Warning: A non-numeric value encountered in /homepages/0/d741589923/htdocs/clickandbuilds/AssociationAnalytics/wp-content/themes/Builder/lib/builder-core/lib/layout-engine/modules/class-layout-module.php on line 499

Warning: A non-numeric value encountered in /homepages/0/d741589923/htdocs/clickandbuilds/AssociationAnalytics/wp-content/themes/Builder/lib/builder-core/lib/layout-engine/modules/class-layout-module.php on line 499

Archive for Data Analytics for Associations

How Associations Are Successfully Using Artificial Intelligence

With AI no longer science fiction, associations are using advanced technologies to convert mountains of data into actionable insights.

At the recent EMERGENT event, hosted by Association Trends, we had the opportunity to jointly present case studies with ASAE’s Senior Director of Business Analytics, Christin Berry.

These success stories include how ASAE has:

Combined artificial intelligence and text analytics to enhance customer engagement, understand evolving trends, and improve product offerings

DOUBLED online engagement with unique open and the click-to-open rates using AI to personalize newsletters

Reduced the need for surveys, identified what’s trending, and measured through Community Exploration

Leveraged Expertise Search and Matching to better identify experts and bring people with similar interests together

I’m Matt Lesnak, VP of Product Development & Technology at Association Analytics and I hope to demystify these emerging technologies to jumpstart your endeavors in association innovation.

Text and Other Analytics

Associations turn to analytics and visual discovery for answers to common questions including:

  • How many members to we have for each member type?
  • How many weeks are people registering in advance of the annual meeting?
  • How much are sales this year for the top products?

Questions about text content can be very different, and less specific.  For example:

  • What is it about?
  • What are the key terms?
  • How can I categorize the content?
  • Who and where is it about?
  • How is it like other content?
  • How is the writer feeling?

It is widely estimated that 70% of analytics effort is spent on data wrangling.

This high proportion is no different for text analytics but can be well worth the effort. Text analytics involves unique challenges including:

  • Term ambiguity: Bank of a river vs. a bank with money vs. an airplane movement
  • Equivalent terms: Eat vs. ate, run vs. running
  • High volume: Rapidly growing social data
  • Different structure: Doesn’t really have rows, columns, and measure
  • Significant data wrangling: Must be transformed into usable format

Like the ever-growing data from association source systems that might flow to data warehouse, text content of interest might include community discussions, articles or other publications/books, session/speaker proposals, journal submissions, and voice calls or messages.

Possible uses include enhancing your content strategy, providing customized resources, extracting trending topics for CEOs, and identifying region-specific challenges.

Learn More


Personalized Newsletter

ASAE is working with rasa.io to automatically identify topics of newsletter content as part of a pilot that significantly improved user engagement.  ASAE and rasa.io first tracked newsletters interactions over time to understand individual preferences and trending topics.  Individuals then received personalized newsletters based on demonstrated preferences.

The effort had been very successful, as unique open and the click-to-open rates have more than doubled for the personalized newsletters.

Underlying technology includes Google, IBM Watson, and Amazon Web Services; combined with other machine learning tools developed by rasa.io.

Community Exploration

ASAE leverages a near-real-time integration with over 10 million community data points combined with enterprise data warehouse to analyze over 50,000 pieces of discussion content and over 50,000 site searches.  The integration is offered as part of the Association Analytics Acumen product through a partnership with Higher Logic.

Information extracted includes named entities, key phrases, term relevancy, and sentiment analysis.  This capability provides several impactful benefits.

Quick wins:

  • Visualize search terms
  • What’s trending
  • Staff and volunteer use
  • Reduce need for surveys

Longer-term opportunities:

  • Aboutness of posts as content strategy
  • Identifying key expertise areas
  • Connecting like-minded individuals

Underlying technology includes AWS Comprehend, Python, and Hadoop with Mahout.

Learn More

Expertise Search and Matching

Another application of text analytics that we’ve implemented involves enabling associations to better identify experts and bring together people with similar interests.  In addition to structured data from multiple sources, text from content including meeting abstracts and paper manuscripts provides insights into potential individual interests and expertise.

This incorporates data extracted from content using approaches including content similarity, term relevancy, validation of selected tags, and identifying potential collaborators.

Underlying technology includes Python and Hadoop with Mahout.

Approaches and Technology

We’re written extensively about the importance of transforming data into a format optimized for analytics, such as a dimensional data model implemented as a date warehouse.

Thinking back to the common association questions involving membership, event registration, and product sales; these are based on discrete data such as member type, event, and day.

Text data is structured for analysis using a different approach, but fundamentally similar as each term is a field instead of, for example, a member type table field.

Picture a matrix with each document as a row and each term as a column.

This is referred to as “vector space representation”.  With thousands of commonly used words in the English language, that can be a big matrix.  Fortunately, we have ways to reduce this size and complexity.

First, some basic text preparation:

  • Tokenization – splitting into words and sentences
  • Stop Word Removal – removing words such as “a”, “and”, “the”
  • Stemming – reduction to root word
  • Lemmatization – morphological analysis to reduce words
  • Spelling Correction – like common spell-checkers

Another classic approach is known as “Term Frequency–Inverse Document Frequency (TF-IDF)”.  We use TF-IDF to reduce the data to include the most important terms using the calculated scores.  TF-IDF is different from many other techniques as it considers the entire population of potential content as opposed to isolated individual instances.

