Ryan Goodman has been in the business of data and analytics for 20 years as a practitioner, executive, and technology entrepreneur. Ryan recently created DataTools Pro after 4 years working in small business lending as VP of Analytics and BI. There he implanted an analytics strategy and competency center for modern data stack, data sciences and governance. From his recent experiences as a customer and now running DataTools Pro full time, Ryan writes regularly for Salesforce Ben and PactBub on the topics of Salesforce, Snowflake, analytics and AI.
We were thrilled to extend an invitation to the unveiling of DataTools Pro Metric Analyst for Salesforce – your key to transforming your Salesforce organization into a beacon of metrics and KPI excellence.
Webinar Date: March 13 2024 9:30 AM PST / 12:30 PM EST
Register to get access to the recording – Week of 3-18-2024
In just 40 minutes, discover how to revolutionize the way you align and agree on KPIs, all with the speed and precision that only AI-aided automation can offer. This is more than a webinar; it’s a doorway to enhancing productivity and insights within your Salesforce org.
What you will Learn?
Plug and Play Salesforce Connected App: Seamless ways to incorporate DataTools Pro into your existing Salesforce org with 1 click.
AI-Aided KPI Alignment: How our batch, AI enhanced meta data analysts fast-tracks consensus on crucial KPIs, making your team more unified and focused.
Real-World Applications: Insightful demonstrations on leveraging DataTools Pro to elevate your organization’s data analysis and decision-making tools.
Interactive Q&A: Have your questions answered in real-time.
Recent AI advancements with large language models have broken through and forever changed how we think about information access and retrieval. Metrics and AI is at the top of my mind as AI agents today provide universal translation and curation of information. Here, in our data tools niche where we wrangle and transforming data into information, we are seeing incredible results using AI to write code and deliver the same results that traditionally required an analyst. We don’t believe AI will replace analysts, but we know already that AI augmented retrieval for researching large bodies of information a job better suited for machines. Enterprises need to re-frame documentation as context data generation for people and AI. You will likely see a rise in “knowledge graphs” as a hot topic. Unstructured data has always been deemed “untapped” gold, so now the race down the yellow brick road is on!
Behind the Curtain: Unveiling the Reality of Modern Bots
Chatbots have propelled large language models into the forefront. The benefit of these AI chatbots to individuals is the way an LLM can breaking down knowledge and experience into personalized bite sized information chunks. We are not too far from this being a shared experience in a collaborative setting. This is where we can see early adopters super charge team productivity. The big opportunity is how how enterprises will use AI to curate and deliver information us using the vast collections of empirical knowledge created over time. It goes without saying there are lots of smart people working tirelessly on these problems. Here at DataTools Pro, we are obsessed with this problem as a small scrappy team.
Does self-service analytics help?
Analysts, data scientists, and data professionals have always been required to distill complex business concepts into quantifiable analytics. Business Intelligence (management information systems) and Analytics disciplines still have the same problems today as 15 years ago. AI, LLMs and data platforms will not solve these problems without radical changes how people work.
Age old “multiple versions of truth” problems still exist. It has moved from spreadsheets to self service reports and dashboards
Empirical knowledge gained from pulling data together becomes disparate in spreadsheets, documents and PowerPoints, and email.
Methods to build a live, connected semantic layers to categorize and measure quantitative performance remain siloed and technology oriented.
Reports, dashboards, and data remain the primary delivery mechanism for performance metrics and KPIs. The need for speed to prepare and deliver self-service analytics has shortcut slow moving BI platforms of yesterday. Similarly, modern cloud data platforms have helped democratized working with structured and unstructured data that historically required database administrators, software engineers, expensive technology components. “Deluge” is the best word to describe the state of most enterprises in regard to the number of data and analytics assets flowing thus creating newer “data mesh” and “data fabric” methodologies to help strategize designing systems and process to tackle the deluge problem. We are experimenting with this ourselves with Azure Co-Pilot while our team, data, and metrics library is small.
What about Self Service via Natural Language Requests?
