Archive for analytics

Partnership Provides Powerful Member Engagement Analytics

(Washington, DC—August 13, 2018) Association Analytics, the leading data analytics software and services company for associations, and Higher Logic, the industry leader in cloud-based engagement platforms, have announced their formal partnership.

Sharing the goal of innovating associations through technology, this dynamic union originated on May 23, 2018. An unprecedented case study detailing the impact of this new integration was released at the ASAE Annual Conference, featuring the predictive membership engagement and revenue indications ASAE has captured.

Higher Logic’s Director of Partnerships, Bobby Kaighn, explained, “What may surprise association leaders is the potency of analytical trends when the Higher Logic community platform is integrated with Association Analytics’ Acumen data analytics platform. You can see the exact retention rate of members who engage in specific community activities, the relationship between community interactions and attendance at annual meetings, and the ratio of membership revenue to the frequency of online community participation.”

Associations dedicate their sources to storing information on everything from finances to event registrations, and most of these sources are continually upgrading their advanced reporting functions to include visual analytics. Now imagine if an association could lay these advanced data sets, like marketing campaign data (from one source), on top of member engagement statistics (from another source). This is where the synchronized connectivity of this partnership innovates the association industry.

Higher Logic’s data-driven approach helps organizations track and manage meaningful interactions along each stage of the member journey. Its expanded suite of engagement capabilities includes online communities and marketing automation, covering everything from the initial web visit to renewal and ongoing engagement.

Julie Sciullo, CEO of Association Analytics, said, “With Higher Logic as a standard Acumen platform integration, we’ve been able to quickly and easily import community data in real-time to interpret past and current data, bringing trends to light for strategic action plans.  The integration with Higher Logic empowers associations to overlay hundreds of data combinations to gauge performance and predict future outcomes.”

With the partnership between Association Analytics and Higher Logic already innovating associations, today’s announcement is shared in celebration of this ongoing and increasing benefit to the industry. For more, don’t miss the latest case study featuring ASAE’s actionable analytics on retention rates, the relationship between specific interactions and attendance at annual meetings, and the ratio of membership revenue to the frequency of online engagement.

Association Analytics (A2) is an industry leader in data analysis and management products, services, and training. We innovate associations by funneling your databases continuously into one powerful visual analytics dashboard for real-time, instant interpretation and decision-making power. The platform is called Acumen and it’s designed specifically for associations. This hassle-free, hosted analytics platform means you no longer need IT staff to run reports. Acumen is intuitive and easy-to-use with out-of-box visualizations & reports that encourage cross-staff adoption. The flexible design allows you to choose only the modules you need and includes seamless built-in integrations with your AMS, LMS, email, and finance platforms. Modules include Membership Engagement, Events, Community, Finance, Membership, Sales/Orders, Email Marketing/Automation, and the Executive Dashboard feature. It’s time you had a single source of truth with a 360 degree view for better, faster decisions, enhanced member experiences, improved staff efficiency, and increased revenue. Learn more at www.associationanalytics.com

Higher Logic is an industry leader in cloud-based engagement platforms. Our data-driven approach gives organizations an expanded suite of engagement capabilities, including online communities and marketing automation. From the initial web visit to renewal and ongoing engagement, we help you track and manage interactions along each stage of the digital customer experience. Organizations worldwide use Higher Logic to bring people all together, by giving their community a home where they can interact, share ideas, answer questions, and stay connected. Everything we do – the tools and features in our software, our services, partnerships, best practices – drives our ultimate goal of making your organization successful. Visit www.higherlogic.com.

An Approach to Analytics both Hamilton and Jefferson Could Embrace

Happy 4th of July!  What a great time to think about data independence, democratization, and governance for your association.  In this post we’ll talk about the balance between the central management of data by IT and data directly managed by association staff.
Leading analytics tools provide great capabilities to empower people to make data-guided decisions. The ability to analyze diverse data from a breadth of sources in a usable way for association staff is a key feature of these tools. Examples include Power BI Content Packs and Tableau Data Connectors. These range from pre-built data sources based on specific applications such as Dynamics, Google Analytics, and Salesforce; to relatively rarer “NoSQL” sources such as JSON, MarkLogic, and Hadoop data. These tools rapidly make data from specific applications available in formats for easy reporting, but can still lead to data silos. Tools such as Power BI and Tableau provide dashboard and drill-through capabilities to help bring these difference sources together.