It is widely estimated that 70% of analytics effort is spent on data wrangling.  This high proportion is no different for text analytics but can be well worth the effort.

Other key foundational processing:

  • Part-of-Speech Tagging: Noun, verb, adjective
  • Named Entity Recognition: Person, place, organization
  • Structure Parsing: Sentence component relationships
  • Synonym Assignment: Discrete list of synonyms
  • Word Embedding: Words converted to numbers

The use of Word Embedding, also referred to as Word Vectors is particularly interesting.  For example, the word embedding similarity of “question” and “answer” is over 0.93.  This isn’t necessarily intuitive and it is not feasible to manually maintain rules for different term combinations.

A team of researchers at good created a group of models known as Word2vec that is implemented in development languages including Python, Java, and C.

Here are common analysis techniques:

  • Text Classification: Assignment to pre-defined groups, that generally requires a set of classified content
  • Topic Modeling: Derives topics from text content
  • Text Clustering: Separating content into similar groups
  • Sentiment Analysis: Categorizing opinions with measures for positive, negative, and neutral

Finding and Measuring Results

With traditional data queries and interactive visualizations, we generally specify the data we want by selecting values, numeric ranges, or portions of strings.  This is very binary – either the data matches the criteria, or it does not.

We filter and curate text using similarity measures that estimate “distance” between text content.  Examples include point-based Euclidean Distance, Vector-based Cosine Distance, and set-based Jaccard Similarity.

Once we identify desired content, how do we measure overall results?  This is referred as relevance and is made up of measures known as precision and recall.  Precision is the fraction of relevant instances among the retrieved instances, and recall is the fraction of relevant instances that have been retrieved over the total amount of relevant instances.  The balance between these measured is based on a tradeoff between ensuring all content is included and only including content of interest.  This should be driven by the business scenario.

This overall approach to text analytics is like that used for recommendation engines based on collaborative filtering driven by preferences of “similar” users and “similar” products.

APIs to the Rescue

Fortunately, there are web-based Application Programming Interfaces (APIs) that we’ve used to help you get started.  Here are online instances from Amazon and IBM for interactive experimenting:

This is a lot of information, but the takeaways are they there are big opportunities for associations to mine their trove of text data and it is easy to get started using web-based APIs to rapidly provide valuable insights.

Learn More


Matt Lesnak, VP of Product Development & Technology
Association Analytics

Your Association Needs An Analytics Translator

If you have a data analytics team or you are in the process of forming one, the analytics translator is a role to consider hiring for. This individual plays a critical role on that team and at your organization. She or he serves as the link between the analytics team and the executive team because this individual takes the information from the data team and translates it on a broader scope, so stakeholders can see how analytics impacts the organization.

Think of the analytics translator akin to a marketing operations manager at a B2B or B2C company. A marketing operations person is a subject matter expert on marketing technology solutions and typically works very closely with the executive teams. That individual also oversees the implementation of a marketing automation system, the data, and the processes to support the organization. A person in this role also works with the demand generation team to support any campaigns they want to execute and provides the metrics and reporting. She or he has both qualitative and analytical skills and possesses superb communication and project management skills to be successful in their role.

So, what does an analytics translator do then? According to Mckinesy and Company, someone in this role “communicates data science features and capabilities to internal and external stakeholders in order to identify business needs and uncover areas in need of deeper data exploration.”

Ideally, an analytics translator possesses the technical fluency in analytics, strong project management and communications skills, and a keen interest in staying current with industry trends.

Now you might be thinking that your data analytics team already has many of these skills, but the analytics translator is truly a unique role that an organization benefits from having. Here are 3 reasons your association needs to bring an analytics translator on staff.

1. Help demonstrate how analytics solves business problems

Analytics continues to play an instrumental role at organizations because it helps staff members make data-guided decisions to help them better align with their overall organization’s goals. Sometimes one person (such as a data analyst) alone cannot convince their executive board that a data analytics platform is a worthwhile investment. In some cases, it takes someone such as an analytics translator to convey the need to purchase an analytics tool because this person has the expertise to communicate how it can truly solve your organization’s challenges in an easy-to-understand way.

Let’s say your organization already has an analytics platform, but your organization’s stakeholders still don’t understand the value of it. That’s where an analytics translator can also help. An analytics translator is in a unique position where she or he can demonstrate the power of data analytics by identifying where it solves a business need. Once this individual discovers an area of opportunity, she or he works with the analytics team who then creates data models that show where the problem is and how best to resolve it. The translator might offer suggestions on adjusting the model so that it produces actionable insights that their executive team can easily interpret. Once the model is complete, this person ensures it’s the final product before delivering it over to the executive team to review.

2. Function as a Project Manager

One of the attributes an analytics translator must posses is excellent project manager skills. In the association world, this individual can operate as an “internal” project manager. She or he serves as a go-between the business leaders and the data analytics team ensuring that an analytics project is successfully executed from start to finish. This individual also can communicate the project’s progress in a digestible way to different team members at the company. If the stakeholders want changes to the project, then they work with the analytics translator who then relays those changes to the analytics team. The analytics translator helps to bridge that gap that exists between those who are a part of the data team and the executive team, so each side is better aligned with one another.