Natural language queries is a feature and not a solution. Many professionals simply do not know what data and metrics are available to start asking questions. This is where AI agents armed with a glossary, semantics and a large body of context data will be transformational. AI co-pilots are still very new, so we are experimenting ourselves what is real vs art of the possible. The keystone is aligning AI and business professionals with a common taxonomy and language and where we are working to build a common thread between business, data, analytics, and soon auto-pilots aiding these teams.
What about data?
Many enterprises do not have enough resources behind data governance and management. I still think this is a massive area of opportunity to somehow democratize and distribute data management in a way that is non-intrusive. Otherwise our point of arrival for AI automation will be autopilots and agents spitting out useless information. Incorrect information leads to mistrust and failed adoption of “co-pilots”.
Data storage is dirt cheap and modern data platforms make it fast and easy to analyze and model data into sophisticated analytics. How do you create universal focus?
All roads to AI metrics and analytics mastery leads back to goals and data governance
Every company has a set of metrics that indicate the health of the business. Your financial metrics within your income statement and balance sheet don’t get a lot of love on social media, but they are the bedrock to your performance (assuming you are a for profit business). Highly sophisticated metrics or amassing hundreds of metrics wont translate to good performance. Universal understanding, consistency, correctness and execution against a finite set of metrics will!
Quick metrics maturity quiz:
Do you have an inventory of all of the metrics your operational team is using?
Who are the owners, stakeholders, and oracles (keepers of institutional knowledge)?
Are you certain those metrics are calculated and deployed consistently across teams and individuals?
Are your business, technology, data and analytics teams aligned how to implement these metrics into analytics?
Fact Finding Process:
Traditionally, this is the process most consultants utilize to thoughtfully acquire and organize your metrics glossary. Many enterprises already have documents, presentations, or spreadsheets with this information formally gathered. Rarely are they universally understood and up to date.
Getting consensus and universal understanding is slow and cumbersome. It is one of the challenges we wanted to tackle at DataTools Pro
From Metrics to Key Performance Indicators and OKRs
There are a number of different organizing principles and methodologies to translate your organizational goals into metrics. A KPI differs from a metric in that it has a specific target, timeline, and direct impact on your organizational goals and objectives. You may have dozens of metrics without targets, and that is okay. There are a number of widely adopted models to help you formally structure and organize your KPIs:
Specific – target a specific area for improvement.
Measurable – quantify or at least suggest an indicator of progress.
Assignable – specify who will do it.
Realistic – state which results can realistically be achieved, given available resources.
Time-related – specify when the result(s) can be achieved.
OKR – Object Key Results
OKR – An objective is a clearly defined, inspirational goal aimed at driving motivation and direction. Key results are specific, measurable outcomes used to track the achievement of the objective.
As you get deeper into the performance management, process improvement, you will discover what works best for your corporate culture.
How are metrics and KPI is evolving with AI
No article is complete in 2024 without a hot take on AI. A lot of the focus in technology and analytics is centered on amassing feeding large volumes of quantitative data into machine learning models to predict outcomes. Now with generative AI, we are vectorizing large bodies of data to train, fine tune or simply retrieve data using natural language requests. Unfortunately, without the right semantics, definitions, governance, and context data, your AI investment won’t feel magical. Our team is racing ahead knowing the path to have meaningful dialogue and results with AI co-pilots requires context. We have taken a novel approach to run along side enterprises on their journey down the yellow brick road to help with our upcoming DataTools Pro metrics analyst!
Join us for a webinar March 13 where we will formally introduce our Metrics Analyst AI.
We have pooled together Salesforce Data Cloud resources from top experts to help get you up to speed. Salesforce Data Cloud is designed to streamline the organization and unification of data across Salesforce’s extensive ecosystem and beyond. Integrating external data sources and salesforce data seamlessly, Data Cloud was designed to ingest, share, manage, and operationalize data, enabling a deeper connection with customers through personalized experiences and targeted engagement.
