Or “determining future performance based on current and historical data” (Investopedia).

That’s predictive analytics.

Predictive analytics models are the (often very sophisticated) tools used to perform this analysis.

Real-world applications of predictive analytics modeling can be found across various industries with many use cases. A few examples: 

• Healthcare – assessing patient risk
• Finance – reviewing creditworthiness
• Retail – predicting demand and sales and managing inventory
• Manufacturing – predicting when a machine will need maintenance (or replacement)
• Marketing – segmenting customers based on potential buying behavior, preferences, and customer value

Modeling is the secret weapon, the special sauce that ensures the predictive analytics is as close to spot-on as possible.

How predictive analytics modeling works in practice

Data. It all starts (and ends) with data. 

Predictive modeling starts with data collection — gathering relevant data. This data can come from historical records (think CRM), real-time feeds (social media, BI tools), structured data (spreadsheets), unstructured data (text, images, etc), and more.

Next up: Data cleansing. As the saying goes, “garbage in, garbage out” (or “bad data in, bad data out”). Your data must be squeaky clean. This step cannot be overlooked or rushed. Find missing values. Remove duplicates. Transform data into an analysis-ready format.

Now, choose the features (or variables) most relevant to the outcome you are trying to predict. This may also be where your team realizes you need to add more features to the model to improve its accuracy.

Pick your model! The model you choose depends on the problem you are attempting to solve. Note: We’ll dive into the various models in the next section.

Training is not just for athletes. Ensuring your model is “trained up” and ready to go is essential. Start this process with a small sample of the data. This is when the model learns to recognize patterns or relationships between the features and the outcome.

Time to test and validate. Feed in a different set of data from the one used during training. You are assessing the model’s accuracy — how well does it perform with new, unseen data?

It’s time to fully deploy your model in a real-world environment where it can start making predictions.

Predictive modeling is not a “set and forget” situation — it requires constant monitoring and updating. Some models degrade over time as data (and patterns) change.

To recap (or TL; DR), here is the 7-step modeling process: 

1. Data collection
2. Data cleansing
3. Feature selection
4. Model training
5. Testing and validation
6. Deployment
7. Monitoring, updating, iterating

There is no one-size-fits-all model. There are different predictive models for various situations.

What are the various predictive models?

There is more than one way to crack an egg (model predictive analytics).

Below is a brief recap of the 6 most commonly used models.

1. Classification Model

This model categorizes or classifies data into predefined labels or classes. It can be binary (two categories) or multinomial (several categories). 

• Binary example: check an email and classify it as “spam” or “not spam.” 
• Mutilnomial example: categorize customer support tickets into various types such as “billing,” “technical support,” or “general inquiry.”

A classification model is beneficial when the output (the prediction) assigns each input data point to one of the discrete categories or classes. 

In marketing, this model is often used to predict customer behavior categories.

2. Regression Model

This model predicts a continuous outcome or numerical value based on one or more input features.

They are often used for predicting quantitative outcomes like stock prices, sales and revenue forecasts, customer lifetime value, etc.

In sales and marketing, regression models can be used to analyze customer behavior — identifying key factors that influence customer purchasing decisions.

3. Time Series Model

This model is a statistical technique for forecasting future values based on historical data, especially when the data is sequential and time-dependent. In time series forecasting, data points are collected at consistent intervals over time.

In the marketing and sales world, time series modeling can be effective in: 

• Understanding seasonal trends (When do most leads enter the pipeline? Which months/quarters see the most significant bumps? etc.).
• Predicting sales growth. The time series model can predict future sales volume by analyzing past sales data, reviewing market trends and economic indicators, and studying consumer behavior.
• New product launches, performing marketing campaign analysis, forecasting customer demand, and more.

4. Clustering Model

This model groups data points with similar characteristics. 

The two areas cluster modeling are used most often in marketing and sales are: 

• Customer Segmentation: Segmenting customers into distinct groups based on purchasing behavior, demographics, tech stack, and engagement levels. 
• Optimizing Sales Strategies: Sales teams can use clustering to identify which customer segments are most likely to respond to specific sales tactics or which products are often purchased together.

5. Anomaly & Outlier Detection

This model identifies unusual patterns (anomalies or outliers) in data sets. 

Outlier detection models can help uncover unusual sales patterns —sudden changes in sales that aren’t explained by typical trends or seasonal variations. 

This model is also used for market and competitive analysis. Anomalies in market data can provide early warnings about changes in the competitive landscape or shifts in market dynamics.

6. Decision Tree

This model uses a tree-like structure of decisions and their possible consequences. 

Simple, yet powerful.

In a decision tree, each internal node represents a “test” on an attribute, each branch represents the outcome of the test, and each leaf node represents a decision.