3. Assist in implementing solutions

An analytics translator serves as an advocate for adopting new solutions at your organization. One of the challenges your organization might face is some staff members hesitation in incorporating analytics into their roles. Shifting to a data-guided mindset is intimidating if your team isn’t accustomed to thinking in that manner. Also, sometimes company politics are a roadblock when attempting to implement a new tool or project across the organization. The analytics translator has the potential to champion the need to use analytics or needs to possess the grit to “fight” that battle because there will be people who are less enthusiastic to the notion.

If the resistance to adopt to new technology or start a new project exists in your organization, don’t feel as if you’re the only one experiencing that. It’s more common than you realize. That’s why it’s essential to have people such as an analytics translator to work as the driving force in implementing new solutions at your organization.

What if there’s budget constraints?

Even if an analytics translator isn’t a role you can fill at this time due to budget constraints, it’s a great position to keep in mind for the future. If hiring someone for that position isn’t possible though, then consider delegating the responsibilities to your data analytics team. It’s likely there’s someone on that team that possess some of these skills, so it’s just a matter of honing them and shifting their responsibilities around. Sometimes this is a better option because this person is already familiar with the company, processes, and data. And remember, an analytics translator doesn’t have to be a data scientist to be successful in this role.

Need help?

Contact us at info@AssociationAnalytics.com or (800) 920-9739 to explore if the Analytics Translator role is right for your association.

How to Structure Your Association’s Data Analytics Team

Having a data analytics team is essential for your organization to move forward with an analytics strategy and to drive a data-guided culture. Before you build your team and identify what roles you should hire and/or fill, you need to determine what your organization’s goals are and how data analytics can help you achieve those goals. It’s also good to recognize what responsibilities need to be created for you to attain those goals.

Another way to assess what roles you need when forming your data analytics team is to consider where your organization is in how they use data. Once you know those, then you can assemble a team of people to assign the projects to.

Here are 5 roles to consider when structuring your association’s data analytics team.

Data Analyst

A data analyst role could be quite versatile depending on how your organization chooses to define this position. This individual will have a data-guided mindset and a curious nature for understanding what the data is trying to convey. If your organization is looking for an AMS, this person could play an important role in the RFP process. Other responsibilities could include pulling data reports based on requests, ensuring data accuracy, and conducting data integrity audits. Besides having an analytical skillset, it’s also best for this individual to have strong relationship skills because she or he will be working with various members on the leadership team to communicate what the data is saying and provide recommendations on what to do.

Data Warehouse Manager

Think of the Data Warehouse Manager as a “gatekeeper” for your data. This individual plays an instrumental role in maintaining the data integrity and ensuring everyone is following data management best practices. If your organization doesn’t have a central data repository, then a data warehouse manager will lead the project in creating a single place where all the data resides. This person will also monitor the database to ensure the data is accurate and consumable for other staff members. This person could also assist in the development of written internal procedures that help with data upkeep and work cross-functionally to communicate these policies.

Database Developer

This role will work closely with the data warehouse manager and will be responsible for the actual creation of the database. It’s up to them to design a centralized database that is user-friendly, reliable, and effective. She or he will continually optimize the database and its functionality as the organization continues to grow and needs change. Depending on how your organization wants to define this role, this person could also have a hand in developing a data dictionary and catalogue. As with the other roles, this person will possess excellent communication skills and is adaptable. When building the database, it’s likely that last-minute changes will get thrown at this individual so she or she needs to be flexible. 

Chief Data Officer

Ideally, you want a data advocate to have a strong presence on the executive team. This role will spearhead the data management and analytics projects performed within their department or team. She or he will also play an integral role in making sure the entire organization understands that data helps drive growth and offers a competitive advantage. Following best practices for database management and governance falls into this role as well. And this person will advocate for that consistency, or it will be impossible to make the best use of the data.

This individual will be in a unique position to inspire a change in the organizational culture. He or she could encourage people to adopt a more data-guided mindset. Without this individual, it will be challenging to push for data to be treated as a strategic asset.

Big Data Visualizer

Think of this role as the “storyteller” of the team. This individual will take the data living in your warehouse and transform it into a visual and informative narrative that’s utilized by various departments. Since this person will be a “storyteller,” she or he could also be involved in writing proposals when wanting to get buy-in for a piece of technology.

Since this person will work with cross-functional teams to provide data visualizations, she or he must have strong communication skills and be able to explain data insights in different ways that resonate with their audiences. This will also involve developing and maintaining a collection of data visuals such as graphs, charts, and dashboards that other team members can access.

As with the previous roles, this person also ensures the data integrity of the warehouse. It’s a responsibility that can’t fall on solely on one team member. Everyone will play a part!

A Final Thought…

There you have it! These are the roles to consider filling when building your data analytics team.

Keep in mind that every organization is unique, and one way to approach it is by examining where your organization is in terms of analytics maturity. And if your association doesn’t have the budget for some of these roles, then that’s okay! Responsibilities can be merged into one role, and you can prioritize one responsibility over another. There’s no right or wrong way to structure your data analytics team. It’s ultimately up to your team to determine which roles are vital to the success of your organization. After all, it’s not the data that’s your most valuable asset. It’s the people at your organization.

Learn More About DAMM for Associations

To learn more about the DAMM for Associations and what a data analytics model can do for your organization contact us and sign up for our monthly newsletter.

