Most of your staff isn’t trained to handle the mountains of data you’re collecting. So we usually rely on data analysts and IT teams to make sense of it all and then distribute it as needed.
But we all know this doesn’t always happen when you need it most. Your people are busy after all. Today though, non-techie people finally have the power to access and apply real-time data through a process called “data democratization.”
With the right checks and balances in place, this game-changer can promote better teamwork, foster collaborative problem-solving, and give your association the competitive edge you need to make decisions on a dime and provide unparalleled member experiences.
Data democratization is the process of making digital information accessible universally in your association—for the tech-savvy, non-tech-minded end users, and everyone in between.
With no barriers to gathering and sharing data, anyone within an organization is empowered to use real-time data in their decision making. If you have a good handle on your information, then democratizing data enables:
Associations typically have member data, internal departmental data, transactional data, and analytics data kept in information silos that don’t “talk” to each other. Data democratization breaks down these walls and inconsistencies in data sets to create a borderless ecosystem of information.
For data democratization to work, every team member needs a certain amount of training on the tools and processes involved. Training and hands-on learning allows every player on your team to grow and stay proactively informed in the state of business, your goals, and how to achieve them.
Data democratization often drives a serious cultural change in organizations as it forces you to re-examine how you manage data to reduce the obvious risks involved in giving everyone access to information. In turn, a shared sense of trust and cooperation grows. And when teams see the value of sharing data, cooperation and collaboration grows even more until it’s just a natural part of your culture.
Democratizing data ensures the right person has the right information at the right time so they can make informed decisions when they need to. Giving more people access to the insights they need leads to less guesswork and more impactful and diverse ideas.
Sounds good, right? Now here’s how you can actually put it into practice...
Increasing access to data opens the door for obvious risks to data integrity if the right safeguards are not in place. So to increase data accessibility, we put data governance safeguards in place. These checks and balances set the bar high for the standard of practice among team members by increasing accountability and transparency.
Reducing these risks requires you to champion better “data governance” and “data management” practices.
Data governance is basically when you create procedures for data security, quality, and usage. A clear framework for data governance and the checks and balances it creates association-wide:
Data management essentially puts your data governance processes into action when it comes to using data for decision-making. Having a “standard of excellence” around how information is managed lays to bed any concerns about potential data mismanagement or misinterpretation.
It also ensures information is consumed and visualized in alignment with your organization’s goals and performance targets so all decisions support business needs.
You can even create data management standards to filter information based on people’s roles or show data points in a visual way. This way, not only is your staff comfortable with the process of democratizing data but you also stay ahead of any potential mishaps or liabilities.
Connecting everyone in your organization to the information they need may require some initial training or technology changes, but the payoff in terms of performance, growth, and freeing up information silos is well worth the investment.
Integration platforms such as middleware and newer microservices bridge the gaps in your systems and data.
Instead of having data silos and one-to-one integrations between all the systems and applications you use, integration platforms are the “software glue” that work with all your third-party vendors. So when you need to access information, you don’t have to manipulate the data. It’s already ready and manageable from a single place.
Customizable data warehouses, more modern data lakes, and even cloud storage are another strategy to make it easy for anyone in your organization to get the data they need to make a decision.
These solutions store and organize information - according to your data governance processes - in one centralized database for easy reporting and analytics.
But you should know that these analytics warehouses aren’t always the best solution because they integrate data, not your actual systems (aka they’re data-level integrations not system-level integrations). So you could potentially be left with member experience issues. For example, your member profile page may not have info about past event registrations or book purchases.
In short - some organizations may find that integration platforms or moving to more natively integrated systems may be a better solution to make data democratization a reality.
With the limitless potential of organization-wide data sharing and accessibility, it’s not a question of IF your association needs data democratization; rather it’s time to figure out HOW to create it.
Getting a leg up on slower adopters and data-stingy organizations, and making data-driven decisions a central part of your culture will unlock the true value of your association.
Before you can implement data democratization though, you first need a handle on what your information is right now. If you want to make sure you have the right data and are using it effectively, check out our webinar Is My Data Good Enough?.
Your association is collecting more data than ever as organizations race to keep up with member expectations and digital transformation initiatives. This data contains the insights you need to grow and differentiate your organization. But the truth is, your teams are busy and resources are too limited to really unpack and use your data effectively.
