In recent years, marketers have seen a significant emphasis on data-driven decision-making. Additionally, there is an increased need to understand customer behavior and key metrics such as ROI or average order value (AOV). With an endless number of data sources (social media, email marketing, ERP systems, etc.) and a rapidly growing amount of data, both executives and analysts struggle to make sense of all the available information and respond in a timely manner.
A well-developed marketing dashboard helps companies overcome these challenges by organizing data into digestible metrics and reports that update automatically. Dashboards provide intuitive visuals that unlock the business value within your data quickly and without the manual effort of getting pieces of information from multiple places. Below, we have outlined four ways that dashboards benefit the entire marketing team, from the executives to the analysts.
1. Dashboards centralize all of your key information in one place.
Dashboards combine all of the essential information that would typically be found across various reports from disparate systems such as your CRM, ERP, or even third-party reports. Questions such as “How does our average order value compare to this time last year?”, “Which marketing channels are driving the most new customers?”, or “What is our marketing ROI?” can be quickly, consistently, and reliably answered without waiting on IT to put the information together or spending your department’s working day going to each software to download a new report and then marry them together. Instead, you can simply refer to a dashboard that highlights key statistics.
2. Dashboards automate manual processes and ensure reliable and consistent reports.
Marketing analysts often spend more time wrangling, cleaning, and validating data for a report than they do gleaning insights from it. Even after these reports are created, it’s difficult to ensure the metrics will remain consistent each time they are delivered to executives. Dashboards are typically built using one source of data that has predefined metrics and inputs, to ensure the reports remain consistent. They can automatically refresh data on a set schedule or surface it as it’s collected. Not only does this create more time for marketing analysts to focus on creating campaigns and incentives based on these insights, but it also allows executives to make decisions using accurate and consistent data points.
This dashboard highlights the trend in a selected metric, including its predicted future value, allowing marketers to quickly pivot when current campaign ROI is trending downward. It also allows the marketing department to demonstrate ROI to the company as a whole, which often leads to an increase in marketing budget.
This dashboard highlights the order activity associated with experimental products to allow the merchandising team to pivot quickly when a new product isn’t working, or it can allow the marketing team to increase advertising dollars if a new product isn’t getting enough attention.
3. Quick and reliable understanding of customer behavior paves the way to a stronger customer relationship.
Dashboards consolidate and visualize the story your data is telling. This often reflects the reality of how customers interact with your brand and clearly points out new trends.
For example, creating a picture that combines key pieces of information across systems, such as how many orders a customer placed and the value of those orders from your ERP with click analytics for that customer from your email marketing platform, allows you to identify like-customers who may respond to similar incentives. These customer profiles can then grow and change over time as you gather more data, leading to insights that allow you to more efficiently target the audiences who are more likely to convert based on that incentive. It also cuts down on the number of incentives or touchpoints you put in front of a customer who isn’t interested in that particular part of your business. Less spam for your customers and more conversions for you.
This dashboard quickly highlighted which customer segment was more engaged when the brand pushed social/email/web content, giving the marketing department the perfect focus group on which they could test new ideas.
This dashboard provides an executive-level overview of marketing performance.
This dashboard shows the comparison in purchase behavior across loyalty and non-loyalty customers. Our clients use dashboards like these to inform when they should incentivize customers to participate in a loyalty program or jump to the next tier and when the loyalty program is actually costing them more money than it’s yielding.
This dashboard highlights the time between orders across your customer base. Say, for example, you have a set of customers who place an order every six weeks, but they haven’t returned for 10. Our clients automate the discovery of these customers and send that email list to their email service provider (ESP) to automatically re-engage that customer base.
4. Dashboards are a gateway to advanced customer analytics.
With your analysts no longer consumed by manually building out reports, you’re on your way to identifying strong use cases for machine learning (ML) and predictive analytics. This form of advanced customer analytics covers a wide variety of use cases.