5 Areas To Assess Using the DAMM—Data Analytics Maturity Model

At ASAE Tech earlier this month, we shared our Data Analytics Maturity Model (DAMM) which is built for associations by association leaders. Designed as an assessment and high-level action plan, DAMM helps staff members identify where their organization is when it comes to taking a data-guided approach to overall strategy.

Keep in mind though each association is unique and the need for data analytics differs so answers to this assessment vary.

While there are several indicators in each stage of DAMM worth examining, here are 5 areas to hone in on because it impacts where your organization lands in the model.



1. Make your data accessible to everyone.

At this year’s ASAE Annual Meeting, we conducted a live poll of our audience and discovered some interesting statistics that hold true at many associations. 68% of association staff members recognize the potential value of data, but they lack the processes and tools to make it useful to everyone.

Stage 1 of the Data Analytics Maturity Model focuses on “learning.” Associations need to evaluate where their data resides and how easy it is for everyone at the organization to access it. Or is the reporting centralized in the IT department? In the past, it might have made sense for IT to pull the reports and to own the data. However, for your organization to flourish, everyone needs to be able to see the data when it’s convenient for them. A sign of an organization’s maturity is when the data silos have broken down. The ability to use data is essential for competitive advantage and should be a goal for the entire company.

2. Instill an analytics-guided culture.

Chances are you do have business leaders in your organization who possess analytical mindsets and understand the value of data analytics. Those are the champions driving your organization to be more data-guided. Those same staff members can form the core analytics team and begin to develop an effective data strategy that be used throughout the company.

Stage 2 of DAMM is the “planning” phase. At this point, there’s the recognition that there’s a lack of trust in the accuracy of the data. Your team is unsure what data they need or even where to find it. The core analytics team is essential in helping to overcome this challenge. 

This same team can also be the ones who experiment with data visualization and create standard operating procedures around data quality management.

All it takes is a small team of interested and motivated staff members to instill a more analytics-guided culture. It won’t happen overnight, of course, but they can be an influential force in encouraging people to embrace analytics in their day-to-day roles.



3. Develop clearly defined KPIs.

Stage 3 is the next stage of maturity for an organization, and it’s all about “building.” By this time, your association has a data strategy in mind and action plan for analytics that has been given the green light from your executive team. Once you’ve gained the executive team’s buy-in, you’ll discover that KPIs need to be established to track your success along the way. How you measure success varies with every department so it’s up to your team to determine what is most suitable to measure your performance. Ideally, these KPIs should go beyond “vanity metrics” and really drill down to what will directly impact the member experience, and these should track back to what your organization’s overall goals are. These take time to refine, but it’s a sign that your organization is maturing.

4. Create a central repository that encompasses all key data sources.

It’s not uncommon to have data living in different systems because there was probably a time when this was a common practice at your association. When your association uses data and analytics to solve business problems, one of the immediate projects to tackle is keeping the data in one repository for everyone. Within this repository, you can find all the key data you need to have a deeper understanding of your members and the services you’re providing them.

Once your organization has a central place for data storage, you’re at Stage 4 which involves “applying” treating the data as an organizational asset and eventually becoming proficient in data analysis because it’s becoming a go-to source for understanding your organization and members. 

5. Implement a data governance program.

According to DAMM, the final stage is when your staff members are actively taking a data-guided approach to run their business. Data is routinely used by staff in their respective roles, and everyone has a 360-view of their members. One way to aid data-guided decision making is to establish a data governance program. Data governance keeps data updated and accurate for everyone who uses it. In order for this program to be successful and ongoing, you need to have a team take ownership of it. Whoever this group of staff members are, it’s up to them to communicate, follow, and continually improve the program so it’s relevant to the association as it continues to grow.

The DAMM for associations and nonprofits is an eye-opening way for your organization’s team to identify where it is in the model and what action steps you can take to move your association forward.

Learn More About DAMM for Associations

To learn more about the upcoming release of DAMM for Associations and what a data analytics model can do for your organization contact us and sign up for our monthly newsletter.

Developing Meaningful KPIs to Get More from Data Analytics

As the days grow shorter and end of the year lists start appearing in our feeds, it’s natural to take a retrospective view on the past year and to begin thinking about what we could do differently for 2018 regarding data analytics.
Where do you want to be at this time next year? How will you get there? And how will you measure your success?
Now is the time to form meaningful key performance indicators (KPIs) that will guide you through the coming year and have your association meeting its goals.
Association Analytics COO Julie Sciullo, along with Sean Hewitt, our Director of Data Governance and Adam Rosenbaum, Director of Information Systems at CASE—will be presenting on this topic at the ASAE Technology Conference in December.
Here’s a sneak peek of what they’ll cover in their session and some tips on how to use data analytics to build useful KPIs.

What Are KPIs?

A KPI is something that can be counted and compared. It provides evidence of the degree to which an objective is being attained over a specified time.
For instance, if your goal in 2018 is to raise the public awareness of your association, then KPIs might be to run X number of ad campaigns that received X number of impressions in the first two quarters.
Here are some questions to ask to determine if the KPIs you have are working in the right way:

  • Can it be counted in the form of a number, percentage or currency?
  • Can it be compared to what is optimal?
  • Is the evidence observed in the same way by all stakeholders?
  • Is it contributing to a significant organizational objective?
  • Is it being measured over a specified period of time?