But if you’re not prioritizing data analytics, then you can’t identify actionable insights to benefit your association. You need a plan right now, but you don’t want to invest a ton of capital or time. That’s why an increasing number of organizations are outsourcing data analytics to a dedicated outside partner.
In fact, Allied Market Research projects the market for outsourced data analytics (aka Analytics-as-a-Service) to grow to a staggering $126.48 billion by 2026. Is it time for you to jump on board to maintain a competitive edge?
In this article, we’ll discuss four signs that it’s time to outsource data analytics. These four red flags include:
Your IT people just don’t have the time to deal with data requests. Oftentimes, IT folks have to respond to more pressing priorities as they arise.
Without reviewing current data, vital renewal dates get missed and time and resources get wasted on uninformed decision-making and poorly crafted business strategies. You also need to make data-driven decisions faster than ever as competition fiercens and market forces often change rapidly.
Relying on existing IT staff is simply not a good long-term strategy for data analytics. Instead, bringing in a dedicated data analytics vendor:
• Ensures you get the right data when you need it.
• Allows you to take advantage of opportunities with precision.
• Increases your productivity and competitiveness across every level and function of your organization.
Solution: Outsourcing data and analytics ensures your members are renewed on time, key deadlines and goals are met, and opportunities are capitalized on without disruption.
Your membership is increasing so you’d think your overall growth should be trending up – but it’s not.
Growing membership but flatlining growth is a clear indicator that there are better ways to spend your money, time, and resources. The problem is you don’t have the time or energy to analyze the data while also keeping a pulse on the competition. And with more competition in the association space than ever before, the urgency for analytics is tenfold.
A dedicated outsourcing partner that is exclusively focused on associations can dive into your data and competitive data, uncover insights about members, and even anticipate member needs and actions.
Plus, any membership-based organization knows that segmentation, targeting, and positioning (STP) is the only way to reach the right members with the right message at the right time through the right channel. Making this happen requires analytics as a core competency.
Solution: Outsourcing data analytics ensures resources are spent in the right places to drive growth.
Simply put, IT people and data analysts are different.
Your internal IT teams may not have the right technical skill sets and horsepower to make sense of the sheer volume of your data and build growth strategies from it.
Deriving actionable strategies from your data requires someone who is dedicated to the cause, not halfway invested. Hiring an association-focused data analytics vendor that guarantees results and a clearly defined return on investment (RoI) ensures the job will be done right. It’s all about accountability.
Delivering on that RoI translates into success for your organization, and in turn, a mutually beneficial relationship for both parties.
Solution: Outsourcing data analytics turns ownership and accountability over to a specialist.
Data IS vital to grow your association. But in order to use your data, you first have to understand what it all means.
But not only are your in-house IT folks swamped and often don’t have the right data analytics competencies, but there’s also a huge shortage of data analysts in the market today. In fact, there’s about 250,000 more jobs than eligible candidates.
Rather than spending tons of money and time having in-house IT teams get trained in data analytics or finding a qualified (and most certainly high-priced) candidate, outsourcing offers a way out that is more affordable and less time-intensive.
Solution: Hiring an outside firm to help with data analytics is a low-cost alternative to increased IT hours or hiring a dedicated data employee.
Analytics is well on its way to becoming a lifeline for associations in today’s swim or sink reality. Outsourcing data analytics allows you to scale up your organization and scale down your resources while adding vital analytics expertise.
Giving ownership of pressing analytics needs to a third-party provider that focuses exclusively on associations can help you:
• Dive into the psyche of members to cultivate deeper connections and tailored journeys.
• Identify what members need, even before they themselves know, to improve retention.
• Get you the right data when and how you need it.
• Instantly resolve ongoing challenges and lingering stakeholder questions.
At Tasio, we provide dedicated data analytics services according to your budget, financial forecasts, and a clear RoI. As a round-the-clock partner, we manage the ins and outs of your analytics and determine data-backed strategies for sustained growth.
Our managed analytics services range from integrating your data sources and managing your dashboards, to building predictive models using artificial intelligence and machine learning.
Are you ready to unlock real results through data analytics at a lower cost of ownership than doing it in house? Partner with us.
We spend a lot of time talking with association leaders about their data quality. Even though there seems to be no end of information that you can gather about your association membership, a big concern we hear all the time is, “Is my data good enough to make predictions from?”
