Archive for Member Engagement

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

Get the Most Out of Your Website Analytics

An association website provides invaluable data and information on what your customers (i.e., members, prospective members, nonmembers, and the public) care about, what they don’t care about, and how they find information. By analyzing this data with website analytics, you can help your association provide valuable, engaging customer experiences.
There are a number of tools available for website analytics. Google Analytics is probably the most well-known. Regardless of what tool you use, there are some basic steps that will help you get the most out of your website analytics:

  1. Define business goals and important conversions. This should be based off the association’s strategic plan. A conversion might include someone joining membership, subscribing to a publication, registering for an event, etc. When listing your key conversions, consider what actions your optimal customer would take. What are the customer actions that have the greatest impact on your association’s ability to achieve its goals and objectives?
  2. Select meaningful key performance indicators and goals. Website KPI’s could include:
    • Visits (Sessions), Unique Visitors (Users) and New vs. Returning Visitors
    • Traffic Sources
    • Bounce Rate and Average Session Duration by Channel
    • Conversions and Conversion Rate by Channel
    • Cost per Conversion, Profit and ROI (Return on Investment) by Channel
  3. Determine audience for reports. Before developing reports or visualizations, consider the audience. Design reports that will be meaningful to the intended audience. Think about the goals of your audience and strive to exclude extraneous information.
  4. Analyze data. Investigate things that surprise you or dive deeper to find relationships.
  5. Take Action. Take action from what you learn and make modifications based on what you learn.

Customer Experience is about the Journey

The constantly growing number of choices for customers mean many aspects of our products and services are in danger of becoming commoditized, but customer experience is not one of them.

Importance of Customer Experience

Gartner reports that “By 2017, due to internet-enabled price visibility, the digital customer experience will be the key differentiator of your organization.”
But it’s not only digital — customers expect consistent, seamless, high value experiences – both digital and in-person. If you don’t give it to them, they will go somewhere else.

Providing Outstanding Customer Experiences

How can associations continue to provide outstanding customer experiences to remain relevant to members and prospects?
Similar to commercial brands, associations have to view every interaction through the lens of the customer.  Sounds simple, but its easier said than done.
At a minimum, associations have to understand and manage their touchpoints. Customer touchpoints are your brand’s points of customer contact. Examples include online advertising, social media, direct emails, customer service, events and conferences, membership joins and renewals, purchases, and surveys.
It’s important to carefully manage each point of interaction. A single negative touchpoint can sour the customer on the overall experience.

Customer Journeys

Interestingly, the research shows that careful attention to individual touchpoints may not be enough. The customer experience with your association happens over a period of time, across multiple channels, and most importantly, across multiple business functions. These multichannel, cross-functional interactions are called Customer Journeys.
Consider a customer registering for an event.
In this example, the customer navigates through multiple channels (print, website, direct mail, word of mouth, social media, in person).
In addition, their touchpoints span multiple departments (Marketing, PR, Events, Publications, Member Services, Finance). Each department has different goals and performance measures for their customer interactions. The challenge is to move away from this siloed approach to managing customer interactions.
When we think of the journey instead of just individual moments, we can see each touchpoint influences the others and the whole journey is greater than the sum of its parts. This cross-functional, customer-centric view is the essential first step to analyzing and improving your customer’s journeys.

Customer Journey Analytics

To understand customer journeys, you need Customer Journey Analytics – analyzing how customers use the available channels and touchpoints to interact with our organizations. You can read more about Customer Journey Analytics in Association Analytics® News and subscribe to our monthly newsletter for more information on the latest trends in nonprofit data analytics.

Using Propensity Modeling to Drive Revenue and Increase Engagement

At the 2016 ASAE Annual Meeting & Expo, Gwen Fortune-Blakely (Enterprise-wide Marketing Director) and Leslie Katz ( Marketing Director) with the American Speech-Language-Hearing Association (ASHA) presented an amazing session on how ASHA is using propensity modeling to move people up the continuum of engagement to drive revenue and membership. Here’s a quick overview of what you need to know about propensity modeling and how it can help your association.

What is Propensity Modeling?

Propensity Models look at past behaviors in order to make predictions about your customers.
It is complementary to segmentation, but different. When segmenting, you cluster customers based on shared traits or behaviors. In marketing, propensity modeling goes a step beyond segmentation by focusing on likely behavior or action. Where segmentation provides insight into customer behavior, propensity modeling provides foresight. It allows you to target customers based on likely behavior as opposed to past behavior.
There are three main types of models: propensity to buy, propensity to churn, and propensity to unsubscribe.

