How to Create a Wealth Model of Your Best Prospects

wealth models

When it comes to fundraising and marketing, acquisition costs can be measured not only in dollar value but also in time. It is important to spend time and dollars on the right kind of prospects. Wealth models can guide fundraisers and marketers to focus their time and efforts.

What is a wealth model?

A wealth model is a statistical model or an algorithm. It is based on a mathematical formula. Data scientists create these formulas to identify common traits among your customers, donors, and prospects. They have the capability to even convert certain qualitative attributes into quantitative scores.

Instinct usually determines the most predictive characteristics of top prospects. Wealth models help validate your instincts by quantifying and measuring data. This means that as a fundraiser or marketer your educated guesses can now be data-driven decisions.

How does wealth modeling work?

The idea is simple. A wealth model helps you answer a specific question. Wealth modeling is like perfecting a recipe. Data scientists take different ingredients in exact proportions to create the perfect model. Correlations between data points help you find your next best prospects. These correlations are much more reliable than any one single attribute.

For instance, let’s look at building a major gifts model for a university. The model must answer a specific question. Let’s say that the query is to find donors with a gift capacity of over $25,000 out of a list of 100,000 people. The wealth model first considers past behavior. Everyone that has made a contribution of $25,000 or higher will be included in the results.

wealth modeling

WealthEngine’s models establish a sample size of 200 or more. This means the machine learning in the model will look for donors with high gift capacity. It also means that the algorithm will go a step further to redefine the gift capacity or metric until a reliable sample size is established.

In the previous instance, if there are over 200 people that have donated over $50,000 the model can recommend this as a benchmark. Conversely, if it is only possible to find over 200 people who have donated $22,000 and more then the model can reset the benchmark accordingly.

How to use a wealth model

The quality of data determines the strength of wealth modeling. Therefore, modeling with reliable data gets you closer to your next best prospects. Wealth models can help segment your database based on capacity and propensity.

Raw scores generated by the algorithm help rank prospects. You can then start to segment top prospects. Refined segmentation helps you personalize offers and nurture prospects.

The benefit for marketers and fundraisers here is that the model brings you to the right neighborhood of prospects. You can prospect with confidence as your decisions are backed by data. The wealth model helps keep the right people and removes ones don’t meet your goal.

You can also customize wealth modeling to your industry and organization. Instead of looking at benchmarks that may not apply to you, you can focus on attributes that truly affect your business.

For instance, if two competing luxury goods companies run wealth models they might find different patterns within the same database. Company A might find that the correlation between zip code and number of cars owned is important. Whereas, company B might find the correlation between age and interest in travel are most important for them.

In this case, companies A and B would not benefit from a generic model for luxury goods companies.

Additionally, wealth modeling can also help you calculate donor lifetime value. This can act as an indicator of major gifts and recurring donations. It can also reduce donor churn and acquisition costs.

How WealthEngine builds wealth models

What makes WealthEngine’s analytics unique is that we build custom models for your industry and organization. This means they truly reflect patterns in data that are relevant for you.

WealthEngine’s wealth modeling goes beyond the industry standard of analyzing 70% of any data set.  In fact, we use 90% of your data file and use the rest of the 10% to validate the file up to 10 times.

Our models generate raw scores to help you prioritize. Further, they create 10 deciles so that data is pre-segmented to show you the top 10% of prospects. You could then choose to target the top 20%, 30% and so on.

Our process uses standard econometrics and tree-based modeling to show you where your top prospects are clustered in a continuum.

Enterprise models give you unlimited opportunities to answer fundraising or marketing questions. You can input every attribute in your database into different wealth models. Each model addresses a specific need. These are custom built for your organization so they highlight patterns that are most relevant to you.

Custom models give you the option to input 6 key attributes into our analytics solution. This is an affordable way to generate more controlled models that are still specific to your organization.

Both types of models are capable of accepting numeric and categorical variables.

Learn more

Find out which model delivers the best results for your organization . Contact us.

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Prospect Automation: Why Old Ways of Prospecting Will No Longer Deliver Results

Use Predictive Modeling to Find Your Next Best Prospect

 

Empower Prospect Research with Artificial Intelligence & Machine Learning

artificial intelligence & machine learning

The applicability of Artificial Intelligence & Machine Learning has been growing in every sector. In fact Forbes predicts that, “AI and machine learning will no longer be considered a specialty and will permeate business on a deeper level.”

At WealthEngine, Artificial Intelligence and Machine Learning are terms that are part of our Zeitgeist. We deal with these technologies every day when it comes to platform optimization. In fact, our new release WealthEngine 9.0 is all about automation and continuous learning.