Downstream Data Integration

This method of downstream integration is commonly described as “data blending” and “late binding”. An application of this approach is a data lake that brings all data into the environment but only integrates specific parts of data for analysis when needed. This approach does present some risks, as the external data sources are not pre-processed to enhance data quality and ensure conformance. In addition, business staff can misinterpret the data relationships that can lead to incorrect decisions. This makes formal training, adoption, and governance processes even more vital to analytics success.

What about the Data Warehouse?

When should you consider using content packs and connectors and how does this relate to a data warehouse and your association? The key is understanding that they do not replace a data warehouse, but is actually an extension of it. Let’s look at a few scenarios and approaches.

  • Key factors to consider when combine data is how closely the data is linked to common data points from other sources, the complexity of the data, and the uniqueness of the audience. For example, people throughout the association want profitability measures based on detailed cost data from Dynamics, while the finance group has reporting needs unique to their group. An optimal approach is to bring cost data into the data warehouse while linking content pack data by GL codes and dates. This enables finance staff to visualize data from multiple sources while drilling into certain detail as part of their analysis.
  • Another consideration is the timeliness of data needed to guide decisions. While the data warehouse may be refreshed daily or every few hours, staff may need the immediate and real-time ability review data such as meeting registrations, this morning’s email campaign, or why web content has just gone viral. This is like the traditional “HOLAP”, or Hybrid Online Analytical Processing, approach where data is pre-aggregated while providing direct links to detailed source data. It is important to note that analytical reporting should not directly access source systems on a regular basis, but can be used for scenarios such as reviewing exceptions and individual transaction data.
  • In some cases, you might not be sure how business staff will use data and it is worthwhile for them to explore data prior to integration into the data warehouse. For example, marketing staff might want to compare basic web analytics measures from Google Analytics against other data sources over time. In the meantime, plans can be made to expand web analytics to capture individual engagement, align the information architecture with a taxonomy, and track external clicks through a sales funnel. As these features are completed, you can use a phased approach to better align web analytics and promote Google Analytics data into the data warehouse. This also helps with adoption as it rapidly provides business staff with priority data while introducing data discovery and visualizations based on actual association data.
  • Another important factor is preparing for advanced analytics. Most of what we’ve described involves interactive data discovery using visualizations. In the case of advanced analytics, the data must be in a tightly integrated environment such as a data warehouse to build predictive models and generate results to drive action.

It’s not about the Tools

The common element is that using data from sources internal and external to your association requires accurate relationships between these sources, a common understanding of data, and confidence in data to drive data-guided decisions. This makes developing an analytics strategy with processes and governance even more important. As we’ve said on many occasions: it’s not about the tools, it’s the people.
Your association’s approach to data democratization doesn’t need to rise to the level of a constitutional convention or lead to overly passionate disputes.

How Do I Create an Analytics Strategy & Roadmap for My Association?

When I think about the many reasons an association should create an Analytics Strategy and Roadmap, I am reminded of Stephen Covey’s great advice: “Begin with the end in mind”.  Business intelligence and analytics implementations deliver the most value wAnalytics Strategyhen the analytics strategy is connected to the overall strategy of the association.  After all, a strategic roadmap is a plan that defines where an organization is, where it wants to go, and how it will get it there.  However just like the famous quote from Lewis Carroll, “If you don’t know where you want to go, any road will lead you there.”

8 Primary Goals for Analytics Strategy and Roadmap

During the 18 years we have been helping associations determine how to use data to make decisions, we have identified 8 primary goals for an Analytics Strategy & Roadmap process:

  1. Align analytics objectives with the organizational strategic plan.
  2. Recognize the importance of data as a key organizational asset that requires oversight and governance to ensure quality.
  3. Assess and document the current state of Data, Technology, Processes and Culture.
  4. Quantify the direct and indirect costs of the current situation (data is not clean, accessible, understood and consistently used for decisions).
  5. Identify achievable desired outcomes and understand their value.
  6. Prioritize these outcomes according to business impact, technical complexity, and organizational considerations.
  7. Educate executives and staff about what is possible and what to expect from an analytics initiative.
  8. Establish a high level plan for implementing the analytics strategy, including scope, cost and schedule.