That’s a topic that we are really excited about tackling, and it’s actually the subject of our webinar coming up next month.
But, as a partial way to answer that question, there’s always a concern with third-party data—how much is too much, which sources are valuable and valid, and how can your association access this data in a way to make it useful?
Before we can really launch into answering those key questions, it’s important to understand that there are actually two types of third-party data that most associations deal with on a regular basis.
The difference between these two things is all about control.
With association-managed third-party data, your association has a bit of ownership over the data, even though it’s being gathered by an outside source. Examples of this type of third-party data include:
Truly external third-party data is information that you have no control over, but still can potentially illuminate your membership and give you more powerful predictions about their behavior, motivations, and likelihood of success within your organization. Typical kinds of data that fall into this category are:
In this case, members are not producing the information from any interaction with your association. The data is just simply there, waiting to be collected and added to your database.
Both of these types of third-party data are excellent places to get new information about your membership so that you can better understand who they are, why they are gaining value from your association, and the kinds of people who will also benefit from your organization.
Once you’ve identified all the sources of data that you both own and don’t own, it’s time to think about how to gather and then use that information to accomplish your goals. There are several steps to this process, and it requires collaboration from several different sections of your association.
Marketing. Here, you can get insights about how people interact with your online and offline products, social media and digital marketing, and individual outside campaigns. Specific data sources include event registrations, email marketing, forum and CMS data, social media analytics (Facebook, Twitter, and LinkedIn), Google Adwords and Adsense, and referrals from unique campaigns for radio, television, billboards, or other campaigns.
Membership. This information is crucial for developing predictive models of your membership and includes raw membership numbers, loss and retention, and engagement information (how often they attend events, participate in discussions, etc.). A lot of this information can be found in-house, but you may have third-party vendors that you need to go through to get data about event attendance or membership registration.
Technology. Especially now, membership engagement is very web-driven. People haven’t been able to come to many face-to-face events or meetings, so a lot of that data is being collected via your LMS and other digital tracking methods. Work with your IT department to gather information about member engagement such as how often they’re on your site, how engaged they are in online discussions, or the last time they logged into the member portal.
When it comes to supplementing your association-owned data with some of these outside sources, the most important thing to consider is your goal and your specialty area. For example, an association that is focused on the healthcare industry may not need data about voting registration, but a philanthropic association with a political bent might find that to be very helpful in profiling their members.
Consider these three guiding questions when working with outside third-party information:
Once you know the answer to those questions, it will be clear which sources are worth mining for additional information and which ones are just not worth the time and effort.
One of the things we don’t see associations doing nearly enough is developing collaborative information relationships to enhance and supplement their data.
Of course, the gut reaction of most association leaders is to hold their member data close to their chest. Not only are they worried that their sensitive data might not be as safe as a potential partner claims, but there are the legalities—can you even share that information without specific consent from members?
The answer is, you can make these collaborative relationships safe and ethical. It just takes an extra step or two.
Keep in mind that the goal is to gain a holistic view of your members. Going to a parallel organization (not a competitor) who serves the same group and interests and sharing information on your common members helps both of your associations and the member in question. Why? Because you get a better picture of that member’s needs and both of your organizations can better serve them.
We are not ones to give you complex legal advice, so that’s not what we’re going to do here. But, consider your information policies. Is there a way to allow for discreet information-sharing under the right conditions? Probably.
And even more important is this: It doesn’t take a lot of detailed identifying information to get real value from a collaboration. Even top-level overviews of membership numbers, engagement, or demographics can be very helpful for both your associations.
Managing your association’s third-party data is a big part of having a proactive plan to develop predictions about how to best help your membership. By taking advantage of all third-party data resources available, you can get a better picture of who your members are and how to help them be more successful in you association.
If you have questions about how to gather the right data (or how to remove it from a difficult system or process), that’s what Tasio specializes in. We work with association leaders to gather the right data to make informed decisions about the future and get a better understanding of where your association stands right now.
Connect with us to learn more about what services we offer for third-party data collection and analysis.
Being an association member should never feel like buying a t-shirt or cup of coffee. Great associations are able to really listen to their members and use that feedback to build their communities. This kind of ear-to-the-ground model transform education, events, and even person-to-person outreach.
So why aren't we using this feedback to make sure each member has their own success story?