  1. Propensity to Buy model looks at customers who are ready to purchase and those who need a little more incentive in order to complete the purchase.
  2. Propensity to Churn model looks for your at-risk customers.
  3. Propensity to Unsubscribe model looks for those customers who have been over-saturated by your marketing efforts and are on the verge of unsubscribing.

How can Propensity Modeling help your Association?

Think about an association that is about to send membership renewal notices. In the past, they send out a packet of materials by mail to all current members. The packet includes an invoice and an expensive, professionally designed brochure that espouses the value of membership. The association’s retention rate is about 86%, which is respectable but what would happen if the association applied a propensity model to better understand their customers?

  1. Increase Revenue. A propensity to churn model would “score” current members and could help identify those members who are at risk. The association staff can use that information to create customer campaigns for at-risk members. This might include in-person visits or phone calls and other personal touchpoints that would help secure renewal.
  2. Decrease Expenses. Propensity modeling also helps associations determine who to target and how, which can help reduce expenses. In this case, the staff might use the model to identify those members who don’t require a brochure and would simply renew after receiving an invoice. Similarly, a propensity model can identify those customers who need extra attention. It may not be cost-effective to have staff call every member, but what if staff knew which members would likely respond best to a personal phone call?

You can imagine other examples where a propensity model can help your association. For example, associations can use propensity modeling to facilitate market penetration by identifying customers most likely to buy. Or you can use propensity modeling to anticipate how much a customer is likely to spend. This can help determine pricing and product offers.
We often draw inspiration from the corporate world. MasterCard Advisors shared an interesting white paper on how you can use behavioral scoring to add precision to targeted marketing.

How do you get started?

5 step process
At Association Analytics, we follow a five-step process for data analytics, including propensity modeling.

  1. Scope. Define your business objectives and prioritize them. We recommend starting small and focusing on developing a model for one specific objective first. This will keep you from becoming overwhelmed. When you try to fix everything, you normally will end up fixing nothing.
  2. Collect. Spend time with your data before doing any modeling. Inventory your data sources and make sure you understand how data sources will help you answer your questions. Then, integrate data into a central location. We recommend a data warehouse and dimensional data models, but you can directly connect data sources to a business intelligence tool like Tableau.
  3. Clean. Don’t spend time on your model before making sure you have clean, complete data. Be sure to identify data anomalies and then correct any issues.
  4. Analyze. Visualize your data to understand likely behaviors in your model. Don’t get trapped by confirmation bias. Keep an open mind and be open to new patterns or information.
  5. Communicate and Take Action. Share the results of your propensity model with key stakeholders. Take action on the results to improve your marketing efforts and advance your association’s mission.

Propensity Modelling can be a valuable tool in order to better understand your customers and to predict their behavior. It can help you improve their experience with your association.

How to Harness the Power of Recommendation

Taking a customer-focused approach to data analytics helps provide optimal value, enhance engagement and understand the overall customer journey. Individuals’ actions provide valuable information that goes further than what is collected with surveys and online profiles. Additionally, actions uncover hidden patterns that can be used to build a recommendation system to guide customers toward other interests.
Here are the most common approaches to creating recommendation systems:

  • Collaborative filtering. This is based on data about similar users or similar items. It includes these techniques:
    • Item-based: Recommends items that are most similar to the user’s activity
    • User-based: Recommends items that are liked by similar users
  • Content-based filtering: Makes suggestions based on user profiles and similar item characteristics
  • Hybrid filtering: Combines different techniques