What Makes a KPI Meaningful?

A meaningful performance indicator ties directly to a strategic objective. Though keep in mind, making the connection between an ambitious objective and winnowing it down to a measurable indicator that’s manageable and measurable is not always an easy task.
If only it were as simple as telling a team to climb up a hill, where you have a precise, calculated performance metric. As you progress up the hill, you can track the distance travelled, the number of teammates who are still climbing, and how far left you have to go. At the end of the journey, you’re either at the top and you accomplished your goal, or you fell short.
The objectives your association wants to achieve probably aren’t as literal as mountain climbing, but it’s worth taking the time and mental effort to figure out what hills you want to climb and then breaking the journey up into measurable pieces.
In the end, the meaningful KPIs are those that move your mountains into molehills, breaking up the bigger goal into attainable chunks that can be effectively measured to make sure you’re on track and that the actions you’re taking are having a positive effect.

How Can Data Analytics Help Inform KPIs?

A meaningful strategic goal (and the KPIs that correspond to it) balances on three important points: strategy, data, and the ability interpret that data. What ties it all together and really allows an organization to take action is understanding what you want to get out of accomplishing that goal and why you want to accomplish it in the first place.
Using and analyzing data to define your strategy takes away assumptions and cuts to the center of the goal’s purpose. When you can look at what the data is telling you and combine it with the “why” of your goal, you can better define the next action steps to take.

Interested in learning more about our session at ASAE Tech or how data analytics can inform your strategic goals? Sign-up for our monthly newsletter.

4 Essentials to Ensure a Successful Customer Journey Analytics Plan

While the customer journey isn’t a new concept, the science behind customer journey analytics is still relatively new, but is slowly gaining popularity. By 2018, 60% of organizations will be adopting customer journey analytics into their organizational strategy.

As technology changes exponentially, you expect to obtain information and make purchases and decisions even faster than ever before. For businesses, that’s a demand that they need to keep up with or they risk losing customers. The same can be said for your association.

In order to stay relevant in the eyes of your existing members and to attract prospective members, you need to have a customer journey analytics plan that makes their customer journey easy, intuitive, and provides an exceptional experience. The journey that you create for customers shapes the customers view of your organization.

Here are 4 essentials to keep in mind before you implement a customer journey analytics strategy at your association.

1. Customer journey mapping vs. customer journey analytics.

Both of these terms might seem similar, but each one has a different meaning so it’s important to have an understanding of them because they go hand-in-hand.

A customer journey map allows you to see things from the customer’s perspective, and the different touchpoints they interact with at your organization. It focuses more on the high-level macro-journey and tells more of a story. However, it doesn’t hone in on data like customer journey analytics does.

According to McKinsey, customer journey analytics is a combination of journey mapping, customer-behavior analyses, channel-opportunity scans, and machine-learning algorithms creates a picture of the end-to-end customer journey. It’s focuses on the micro-level journeys and is more comprehensive. Customer journey analytics can show you that your members take different paths in a variety of channels to get to the end goal. And most importantly, it’s based on data.

Think back to your last Annual Meeting. What was the process someone took to attend your conference? Were there touchpoints that led to their decision to register? Did that person see your Annual Meeting in an online ad, blog post, or email? Perhaps they learned about it from a colleague? And what happened after the event? One would hope they plan to attend again next year based on their touchpoints. That’s the power of a customer journey. Knowing those touchpoints help you make data-guided decisions and inform your overall organization’s strategy.

When you combine both the analytics and the mapping, you have a powerful combination that can transform your organization because you have a deeper understanding of the customer-journey and it gives you the ability to predict future behavior as well.

2. Visualization is key.

Once your team maps out a customer journey with the goal of converting your leads and building brand loyalty, you need a way to present the story in a visual way. Often times, it’s easier to grasp a concept or idea when it’s shown in a visually appealing way. Seeing data story empowers your team to make data-guided decisions because it’s easier to spot trends and make decisions faster and more confidently. You can also forecast what can happen in the future and make adjustments to the customer journey. Your data story should also help your team better align with your organization’s overall goals or re-prioritize them if needed.

Consider these 3 factors when developing a data story:

  • What’s the visual going to look like?
  • Who is your audience or stakeholders?
  • What decisions can be made to improve the customer experience?

If data isn’t present in your story, then all you have is a picture and that’s not enough to determine how best to optimize the customer journey.

3. Break down the silos.

Most people tend to evaluate data in silos because it’s scattered in different systems and channels. When data isn’t in a central location that’s accessible to everyone, you don’t have an accurate representation of the customer journey. Everyone is interpreting it differently because they don’t have all the data just certain touchpoints.

Customer journey analytics is not a one-department project. It’s an organizational-wide effort that everyone should be involved with. While one department might be focusing on the touchpoints, everyone should have an understanding of the journey a member takes and what it’s informing you to do.

Breaking down the silos won’t happen overnight or even in a week. It’s a process that will take time because you have to shift your team’s mindset and how they interpret data as a whole.

4. Get employee advocacy.

Getting staff-wide buy-in to the idea of customer journey analytics is a challenge depending on the organizational culture. If employees are typically slower to adapt to change and embracing new technology, then you have to build a solid case to prove implementing a customer journey analytics plan is worthwhile and necessary for sustaining your organization.