At the heart of Data Cloud’s capabilities is the creation of unified customer profiles. The holly grail of a single unified view of a customer to drive understanding and hyper personalized engagement. Data Cloud isn’t a simple Salesforce feature. It is a suite of capabilities that requires a cross breed of skills to succeed.
New Azure Copilot Studio custom actions have opened the door for us to connect live, connected Salesforce metric and data dictionaries into the MS Copilot experience. Over the weekend I jumped into Azure and setup a functioning Azure Copilot, trained on our website data, that is available for you to try out below. A little bit of work and reading landed us in the same place we found ourselves a few weeks ago while testing OpenAI’s GPT actions for the first time. In a similar process, I embedded our DataTools Pro app as an action, in the same time it took to finish a cup of tea.
Unlike OpenAI, Azure OpenAI and now Azure Copilot are designed with enterprise in mind with the full suite of Azure services behind it.
Microsoft Copilot Bot Live Demo
This weekend, I dug in and with only a few clicks, built a co-pilot built a co-pilot based on the DataTools Pro website. With a little more work, we were security connecting in real time to DataTools Pro API and surfacing Salesforce metrics as context to Copilot our own business. We will continue to update this live demo below with our DataTools API demo account connected to Salesforce Essential Metrics.
Ask Questions about DataTools
Azure OpenAI and Copilot won’t fix your data
We are in unprecedented times with the speed that these AI advancements are rolling out and evolving. The real benefit of a Copilot is:
Increasing speed and ease for consuming large bodies of information
Improving the level and depth of understanding (for people who are inquisitive)
Translating and communicating information (text and visual).
While the innovation and art of the possible is very exciting, a sobering reality is you still need to double down on the same data and metadata management and governance.
The path is clear with Azure AI services.
Microsoft has done an incredible job weaving AI into the existing suite of data services and tools..
In this guide, we will walk you through the process of setting up Tableau Salesforce Cloud using the latest and greatest native integrations. Tableau Cloud natively integrates with Salesforce for enhanced security and access as the two clouds have become tightly knit together. In addition to the nuts and bolts, we will focus on key use cases how Tableau can provide valuable insights beyond standard Salesforce reports and dashboards. Tableau’s capabilities for deeper analysis, data manipulation, end-user ad-hoc analysis, and access to diverse data sources make it a powerful complement to Salesforce’s offerings.
Tableau Cloud Setup
Setting up Tableau cloud is as simple as signing up and provisioning an account through the online setup form. Once provisioned you can immediately start connecting and building.
Salesforce SSO for Tableau: Security and Access
Salesforce cloud natively supports Salesforce for user access and authentication. This allows you to extend your user management and access into Tableau so you are not needing to duplicate work.
Simply check “Salesforce” so when you invite users they will need to utilize their Salesforce username and password. If you use Multi-Factor Authentication MFA with the Salesforce authenticator app, you do not need to perform any additional configuration for it to work.
Embedding Tableau inside of Salesforce
For Salesforce organizations, Tableau should be a seamless experience that resides side by side with standard Salesforce.com dashboards. To accomplish this goal, we typically utilize the Tableau lightning component. With Tableau cloud, you can utilize the “Default Authentication type for Embedded Views”, ensuring a secure and seamless experience for end users.
The best user experience is one that reduces friction. We typically embed dashboards inside of Lightning pages and also make use of tabs to isolate Salesforce dashboards and Tableau dashboards side by side based on topic.
To allow embedding of Tableau inside of Salesforce as of Winter 24, simply go to Setup and enable Tableau embedding.
Tableau for Salesforce Use Cases
Before embarking on a Tableau Salesforce its important to understand key uses cases where implementing Tableau makes sense above and beyond standard Salesforce reports and Dashboards.
Deeper analysis
When we refer to “depth of analysis” we mean taking a single subject and exploring history, relationships, and paterns that impact the subject.
For example, if you see that yur lead to opportunity conversion rate is lower than expected, you may ask questions related to sales rep activity including speed to lead, number of calls, number of reps to leads and other ratios. When building Tableau dashboards and supporting reports, you can drill and explore these relationships over time with greater ease and relate them together.