A common marketing use case for ML is predicting customer lifetime value (CLV/LTV) prior to investing marketing dollars on acquisition by matching a potential customer to the profile of existing customers that either yield high net profit or end up costing your business money. Another great marketing use case for data science is predicting the probability of conversion for a specific campaign or promotion based on a customer or customer segment’s previous behavior with like-campaigns. Your branding and merchandising teams may want to focus on identifying products that would yield a higher profit or increase in orders as a bundle instead of being sold individually.
Regardless of the use case, your dashboards will put you in a strong position to have a more targeted and therefore effective data science use case.
This is an example of a marketing dashboard that helps better understand customer demographics.
This dashboard gives marketers a place to test theories on customer demographics that would yield the highest LTV for a specific campaign.
Implementing strategic, goal-oriented dashboards significantly improves your marketing efforts at all levels. They provide analysts with the ability to spend their time acting on information rather than searching for and cleaning up data. More importantly, they enable executives to make informed decisions that ultimately increase ROI and ensure marketing budget is spent on impactful efforts.
2nd Watch has a vast array of experience helping marketers create dashboards that unlock valuable insights. Contact us or check out our Marketing Analytics Starter Pack to quickly gain the benefits listed above with a marketing dashboard specialized for your company.
Successful private equity firms need the ability to easily access and analyze data across their portfolio. Accurate financial, operational, sales, and other data must be available on demand to measure performance and make smart decisions. However, as a private equity firm’s portfolio grows, so do the complexities of analyzing data across portfolio companies.
Once data is centralized and standardized, private equity firms can significantly improve the way they analyze data by using interactive private equity dashboards built on modern business intelligence tools like Power BI, Looker, or Tableau. These interactive private equity dashboards not only provide a holistic view of data for all existing companies in a portfolio but also enable everyone from C-suite executives to analysts to easily run financial, operations, and sales reports to inform decisions with up-to-date information.
Interactive private equity dashboards can alleviate a variety of challenges across your company. Here are just a few examples of the enterprise-wide return on investment for incorporating dashboards into daily functions:
Centralized Data: Viewing reports in a centralized location leads to significant time savings. It cuts out the need to generate disparate reports for each data source.
User-Friendly Dashboards: Intuitive dashboard design means metrics are easy to read and always up to date, eliminating reliance on IT to provide refreshed data.
A Holistic View: Executives can easily assess portfolio company performance using reports that compare financial information across the entire enterprise.
Improved Access: Business users can quickly access data ranging from sales and marketing data to inventory and operations data.
Anticipation of Demand: Managers can utilize dashboards to forecast fluctuations in the next business cycle and set measurable goals.
At 2nd Watch, we have helped many private equity firms modernize their data analytics and reporting solutions, including creating interactive private equity dashboards. In our experience, the end vision of how interactive dashboards can fit into your day-to-day functions is often a bit foggy at first. That’s why we created sample dashboards in Power BI to help clarify how they can unlock the benefits mentioned above.
These interactive private equity dashboards illustrate how to drill into a holistic, enterprise-wide view, as well as how to upgrade your cash conversion cycle (CCC) and cash balance reports and integrate them into your daily functions.
Enterprise-Wide Financial Dashboard
This dashboard highlights essential financial KPIs at an enterprise-wide level. It can include information ranging from simple revenue or gross profit metrics to expense overhead or financial absorption. From this report alone, executives gain a snapshot of their overall financial health. This information can be quickly filtered to specific dates, regions, branches, and departments.
The dashboard can also benchmark your key metrics against your budget or the previous year’s metrics. This visual supplies managers with an overview of current KPIs to assess different areas of performance while creating actionable insights in a single screen.
From the enterprise-wide financial dashboard, any KPI requiring further investigation can be drilled into by clicking on the specific visual. For instance, clicking on the “Revenue” box will navigate the user to the revenue detail report. The report generates additional KPIs – such as revenue PY and YoY change – to provide further insights. This allows managers to get a distinct idea of their historical performance or see more specific details about particular metrics.
Within this visual, users can further slice and dice their KPIs by dates, departments, or other relevant attributes.