Recommendation systems results are similar to those on sites that suggest products and people, like Amazon and LinkedIn. Collaborative filtering leads to more of a self-learning process, since it is entirely based on actual activity and not data provided by users. There are scenarios where the others are more appropriate that we’ll address soon.
Similarity between users or items is measured by “distance” calculations from those long-ago geometry and trigonometry classes. You can use the results with a visualization tool such as Tableau, creating a similarity matrix and quickly identifying relationships.
correlation_matrix
It is sometimes helpful to group individuals and items into categories, which can be done by combining similarity scoring with data mining techniques like cluster analysis and decision trees.
Recommendation systems generally require data structured by columns instead of the row-based data that is best for interactive data discovery. Similar to text analytics, the items themselves — meetings, publications, donations, and content — represent large columns. It’s used by specialized R packages for the recommendation system features described in this book.
These algorithms generally need binary values, like a “yes” if someone purchased an item and “no” if he did not. But if users can rate items on a scale of 1-5, what does a score of 3 mean? Normalizing scores based on individual and overall ratings is a good way to answer this question.
The data requirements are really not as onerous as they may sound. Once data is in the right format for the R analysis tools, your imagination can take over to drive actionable association analytics. Content-based filtering works well for new users, and a hybrid approach can help prevent a “filter bubble” where some people get a too-narrow set of interests from similar recommendations.
Data from meeting registrations, membership history, donations, publication purchases, content interaction, web navigation, survey responses and profile characteristics can be used to guide association customers. Additionally recommendations can bring people with common interests together. This new insight can be used to enhance all customer interactions, ranging from email marketing to dynamic website presentation to event sessions.

How Analyzing Social Media is Like Walking Across Bridges

How do your customers connect with one another? Social media mixed with a historic mathematical theory can help you find those patterns and bridge gaps.
Combining social media with other external data can help your association use a range of personal interaction and engagement to move toward a more customer-focused approach. Analyzing social networks is often done through the mathematical concepts of graph theory and network theory, showing relationships between individuals. Richard Brath and David Jonker described using these concepts for business in their book.
socialnetwork1
Using technical analysis helps you identify people who are “connectors” and link to several groups, “influencers” who help groups form, and cliques that would otherwise be difficult to detect. Setting people up as a simple shape and line graph can be understood by:

  • Counting incoming and outgoing links between people.
  • Looking at the density of direct connections.
  • Examining the shortest and longest paths between people.
  • Considering how far the shapes are from one another.
  • Seeing how people tend to cluster together.

Graph analysis comes just from activities and does not make use of other attributes, like demographics. You can supplement this analysis with other data that you have. You can also use this kind of analysis to identify customers similar to the ones you found through social network analysis. It may be interesting to study things like how members’ interaction levels differ from those of non-members.
To bring social media data together with your other customer information, it should all come together in a data mart. In addition, you can also introduce text analytics to provide additional context. Different social media platforms make data available through application programming interfaces (APIs), which all have their own technical integration options and data scopes.
socialnetwork2
While social media analysis has been getting more popular recently, the graph theory that is used to break it down is centuries old. Mathematician Leonhard Euler famously used this theory in the 16th century to solve the “Seven Bridges of Königsberg” problem, devising a way to walk through a city while crossing each of its seven bridges only once.
While crossing physical bridges may not be on your list of priorities, social network and graph analysis can help cross several metaphoric bridges in your association, including:

  • Segmenting customers: Coming up with similar people, based on links and attributes.
  • Analyzing influence: Finding people with large numbers of connections and activity.
  • Analyzing the market basket: Figuring out items that are commonly purchased.
  • Finding general correlations: Seeing relationships between people, products, events and other things.
  • Website visit analysis: Determine which webpages are the most popular.

Visualization techniques can communicate the message in social network data through node size, node color, link weight, link colors, and labels. You can use a combination of visualization choices in Tableau to tell social network stories.

Advancing Trust in the Charitable Sector

I recently had the opportunity to attend a fascinating international conference on building donor trust, jointly presented by the BBB Wise Giving Alliance and the International Committee of Fundraising Organizations (ICFO).
Art Taylor, President and CEO of the BBB Wise Giving Alliance, explained that the four pillars of trust include ensuring good governance, providing quality financial information, being truthful in public communication, and continuously striving to achieve results.  He stressed the impact of trust as breaches force those organizations who stand for doing things the right way have to work even harder to maintain trust.trust
People often do not conduct research prior to making charitable donations.  Each of the four organizations recently accused of fraud had detailed information available on the BBB Wise Giving Alliance website indicating that they did not meet all standards.  This is why the BBB Wise Giving Alliance seal is vital in advancing trust.
The conference included four very interesting panel sessions addressing the most challenging trust issues.
Cultural Differences that Influence Trust in Charities & Fundraising Strategies
Cultural differences exist that affect how characteristics of charitable organizations which are used as a basis for trust are perceived:

  • Public executive salary data: publishing salary data is a priority in some countries, while privacy of such data is a priority in other countries.
  • Board member compensation: board members may be expected to volunteer their time.
  • Financial statement publication: detailed consistent financial statements are considered the norm in some countries and an added burden in others.
  • Tax exemption status: the threshold involving the level of contribution to the public good and how that is defined varies.