More flexibility to slice and dice data
Slicing and dicing data in many cases requires analysts or in the world of Salesforce reports saving data to Excel. Tableau was born and designed for visual exploration of data where you can filter, drill and modify the subject of your analysis with.
End User Ad-hoc analysis
Salesforce provides an amazing ad-hoc reporting capability, granting business professionals with the power to produce powerful reports. While the report developer has a full fledge reporting solution, end consumers of the report are limited to basic filtering. Tableau on the other hand provides end user ad-hoc analysis for changing dimensions, drilling, and constraining information.
Access to more data sources for analysis
Salesforce reports and dashboards are limited to the data available inside of Salesforce. Tableau on the other hand opens the door to connect more data sources with Salesforce.
Connecting Tableau to Salesforce Data
Tableau provides a native Salesforce data connector, allowing direct access to Salesforce data objects. This is quite useful for real-time access to Salesforce data, or static extracts that harness the full power of Tableau data.
Native Salesforce Connector
Unfortunately, the Tableau integration with Salesforce data is imperfect. Using the standard Tableau connector for Salesforce prevents Salesforce formulas in the results. This limitation has long existed as an enhancement but is not obvious.
Working with Data Time Fields
Small variances in metrics can occur when using DateTime fields as a result of data extractions rendering in UTC instead of your local time zone.
Connecting Tableau to Salesforce Data Cloud
With the recent release of Salesforce Data Cloud, Tableau has a new modern approach to data access that bypasses some of the traditional limitations. We will cover this topic in detail with an upcoming post!
Salesforce metrics and KPIs are important tools to define how you will manage and monitor your Sales and Marketing efforts. Metrics and KPIs playing a crucial role in aligning strategies with business goals. In this article, we dive into vital Salesforce Lead and Opportunity Pipeline Metrics, emphasizing the importance of consistency and clear definitions. Whether a stakeholder, Salesforce admin, or a member of a data analytics team, here, you’ll gain insights, answers to common questions and examples.
5 Salesforce Lead Metrics you Should be Tracking
While different organizations and industries have varying definitions for leads, prospects, and customers, the following metrics are designed for organizations where lead generation and handoff occurs inside of Salesforce.
Qualified Leads Generated – How many qualified leads are delivered to sales? The qualification definition will vary per organization, as some use MQL (marketing qualified leads) and others use SQL (sales qualified leads). Having the qualification definition also helps identify un-workable leads which creates a feedback loop to improve lead generation channels.
Lead Generation to ResponseTime – How fast are you making contact with leads after a prospect is delivered to your sales organization? Turn time for some businesses are measured in hours but for some they are measured in minutes.
Lead Conversion Rate – How many leads need to be worked to generate a deal with revenue potential? Many organizations convert leads and create opportunities different stages of the Sales cycle. We recommend measuring from a key, well defined the point in your Sales funnel that is unlikely to change over a long period of time.
Lead to Close Win Ratio – How many leads do you need to generate to close deals?
Cost per Lead and Closed Won – What are your marketing campaign costs relative to lead generation and deal closure? For organizations that track their marketing campaigns and spend inside of Salesforce, this metric can be tracked using Salesforce campaigns. Not all organizations track marketing spend inside of Salesforce unfortunately.
5 Salesforce Opportunity Pipeline Metrics you Should be Tracking
It goes without saying the count of won opportunities, revenue and margin are important and common sales metrics. Here are 5 additional metrics that you can look to for inspiration.
Deal Win Rate – How many fully qualified Sales opportunities are closed? This metric is used to measure the effectiveness of your sales team.
Outbound Activities to Close – Measuring how many phone calls, emails, and SMS are required to close deals is effective at aggregate to measure top and bottom performers and understand globally what it will take to move customers through each stage to a win.
Average Deal Size – Understanding your average and potential median deal size are important to understand market shifts, targeting, sales effectiveness and is typically a driver for forecasting and predicting future deals.