How do I Create an Analytics Strategy and Roadmap?

There are many steps to do this well, and here are the key ones to get you started:

  1. Review your association’s strategic plan and identify measurable objectives and outcomes which can be achieved with the optimal use of data
  2. Create an Analytics Scorecard by honestly evaluating your association’s Data, Technology, Reporting and Organizational Culture
  3. Evaluate your current Data Governance Process, including quality, accountability, semantics, integration, etc.
  4. Assess each data source using an impact/complexity matrix
  5. Identify metrics, Key Performance Indicators (KPIs), and business questions that can be answered with data
  6. Create a risk matrix that maps the probabilities of each risk – including the cost of not taking action with analytics
  7. Review internal staff resources and identify staff augmentation and training needs
  8. Understand the options available for data warehousing and analytics tools such as Power BI or Tableau

The Ultimate Outcomes

Our experience has shown that key outcomes an association will discover as part of their Analytics Strategy and Roadmap initiative are the clear need to establish and maintain a data governance program, implement a “single version of the truth” and embrace a culture of analytics.  These things only happen when leadership recognizes data as an asset and key data sources are combined (and consistently updated in a data warehouse) and staff are rewarded for adopting an analytical mindset.  The best way to determine if this is the case for your organization is to begin with the end in mind and undertake an Analytics Strategy and Roadmap project.

How to Determine What Data to Combine

There is a lot of value in combining data from one business area with data from another business area. Similar to a jigsaw puzzle, when we combine data sets and put the pieces together, we get a complete picture of customers, events, and activities. But how do you know what data to combine?

Take Inventory of What You Have

To get started, take inventory of the data you already have available in your business area. Let’s take members for example. Membership teams often require a high level of granularity. They also have years of membership data that can be leveraged. The data they have may be stored in their Association Management System, Customer Relationship Management system, and their Financial Management System.
After identifying the data sources, consider what data is stored in each data source. Identify the file type and how you extract or integrate the data with other systems.

Consider What’s Missing

To determine what data could augment your existing data source, think about the aspects of the customer or activity that you care about.
What information could help answer your business question? If you don’t have a business question, what information would provide additional insight on customer behavior?
The membership team typically has data on when a member joined, membership type, length of membership, contact information, and dues payments. What other information would help them understand members? It may be helpful to combine membership data with components from other areas such as the number of events attended in the past two years, the last meeting attended, age, member status, tenure in the industry, and total spending in the past year.

Combine and Analyze

Combine the data and analyze it. Look for trends ad relationships. Distill down the information so that each component of activity that is of interest is presented as attributes of that person.
 
The table below shows some combined information as it relates to the Top 10 and Bottom 10 thread topics from an association’s online community. Using the information, we can see what a correlation may exist between a person’s attributes and the most active threads. From the data below, it looks like younger individuals with less membership tenure and professional development are replying and posting to threads generated by younger authors than the bottom threads. Perhaps action can be taken to target the younger members with messaging encouraging them and providing the benefits of authoring and responding to community posts.
thread-stats
Once you combine data, you can determine if there is actually a relationship between two data sets. You can also see if you need additional data to augment your analysis. Using business intelligence tools, like Tableau, allows you to easily connect data sets and experiment.