This means you get accurate and up-to-date data to fuel personalized, wealth-aware campaigns.

Artificial Intelligence, Machine Learning, and Prospect Research

Artificial Intelligence helps analyze your database and build predictive models. You can use these models to identify your next best prospects. Further, Machine Learning automates the learning process to give you refined, personalized insights. In effect, the more you use the platform, the better it gets at serving your needs.

For instance, you could give us a list of 100,000 people with over 100 attributes for each record. This seems like a lot of data, but the Artificial Intelligence that powers the engine quickly analyzes these records. The engine recognizes patterns among top donors. These patterns are different for every organization. Therefore, your custom model helps you find new prospects like your top donors.

Machine Learning continuously refines your model. You can find top prospects in your database and convert them. While you do this, the algorithm sharpens itself to make the process faster and more targeted.

How do Artificial Intelligence and Machine Learning empower your organization?

The idea is simple. Models built specifically for your organization result in increased fundraising and marketing efficiency. Our predictive score is a strong indicator of conversion. It helps lower acquisition costs by enabling you to focus on top prospects.

When causes and corporations become efficient, they level-up entire communities! McKinsey reports that Artificial Intelligence is seeing niche applications in several social sectors. Plus, the technology has great potential to be scaled up.

Besides Artificial Intelligence and Machine Learning, we have defined a set of qualities that will empower us to uplift you.

What does AI/ML mean for us?

Our company is operating on a theme that we call Level Up! Every aspect of our business is being pushed to perform at a higher level. The notion applies to us, our platform, partners, and our approach to customer engagement.

Further, it means leveling up the work that our customers do- making their goals and missions our own. Through them, we will work towards uplifting communities and causes.

We believe that Automation, Integration, Monitoring, and Learning are key to achieving this.

Automation

Automation helps us present a tested and unified recipe in the form of a customer or donor profile. The process involves taking bits of data or ingredients in varying proportions from different sources and orchestrating them into a comprehensive profile. Our profiles can help you understand a person’s capacity, propensity, and intent.

 Automation also helps ensure that you are always working with the latest information. This reduces the margin of error and acquisition costs. We automate the most manual, error-prone aspects of the process through Artificial Intelligence.

For instance, if you are a bank you can go beyond generic service offerings. An updated profile allows you to offer products based on current life stage and needs.

Integration

We take efficiency one step further through integration. Having updated profiles is great, but if we can provide this to you within a familiar platform, it is even better!

 Our Salesforce connector app is one such integration. It allows you to see richer data on each profile within the ecosystem that you already use.

 Integration reduces your effort and helps you focus on driving engagement.

Monitoring

Monitoring enables the platform to automate and provide a seamless user experience. It provides insights that help us enhance the profile frequently. The process also helps us refine our integrations for each sector.

For instance, giving history may be a top data point for a nonprofit. The same data point may be nothing more than an interesting side note for a luxury marketer. Monitoring powered by Machine Learning creates a tailored experience for you. When you access the platform, you will see a dashboard built on data and features that are most relevant to you.

Learning

Nothing is more important than continuous learning. We are interested in two types of learning. Insights at the platform level can help enhance your experience continuously.

At a higher level, we keep our eye on about global wealth, charitable giving and macroeconomic trends that could affect you. This helps us champion your causes and thereby level-up communities.

Leveling Up Together

Artificial Intelligence and Machine Learning enable us to offer relevant and updated insights. Our focus on Automation, Integration, Monitoring, and Learning empowers your prospect engagement.

When engagement and personalization come into greater focus, corporations can better serve customers. These customers can better support causes. This means that we each help the other to level up!

Learn More

Power up your prospect research to find your next best donors and customers.  Contact us for a demo on how to implement AI/ML to power up your prospect research.

Customer Segmentation Models for Increased Conversion

customer segmentation models

Fundraisers are marketers are well aware of the power of customer segmentation analysis. Customer segmentation models can increase personalization and engagement.

Importance of Customer Segmentation Models

Personalization is going to be a major theme in marketing. Tailoring the experience to your customer or donor’s interests makes your message resonate. Robust customer segmentation analysis is the foundation for personalization.

In a diverse marketplace, organizations need deeper insight into demographics, lifestyle and interests. To this end, customer segmentation models help you understand the nuances of the market.

Once you create main segments, you can analyze them further to improve personalization.