This model is often seen when performing churn analysis, helping identify critical factors contributing to customer churn and empowering businesses to take proactive measures to retain high-risk customers.

So which is the best predictive model? As with most things in business (and life), it depends. More than anything, the “it depends” is related to the problem you are trying to solve. And often, these models are used side-by-side, not simply as one-offs.

Demandbase runs on predictive models

Predictive models are a powerful and effective way to forecast future events (sales, marketing trends, etc.) based on historical and current data. 

Using the FIRE method, Demandbase customers use our B2B predictive analytics tools to set up models for scoring accounts based on company Fit, high Intent actions, journey stage to nurture the Relationship, and Engagement across your website, email, inbox, CRM, and marketing automation (FIRE).

Get on a path to predictive revenue today.

The post Predictive Models – Future Insights from Past Data appeared first on Demandbase.

For this month’s blog, I snagged a few moments with one of the busiest people in Demandbase, Mark Walter, leader of the Demandbase Technical Consulting Team. After working with hundreds of clients across a myriad of industries, Mark and his team have some pretty unique capabilities. They’re able to paint a vision of how to solve a client’s business needs, through technical demonstration, analyzing client pain points, and creating a compelling strategy. In short, they have the technical chops to address even the most complex technical challenges for our clients.

Here’s what Mark has to say about Demandbase Technical Consulting Services…

What does a technical consulting team do?

Our mission is to surprise and delight customers by delivering technical content in a way that interests and excites our clients based on their role in their organization. Beyond that, our team plays three primary roles. First, we’re involved in the foundational service packages that bring us into contact with many of our clients on a quarterly or monthly basis — where we provide technical guidance on platform setup and optimization. Next, we support our enterprise customers through ongoing hours-based projects. We serve as a technical resource to guide our clients through best practice usage and platform utilization. And lastly, we support customers with technical integrations that occur after they’re out of the implementation phase to ensure the new integrations are set up correctly.

What backgrounds do they have?

All of our technical consultants (TC’s) come from technical backgrounds, of course, but each one also brings a unique ability to communicate and interact with customers — something that is rare in a technical persona.

What superpowers do they possess?

One of the things we have unique knowledge of, because we’ve seen so many client tenants, is the implications of decisions made in the platform. We have an insider view of so many customer configurations, and we’ve waded in the technical weeds with so many clients that it’s easier for us to identify problems quickly or head them off altogether. Easier than it would be for you to do it on your own. 

Compared to the implementation team that works with our clients for the first few months, overseeing the initial configuration of the platform, we come in a bit later and have the opportunity to see the more prolonged downstream impact of platform setup. This puts us in an excellent position to help refine and optimize for our clients, really taking their performance to the next level and aligning it with their business strategy.

Are there specialties within the team?

Each of us has found our niche inside of the platform. One of our team members focuses on journeys and scoring models, while another has expertise on intent and the new workspaces feature. For me, I focus on the analytics-how we present the data in charts and graphs, helping our clients learn from the data.

What value do you bring to customers?

We all enjoy our client interaction, and bringing the platform to life for customers is our specialty and passion. We help you get excited about Demandbase, not only because we help you build confidence in how the software is set up, but because we teach you how you can see results on your own through analytics, intent dashboards, reports, etc. 

What is something unique you do to engage clients in a virtual meeting?

One of the things we have brought to Demandbase is the way in which we conduct our technical sessions. We have the client log in and share their screen. In this way, we help the user learn the platform, with our guidance, and this really helps with knowledge transfer, and retention and keeps everyone engaged in our sessions.

What three words describe Demandbase Technical Consulting best?

• Confident :  I like the word confident because it’s essential for building credibility and rapport with our clients. And we’re confident in our skillset and technical competence for sure!
  
• Comprehensive : We need to know so much about the platform to be ready to answer any question on the spot. Good thing, we have a ton of knowledge that we pack into our brains that we’ve learned along the way as both users and technicians of the platform. This depth and breadth of knowledge helps us react and learn with our clients in any number of technical and business discussions.
• Analytical : Not only do we analyze from a technical perspective, but we’re also always gauging how deep we can go in the conversations with people we’ve just met. Are they execs who need us to explain things in a simple, non-technical way? Or are they technical individuals who want and need to dive deep into the platform details?
  

What’s your favorite part about leading the technical consulting team?

Our team is a wonderful group of people, with top-notch talent, and we all get along well. We get feedback regularly on our team members, and (not to boast) we’re all excelling in our roles. While we have a lot of work, we’re always hungry for more!

The post Meet the Team with Hidden Superpowers: Demandbase Technical Consulting appeared first on Demandbase.