Some of the ramifications of having a poor customer journey include but are not limited to:

  1. Customer defection
  2. Negative reputation
  3. Higher call volumes
  4. Lost sales
  5. Lower employee morale

As you develop your customer journey analytics plan, involve the key stakeholders, particularly the ones that are enthusiastic about the idea. They are the “champions” who support the plan and ensure that it’s set up for success long after it’s implemented.

When it comes to gaining buy-in from the rest of your staff, tailor your case by using terms they understand and relate to. Avoid using technical jargon if you’re not speaking with a technical group. Demonstrate how customer journey analytics empowers them to make better decisions because they have data to guide them.

Another way to transform your association into a more data-guided culture is to incorporate it into your onboarding process for new hires. By instilling that mindset from the get-go, you contribute to the change in the long-run.

With customer journey analytics in place, you can differentiate yourselves from other organizations and deliver an excellent customer experience that keeps your members coming back for more. It has the power to transform your organization, but in order for that to happen, you need to change your traditional association approach to one that is customer-centric.

And over time, the more data you acquire from your analytics, the more equipped you’ll be to adjust your customer journey to give your members what they need when they need it more effectively.

Ready to plan?

Contact us at info@AssociationAnalytics.com or (800) 920-9739 to discuss your association’s customer journey analytics plan.

What Data Analytics and the Mona Lisa Have in Common

Surge 2017 Virtual Conference: Embracing the Right Mindset to Take Data Analytics to the Next Level

With so much data available at our fingertips, deciding how to actually use it can easily become overwhelming. While there’s no one secret to leveraging data analytics, those who do embrace an analytical mindset in their association will be able to make evidence-based decisions quickly and confidently.
I sat down with Wayne Eckerson, author of Secrets of Analytical Leaders, to discuss how association leaders can adopt an analytical mindset and leverage big data to make powerful decisions.
Our full discussion will be presented at the Surge 2017 Virtual Summit on Wednesday, November 8, at 12:00 EST. This is a great opportunity to pick up some practical steps for how to be a role model for effective decision-making in your organization.
Here are three sneak peek secrets from our interview…
Data analytics for associations | Wayne Eckerson
1. “Purple People” Are Best Suited for Today’s Associations
Eckerson introduced the concept of “purple people” in the first chapter of his book, and it’s an idea that has resonated widely.
Think of it this way: the classic persona who is most interested in data analytics are the IT professionals—those who are responsible for measuring data, running reports, and setting up dashboards. Let’s call them “red people.”
Then the “blue people” are those who are business-minded. They have an understanding of business objectives and priorities.
“So you can’t be just red in IT or blue in business,” Eckerson says. “You have to be a blend of both. Of course, when you blend red and blue you get purple. So, a lot of the folks that we traditionally address really like that phrase because, in their own experience, that’s really what the level of success for them was—to be able to understand the business at a deep level, translate that into technical requirements, and then give them back what they needed as they wanted it.
“We see things coming together and that need for a purple person is just as strong as ever. That purple person can come from the business, can come from IT, but there’s gotta be a meeting in the middle.”
2. Now Is the Best Time to Dig into Data
Now is the opportunity for associations to take advantage of the things that are happening with data so that they can make decisions with confidence.
One of the problems is that if we don’t know what to expect, a lot of times we use instinct, politics, and tradition. That’s dangerous when the pace of change is increasing. We now no longer can just rely on our wisdom and our experience.
We need to combine our wisdom and experience with data so that we can make what I like to call “data-guided” decisions. And there’s never been a better time to do this.
3. Big Data and the Mona Lisa Have Something in Common
We’ve all heard the expression “a picture is worth a thousand words,” but a picture is also worth a thousand numbers because you can present data visually. A lot of dense data can be presented quickly visually. But even the best data visualizations that we’ve developed are still only half of the solution. We need to have this ability to tell the story about what the data means.
So, when we use data visualization and we can combine a story that explains what it means, it becomes a much more compelling case for business leaders to make decisions quickly. This is a fabulous opportunity and time in the history for association leaders to advance their careers by being able to understand and explain the story and the data.

Our discussion at the Surge 2017 Virtual Summit contains an in-depth analysis of big data and adopting the analytical mindset. Register for the Summit to access the full session, including:

  • The difference between power users and casual users
  • The best way for an association or nonprofit to get started on a business intelligence journey
  • How to measure the success of an effective analytics program
  • How to determine the ROI for a data analytics project

If you’d like to learn more about events and topics such as this in the futuresign-up for our monthly email newsletter.