For instance, let’s say you’re a Major Gifts Officer at an animal rights advocacy group. You are looking for donors with a gift capacity of over $25,000. You can refine segments that fit this need further based on a geographic region. The next step is to filter for prospects who have given to animal rights groups in the past. This allows you to filter for pet owners within the refined micro-segment.

You can then personalize your message in a way that speaks to this micro-segment. Focusing your marketing budget on targeted micro-segments reduces costs. Moreover, the likelihood of conversion increases.

Customer Segmentation Analysis

Segmentation models reveal patterns that help you increase engagement within these micro-segments. They also enable you to personalize marketing at scale.

WE Analyze, our analysis and modeling solution can create such models from your data. In the previous instance, you found your micro-segment. Let’s say they are San Diego area millionaires. They have a net worth of at least $5 million. You narrowed your list down further to those who have donated to animal rights groups and are pet owners.

Customer segmentation models can identify common traits and patterns within this group. You can then apply these patterns to your database to find more prospects like your chosen segment. A customer segmentation analysis can also help to drive predictive prospecting, including finding lookalike prospects that match the profile of your best donors (or clients).

Customer Segmentation Models and Personalization

Customer segmentation analysis helps drill down into smaller sub-segment to fine-tune messaging. A personalized message that reflects customer interests is more likely to resonate. If your customer relates to the message then he is more likely to engage with the brand or cause.

For example in your micro-segment, you can create smaller groups of prospects based on age and type of pet. 

This will help you personalize your pitch to them. Further, it can guide you when selecting a medium that is most effective for each sub group.

Segmentation Analysis and Lifetime Value (LTV)

Customer segmentation models increase engagement over time. This results in improved customer LTV. Which means that the customer or donor stays connected to the organization. LTV is a great metric for you to rank your prospects.

Prioritization means your budget and message focus on the most responsive segments. Thus, your efforts will see increased conversion at reduced costs.

Segmentation Analysis and Models are Cyclical

The more you use customer segmentation analysis and modeling the better it is for conversion. We have already established that micro segmenting helps personalize your message and increases engagement.

Consequently, increased engagement results in higher conversions and lower costs. The other important attribute of these models is that they learn to get better over time. Every customer segmentation model helps WE Analyze learn from it. Your models become more refined, thus creating better segments for your next campaign.

customer segmentation analysis

Increase Conversion Rates

Learn more about how WealthEngine’s data science team can help you increase conversion rates through customer segmentation models. Contact us.

Further Reading

Why You Need a Concierge Marketing Strategy

How to Calculate Donor Lifetime Value to Predict Future Donations

 

 

How to Calculate Donor Lifetime Value to Predict Future Donations

donor lifetime value

Knowing donor lifetime value for each of your contacts can make your nonprofit fundraising efforts go much faster. You can use it to forecast the future giving of your constituents. Here’s how to calculate donor lifetime value, which can help you determine which donors to nurture more closely.

What is Donor Lifetime Value?

Donor Lifetime Value is an estimate of how much you can expect a particular donor to contribute to your organization over their lifetime.

There are many factors that go into calculating what this number should be. Not only should you take into account a donor’s wealth indicators, you should also look at their propensity to give to related causes.

One WealthEngine client found that by simply asking a specific 1% segment of their donors to contribute just $100 more in a year, they would generate over $200,000 in additional funds. In five years, this would generate more than $1 million in new funding from “underperforming” donors.

The beauty of creating such a model is that it can pinpoint exactly who to target and what your ask amount should be.

Creating a Donor Lifetime Value Model

The donor lifetime value wealth model takes into account the giving history to your organization, donation frequency, contributions to similar organizations, and other factors.

WealthEngine’s deep insights into donation habits starts with financial data about a donor to determine their giving capacity. The model then uses philanthropic, demographic and lifestyle data as part of the data set to predict how much a donor is likely to give.

Aside from these factors, our data science team uses our proprietary database, which has profiles on over 250 million Americans, to calculate the donor lifetime value.

These insights can help you estimate the churn risk and future giving behavior of your existing donor base.  Armed with this information, you can maximize fundraising ROI across the donor’s lifetime instead of focusing only on your next campaign.

How WealthEngine Predicts Future Donations

WealthEngine’s methodology uses machine learning techniques to determine donor lifetime value.

  1. We start with randomized partitions of known giving history. Some segments of this data are used for calibrating the model and others are used to validate it. We also evaluate various types of predictors using the data sets our clients provide.
  2. Then, we apply machine learning algorithms that iterate and learn from each round of data analysis.
  3. Once the model is created, we cross-validate it to view the model performance. This gives an overall confidence level in the donor lifetime value model.