The easy answer is that it’s just too hard. Every member is coming from a different place and looking to reach different goals with their membership in your organization. They have different levels of activity, different interactions with your marketing and educational offerings, and the best you can do is try to focus on generalized behaviors and hope that what you’re doing works.
But there is an alternative.
With the right kind of data and analysis—powered by AI-driven predictive models—you can actually listen more to the unique needs of your members and help them develop personalized trajectories that will leave them fulfilled and keep their membership renewing year after year.
The term “predictive analytics” sounds intimidating, but it’s actually something you already use to your advantage every day. Every time you go to Gmail to send an email, the AI-driven predictive analytics machine is working on your behalf. With every word, a few things are happening behind the scenes:
As you can imagine, that’s a massive amount of data to review in a millisecond.
But because it has so much data to pull from, it can give you very likely predictive text when you pause long enough to indicate that you’ve run out of words to say.
If this kind of power can help you do something as simple as send an email that sounds intelligent, you can imagine that the same power behind your member data would allow you to give them a higher level of member success than ever before.
One of the ideas that we’re really excited about right now is growth-based member journeys. Basically, we take historical data about your successful members (those who have reached certain milestones in their careers, those who engage fully in the community, etc.) and reverse-engineer their journey to provide a clear pathway of how a new member can be successful.
Depending on what kind of data you have, you will be able to develop a program of events, courses, and marketing materials that have the highest potential for providing value in the long-term. This doesn’t just give you a recurring stream of revenue, but also gives your members exactly what they came to the association to find—opportunities for growth and advancement.
Unfortunately, this kind of data-driven pathway is not a one-size-fits-all venture. Associations typically tell us that they don’t have the time and budget for more than a “spray-and-pray” model of marketing and service offerings. But while these initiatives may work for 51% of members, they often leave the other 49% disillusioned and unlikely to renew their membership.
The right kind of data analysis allows you to segment your membership into cohorts to really understand their motivations and the best outcomes for their particular member success story. And, while it’s a good first step to segment your members by age or location, these aren’t the kinds of segments we really mean (although they’re better than nothing). The most valuable types of segments include things like:
These behavioral cohorts are much more effective at showing you which stage of the member success journey they are in and whether or not they are currently finding value. Then, just like Google can predict what words you’ll type into a search bar, the predictive model can predict what next steps will help them find the most value in your association.
It is possible to provide a better level of member success than you are currently providing. All it takes is good listening through better data and a professional eye to develop the personalized journeys that will benefit your members most.
If you are interested in how Tasio works with associations to create these customized member success plans, please connect with us here. We are always happy to help an association develop a long-term strategy for happier members and exponential membership growth.
The best member data to input into your AI model includes a combination of the type of data, amount of data, recency of data, and responsiveness of the data model. While there’s no silver bullet approach for all associations, considering these member data characteristics through focused Data Design Sprints will help you make better predictions on behalf of your organization.
When we think of AI, our gut instinct is often to think of robot brains that are infallible and maybe even a little bit creepy.
The reality is that an AI model is simply a program that makes educated guesses based on the data you feed it. This is why, at Tasio, we stress the importance of not just having data, but having the right data and knowing how to use it.
So what exactly is “the right data” to help train an AI model? In this article, we’ll talk about the characteristics of the data you need to build a machine learning model that can reliably make predictions on behalf of your association. We’ll specifically focus on:
We’ll also discuss how you can run Data Design Sprints to continually develop prediction goals and recipes to test your hypotheses.
When you're in the process of teaching an AI system how to predict membership loss or retention, there are really four key areas that you need to focus on to make sure that you have "the right data."
Some types of data you need to properly train your machine learning model are relatively constant such as profile data, while transactional and behavioral data are more variable.
Profile data includes data points like:
Transaction data may include things like:
Behavioral data may include:
You also can’t forget about master data (e.g. different products and services), as well as reference data that’s typically governed by regulations and compliance standards. While we wouldn’t call master or reference data points member data per say, they both without a doubt impact the member experience.
The amount of data you need to train an AI model depends on the type of problem you want to solve. But there are definitely some general rules of thumb.
If you’re trying to predict 12 months out into the future, you should have at least 12 months (or 365 days or 52 weeks) worth of data to train your model. In other words, you should have a data point for every month out you’re forecasting to have valid results.