Cash Conversion Cycle Interactive Dashboard
As a financial executive, you want to be able to quickly understand CCC trends across the enterprise. The visual below demonstrates how to display your company’s trends in a digestible manner. This dashboard shows the number of days required to convert investments in inventory into cash and also trends this data against previous months. For one of 2nd Watch’s private equity clients, a similar report enabled financial subject matter experts to quickly understand how their CCC affects their bottom line and cash flow, as well as how it influences the amount of external funds needed.
Cash Balance Interactive Dashboard
Cash balances are another immensely important criterion that private equity associates often need to access quickly. A dashboard summarizing cash balances for each account and the cash balance trend over time enables associates to track up-to-date financial data when rolling up companies. This type of dashboard also helps executives gain more visibility into their business and understand how to better assist their clients.
These days, the rich information required to successfully run any private equity firm calls for a strong data strategy. The Power BI interactive dashboards demonstrated above are only a few examples of the wide variety of options available to you.
At 2nd Watch, we have helped private equity firms implement entire data analytics solutions from start to finish and have overseen focused analytics projects to help clients attain more modern analytics. Contact us for a complimentary 60-minute whiteboard session to get started.
Looker is one of several leading business intelligence (BI tools) that can help your organization harness the power of your data and glean impactful insights that allow you to make the best decisions for your business.
In this post, we’ll provide a high-level overview of Looker, including a description of the tool, why you should use it, pros and cons, and easily integrated tools and technologies to augment your Looker reporting.
Overview of Looker
Looker is a powerful BI tool that can help a business develop insightful visualizations. Among other benefits, users can create interactive and dynamic dashboards, schedule and automate the distribution of reports, set custom parameters to receive alerts, and utilize embedded analytics.
Why Use Looker
If you’re looking for a single source of truth, customized visuals, collaborative dashboards, and top-of-the-line customer support, Looker might be the best BI platform for you. Being fully browser-based cuts down on confusion as your team gets going, and customized pricing means you get exactly what you need.
Pros of Looker
Looker offers performant and scalable analytics on a near-real-time basis.
Because you need to define logic before creating visuals, it enforces a single-source-of-truth semantic layer.
Looker is completely browser-based, eliminating the need for desktop software.
It facilitates dashboard collaboration, allowing parallel development and publishing with out-of-the-box git integration.
Cons of Looker
Looker can be more expensive than competitors like Microsoft Power BI; so while adding Looker to an existing BI ecosystem can be beneficial, you will need to take costs into consideration.
Compared to Tableau, visuals aren’t as elegant and the platform isn’t as intuitive.
Coding in LookML is unavoidable, which may present a roadblock for report developers who have minimal experience with SQL.
Select Complementary Tools and Technologies for Looker
Any SQL database
Was this high-level overview of Looker helpful? If you’d like to learn more about Looker reporting or discuss how other leading BI tools, like Tableau and Power BI, may best fit your organization, contact us to learn more.
A scalable, visually impressive business intelligence (BI) tool like Tableau, especially in conjunction with a well-defined data warehouse, may be what your organization needs to vastly improve your data-based decision-making (and to wow company leaders).
In this post, we’ll provide a high-level overview of Tableau, including a description of the tool, why you should use it, pros and cons, and easily integrated tools and technologies to augment your Tableau reporting.
Overview of Tableau
Tableau is a highly scalable tool that produces sophisticated, well-designed visualizations. Role-based licensing can dramatically affect the price, but Tableau’s intuitive user experience and extensive features can make the cost worthwhile.
Why Use Tableau
If you have a well-defined data warehouse already in place, consistently up-to-date ETL, and a data-savvy team looking for data discovery across various source systems and applications, Tableau may be a good fit. Bar none, this is the tool to use for the most impressive visuals.
Pros of Tableau
Tableau offers quick, accurate, and flexible report building and analysis.
Tableau’s user experience is intuitive, including drag-and-drop reporting capabilities.
Tableau has a native function that analyzes performance problem areas, telling you which worksheets, queries, and dashboards are slow and even showing you the query text.