The interpretation of data and supported conclusions can be very different when there is not a common understanding of data.  We find this same situation frequently in our work with association analytics.
Gaining Donor Trust of New Generations
The panel focused on the popular topic of millennial donation behavior, while many of the conclusions surrounded the importance of taking a broader view of customer engagement along with user segmentation.
A common characteristic of millennial donors is that they value shared experiences with friends.  To accommodate this, it is important to provide social communication tools.  A specific example is a successful campaign where a charity did not request donations directly from previous donors, but instead requested that they ask their friends to donate.
The concept of engagement is relative to individual expectation.  Millennials treat assets other than financial donations as equal, such as time and social actions.  This is reflected in time and donation patterns that are common with millennials – they take longer period of time to donate and have a larger quantity of relatively small donations.  Organizations must build trust over time and consider broader forms of engagement.  Once millennials are engaged, giving feels very natural.
Treating all donors the same is not a good idea for many reasons.  Not only is it not as effective, but one panelist described an example of a charity directing donors to a popular online platform resulting in high transaction costs for large donors who normally use other channels.
One of the most popular panelists was Josh Hoffman-Senn, the co-founder of Causemo, a startup company that provides technology to support causes through apps and websites by presenting donation opportunities within the natural flow of the user experience.  For example, a game app can offer players the option of donating to a list of charities with the BBB Wise Giving Allowance seal in order to advance to the next level of the game.
In summary, organizations need to adjust expectations concerning the size and timing of donations; place a greater value on all forms of engagement; and make a concerted effort to segment donors based on these and similar characteristics.
Communicating Charity Value and Inspiring Trust
A common theme of the panel members was the importance of articulating the impact of donations as part of all donor communication.  The panel also included the presentation of public opinion surveys that unfortunately indicate a lack of trust in charities along with high demands for accountability.
Expectations of charities by donors and the public include:

  • Serving an important need
  • Providing effective and lasting impact
  • Efficient use of funds
  • Ensuring transparency
  • Meaningful participation and feedback

Prescriptive actions that are needed in the near-term include:

  • Measuring program effectiveness
  • Timely and accurate communication to donors
  • Ensuring value to donors
  • Common standards for enforcement
  • Clear regulation and enforcement

Many of these prescriptive actions should be a natural part of any analytics strategy for associations or charitable organizations.
Donor Trust in Charity Data Security & Privacy
The same valuable data to guide decisions is also very vulnerable to misuse.  The panel stressed that most data breaches are internal and do not involve malicious intent as people often unknowingly misuse data.  This demonstrates the importance of data governance and staff education.  The discussion also addressed the differences between data privacy, which is very much policy-driven, and information security, which includes ensuring that customer data is not improperly changed.
A panelist with the Federal Trade Commission discussed legal issues and noted the key challenge of there is no single law governing privacy and data security in the United States and privacy laws are based on the residency of individuals.
Customers should ultimately be in control of their own data based on clearly communicating choices.  Bill Karazsia of the National Student Clearinghouse nicely summarized how charitable organizations should view trust and data privacy: the question they should ask themselves is “What should we do with customer data, not what can we do with customer data.”
Data analytics plays a strong role in data security and privacy:

  • Identifying sudden data changes that might indicate data governance issues such as customer service calls, customer opt-out changes, and website clickstream involving preference pages.
  • Ongoing staff training to ensure use is aligned with privacy policies.
  • Defined data cleaning and quality process to ensure changes to customer data are accurate.
  • Ensuring a common understanding of detailed customer data.

Like other initiatives, building trust requires ongoing consideration of technology and culture.  Art Taylor elegantly noted that if we understand the values, we understand many of the most important things about an organization.  He stressed that values must live and breathe throughout the enterprise, as values drive culture and culture drives what you do.
A big challenge for charities is balancing clearly beneficial investments that improve business performance with the perception of having high overhead that is commonly used as a measure of trust.  Organizations should place the same emphasis on transparency, communication, and value as direct programs to advance donor trust as such investments provide sustainable benefits throughout the organization to ensure a lasting impact of donations.