Another key factor is gathering a good amount of data from a number of sources. For many associations we work with, data comes from AMS systems, Google Analytics, and other third party sources. Remember: the more volume of reliable sources of data, the better the predictions.
The recency of member data is the amount of time since members’ last data history (whether it be their last activity on your site, webinar viewed, etc.).
Again, there’s no “one-size-fits-all” for determining how recent member data needs to be to feed your AI model. Keep in mind that many failed AI models produced inaccurate or unintended predictions not because of the data they had but because of the data they didn’t have. So the more recent, the better.
A good AI model is able to make predictions based on data and then learn and become even more capable and more knowledgeable from feedback and subsequent data. So you must consider the development and use of your AI model in response to changing circumstances.
This is where that training data and feedback data really come into play.
A Data Design Sprint is a great way to select the right member data to feed your AI model.
The Data Design Sprint was created by Sam Chow, PhD, a top AI Product Manager, to compress months of work and debate over your AI model into a single hour.
Instead of launching your model only to realize later that it predicts member behavior poorly, you can walk your team through a member data brainstorm beforehand to get a tactical roadmap in place.
Building a machine learning model is all about experimentation. So you’ll need the right people and strategic brainstorming processes during your Data Sprint to capture and test hypothesis and prediction goals.
Here are the main requirements and best practices for a Data Design Sprint:
Here’s how Chow breaks down the Data Sprint process and time spent:
Member data is your most valuable asset to becoming indispensable to the professional community you serve. It’s a precious thing that helps you provide a more valuable member experience given individual needs and interests.
But there’s no silver bullet approach to deciding what types, amount, and recency of member data are needed to train your AI. What works for one model may not be the best for another. It’s all about the process and continued experimentation through good training data and feedback data that you can gain by conducting focused brainstorming huddles and Data Design Sprints with your team.
Want more information on how your association can better identify and handle member data to increase retention? Download the free Association Retention Playbook to get a proven 5-step strategy you can start implementing right now.
Part 6 of a 6-part series.
You can build a great organization and grow pretty quickly, but you can’t sustain it unless you’re actually providing something people want and keeping them around. That’s why member retention is the holy grail—and, unfortunately, one of the greatest pain points—of associations today.
Knowing what people want requires data, and lots of it. And even if you have all the data and processes for collecting feedback from your members, you still need processes and workflows to make sense of that data. This is where predictive analytics comes into play.
Predictive analytics puts us on the path to finding the holy grail; to finding amazing member retention strategies, the kind that just makes you a blow-away awesome association.Now if you’ve been following this blog series on using predictive analytics to grow your association, then you may have noticed we keep circling back to how each of the applications we’ve covered—member targeting, financial forecasting, risk analytics, market intelligence, and financial modeling—help you retain more members.
But these predictive analytics applications don’t just individually contribute to your member retention rate; they also work synergistically to help us make better roadmap decisions and member experiences that keep people coming back for more.
The ways in which predictive analytics applications work together to retain members are innumerable. Here are just a few of the ways these elements go hand-in-hand to inform member retention activities and “hack” the growth of associations.
So, in essence, predictive analytics and modeling promote member retention by discovering what makes your individual members tick and why. These algorithms and machine learning techniques also go beyond just identifying what members want from their association membership.
They also help you segment your lists and tailor your targeting strategies so you can best serve the wants and needs of your members. Then you can dig into the analytics to refine your custom targeting approaches and membership retention best practices to continually deliver value and solve the problems of each and every one of your members.
Let’s say you have a network of members that want to connect and/or mentor each other. Identifying this need using good ole’ fashioned outreach (e.g. online community engagement, email campaigns, focus groups, individual outreach) or predictive market intelligence may then prompt you to consider implementing a mentorship program. You could then forecast engagement and risks, and model the impact and strategy of implementing such a program all with the power and insights of predictive analytics.
You can also use analytics to predict new member retention opportunities such as untapped continuing education needs within your community. You could then apply predictive analytics to turn this vision into reality by:
Member retention is certainly NOT a simple equation. If you want your members to stick around, you have to demonstrate the value of being in your association. In other words, you have to literally help them on their professional journey and assist them with whatever the reason they decided to join your association in the first place. And this starts on day one of their membership.