Using a Donor Lifetime Value Model

Using these machine learning techniques, WealthEngine helps nonprofits determine very useful insights like:

  • Churn likelihood: probability that the customer will not donate anymore after their last donation
  • Next gift amount: expected amount of the donor’s next donation
  • Future gift count: expected number of donations within a 20-year period
  • Future gift total: expected dollar value of donations within a 20-year period
  • Total Donor Lifetime Value: Past plus expected future donation amounts

Equally important, the donor lifetime value model can help you identify the high-value donors at risk, including those with:

  • Moderate-to-high churn likelihood
  • High expected next gift amount or future gift total

When you know these valuable insights, you can identify approaches to increase the number and amount of gifts from your donors. You will know the potential for donor fatigue that occurs due to frequent contribution requests. This can generate higher conversions while saving your marketing investments.

Calculating Donor Lifetime Value for Your Nonprofit

WealthEngine offers multiple donor lifetime value modeling options. In addition to custom models, our popular 4-pack of pre-built models can shine a light on specific major gifts, planned giving and other opportunities within your existing base of donors.

Contact WealthEngine to learn more about how to calculate and apply donor lifetime value to accelerate fundraising for your nonprofit.

Who are the Forbes 400 Billionaires?

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Of course they’re all wealthy but what else separates these billionaires from everyone else? 

We took a look at the top billionaires in the country to see what their interests are, how many properties they have, who they donate to, and what their demographics look like. We also analyzed how these billionaires differ by age. How do the youngest stack up against the oldest? What makes each age group tick?

Continue reading “Who are the Forbes 400 Billionaires?”

Who are the NFL Fans? We’ll tell you.

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Football season is in full swing, and teams are in competition yet again on the road to Minneapolis on February 4th. There’s volumes of data on the players, but what about the fans?

We’ve analyzed the 32 towns to see who drives what car, who owns a business and where the millionaires are. Do wealthy towns have winning teams? Check our stats below to see how your team and town stack up.

How does WealthEngine do this? With great data and cutting edge analytics. We can find football fans, car enthusiasts, millionaires and spenders. We do it every day for our over 3,000 customers. To learn how WealthEngine can help your company, request to be contacted here.

America’s Wealthiest Singles 2017

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In honor of Valentine’s Day we analyzed the wealthiest bachelors and bachelorettes in the country. Take a look at the infographic below to see where these singles live, what they’re interested in, how charitable they are, how many properties they own, and more.

Curious how we found and analyzed this list? We started by creating a list in WE Prospect of single men and women in the US with a net worth greater than $5MM. Then we used WE Analyze, our predictive lead scoring and analysis platform, to visually see the composition of the singles. The best part? We got all of this information within minutes.

Want to see it yourself? Contact us for a demo.

How Can Luxury Marketers Use Social Currency to Drive Business Results?

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Do you know your customers? No, do you really know your customers? As a luxury provider, you may not know them as well as they know you. You’re used to delivering a personal experience to customers you’ve won over, but the only way to attract new customers is to really understand what makes them tick. And that means creating “social currency” with them.

Simply said, social currency is the stuff we talk about with our friends, colleagues, and family. In a marketing context, it concerns the extent to which we share a brand or information about a brand with others in our everyday social lives at work, home, or in virtual social networks.

One of the biggest challenges facing luxury marketers today is how to take social media beyond just another communications channel, and use it to drive bottom line results. Social currency provides the answer.  By creating social currency — that person-to-person buzz — marketers can support their objectives of:

  • Increasing brand awareness
  • Maintaining brand relevance
  • Improving customer experiences with their brand

Marketers can successfully drive the conversation and create more social currency by learning more about their customers and speaking directly to them. With big data, data analytics and strategic social media initiatives, marketers can now know more about their customers than ever before, and personalize communications to an extent never before possible. From purchase behavior to demographics to life style affinities, marketers can not only personalize the dialogue, they can also deliver the message in the channel that will get the best response.

As a luxury marketing professional, it is extremely important to understand the mindset of wealthy consumers, and that not all wealthy consumers are alike.  You must take into consideration both the demographic and psychographic profile of your clients. Most importantly, you must go beyond judgment into a deeper understanding of what makes your target audience tick.

The better you understand about what matters most to your customers and the better you can speak directly to those needs, the more social currency you will create. Social currency will deliver more brand awareness, brand relevance, and better customer experiences, which will drive bottom line business results and referrals.

If you’d like to learn more about using big data and analytics to get into the mind of your customers, check out our free ebook: The Luxury Marketer’s Guide to Engaging and Winning HNW Customers. This simple guide will show you how to understand your customers beyond their purchasing history and walks through the strategies that innovative luxury brands are using today to increase marketing and campaign effectiveness.

