals compared to key benchmarks including their quota and prior year's attainment. Sales people are also constantly being asked how their pipeline looks overall as well as which deals they are forecasting to close this month/quarter. The display above provides a way for them to get visibility into their entire pipeline and see summary values for the rollup of that pipeline at each stage while maintaining the flexibility to drill down and look at only one month at a time to insure that the data they have in CRM accurately reflects the right value and close date on all their deals.

3. The context on the chart provides critical information to eliminate possible confusion. In the upper right, it is noted that all currency values have been converted the local user currency (in this case USD). Second, the chart is available in the UI of Dynamics CRM but can be exported to Excel or PDF for viewing offline and to allow for printing. Third, the user selector in the top frame of the report defaults to the user running the report and returns data from not only the user selected, but also all individuals who are setup in CRM as subordinates of the selected user. Finally, in the lower left it has been clearly identified when the report was run in order to minimize the possibility of confusion that results from reviewing outdated materials.

This is by no means the only design, or best design for any given organization, but it can serve as a solid foundation from which to build a sales culture oriented around data driven decision making. I'll follow up with additional posts where we'll look at other examples that add some nuance to this display. Some things we'll focus on in upcoming posts -

Quotas - What's the best way to make sure they are displayed effectively?

Activity tracking - How do we add more information to the display that also gives a perspective on how well sales reps are using CRM?

Dynamics CRM Sales Manager Dashboard

JeffF — Sun, 11 May 2008 23:21:00 GMT

Last week's post focused on defining a baseline for Sales Analytics. This week, I wanted to follow up with a concrete example for Sales Managers. Future posts will focus on some alternative views to this display as well as views that are critical for field sales representatives and Marketing managers.

Again, this is intended to represent the type of outcome you can get with an initial release of a sales dashboard when working with the standard Dynamics CRM application and reporting tools. Let's take a look -

Note: Feel free to click this link for a full sized image.

A dashboard like this can be a good start for a sales organization for a number of reasons. Let's breakdown why this format can work, and who it is primarily geared for -

1. The display is fairly data dense and conveys quite a bit of information in one visual layout.

2. The specific content displayed tells a story that is critical for the manager in question. In this case, the dashboard is broken down to tell the manager first what's in their forecast set to close in the current quarter. This chart in the upper left provides visibility into how the team is performing and which reps have deals at which stages. Second, the chart in the upper right displays current performance for the manager and their team with respect to closed deals with background context for prior year's sales and quota. Thus, the top frame of the report provides immediate visibility into how each rep is performing with respect to forecast deals at key stages in the sales process, and how the team as a whole is performing compared to prior year and quota.

The lower frame then provides visibility into a handful of second tier metrics. In this case, the manager see a list of the largest open deals in descending order with the ability to drill through into the most recent activities associated with those deals. The middle chart ranks individual sales reps on the manger's team based on both their closed deals this YTD, but also based on the % of deals that the rep has successfully closed in this year. The final chart provides full visibility into the entire pipeline for all reps showing the volume of deals and dollar value at each stage.

3. The context on the chart provides critical information to eliminate possible confusion. In the upper left, it is noted that all currency values have been converted the local user currency (in this case USD). Second, in the lower right, we have the option to click a link and download the data to Excel which will allow users to perform more detailed analysis, or to validate the display if their are concerns as to the validity of the data. Third, the chart is available in the UI of Dynamics CRM but can be exported to Excel or PDF for viewing offline and to allow for printing. Finally, in the lower left it has been clearly identified when the report was run in order to minimize the possiblity of confusion from reviewing outdated materials.

Drill through reports - The dashboard above is a good starter, but how can this grow to support more detailed questions as more and more users adopt the solution.

Pipeline trending - The dashboard above provides a snapshot of a point in time with respect to the forecast and pipeline, but many sales managers would also like to understand if the pipeline is growing, shrinking, or staying stagnant over time. This is a critical metric that we'll work into a revised version of this view with upcoming posts.

Executing on Sales Analytics - Creating a Foundation

JeffF — Tue, 29 Apr 2008 19:02:00 GMT

Last week’s post on planning an analytics strategy for sales outlined a series of steps organizations need to take in order to be fully leveraging customer data to make the best decisions. Today, I’ll focus specifically on getting to the basic stage. This may sound simple, but this stage is all about deeply understanding the things that sales teams care about, and then applying best practices for visual display to give the sales team simple effective tools that work the way they do.

With that in mind, here is a short overview of the process I follow when working with organizations that are struggling to get beyond using their CRM system as a data repository.

Identify the Questions Users Want to Ask of CRM Data

An analytics strategy must take into account that users are looking to both monitor their current state, and analyze the specific results. The monitoring function is oriented around comparing the current state of performance against both stated and unstated goals. The analysis function is oriented around drilling into the data to validate its accuracy and to gather clues as to how to improve performance.