The great thing about predictive analytics is it helps ensure valuable data points don’t fall through the cracks—starting on day one. It ensures you’re solving the right problems, engaging members the right way, and tapping into the thoughts and voices of your crowd.
Without processes and techniques to understand and apply the data your organization has collected overtime, it’s easy to get caught in this vacuum kind of thinking where you’re giving your best guesses like, “Yeah, this makes sense because of this.” Instead, you can now use algorithmic forecasting and modeling with an organized dashboard that explicitly says, “Yes, A, B, and C are the right things to do for this type of member because of X, Y, and Z.”
Ready to fine-tune your member retention plan? With the impact of predictive analytics on associations today, it’s never been easier to get a handle on your data and use it for the benefit of your members. Download our free ebook, The Association Retention Playbook: The 5-Step DIY Membership Retention Strategy and Workbook today to get moving on the right track.
Too often associations focus intently on their mission and vision while largely overlooking finances and operations until a crisis looms.
But did you know financial modeling can help you more clearly understand both financial and nonfinancial issues? It’s an invaluable management tool that is key to delivering the programs, services, and products that are needed in your market.
Today, financial modeling has become even easier and more illuminating with predictive analytics. You can now translate historical data, current conditions, and assumptions about market behavior into numerical predictions.
These predictive models, in turn, can help you see the impact of different events and scenarios to support better decision-making.
Financial modeling gives you a numerical representation of your internal operations and external influencers in the past, present, and forecasted future. It essentially allows you to calculate (and explain) the impact of a future event or decision.
But there’s no one size fits all when modeling. Every industry sector, organization, executive, and investor is unique so your financial model needs to be customized accordingly. You also have to consider who you’re building your model for (aka your audience) and how they prefer to consume information.
Is it to be used by your membership department? If so, they may care about members more than revenue or dollar signs. They probably also care about member engagement and how the model tracks it and determines the elements that resonate. In this instance, you’d want your model to include operational data as well as revenue data.
On the other hand, if your audience is your board or finance committee, you may want to hone in on funding and revenue data.
Financial modeling allows you to boil your whole organization down to the key programs and drivers that matter most from a financial perspective and other perspectives as well. At the same time, it also helps get people in your organization on the same page and “speaking the same language” when it comes to key economic drivers.
A financial model is not a budget; rather it allows you to play out different scenarios and see what the impact is. By quantifying (and then validating) your financial model, you can determine whether you can turn your vision into an economically viable organization.
Building out different scenarios of what could happen allows you to prepare for future outcomes, especially if things don’t go as planned. Planning for worst-case scenarios helps you anticipate potential cash flow and funding needs. It also helps keep everyone informed of how your organization is performing compared to financial targets and benchmarks.
Finance modeling is a critical part of the fundraising process. Investors and donors almost always ask for a financial plan when you engage them to raise funding. Some typically want more details than others. But either way, building a real model allows you to provide both high-level and detailed data.
Plus, calculating exactly how much funding you need and when will surely help you plan your fundraising strategy. It can also help you engage potential investors in a more meaningful conversation using charts, graphs, and data visualization rather than simply presenting an income statement or balance sheet. Having a model to say when an event might happen and how it will impact your organization is a sure-fire way to instill confidence.
Financial modeling can also help you benchmark and manage your budget projections, assumptions, and current finances. This can open a dialogue about the appropriate way to value economic drivers and the effectiveness of your fundraising approaches, potentially prompting you to re-formulate your strategy.
It’s important to note that a financial model is only as good as the inputs that go into it. But thankfully with predictive analytics, you can make your model more accurate than ever.
Building a financial model today is not really an issue considering the countless templates available online. The REAL problem though is actually doing the modeling and crunching the numbers.
For example, how do you forecast membership? What is the market size of your target audiences? And how much should you spend on events? Financial forecasting with predictive analytics is a great way to get these numbers and enable the predictive modeling of future scenarios.
Here are some of the ways in which predictive financial modeling can help your association:
If you wanted to get even more specific, you could even use financial modeling software to project peak funding needs and investment opportunities down to the week or even day.
In reality, your predictive model could act like a very detailed checkbook in which funding, expenditures, interests, and different investments—vendor by vendor, line item by line item—can be projected and continually compared to your historical and current performance.
Typically, though, you’d want to err on the side of caution for anticipated expenses and projected availability of funds when doing such predictive modeling.