Tableau offers extremely flexible and beautiful visualizations that you can’t easily achieve with other tools.
Cons of Tableau
Users must shift from one desktop tool to another and then to the web to complete various tasks, similar to Power BI.
Also like Power BI, there is no parallel publishing capability, reducing dashboard collaboration.
You need to invest heavily in your data modeling and ETL to avoid performance degradation within Tableau.
Tableau performs much faster when executing large queries on extracts versus live connections.
Select Complementary Tools and Technologies for Tableau
If you found this high-level overview of Tableau helpful, you may be interested in learning more about Tableau reporting or how other leading BI tools, like Looker and Power BI, may suit your organization. Get in touch to learn more.
When paired with a modern data warehouse, implementing a leading business intelligence (BI) tool, like Power BI, can help your organization use your data to gain powerful insights to help you make the strongest decisions for your business.
In this post, we’ll provide a high-level overview of Power BI, including a description of the tool, why you should use it, pros and cons, and easily integrated tools and technologies.
Overview of Power BI
Power BI is a financially attractive alternative to the likes of Tableau and Looker, and it’s easy to learn for developers and business users alike. Companies already relying heavily on Microsoft tools may want to add Power BI to their toolkit to facilitate faster and deeper analysis.
Why Use Power BI
Power BI is initially cost-effective, especially if you’re already part of the Microsoft ecosystem. After upfront technical user assistance, business users can easily self-serve to build data visualizations and gain insights. However, dashboard collaboration is limited.
Pros of Power BI
Pricing is more reasonable than other big-name options, though add-on features can bump up the cost.
Power BI offers row-level security.
Drillthrough pages are useful for in-depth data exploration and decluttering of reports, while cutting down query execution time and improving runtime.
Power BI documentation is replete with tutorials, samples, quickstarts, and concepts, in addition to strong community forums to answer technical questions.
Cons of Power BI
A desktop component is required in most instances.
When working with large datasets, developers will experience some slowdown as they customize and publish reports.
Row-level security can slow down system performance as Power BI has to query the backend and generate caching for each user role.
You need to be all-in on a Microsoft ecosystem to maximize the benefits of Power BI.
Select Complementary Tools and Technologies for Power BI
We hope you found this high-level overview of Power BI helpful. If you’re interested in learning more about Power BI or other leading BI tools, like Looker and Tableau, contact us to learn more.
The widespread adoption of the value-based care model is encouraging more healthcare organizations to revisit the management of their data. Increased emphasis on the quality of service, elevating care outcomes along the way, means that organizations depend more than ever on consistent, accessible, and high-quality data.
The problem is that the current state of data management is inconsistent and disorganized. Less than half of healthcare CIOs trust the current quality of their clinical, operational, and financial data. In turn, the low credibility of their data sources calls into question their reporting and analytics, which ripples outward, inhibiting the entirety of their decision-making. Clinical diagnoses, operational assessments, insurance policy designs, and patient/member satisfaction reports all suffer with poor data governance.
Fortunately, most healthcare organizations can take straightforward steps to improve their data governance – if they are aware of what’s hindering their reporting and analytics. With that goal in mind, here are some of the most common challenges and oversights for data governance and what your organization can do to overcome them.
Most healthcare organizations are now aware of the idea of data silos. As a whole, the industry has made commendable progress breaking down these barriers and unifying large swaths of raw data into centralized repositories. Yet the ongoing addition of new data sources can lead to the return of analytical blind spots if your organization doesn’t create permanent protocols to prevent them.
Consider this situation: Your billing department just implemented a live chat feature on your website or app, providing automated answers to a variety of patient or member questions. If there is not an established protocol automatically integrating data from these interactions into your unified view, then you’ll miss valuable pieces of each patient or member’s overall story. The lack of data might result in missed opportunities for outreach campaigns or even expanded services.