6 Surprise Findings to Help Your Association Become Data-Guided

Our gut instincts play a role in our decisions – both in our personal and professional life. And more times than not, we can trust our intuition when making decisions. However, in our associations, relying on gut isn’t enough to achieve strategic goals and to stay relevant. We also have to be data-guided in our decisions. Both go hand in hand. This is because today we have more decisions to make than ever before, and the pace of change itself is increasing exponentially. It’s really just not possible for associations to rely on instinct, politics or tradition in today’s world.
A few weeks ago, Ric Camacho, Chief Technology & Digital Officer, Specialty Food Association Inc. and I presented on Business Analytics Projects and Initiatives at ASAE Annual. During this year’s ASAE Annual Meeting in Toronto, we conducted a live poll of our audience during our session about Business Analytics Projects and Initiatives and what we discovered was surprising. Although these findings are from polling the attendees of one session, based on over a decade of experience we find these statistics to be representative of what we find holds true at most associations.
Here are 6 surprise findings from the survey results:
1) Data isn’t easily available in one centralized location for easy reporting and analysis.
Only 8% agree that the data is available in one centralized location, while 72% disagree and feel that their data isn’t easily accessible in one location. When data is scattered in different systems and spreadsheets, this leads to data silos. The data that one group has in their system can be just as valuable for someone else at their association – especially when combined with other data. And while it’s tempting to put your data in a system that is convenient for you, that’s not helping in creating a more data-guided, transparent culture either. Everyone at your organization should have the ability to drill down into data visualizations to pull data at any time. Combining data in one easy-to-access environment creates transparency across the organization, which in turn helps staff members make better decisions about both strategy and execution.
2) Business staff don’t have the tools to easily access data with having to rely on IT.
72% indicate their staff have to rely on their IT team to access the data they need. Ideally, the data shouldn’t be only accessible via IT. If you can have access to and can quickly understand the meaning of data when you need it, think of the all the time you will save! You can spend more time examining it and making your next move. Data is an asset for everyone at your association, regardless of which department they’re in. By having easy access to data, you can better define business goals and develop strategies to attain them. You can also track KPIs on a regular cadence when they are available across the organization and updated automatically every day. Relying on another department to perform a task that you should be able to do yourself is a burden to both you and the department pulling the data, particularly if the other department has a lot on their plate. You feel more empowered in your roles when you have the ability to pull reports that allow you to do your job more effectively – which also makes you happier and more fulfilled!
3) We don’t use visualization tools to understand our data and make decisions quickly.
69% of attendees believe that their existing data analytics tools aren’t visually appealing. Believe it or not, you can showcase your data in an attractive and meaningful way. Most people are naturally drawn to attractive visuals. When presenting your data story to your executive team, you’re more likely to capture and hold attention if the data is presented in a visually appealing way. Telling your data story using a dashboard instead of a traditional spreadsheet is a great way to demonstrate patterns or trends which help you make more strategic decisions because you have the data to back up your ideas.
4) We don’t use data to effectively segment and target marketing.
One way to annoy and potentially lose your members is by spamming them with irrelevant content. Each member at your organization is unique and has different interests. And you can determine what content is valuable to them if you examine your member data and demographics. It’s up to your team to determine what segments make the most sense for your organization. By segmenting your audience, you show that you care about your members and their needs and want to offer them something of value.
5) Data is recognized as an asset and analytics is seen as critical to strategic success.
Another surprising revelation from this survey is how many people understand that data is an integral part of an organization’s success. However, they aren’t utilizing it in the best way possible for the reasons listed above. Getting organizational buy-in for an analytics platform can be an uphill battle if your CEO and board don’t understand the need. It can be a cultural realignment if that’s not how decisions have been made in the past. However, it’s not impossible to change the culture if their goal is to further the organization’s mission, and you can’t do that relying on instinct alone.
6) Data analytics has the support of the CEO and board.
68% of association staff have the support of the CEO and board that data analytics plays a key role in driving their association’s mission – and this is great news! Getting buy-in from the executives is half the battle so once you have their support for using data to make decisions, then it’s easy to explain the need for an analytics platform. Data analytics is something that everyone at your organization can benefit from – but remember the “tool” is not the solution. It’s a process, not a project. The important thing is to begin where you are and create a roadmap that will meet your needs both now and in the future.

Ready to Plan?

Contact us at info@AssociationAnalytics.com or (800) 920-9739 to discuss your association’s analytics strategy and roadmap.

5 Ways to Kickstart Your Association’s Business Analytics Projects

In the blink of an eye, another summer has passed along with ASAE Annual. Last month, the Association Analytics team made their way to Toronto, Canada and as always, it was filled with informative sessions, incredible speakers, and fun conversations. This year’s overarching conference theme, “What Inspires”, can be easy to answer for some. For others though, it can be challenging to explain. If you’re an association professional, then chances are your organization’s mission inspires you each day. And your members are the lifeline that keeps your association alive. However, before you can effectively reach your members, you need to examine the data (through analytics projects) to better understand their needs. Then, you can find ways to inspire them to stay engaged with your organization.
This year, Ric Camacho, Chief Technology & Digital Officer, Specialty Food Association Inc. and the team at Association Analytics had the opportunity to present a pre-conference masterclass session, “Kickstart Business Analytics Projects and Initiatives”. If you were unable to attend this year’s conference or just want a refresher, we have you covered. Here are 5 key takeaways from the Kickstart Business Analytics Projects and Initiatives Session from ASAE 2017.
1) Data Is an Asset.
Data is an integral part of your association because it’s one of the elements that makes your organization unique. The data living in your software systems can tell you a lot of about what your members needs are and how you can keep them engaged or even re-engage with them. However, if your data is not managed, then it’s not a viable asset to your organization. Data management is essential in order for you to make informed decisions that guide your association’s strategy.
Here are just a few of the ways an association can use data to better understand their members:

  • Profile optimal customer by segment
  • Determine who is “at risk” (for not renewing, not attending, not donating, etc.)
  • Identify new audiences
  • Cross Sell/Up Sell/Next Sell
  • Create new products and services
  • Measure performance

Like I said, just a handful. Data can even help you tell your association’s story (aka mission) which will resonate with prospective and current members. But how do you let data drive your mission? You can achieve that by creating an analytics strategy.
2) How to Create an Analytics Strategy and Roadmap

An analytics strategy should be rooted within the overall association strategy. However, the process of creating an analytics strategy and roadmap can seem daunting. It doesn’t have to be though. There are some key steps that your team can take to get started. Once you’ve completed this strategy and roadmap, you can present it to the leadership team and show the importance of embracing a culture of analytics. Ultimately, if everyone can adopt the data-guided mindset, then the leadership team is more likely to see the benefits of it and will also treat data as an asset.
3) A Picture Is Worth a Thousand Numbers
Wait, why does this sound familiar? Yes, we’ve all heard the old adage “A picture is worth a thousand words,” and the same can be said for numbers. For many people, data is more likely to resonate with your team if you showcase it in a visually appealing way. Now, you might be thinking how can data be “pretty” and still be valuable. Well, guess what it’s possible to have both. There are programs, such as Power BI, that can provide dynamic reporting in an attractive and useful way so you can present your data story to executives. Ideally, if data is provided in a visual way that’s easy to decipher and process, then it will be easier to convince your team to embrace it as a way to better engage with your members.
4) Five Actionable Steps to Begin Using Data Analytics

Let’s say your team is on board with using the data to make decisions. Here’s an all too familiar example. Your association team is planning their next Annual Meeting. You want to find out what the registration numbers have been for the past few years, the retention rates at each meeting, and how many first-timers have been attending. Once you’ve determined the scope, you’ll need to collect this data from your registration reports. Next, you’ll have to clean it and extract what is relevant to you. From there, you can analyze it, and determine KPIs and goals for your upcoming Annual Meeting. Keep these 5 steps in mind as you begin using data analytics: Scope, Collect, Clean, Analyze, and Act.
5) Culture Change and the Analytical Mindset
When your team is accustomed to doing things a certain way, and a proposal to change the existing process is made, it’s likely you’ll receive push back from some people. As humans, we are naturally inclined to be distrustful of change, but it’s important to reinforce the reasoning behind doing something different. If your organization isn’t used to treating data as an asset in order to make strategic decisions, then it can be a major adjustment. That’s when it’s imperative to build a case for it and to turn to team members, such as your CIO and IT Directors at your organization. Chances are people in these specific roles understand the power of data being a single source of truth. Another way to instill a culture change at your association is by incorporating data literacy into your on-boarding process. By establishing that analytical mindset from the get-go, team members are more likely to use data to make informed decisions. Don’t get discouraged if you don’t get immediate buy-in to this idea though. Sometimes it just takes persistence on your part and even showcasing the loss in ROI to demonstrate the need for data.
If you can successfully get your entire team to adopt an analytical mindset, then you’ll will be able to truly see if your organization is truly moving in the right direction of achieving their mission.

Ready to Plan?

Contact us at info@AssociationAnalytics.com or (800) 920-9739 to discuss your association’s analytics strategy and roadmap.

Moving the Needle on Member Data at NCACPA

Nikki Vann, CPA, is the Director of Finance & Administration at the North Carolina Association of CPAs (NCACPA).  She, along with Jennifer Rowell, Director of Member Engagement (also from the NCACPA) and I were honored to deliver a presentation entitled, “Moving the Needle on Member Data” at the American Institute of CPAs (AICPA) Annual Conference in Key Biscayne, Florida during July 2017.

“Our journey with business intelligence started as our Board discussed our strategic priorities,” Nikki explained. “They realized we couldn’t move forward with any of them without data. To better serve our members and their needs, we needed to understand the story of their actions through data.  Most recently, our team created 12 operational goals, and decided how to measure them.  Now we go through them at the staff meeting and we tie our efforts to those goals. We have made so many improvements to the way we use data as an asset.  For example, we used to go through the entire budget process with our executive committee and talk about each line item.  We realized we were dealing with highly intelligent leaders, so instead we decided to talk about the reason why we built the budget the way that we built it. Let’s talk about the changes we are making to how we do things because the data is telling us to.  Now at every board meeting, this is what they want. “

I see this trend continuing in high performing associations – the time that used to be spent pulling all the data together, can now instead be spend on deciding what to do about it and taking action.  Plus we all know the saying, what gets measured, gets done.   The idea is to use data to set a goal, then make a plan, and use data to measure results.  As Nikki says, “There is no success if you cannot measure it, and if it’s not quantifiable.”
One of the other significant accomplishments demonstrated by NCACPA during the presentation is the ability to visualize and analyze member engagement.  Jennifer added, “We are proud that during our work with Association Analytics we have connected our Abila AMS data with Higher Logic to get a wider view of what is important to our members, and what they are talking about.  We now have more confidence when we make decisions, and we’re also making better decisions because of it!”

Warning: A non-numeric value encountered in /homepages/0/d741589923/htdocs/clickandbuilds/AssociationAnalytics/wp-content/themes/Builder/lib/builder-core/lib/layout-engine/modules/class-layout-module.php on line 499