Be it software solutions, artificial intelligence, or go-to-market (GTM) strategies — the common thread behind the value is quality data. In GTM, data helps businesses find the right people and companies to sell to and make sure they’re reaching them in the right way. The right data underpins everything from building target account and contact lists to fueling sales prospecting and cleaning existing databases. And with buying centers more fragmented and buyer journeys more complex than ever, B2B data must go beyond mere lists of contacts and companies to include things like buying intent and existing tech stacks. 

This is why I was pleased to see Demandbase’s inclusion in Forrester’s recent landscape report on B2B marketing and sales data providers. At a time when data quality and privacy compliance are top of mind for B2B leaders, and generative AI is transforming the way companies use data, the report offers practical guidance on the changing dynamics in the B2B data provider space and how to evaluate potential partners.

Key Capabilities to Look for

Forrester defines B2B marketing and sales data providers as: Solutions that offer comprehensive data, insights, and data management services to optimize marketing and sales efficiency and effectiveness.

Every business has unique goals and challenges, and the report suggests you begin by understanding the key capabilities required for your highest priority scenarios.

There are two categories of capabilities to evaluate. First, the accuracy and completeness of the data across all key data types, and second, the core capabilities of the provider. 

The core B2B data types include:

• Account Identification: Match unidentified signals to accounts and interpret patterns of engagement
• Company data: Pinpoint the companies that matter most with information like industry, size, location, and news.
• Contact data: Connect with decision-makers using data such as name, title, email, and phone, plus social profiles and connections.
• Technographic data: Know what technologies a company already uses and what they’ll buy next, so you can identify accounts that are the perfect fit for your business.
• Intent data: Determine which accounts are in-market for the products you sell.

But how do you assess whether a provider can deliver accurate and complete data at scale? Don’t take the provider’s word for it. Don’t just think about the volume of their data — think about the quality as well. In order to select a vendor of choice, you need to get hands-on with sample data sets. Conduct apples-to-apples comparisons with the accounts and buying groups you know best. And ask questions about compliance and privacy practices, especially around contacts.

Forrester recommends evaluating other capabilities as well, including real-time search and import of data, matching capabilities, and native / API integrations. The key is to match the capabilities you need to the use cases you have. 

For example, if sales prospecting is critical, prioritize providers with seamless CRM integration and tools that put AI-driven insights directly into seller workflows. If you’re focused on ICP profiling or territory planning, look for robust firmographic and technographic data you can slice to uncover market potential. And if data unification and quality are top of mind, you’ll focus on API-level integration with CDPs, data lakes, and data warehouses as well as data hygiene capabilities and value-added services.

View Your Data Provider as an Ongoing Partner 

B2B data partnerships can’t be one-and-done purchases. The technology and depth of data you need will change over time. Expect to collaborate closely with providers on privacy standards, product innovation and more value from your data. At Demandbase, we make large investments in these collaborations because they directly strengthen our customer relationships and ability to drive growth. We also don’t try to trick our customers into automatically renewing with complex agreements and overly-aggressive opt-out periods.

In today’s landscape, data providers must be strategic advisors that actively guide customers into the future. Forrester’s report emphasizes the increasing importance of this consultative approach.

Key Takeaways for Selecting Your Next Data Partner

As you dive into vendor evaluations, keep these recommendations from Forrester’s report in mind:

• Thoroughly vet potential new primary providers, as switching later causes major disruptions.
• Conduct extensive side-by-side comparisons using sample data sets.
• Align capabilities to your core business scenarios and requirements.
• Ask detailed questions about privacy practices and opt-in data collection.
• Expect significant integration and training investments when changing providers.
• Seek long-term, consultative partnerships that enhance data value over time.

With the right data partner, your marketing and sales teams will be equipped to identify your total addressable market, refine target account selection, engage elusive prospects, and exceed revenue goals. The data landscape is more complex than ever, but by approaching partnerships strategically, you can thrive in today’s B2B marketplace.

To gain access to the full “The Marketing And Sales Data Providers For B2B Landscape, Q4 2023” report (available to Forrester subscribers or for purchase), visit here. 

The post The Marketing And Sales Data Providers B2B Landscape, Q4 2023: How to Select the Right Provider for Your Needs appeared first on Demandbase.

B2B, B2C, or Retrofitted B2C: What’s the difference and why does that matter? 

(In a hurry? Check out this 1-minute video clip.)

I’ll warn you, I’m going to get in the weeds, but I think you’ll appreciate the details.

Let’s start by examining the differences between B2B and B2C buying journeys. In B2C, you usually have a large market, mostly made up of individual buyers making simple buying decisions that are low cost and don’t involve multiple decision-makers. B2B is the opposite. Most purchases are complex. They take time and involve a whole committee of people, each with a different role in the buying process. 