Lifetime Value $ – For every customer, what is the total average value over time?
Churn Rate – For customers that are won, how many of them churn at the end of their service period or no longer make second purchases within a specified timeframe?
3 Salesforce Metric Tips for Success
Consistency of your metrics and KPIs are measured over time is most important. If the definitions change often, your ability to effectively use the metrics diminishes. Ensure you use clear definitions for points in your sales process.
Clear and concise metric definitions will ensure your business stakeholders, Salesforce admins, and your data and analytics team are aligned.
Ownership of every metric helps ensure accountability not only for monitoring. This also helps ensure changes in definitions and assumptions have a point person for approval.
Common Salesforce Metric and KPI Questions
What is a Salesforce Metric
A Salesforce metric measures performance over time where Salesforce is typically the system of record where the business process and transaction occurs. Salesforce metrics like sales revenue or lead conversion rate are created, calculated and measured with reporting inside of Salesforce or using 3rd party reporting and dashboard tools directly integrated with Salesforce.
What is a Salesforce Metric vs KPI?
A Salesforce metric tracks measurements over time while a KPI or Key Performance Indicator typically has not only a definitive target, but also a timeline and linkage to business goals and objectives.
A KPI should indicate the current and historical performance (Sales revenue is a common KPI for sales), while metric could help identify the leading indicators that influences sales (outbound calls, talk time).
How do I manage Salesforce metrics?
Many organizations simply manage Salesforce in Excel or word, which is fast and easy but requires a tremendous effort and cross functional ownership. There are a number of free solutions that help automate and streamline collecting, organizing and sharing metrics and tracking changes over time.
How do I ensure consistent Salesforce metrics?
Consistency in naming and consistency in measurement are two very common challenges within Salesforce. Tracking and managing aliases or synonyms for metrics over time is important but the measurements and application of metrics in reports needs to be consistent.
How do I design Salesforce KPIs?
The best advice is to ask your business leadership first what is the objective or goal that is most important? From there what are the top 3 things we should do to reach that goal? That is the framework for your KPIs. Setting a target and timeline to achieve the target in many sales organizations are monthly or quarterly. The most important thing is not to get hung up on terminology. If you are setting and agreeing to measurable goals and outcomes, that is most important. Consistency in terminology and approach is most important.
What is DataTools for Salesforce Metrics?
We built DataTools Pro to help inventory, manage, and track implementation of metrics and reporting inside of Salesforce. Bringing the same techniques we use for large scale enterprise Business Intelligence solutions, we have paired it down to make it very simple for Salesforce users.
Inventory metrics
Track aliases / synonyms for metrics
Align dashboards and reports to metrics
Detailed definitions and ownership
Align metrics to topics and lines of business
How do I create Salesforce Metric and KPI Dashboards?
Salesforce provides powerful and flexibility reporting and dashboard tools that ship standard with Salesforce. As the sophistication of your reporting and tracking requirements grow or complexity of calculations increase you may need a solution like Tableau (owned by Salesforce) or one of the many powerful point solutions.
When designing and mapping customer journeys, starting with technology is a sure fire way to not achieve your marketing and sales goals. In this article on Salesforce Ben, Ryan Goodman, highlights some of the key persona attributes and expectations for moving customers through the journey.
When it comes to optimizing your business processes and data analytics, Salesforce and Snowflake stand as two potent platforms, each with its own ecosystem of developers, stakeholders, and users. The Salesforce Snowflake Integration is an essential conduit that amplifies the bond between these two cloud platforms.
Native Salesforce Snowflake Integration: A Milestone in Native Data Sharing
Earlier this week, Salesforce and Snowflake made a groundbreaking announcement: the general availability of native Salesforce data sharing for Snowflake, via what is colloquially referred to as “BYOL” (Bring Your Own License). This is a significant advancement, especially for Snowflake users familiar with the benefits of zero-copy sharing, a core Snowflake feature. With this integration, gone are the days when you needed layers of additional software, services, and complex processes to bridge the two platforms. This is where the Salesforce Snowflake Connector comes into play, simplifying data access and queries between Salesforce and Snowflake.