Planes, Trains, Automobiles… and Meetings

Each Labor Day weekend, analysts discuss the impact of gasoline prices and other factors on travel driven by an extra day off and the symbolic end of summer. Some estimates indicate that a greater number of travelers by car saved a collective $1.4 billion this year, much of which was spent elsewhere during trips.map-clipart-travel-map
Deciding if and when to travel greater distances is a more complicated process since it often requires relatively expensive air travel with a commitment in advance. The price of airline tickets is significantly determined by the timing of purchase relative to travel. Analytics indicate that the optimal time to purchase tickets is generally between three and eight weeks prior to travel. The specific timing changes based on the time of year and also varies significantly by departure and arrival airports. It unfortunately does not require advanced analytics to observe that prices tend to rapidly increase beginning two weeks prior to travel.
Associations effectively create similar opportunities for customers through meetings and events. Associations spend considerable time and effort on planning and marketing activities based on a range of customer data including prior event participation, topical interests, individual engagement, meeting sessions, organization characteristics, continuing education requirements, and demographics. Registration patterns are closely monitored against prior year’s activity and current goals. Sometimes these patterns are not consistent with historical data. In some cases a trend of early registrants fails to continue and in other cases a late surge of registrants follows a period of marketing efforts.
Associations can supplement their AMS data with external sources to help explain such patterns. The U.S. Census Bureau makes very useful geographic data available including zip codes with latitude/longitude mapped to several other data points. The Census Bureau also offers an application programming interface to obtain detailed geographic data for specific addresses. This data can be used to calculate the distance between two points (it’s kind of a long story that involves the trigonometry I never thought I would use such as sine, cosine, and inverse tangent). Tableau data visualization software provides out-of-the-box geographic visualization capabilities based on data including zip code, area code, city, metropolitan/micropolitan statistical area, and even congressional district.
Various surveys and other analytics estimate that a common comfortable distance for car travel is around 200 miles.  Here is a map created using Tableau showing the population density by zip code within a 200 mile radius of Phoenix:
Phoenix
Here is a similar map based on the same color scale for Chicago:
Chicago
A far greater number of individuals are likely to consider driving to a meeting in Chicago than a meeting in Phoenix. It is important to note that this straight line distance is clearly a rough estimate and encountering a large body of water such as Lake Michigan would challenge even the most dedicated meeting attendee. Fortunately you can leverage more advanced data sources such as the Google Maps API to estimate driving distances.
This data provides associations with great analytics opportunities to drive meeting participation by aligning with customer travel decisions. For example, marketing campaigns can consider travel decision timing and target specific airport markets. Registration pricing deadlines for individual events can incorporate location analytics. Your association can also create opportunities for strong customer engagement through efforts to help groups of potential meeting attendees organize buses together or even carpool.
Deriving travel distance analytics from customer location data is an example of gaining value from data. This demonstrates why it is important to consider analytics when designing business processes throughout the organization and not just as part of analysis after the fact. Increased meeting attendance often contributes to other benefits including member retention, publication revenue, engagement, and membership recruitment.
Understanding the impact of customer travel scenarios is a great way to leverage association analytics to create the future and grow your number of happy conference attendees.
 

Association Analytics: Begin with the End in Mind

Often association leaders ask me, “Where is the best place for us to begin our data analytics initiative?”  I like this question and am reminded of Steven Covey’s expression that before we undertake a new initiative, we should “begin with the end in mind”.  When it comes to data analytics for associations, this means starting with your strategic plan.
One association’s strategic plan, for example, specified that they would grow meeting attendance by 10% in 3 years.  This was their largest source of revenue.
In the past the association made decisions by looking at historical trends and was unable to confidently estimate expected attendance, revenue, venue amenities needed, onsite staffing levels and more.  In fact, the more events they held, the greater the risk of an incorrect estimate.
Yet because of the strategic initiative to increase attendance, the first thing they tried was to increase the number of events and market them by email campaigns and direct mail.
The visualization below is a simple bar chart showing the number events by year. The higher the bar, the more events. The color shading of the bars indicate the number of registrants. The darker the green, the more registrants. We can quickly see 2010 had the most events, but not the most registrants.  In other words, although the association increased the number of events, this did not increase the number of attendees. In fact, holding more events actually caused a decrease in meeting profitability as a whole, since there were more expenses associated with fewer registrants.
events
By reducing the number of events by combining smaller, similar events to a larger meetings, the organization was able to dramatically exceed the target set by the board.  In other words, by holding fewer events, they had more participants.

What is the Cost of Not using Data?