Business Intelligence vs Predictive Analytics vs Prescriptive Analytics

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You say you want a revolution
Well, you know
We all want to change the world.

Business Intelligence, Predictive Analytics, Prescriptive Analytics…

The Big Data revolution’s got all kinds of scientific terms buzzing around today’s boardrooms at warp speed, smashing into each other on the path to becoming enterprise-wide solutions to business success. But what do they really mean? And more specifically, what do they really mean to marketers?

Continue reading “Business Intelligence vs Predictive Analytics vs Prescriptive Analytics”

Predictions for Data-Driven Marketing in 2016

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We know that data-driven marketing is becoming more critical to marketers and fundraisers. One doesn’t need a crystal ball to know it will continue to influence what we build and execute in 2016. Let’s be clear, it’s not about more data, but data that can drive business decisions. Company leaders, especially within the C-suite, are considering how they will define their own vision of a data-driven company, the metrics required and how to adopt predictive prospecting as a strategy.

In a recent webinar, Cool Perspectives about Data-Driven Marketing, three of Gartner’s Cool Vendors in Data-Driven Marketing came together for a panel discussion. Here are some of their predictions of what’s to come in 2016.


“There are three big trends affecting data-driven marketing that will accelerate through 2016. Big data technologies combining open source and commercial advances are making new types of data available at scales and speeds marketers couldn’t imagine a few years ago. As a consequence, marketing itself is able to deliver more and more personalized insights and messages, rather than averaging experiences. And there is a third shift toward synchronous experiences, where what is happening now or in the very recent past can inform things like offers, messages, content, and analytics.”

Martin Kihn, Research Analyst, Co-author of Cool Vendors of Data-Driven Marketing
Gartner

“Taking a data-driven approach starting with pre-engagement will continue to be instrumental. For years, our non-profit customers have been using predictive analytics for fundraising to determine who to ask and what to ask and when to ask. While marketers have used analytics to determine buyer behavior, they’re now incorporating wealth intelligence to determine what offers and upgrades they can present in a very personalized way. The results are pretty outstanding. Marketers are seeing higher conversions and more loyal customers. While privacy around what personal data is collected and how it is used is still a valid concern for consumers, I think we’ll start to see a higher level of trust being earned by the companies who use their data to focus on how they can create an exceptional experience for the customer through smart segmentation and customized messaging.”

Mike Lees, Chief Marketing Officer
WealthEngine

“Google is the modern operating system for digital marketing.  Adobe, Oracle and Salesforce may get the press, but Google dominates in terms of marketer’s mindshare, marketshare, and ultimately execution.  Google Tag Manager, Google Analytics and Google Adwords have created digital marketing’s data layer and the standard analysis engine to understand, optimize and execute on digital marketing investments.  Next up for Google is to help marketers build a truly enterprise marketing data warehouse and analytics engine in the cloud.  

Google Cloud Platform & Google BigQuery deliver on this next piece of marketing infrastructure today, creating the first end-to-end data-centric marketing cloud. It’s clear now that CMOs will own the tech marketing stack and Google will be their go-to provider. Other marketing tech vendors will need to be interoperable with the Google Cloud and deliver their own unique value in this ecosystem.  Only a data-centric company like Google can deliver this new kind of marketing cloud.”

James McDermott, Chief Executive Officer
Lytics

“Few CMOs will argue that they don’t own their brand anymore. We have lots of user data thanks to social media, but social media is changing consumer purchasing behavior far faster than companies are translating social data into useful insights. Businesses must begin to iteratively add data tools if they are to overcome this challenge.

Office cultures don’t change overnight. Many companies fear that data will require a top-to-bottom cultural overhaul, which causes them to hesitate instead of adding new data tools. Fear barriers only begin to lower when people begin to iteratively embrace a new concept. With these iterations, change starts to take hold, and companies begin to find the equilibrium between what’s always worked and what’s necessary to remain competitive as the uncertain future unfolds.

In 2016, I believe the fear barrier around data-driven marketing will finally begin to lower as people recognize that they don’t need to eliminate the old ways of doing things, and instead need merely to augment their existing toolbox with new capabilities. On-demand data-driven marketing tools will give CMOs new options as to what they discard, what they keep, and most importantly how they can better use the tools and processes that their company cultures can handle.”

Malcolm De Leo, Chief Evangelist
Quantifind


Interested in learning more about how data can be the critical element that drives your company’s success? Request a demo now.