A financial model is not something you create once. Rather, it should be built into the rhythm of your organization and then rolled forward on a weekly or monthly basis.
Best-case scenario, your management team should use this model in real-time at every management meeting. That way you can model out the effect and strategy behind trying different inititiatives.
Ready to get a pulse on current and future cash flow and operations? Browse our comprehensive new guide The Association Retention Playbook: The 5-Step DIY Membership Retention Strategy and Workbook to start planning for better organizational health.
Part 4 of a 6-part blog series.
You’re probably already analyzing market surveys to find member attributes and motivations, right? But did you know you can design a predictive model to automate this process AND create more engaging programs for your members?
Predictive analytics gives you power—power to have better programs, pricing, and marketing campaigns. Combining your data with the strength and agility of predictive models allows you to make more informed decisions and achieve better results.
In fact, predictive analytics is essentially the secret sauce to developing, managing, and scaling your association. If used properly, it can truly give you the real-time and predictive market intelligence you need to increase sign-ups, retention, and subsequent revenue.
Yet, while good analytics give forward-looking insight into your audience and competition, many associations struggle to leverage data in a meaningful way.
In order to be helpful for market and competitive analysis, the predictive analytics process first requires defining a need. For example, do you want to discover new growth areas? Optimize your offerings and pricing? Anticipate future industry disruption?
After defining your needs, you can then collect data from relevant sources like interviews and surveys. The data mining process will then prepare the data in one place for analysis. Next, analytics software mines through the prepared data and extracts predictive insights. These forward-gazing insights can then be used to drive decision-making.
So the goal here is to use the data you already collect to uncover actionable insights and smart “moves” to make.
So how is market and competitive analysis helpful for your association? It allows you to:
Now that you have a general idea of the importance of market intelligence, let’s take a deeper dive into how predictive analytics can help you:
Market intelligence analytics helps you understand the rational as well as the emotional appeal of your association’s offerings and services. You can then tailor your own offerings to match these desires and expectations of your members. Integrating this framework into your long-term strategic planning can help you search for new areas of growth as well.
You can even use predictive modeling, a tool used in predictive analytics, to forecast future outcomes using known results. So, essentially, you could build a model to help you understand and prepare for what could happen in the future.
Events Example: A great example of this is with events. Let’s say you have a membership event like a conference. The data you collect from the event (how many attendees, the most popular classes, average membership sales, etc.) can give you a baseline of information about what is engaging your membership. Then, you can use predictive analytics to identify not only what worked and what didn’t, but adjacent events and courses that can open up new revenue streams.
Market forecasting using analytics helps you ensure a sustainable competitive advantage.
It allows you to run one or more algorithms on specific data sets to come up with different possible outcomes. Such market intelligence methods can bring full transparency to the design, technical, and growth decisions of your competitors.
As you get a handle on the process, you can refine and optimize your search criteria to unlock new market opportunities as they arise and deliver even more targeted and impactful member experiences.
Through predictive intelligence, determining the direct relationship between demand and price for any program, product, or service offering becomes seamless.
No longer are you limited to matching offerings on a 1-to-1 basis. You can now program your predictive models to match by any attribute. This way you can be notified of any competitive offerings and pricing changes. By monitoring the pricing and details of similar offerings in the marketplace, you can ensure your programs are always competitive to better manage your revenue and achieve your organizational objectives.
In fact, predictive analytics is one of the most effective ways to model product pricing and revenue strategies.
With predictive analytics and modeling, it’s now easier than ever to prepare for emerging and disruptive technology and market changes.
Predictive market intelligence can essentially guide you through changes your association can make today to improve for tomorrow. Yet it also provides external market knowledge that lends predictive insights into emerging industry trends, expectations, and technologies. This foresight is a huge competitive advantage for any technology-focused organization with the know-how to manage it.
Using historical data and real-time trends, you can even create a model for forecasting negative market disruptors and then take preemptive actions to reduce their impact. Every innovation and forward-viewing change to your offerings can help you execute your strategic plan and invest in the long-term satisfaction of your members.
Your data is already providing the keys you need to grow your organization and become a powerful player in the association industry.
Find out more about how you can unlock those secrets with our free ebook, The Association Retention Playbook: The 5-Step DIY Membership Retention Strategy and Workbook.