When making business decisions it’s important to weigh the costs of:

  1. Bad decisions
  2. Decision delay
  3. Lost opportunities
  4. Damage to image/reputation

Often these costs are much greater than the costs associated with analyzing data. Taking a “decision tree” approach can be a logical way to evaluate where to start:

  • Are there areas in your strategic plan where data can answer questions, provide insight and direction?
  • Which areas do our customers care about? Will it make a difference to them – add more
    value, improve their experience – advance our mission?
  • Do we have the data – or can we get it?
  • Will we be able to take action on the results of the analysis?

 

APTIFY USERS CONFERENCE- October 18–21, 2015- Denver, Colorado

Data to the People
Data is essential to making decisions, but it’s really the people with vision and insight who decide what to measure and take action on the results. Hear how the American Society of Association Executives is using the power of data discovery and data visualization to shift the culture to embrace evidence-based decision making. Learn how to get started and what questions to ask.  Walk away with a new appreciation of the power of your data to tell a story and the importance of communicating that story to others.
Speakers:
Debbie King, PMP, MCITP, CSM, CEO, DSK Solutions, Inc.
Christin Berry, CAE , Sr. Director, Business Analytics, ASAE: The Center for Association Leadership
 
Scheduled Time: Wednesday, October 21, 8:30am
Session Length: 75 minutes
More Information

How to Choose the Right Visualization for your Association

Choosing the right data visualization is as important as choosing the right outfit to wear to an important meeting. Although your alma mater’s sweatshirt is perfect for the ball game, a suit and tie is more appropriate when trying to convince your board to increase your budget. Similarly, you are going to catch some flack for showing up to the game in a suit and tie! Choosing the right visualization for your audience is similar to choosing the right outfit for the function.
Did you know that the human brain is able to process images three times faster than text? From our primitive beginning, we’ve depended on our brain’s ability to detect subtle patterns and interpret meaning. So, how do you choose the right visualization? Let’s take a look at some common types of visualization and when they should be used to effectively communicate the story your data is telling.

Tabular

table

  • Best used when exact quantities of numbers must be known.
  • Numbers are presented in rows and columns and may contain summary information, such as averages or totals.
  • This format is NOT favorable to finding trends and comparing sets of data because it is hard to analyze sets and numbers and the presentation is cumbersome with larger data sets. It is estimated that the visual working memory has a capacity of about seven items. This means that you can store up to 7 bits of information (like numbers) in your brain’s “RAM” simultaneously. If you build a table with financial information for each month of the year for different areas of your association, it becomes difficult to find outliers or even the most profitable month.
  • This kind of visualization is likely what many association staff are accustomed to (think of all those excel spreadsheets floating around your office) so you may need to use a tabular format in conjunction with one of the other types listed below to convey the information.
  • A variation of the tabular chart is a highlight table. A highlight table applies color to the cell based on its value. The use of color can make outliers stand out more.

 Line Charts

line chart

  • Best used when trying to visualize continuous data over time.
  • Line charts use a common scale and are ideal for showing trends in data over time.
  • Example: membership or registrant counts throughout the year compared to previous years.
  • Trend lines and goal lines can also be added to compare actual counts with certain benchmarks.

Bar Charts

bar chart

  • Best used when showing comparisons between categories.
  • The bars are proportional to the values they represent and can be shown either horizontally or vertically. One axis of the chart shows the specific categories being compared, and the other axis represents discrete values.
  • Example: Bar charts can be helpful when looking at certain segments of your customers, registrants or members.
  • Goal lines can also be added to compare the actual counts with your benchmarks.
  • A variation of the bar chart is the stacked bar chart. This incorporates the use of color to visually show how certain segments add up to the total. In the example above, it’s easy to see that while 2010 Conference attendance counts are higher, the number of Paid attendees actually decreased from the previous year.
  • Another variation of the bar chart is called a bullet chart. This chart allows you to take a single measure (for example, revenue) and compare it to another measure (for example, revenue goal). It also can display percentiles.

bullet chart

Pie Charts

pie chart

  • Best used to compare parts to the whole.
  • Pie charts make it easy for an audience to understand the relative importance of values.
  • Using this format for more than 5 sections is not recommended as it can become difficult to compare the results. Too many sections make interpretation difficult because the difference between the sections can become too narrow to effectively interpret.
  • Often, even when wanting to compare parts to the whole, a bar chart can be more effective.