Skill Enhancement through Certification Paths
Salesforce Data Cloud serves as a data hub orchestrating a wide range of business activities—be it CRM, marketing, or or any web/mobile activities. To encourage this, Salesforce recently launched its Certified-Data-Cloud-Consultant learning path. This will help Salesforce organizations readily find skilled professionals adept in Salesforce Snowflake Integration.
Salesforce Runs on Snowflake: Following the Leader
In a revelation that should add credibility and assurance to the Salesforce Snowflake Integration, Salesforce’s internal data and analytics have migrated to run on Snowflake. This shows Salesforce is not just advocating for the technology but using it themselves, setting the stage for rapid advancements in Salesforce and Snowflake connectivity.
Transforming AI/ML Workloads
The Salesforce and Snowflake partnership holds tremendous promise for accelerating the time-to-value from your Salesforce data assets. From curating data to deploying ML models, the integration, facilitated by the Salesforce Snowflake Connector, will enable enterprises to leverage their data in novel ways, including the utilization of advanced AI features. There are many first and third party powered solutions to weave your model deployment efforts.
Need Help Navigating these Waters?
We have been in front of Salesforce and Snowflake integrating analytics apps for years. We recreantly wrote the Salesforce data synchronization to Snowflake Guide and can’t wait to extend this into DataCloud. We have an incredible partner network that can help you implement any Salesforce or Snowflake Cloud components (CDP, MarketingCloud, Tableau).
Snowflake has rapidly emerged as the go-to platform for data-centric enterprises. Its ability to centralize and harmonize diverse data types makes it an exceptional foundation for any data strategy. Machine Learning Operations (MLOps) have matured significantly over the years, thanks to Platform as a Service providers like Amazon, Google, and Microsoft, who have developed comprehensive solutions that span the entire model lifecycle. However, a plethora of platforms and tools exist in a very crowded and confusing marketplace. So, what should a customer with Snowflake, a data and analytics team, and a desire to get models to production quickly and practically do?
Predactica: The is a Glimpse of the Future of MLOps in a Snowflake-Centric World
One name that should be on your list is Predactica.. This innovative solution is engineered to fit seamlessly with Snowflake. Predactica elevates MLOps by offering a natively integrated, end-to-end machine learning solution within Snowflake. Unlike other platforms that require disparate workflows and additional data pipelines, Predactica unifies these operations, making it the ideal companion for Snowflake-centric enterprises.
The result is a unified, agile, and compliant system that dramatically reduces the time-to-market for new models while ensuring their long-term reliability. Risk modelers and data scientists can now focus on the nuances of data, feature engineering, explanation and fine tuning.
Snowflake as Center of Gravity for Enterprise Data
With the introduction of Snowpark, Snowflake has also paved the way for native model deployment, allowing organizations to manage the entire model lifecycle within the data platform. This is done using the same tools, Python libraries, and workflows that data scientists, data engineers, and DevOps professionals already use. However, the rapid evolution of MLOps calls for a more streamlined, low-code solution that can natively integrate with Snowflake. This is where Predactica comes in to compliment or potentially replace external ML platforms and expand aspects of your MLOps to more contributors.
The Competitive Edge: Agility, Compliance, and Real-Time Monitoring
Another often-overlooked aspect of the machine learning lifecycle is monitoring model performance over time. Models, especially in credit risk, are not “set and forget.” They require ongoing attention to ensure they do not degrade and continue to make accurate predictions as market conditions and customer behaviors change. Predactica addresses this crucial need by offering built-in performance monitoring features. These tools enable teams to catch performance drift early, allowing for timely model adjustments and ensuring that your decision-making remains both agile and accurate.
Conclusion
The collaboration between Snowflake and Predactica represents a leap forward for organizations looking to democratize model development and accelerate speed to value.. Don’t take our word for it, setup a meeting or sign up for a trial and let us know what you think! Sign up for a Predactica Trial