The Story in your Association’s Data

Did you know that a 2% increase in retention has the same effect as decreasing costs by 10%  and the most effective way to increase retention is to increase customer engagement.  I use the word “customer” instead of member because nonmembers engage with us as well. DSK Data Story
So how can we increase engagement?  The answer is hidden in your data.  Data is one of the most important assets an association has because it is unique in its detail and context and can be used to ensure that you are optimally positioned for growth, while remaining relevant and viable in the increasingly competitive world of trade associations, individual membership societies and voluntary organizations.  Information you glean from your data can put your organization in a unique thought leadership position and enhance your ability to communicate to your target audiences. Your data also tells the story of customer engagement and who is at risk.
The evidence is clear: Data-guided decisions tend to be better decisions. Harvard Business Review has gone so far as to say, “Leaders will either embrace this fact or be replaced by others who do.”  Organizations that learn how to combine domain expertise with data will pull away from the competition.
But research conducted by MIT shows that only 20% of organizations believe they have access to the data they need in order to make good decisions.  This is because in the past it was difficult for business leaders to quickly access and understand their data. Traditionally data was stored in a way that only IT staff could access and when business leaders asked questions they had to wait while IT created reports.  Once the reports were delivered, often they spawned new questions, which meant waiting again for more reports.  Business leaders could not confidently ask questions by interacting directly with their data.  This process hasn’t worked well for either IT or leadership.
The answer is providing a single version of the truth with a visual interface that allows business leaders to have an interactive “conversation” with their data.  Data discovery techniques combined with visualizations allow association staff to ask new questions of the data in real-time, without asking IT staff for assistance with every request.  This is not only more efficient from a time perspective, but it more closely models how we think and makes it easy to ask ever more insightful and precise questions of the data. It also frees up the IT staff to work on other initiatives.
Although it is important to get the technical aspects of data analytics correct – it is really about people and change.  It is people who decide what to measure and people who take action on the results of the measurements.  This is why we say it is important to make “data guided” decisions, not “data driven” decisions.  When starting an analytics initiative, the first step is to identify and prioritize the business questions.

  1. What are the questions you have, the answers to which if you knew, would enable you to position your association to grow and advance your mission?
  1. Are there decisions you are making that you feel you do not have all the information you need to make the decision? What are those decisions?

Once we have prioritized business questions, the next step is to determine if we have the data we need to answer them, and if not, we develop a strategy to obtain it.
I’ve heard many stories of stalled analytics engagements where the first step was to look at the data to see if they could find “something interesting” – in the hopes of finding a magic bullet that would lead to growth.  I find Steven Covey’s adage, “begin with the end in mind” to be sage advice and recommend gaining clarity on our business questions as the first step on the business analytics journey.  Often one of the most important questions, is “How are our customers engaging with us and which ones are at risk?”  The answer to this question is hidden in your data, waiting for you to understand the story it is telling.

Psychographics vs. Demographics: Using Data to Build Association Relationships?

Most of our clients are interested in identifying the characteristics of members who are profitable and engaged and those that are not. But how do you identify and attract new people and organizations that have the propensity to be engaged? View your current audience the way a marketer would, using demographic data and psychographic information.
DSK build association membership Demographics may include characterizations such as age, industry segment, company revenue, number of years in business, location, etc. The benefit of studying your members in this way is that you begin to see how the behaviors for different demographic segments differs. Perhaps those companies that have been in business the longest tend to renew at a higher rate than those that have just started; conversely it may be that those who have just started are more interested in having their staff attend professional training courses. One downside to segmenting your membership based on demographic information is that the generalizations can be broad and may produce limited insight. The groupings may also be too general to offer targeted information resulting in a small yield of return.
Modern marketers are now targeting customers through psychographics. Psychographics is the market research or statistical way of classifying population groups according to psychological values. Examples of psychographic data may include category of the most commonly accessed web sites, amount of money spent on certain products, distance traveled to events, volunteer activities, etc. Psychographics can provide much more useful information about customers. Not only can you aggregate the data about your members and customers that is stored within your AMS, you can also use social profile data and behavioral data by viewing web site activity.
Amazon.com was an early innovator, introducing customers to “recommended products” and “users like me also bought.” Using algorithms, they are able to predict what their customers are interested in. Because it is likely that you already have a lot of data on your members and customers (maybe even more than you realize), you too can leverage psychographics within your association.
Using data to determine psychographic groupings can also be more reliable that self-selected demographics. While performing analysis for an association, I looked at the demographic question which asked what category they were interested in. I then compared it to the category of web pages they visited the most. What was interesting was they did not always correspond. The reason for the difference may vary. Perhaps they answered that question 2 years ago and have changed job positions, perhaps they didn’t understand the category they were choosing. Regardless of the reason why, it’s now possible and more reliable to categorize customers based on their behavior.
There is a major shift in marketing, moving away from the broad-based, “spray and pray” advertising to highly targeted, relevant advertising. This is done using psychographic groupings and is a technique that you too can utilize for a better understanding of your customers. When we receive information about something we are interested in, we are not usually offended, and if the approach is correct, we even value it.  But when inundated with irrelevant promotions studies show that customers consider it invasive.  Using psychographic profiling, you can improve rapport with your customers as well as increase your marketing efficiency.