Adding any new technology (e.g., live chat, healthcare diagnostic devices, virtual assistants) creates a potential threat to the comprehensiveness of your insights. Yet by creating a data pipeline and a data-centric culture, you can prevent data siloing from reasserting itself. Remember that your data ecosystem is dynamic, and your data governance practices should be too.
Lack of Uniformity
None of the data within a healthcare organization exists in a vacuum. Even if the data within your EHR or medical practice management (MPM) software is held to the highest quality standards, a lack of consistency between these or other platforms can diminish the overall accuracy of analytics. Worst of all, this absence of standardization can impact your organization in a number of ways.
When most people think of inconsistencies, it probably relates to the accuracy of the data itself. There are the obviously harmful clinical inconsistencies (e.g., a pathology report indicates cancerous cells are acute while a clinical report labels them chronic) and less glaring but damaging organizational inconsistencies (e.g., two or more different contact numbers that hamper communication). In these examples and others, data inaccuracies muddy the waters and impair the credibility of your analytics. The other issue is more subtle, sneaking under the radar: mismatched vocabulary, terminology, or representations.
Here’s an example. Let’s say a healthcare provider is trying to analyze data from two different sources, their MPM and their EHR. Both deal with patient demographics, but might have different definitions of what constitutes their demographics. Their age brackets might vary (one might set a limit at ages 18 to 29 and another might draw the line at 18 to 35), which can prevent seamless integration for demographic analysis. Though less harmful, this lack of uniformity can curtail the ability of departments to have a common understanding and derive meaningful business intelligence from their shared data.
In all of the above instances, establishing a single source of truth with standardized information and terminology is essential if you’re going to extract accurate and meaningful insights during your analyses.
To combat these problems, your organization needs to decide upon a standardized representation of core data entities that create challenges upon analysis. Then, rather than cleansing the data in their respective source systems, you can use an ELT process to extract and load structured and unstructured data into a centralized repository. Once the data has been centralized, you can evaluate the data for inaccuracies, standardize the data by applying data governance rules against it, and finally normalize the data so your organization can analyze it with greater uniformity.
Even when your data is high-quality and consistent, your organization might still fall short of data governance best practices. The reason why? The accessibility of your data might not thread the needle between HIPAA compliance and appropriate end-user authorization.
Some organizations, dedicated to protecting the protected health information (PHI) of their patients or members, clip their own wings when the time comes to analyze data. In an attempt to avoid expensive HIPAA violations, they restrict stakeholders, analysts, or other covered entities from accessing the data. Though it’s essential to remain HIPAA compliant, data analysis can be conducted in ways that safeguard PHI while also improving treatment quality or reducing the cost of care.
Your organization can de-identify records (removing names, geographic indicators, contact info, social security numbers, etc.) in a specific data warehouse. Presenting scrubbed files to authorized users can help them gain a wide range of insights that can transform care outcomes, reduce patient outmigration, reduce waste, and more.
Elevating Your Overall Data Governance
With all of these challenges in sight, it’s easy to get overwhelmed about the next steps. Though we’ve provided some actions your organization can utilize, it’s important to recognize effective data governance is as much a change in your mindset as it is a series of best practices. Here are some additional considerations to keep in mind as you work to improve your data governance:
You Need a Defined Data Governance Strategy.
An ad hoc approach to data governance will fail in the long run. There needs to be agreement among your data stakeholders about data availability, consistency, and quality. Often, it helps to start with a pilot project on a single line of business or department to ensure that all of the kinks of the transition are ironed out before your data governance strategy is taken enterprise wide.
Even then, compromise between standardization and distributed action is important so users within your organization are following the same best practices as they conduct dispersed analytics.
Your Culture Likely Needs to Change.
Eliminating data inconsistencies or adjusting inaccuracies are temporary fixes if only your executives are committed to making a change. Employees across your organization need to embrace the ideals of effective data governance if your organization is going to gain useful and accurate intelligence from your data.
Is your organization suffering from poor data governance? Find out the ways you can improve your data management by scheduling a whiteboard session with a member of the 2nd Watch team.
Jim Anfield – Principal, Healthcare Practice Leader 2nd Watch