Given this understanding of the fundamental role of reporting, it becomes critical to accurately detail the data that each community of users is looking to monitor and analyze, and deliver a concise package of reports tailored for each community’s role.

The process for identifying the right data for each community is as much art as science. My experience with Dynamics CRM is that there is no perfect requirements gathering template, and it is more effective to start by detailing the types of questions the reports are intended to answer.

In the majority of sales teams, the following questions have been identified as being critical to users.

Account Executives (In typical order of priority)

· Am I on track to make my quota?

o The user is looking to go to one location that will provide insight into the following pieces of information –

§ What has been the actual revenue booked for the closed months in the relevant period (QTD, YTD, HYTD) compared to my quota?

§ What is the forecast revenue for the open months in the relevant period (QTD, YTD, HYTD) compared to my quota?

· What’s in my pipeline?

o The user is looking to go to one location that will provide insight into the following pieces of information –

§ What deals are in front of me today that have forecast revenue commitments for the upcoming months?

§ Does CRM accurately reflect what I expect to close?

§ If I lose deal x but win deal y, am I still on track for the results I expect?

§ How much of the revenue in deals I’m working in is mine?

· What deals will make or break my quarter?

· Am I getting a good return on the clients where I invest my time?

· How am I performing against organizational goals and past results?

o What is my win rate relative to last year/my team/the overall organization?

o What is my lead volume relative to last year/my team/the overall organization?

o What is my average deal size?

Sales Managers (In typical order of priority)

· What revenue will my team deliver this quarter?

o What is the booked revenue for the months in the current period?

o What is the forecast revenue for the months in the current period?

o What deals will make or break this quarter?

o Are the hedge factors accurate on key deals?

o What was in my pipeline last quarter at this time and what did we close?

· How is my team’s pipeline trending quarter over quarter?

· How effectively are we working with strategic accounts?

o What revenue have we booked and do we have forecast for a specific account regardless of whether the deal is with them direct or via an agency?

o Are we actively talking to key buyers at strategic accounts on a regular basis?

o Is the size of our campaigns increasing?

· How is my team performing against organizational goals and past results?

o What’s our delivered revenue against plan? Compared to last year?

o What’s our Win Rate? How does that compare to last year and to targets?

o What’s our average campaign duration and dollar volume? How does that compare to last year and to a target?

· How productive are my Account Executives?

o Which accounts are they active in?

o Are some more active than others?

o How effective are they at closing the leads they get?

This list of questions can now be used to drive the data that flows into mock ups as a designer is constantly checking this list and evaluating how well the mock up delivers answers to these questions.

What are the best practices for visual display that I should apply to answering these questions?

The following are a set of design considerations that we factor in when developing a mock up or beta report regardless of whether this is an operational report or dashboard. The intent is to use these best practices to ensure that the users will easily and readily adopt the delivered dashboards.

Develop information dense displays

The delivered reports and dashboards should focus on delivering users as much information as is readable in one viewable area to allow the users to get as full an answer to the questions they were contemplating that lead them to run the report. Note that this goal can be achieved to varying degrees depending on the selected platform.

Select between charts and data tables based on the intended user

Visual report displays (charts and graphs) are particularly useful when rendering summarized information in the context of a target or trend to users who are not intimately familiar with the underlying rows of data. Tables of data tend to be more useful when the intended audience has day to day working knowledge of the rows of data, and they intend to evaluate the appropriateness of each row while reviewing the report.

Allow users to interact with the data to the extent they have a desire to

A core test of a reporting system is whether users are able to intuitively interact with the data to perform analysis without being forced to interact with the data in order to satisfy the initial question that brought them to run the report.

Clearly identify data sources and calculations

Credibility in reporting systems is in large part derived from users understanding how charts and tables are derived for their transactional systems. Therefore, it is essential that users interacting with the report can know immediately the source (or sources) of data, and can see the precise calculation performed in order to deliver any chart or data point.

Clearly identify the timeliness of the information

One goal of most reporting systems is to allow users to run reports at ad hoc intervals. This puts great power at the finger tips of the users, but it creates confusion when two different users attempt to compare the same report run at different points in time. Additionally, reports sometimes are sourced from multiple unique data stores with each data set updated at different intervals (i.e. CRM real time and the data warehouse nightly). Given these discontinuities in time, it is highly beneficial to clearly state in the user interface the date through which the core data elements are up to date.

Insure the display can be viewed online, offline, or on paper

Not all users will interact with any given report in the same setting or for the same purposes. This basic understanding leads to recognition that any published report should allow for a method through which uses can interact when they are online, when they are working with a laptop disconnected from the corporate network, and when they are meeting in a conference room entirely disconnected.

On my next post, I’ll round out this discussion with a couple of sample dashboards that deliver answers to the key questions and I believe deliver on the best practices for visual display identified above.