Member Engagement: The Most Important Metric for Associations

We want to improve our member engagement! How many members are engaged? How much more likely are engaged members to renew? Who isn’t engaged and should be? These are common questions our clients ask us when starting a business intelligence initiative. A survey report released by the accounting firm Tate & Tryon, titled “Membership Metrics: A Review of Current and Best Practices”, describes member engagement as the metric that is the least calculated yet also perceived as the most critical by association executives. The reality is that many associations are desperate to measure overall member engagement but are struggling to figure out how exactly to do it. The answer is use data!! Sure, data analytics is the solution, but how do you get there? Here are the challenges most of our clients face and how to overcome them: DSK BI Member engagement

Defining member engagement

Everyone seems to define member engagement differently or has a different ideas about what an engaged member looks like. Some think that being a member and coming to conferences constitutes an engaged member. Others think that serving on committees or purchasing publications is engaged. Still others may think that being active on social media or opening and responding to association emails counts as an engaged member.
An association must, from top to bottom and laterally across departments, decide on what metrics they want to measure to determine member engagement. Different activities can and should have different weights but make sure this definition aligns with your association’s strategic plan. It’s ok (and preferable if you have limited resources) to start small with a few key metrics and adjust your weights and metrics as you phase through analysis.

Missing or inaccurate data

Once you have determined what you want to measure, do you have all the information available? Is it all available from one location or more than one and is there a way to tie all the different sources to one individual?
Take steps now to ensure that there is a way to connect or relate your records in multiple sources. For example, if you use a third party event registration system, make sure it contains the customer ID from your AMS or CMS. Also be sure to use custom variables or custom dimensions in Google Analytics to track the primary key or ID. Set up some level of integration with your CMS and your email service and any other systems your members interact with.

Limited Accessibility

A membership manager may have to constantly ask the marketing team for open rates, the events team for registration counts and the programs team for certification counts. Then they may have to compile all that information to figure out why their retention rates aren’t rising as expected. Make sure that your decision-makers have access to the information they need in real-time and that everyone is performing analysis on the same version of the truth.
When thinking about member engagement for your association, here are some concepts to consider.

  1. Think long-term. It is often helpful to take a long-term view and focus on the lifetime value of the member, not just short-term metrics, such as email response rates or page views. Depending on when you converted to your current systems and the approach you took, it may be difficult to get accurate data dating back more than a handful of years. Plan now to be able to use your data effectively in the future.
  2. Think outside the registration box. Think about all the ways your members interact with you that go beyond event registration and membership purchases. This may be inquiries (either in person or through your website) or advanced website metrics.
  3. Assign weighted values. Think about all the ways your members interact with you and what you would like to include in your member engagement metrics. Once you have all these behaviors in mind, you can assign each behavior a point value. These point values should be based on their importance to your association and its strategic mission.
  4. After you have collected engagement data you can correlate your engagement measures with various outcomes to see whether the behaviors you selected actually are predictive of the outcomes you desire. For example, you may discover that if a person gives generously to the foundation or PAC, they are more likely to renew their membership regardless of their product purchases. You may discover correlations you did not expect. You can adjust your point values based on the results of your analysis instead of gut feelings or low-response rate surveys. How cool is that?

So, if you are wanting to measure your members’ engagement, get started today, even if you have to take small precursor steps. There’s no magic wand you can wave to get a member engagement score, but thoughtful planning and analysis can be of significant value to your association.