In the B2C scenario, you do one of two things, either mass branding to a large population of people who fit a certain profile or retargeting to people who’ve shown interest in your product. Think boots. If you recently checked out boots at an online retail site, the B2C advertiser will serve you an ad for those very same boots, hoping to entice you to buy now for the low price of $59.99. Done. Easy decision.

In B2B, most of the time there’s no budget for mass branding. After all chances are pretty good there are a lot more individuals interested in boots than in your B2B solution. And retargeting does not result in conversions. Why? Because B2B buying decisions take longer. Relying solely on either of these strategies will result in a massive waste of budget. B2B digital marketers only want to target people at accounts that are a good fit for their company and likely to make a purchase. 

The problem is that B2C ad tech has no concept of an account or buying group. They choose their targets based on demographic data such as age, gender, ethnicity, level of education, income, and so forth. Not only is this irrelevant in B2B, but the “facts” they gather are often wrong. 

This also means that B2C platforms can’t measure results that matter in B2B. You can track individual impressions and clicks, but there’s no way of knowing if your ads are generating engagement, pipeline, bookings, or revenue from your targeted accounts.

Enter Retrofitted B2C

To address these problems (and to compete with Demandbase), other B2B advertising solutions adapt B2C ad platforms to provide some account characteristics. What they do is upload IP addresses and cookie IDs to their partner platform to serve ads exclusively to a specific group of accounts. That might sound great, but the underlying technology can’t optimize bidding to accounts, so all sorts of problems arise, like individual accounts sucking up all the campaign budget and the inability to prioritize bidding at the account level. What’s more, some companies have an incredibly large pool of IPs and cookie IDs that represent mostly people outside of the buying groups, which leads to targeting people within the account who don’t care about the product being advertised. In other words, wasteful spending.

Perhaps the most significant flaw with retrofitted B2C ad tech is that it can’t tap into the holy grail of B2B targeting data that uses real-time B2B intent signals to prioritize the IPs and cookie IDs to use within each account. Without this, IP-based targeting is all you get. 

I’ll admit, the retrofit is better than nothing. But there’s still too much wasted spend, and there’s a better way.

Meet Demandbase Piper

Earlier this month, we rolled out the red carpet for the Demandbase Piper B2B DSP — the new name for our proprietary B2B ad tech. The technology has been around, honed, and perfected since 2012, but we decided it was time to give it a name as distinctive as its capabilities. 

Piper stands for Pipeline + Revenue and it was built from the ground up to tackle the unique challenges of B2B, with a native account object and the ability to use real-time B2B intent signals to optimize targeting and bidding.

It only serves ads to people associated with a target account, and prioritizes impressions to the buying group members in an active buying cycle. How does it do that?

Suppose you were able to narrow your targeting to an individual with the right title at an ICP account and they’re showing interest in your product (something other ad tech is not able to do, by the way), you might think you’d hit the jackpot. But not necessarily so. That still doesn’t mean the company as a whole is in a buying cycle. You need to be able to analyze behavior across the entire buying group to determine if the company is actively considering a purchase. Demandbase is able to do just that through our Demandbase One platform. It considers predictive models, engagement minutes, intent, anonymous web activity, and more to decide who to advertise to, and feeds those audiences directly into the Piper DSP. 

In short, Piper, combined with Demandbase One, is able to identify ICP accounts and their buying groups and track intent and other engagement behaviors across the groups. These are the two absolute essentials for B2B advertising. Anything less will result in wasting your ad dollars on the wrong targets.

In addition, with Piper and Demandbase One, you can:    

• Track each buying group member throughout their journey, so you can target them individually and customize your messages for their role and stage in the buying process.
• Measure business goals, not vanity metrics, so you can see how impressions and clicks contribute to pipeline, deals, and revenue — at the account and individual level.
• Pace your campaigns and balance impressions across your target accounts and buying group members, so you don’t accidentally spend your whole budget on a few large accounts.
• Advertise only on whitelisted, context-appropriate sites. You’ll never see a Demandbase ad on a celebrity rag or radical political site. That can’t be said of other “B2B” advertisers.
• Automatically optimize bidding to achieve results that drive revenue, such as account lift, engagement, and account reach.

That’s everything you’d want and expect from an advertising solution that’s fluent in B2B. Can that be said of your ad tech? If not, contact us and we’ll gladly introduce you to Demandbase Piper. 

In case you’re wondering, DSP stands for demand side platform. It’s the inside term for a software platform that facilitates ad bidding and targeting across multiple ad exchanges. In other words, “ad tech.”  

The post Is Your Ad Tech Fluent in B2B? appeared first on Demandbase.