Planning an Analytics Strategy for Sales

JeffF — Fri, 25 Apr 2008 21:30:00 GMT

Any strategy must take into account the current state of the sales team and its current tools. For the purposes of this discussion, I’ll focus on a sales team that has Dynamics in place and is using a combination of Excel and out of the box reports to manage key information like their sales forecast, lead volumes, and CRM adoption.

Given these assumptions, I think it’s critical that the strategy recognize that analytics is a continual process and the best outcomes occur when an organization takes a systematic approach to go from basic tools like those mentioned above to being a fully functioning leader in using data to deliver competitive advantage.

I believe that process involves a set of 3 steps that move from basic to advanced analytics. I’ve outlined these steps below –

Basic - The first step beyond working with Excel sheets and out of the box CRM reports. This stage is characterized by providing core information in a way that is readily consumable so that executives and managers better understand the current state of the business. This implies both presenting data in a way that is clear as well as the integration of data so that individuals can see a complete picture of the information that’s critical for them in their role. The goal of getting to this stage should be that individuals feel like they have analytics that helps them better understand their business, that’s based on data they trust, and that is timely and available on demand.

The primary outcomes at this stage are two-fold:

1. Managers and executives have access to compelling role based dashboards for Sales Forecasting and CRM adoption.

2. Through usage of dashboards business managers and executives develop a greater appreciation for and desire to drive the cleanliness and accuracy of data that is funneled into CRM.

Intermediate - The second step on the analytics ladder is to deepen the extent to which the data facilitates decision making by being more relevant within the context that it is used, and providing increased capabilities for ad hoc data analysis. This implies that when a sales manager is looking at the pipeline for their reps they are seeing probabilities not only based on the reps perspective but also based on past performance in deals that are similar. It also implies that analysts can really dig in and begin with a problem and ask questions of the data without needing to know how to write code or needing to request that IT write reports.

The primary outcomes are two-fold:

1. Managers and executives have access to context sensitive information when looking at data that allows them to improve the accuracy of their decision making (i.e. when looking at a pipeline it displays probabilities based on historical close rates for the relevant sales rep/industry/account).

2. Analysts have access to robust Ad Hoc tools that allow them to answer questions as quickly as they can think of them without having to stop and write code or request that IT create a new report.

Advanced - Advanced organizations leverage what they’ve learned in the first two stages to establish key performance indicators and targets that they can use to manage their most critical metrics, and they actively mine historical data to predict future outcomes.

Some key areas where data mining and predictive analytics come into play for sales organizations include: forecasting based on historical win rates, mining data to project customer lifetime value and using that analysis to prioritize accounts, mining the results of campaigns to continually focus investments where they will generate the greatest return.

The primary outcomes include:

1. Having intuitive analytics displayed inside Dynamics applications so that sales managers can make data driven decisions without having to leave their primary environment.

2. Improved confidence in sales forecasting models and reduced volatility in the organization as forecast accuracy improves.
3. Improved ability to target customers with the right offerings and an increased ability to accurately project when those deals will close.

Next week, I’ll drill into the basic stage and work through a process that delivers on the outcomes expected at this level.

What is analytics and why does it matter?

JeffF — Wed, 23 Apr 2008 20:11:00 GMT

There are so many definitions for analytics out there that I thought I would start here with a simple one. Analytics is the process of working towards an optimal decision by making the best usage of existing data.

With that in mind, I don’t view analytics as a specific technology (data mining or OLAP cubes) or a specific technique (regression analysis), but rather it’s getting to the best decision possible by leveraging the right data and applying good critical thinking skills.

With respect to Dynamics, analytics is both the computational processes and human skills it takes to turn raw web, customer, and order data into insight that help to -

· Target the right prospects with the right offers

· Invest time with the right leads

· Accurately predict monthly and quarterly revenues

· Understand how competitor behavior is affecting your business

· Effectively launch new products at the right time in the right market

· Test offers and campaigns to determine how to get the most out of limited budgets

That said, the question I always am asking myself is, “How do you know it leads to better decisions”?

There’s quite a bit of anecdotal evidence, and a couple of interesting articles along those lines include this Destination CRM article – “Analytics Drives the Success of CRM Projects” and an HBR series “To Make the Best Decisions, Demand the Best Data”, but I find that with most organizations if they take the approach of crafting a phased strategy for building out analytics and then execute systematically on that strategy, this question tends to go away as sales and marketing managers decide for themselves that the analytical tools drive improved decision making and increased profitability.

Welcome to the Dynamics CRM Analytics Blog

JeffF — Wed, 23 Apr 2008 19:08:00 GMT

This blog is focused on helping people make good choices around the adoption of sales and marketing analytics tools. Posts will focus on developing a comprehensive strategy and ROI justification for analytics as well as examples of how analytics can leverage Dynamics CRM data to deliver insightful dashboards for executives and tools for analysts.