In addition to difference chart types, the use of filters and sorting is important to increase the association staff person’s ability to explore the data in more detail.
The goal of any visualization should be to communicate the information in the most concise and impactful way by using the appropriate visualizations for your data.  Effective visualizations enable your audience to quickly understand the story in the data and speeds the ability for association staff to reach key insights.

The Value of Data Discovery for Associations

The Magic Quadrant

In February 2013, Gartner Inc. released an important report entitled Magic Quadrant for Business Intelligence and Analytics Platforms which details the current state of the business intelligence (BI) market and evaluates the strengths and weaknesses of several of the top vendors. It’s interesting to note that in this report, Gartner emphasized the emergence of data discovery into the “mainstream business intelligence and analytics architecture”, something we have been highlighting at DSK Solutions for years.
What is data discovery? Associations and nonprofits are sitting on large quantities of data and don’t always realize the value of this powerful asset. The old days of spray and pray are gone. Remember direct mailing blasts? How ineffective! Associations were shooting in the dark and wasting resources that could have been allocated to better serve members. Unfortunately, some associations still rely on this marketing approach, but there is a better way: segmented target marketing based on data.
All of your data – including CRM or AMS (customer data), general ledger and budget (financial data), and Google Analytics (Web data), can be pooled together to illuminate your member strategy. Think of each data source as a small flashlight that reveals a little bit of the path in front of you. When your data sources are pulled together, the path becomes much clearer. When analyzing your data with data discovery, it becomes possible to discover things you did not know before.

Necessary Steps

Clients frequently come to us seeking guidance on how to begin the task of leveraging their data to inform better decision making. Before you can embark on data discovery, you have to do two things:

  • Ask the right questions.  What is meaningful to your organization? What are you trying to find out about your members, prospects, products, services and profit?
  • Clean your data. If your data is filled with duplicates, inaccuracies, inconsistencies and other forms of noise, your analysis will be flawed. Remember: Garbage in, garbage out.  Quality data as an input allows for accurate analysis as an output, which results in the improved ability to make good decisions.

These two steps form the foundation of the data discovery process. Almost always, the answers you derive from your data will lead to more questions. It’s okay to ask why. In fact, you should be asking why! Start by asking questions like these:

  • How dependent is your association on dues revenue?
  • What is the price elasticity of membership (Full Rate v.s. Discounted Rate)?
  • Which members are at risk for not renewing?
  • How far (in miles) will registrants travel to attend a meeting?
  • Which products or services have the highest profit?

Then start asking “why”.  Remember the idea of the Ishikawa (or fishbone) diagram?  It’s an easy and useful way to begin thinking in terms of cause and effect – you ask “why” 5 times, until you arrive at the root cause of an effect.  Now with interactive data discovery you ask these questions directly by interacting with the data in a visual way!  At DSK we describe it as “having a conversation with your data”.  For example, a certification department of an association wanted to look at their pass/fail ratio for an exam.  Using data discovery, they discovered many more college-aged people were registering and doing poorly than in the past.  In the process of asking “why” the failure rate was increasing, they discovered an opportunity not only to publish a new study guide, but also they located an entire new source of prospective members and created a new membership type to serve the college market.
Data discovery is an iterative process where you ask questions of your data in an interactive way. Drilling down both vertically and horizontally into your data allows you to not only answer the questions you know you have, but shed slight on those unknown-unknowns and enables associations to make better decisions.

 SS 2 Filtered on Type 2

The Analytics Convergence

Data-guided decisions permeate our everyday lives as individuals, but how can you harness that power for your association and your members? The field of data analytics and big data is exploding with opportunity. Businesses are encroaching on the areas that used to be the private domain of associations – content, networking, events, etc. – because they are employing the power of analytics. But now, the process and tools needed to analyze and interpret data are much less expensive and easier to use than before. Are you ready to have a conversation with your data? Each level of business intelligence has a unique language to make your data speak!
The following presentation was created by Debbie King, CEO of DSK Solutions, Inc. and David DeLorenzo, CIO National League of Cities. This information was first featured at the 2013 ASAE Finance, HR, and Business Operations Conference.