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.

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Outreach is an incredible tool for tasks like sales execution, task management, and monitoring email sequences. However, to effectively prospect an account or individual, most sellers need to switch between Outreach and other systems, such as their CRM, to get the full picture. This constant toggling between platforms is time-consuming, can lead to missed opportunities, and frankly, it’s a headache. Yet the scenario described is an all too familiar reality for the modern day seller, which is why Demandbase is thrilled to announce a native integration with Outreach. 

This powerful partnership introduces native access to rich account and contact insights directly within your Outreach platform, so you can seamlessly personalize your sales outreach, and revolutionize the way your sales teams connect with prospects.

Streamlining Sales Engagement

Demandbase has long offered an unparalleled set of 1st- and 3rd-party data attributes, along with the ability to assign and run Outreach Sequences directly from Demandbase One. This makes the automation of sales and marketing plays across the customer journey easy, but is one-directional in nature. 

Recognizing the human desire for convenience, Demandbase strives to meet sellers where they already are, which is frequently within Outreach. With our new Outreach integration, sellers can now:

• View Demandbase Sales Intelligence on the Account, Prospect, and Opportunity tabs in Outreach, providing industry-leading company and contact-level insights.
• Access the full capabilities of Demandbase on the Account and Prospect Overview Tabs in Outreach
• Interact with Demandbase’s Sales Intelligence through a custom tab, enabling searches across Demandbase’s entire 3rd-party company and contact database of more than 92 million companies globally and 150+ million contacts, to build prospect lists, set up connections, and more, all from within Outreach’s UI.

This means that sellers can access Demandbase company and contact-level details throughout Outreach, no matter what their preferred workflow may be.

Enhancing Efficiency and Impact

Sellers aren’t the only winners here. With this integration, sales and marketing teams can collaborate more effectively, aligning around coordinated messaging throughout the customer journey. Sales can reference the same insights as marketing and easily track the various ways marketing engages with accounts in their territory.

Take the Engagement Tab as one example. Sellers can view every marketing and sales engagement with an account or prospect to keep tabs on the prospect’s participation in marketing campaigns and adjust their engagement accordingly.

The integration empowers sellers to tap into the full potential of Demandbase in a format tailored specifically to their preferred workflows. They can easily access highly relevant insights to make informed decisions, identify sales opportunities, and engage with prospects more effectively. 

Future Innovation

Our commitment to empowering sales teams doesn’t end with this integration. We have exciting plans to further enhance the experience in the coming weeks and months, including the introduction of our Prescriptive Sales Dashboards. These dashboards will provide a unified and prioritized view of a seller’s territory and account lists.

Everything You Need in One Place

The Demandbase + Outreach integration represents a significant milestone in revolutionizing sales outreach. By seamlessly combining the power of Demandbase’s rich account insights and Outreach’s sales execution capabilities, sales teams can now deliver highly personalized, targeted messaging at scale. This integration streamlines workflows and empowers sales professionals to make data-driven decisions, resulting in increased response rates, stronger pipeline generation, and ultimately, better sales outcomes.

Join us in unlocking the power of personalized sales outreach with Demandbase and Outreach today. Visit https://www.demandbase.com/smarter-sales-intelligence/ for more information.

The post One Platform, Endless Insights: Access Demandbase Intelligence without Leaving Outreach! appeared first on Demandbase.

As more and more AI-powered tools enter the martech landscape, opportunities abound to help B2B companies better achieve their revenue targets. 

However, another trend has kicked off our year.

Many organizations are starting 2023 with fewer resources –– be it because of budget cuts, attrition, or increased responsibilities for team members. 

The good news? Artificial intelligence has the potential to help B2B companies achieve their revenue goals in several ways. And there are tools to help you for virtually every use case. This landscape from Sequoia Capital’s Sonya Huang dates back to October 2022, and since then, the space has exploded:

As promised: here is our @sequoia Gen AI market map V2! Very thankful for the hundreds of folks who wrote in additions and edits.AI people are amazing pic.twitter.com/dXyHR9dSvR

— Sonya Huang (@sonyatweetybird) October 24, 2022

Consider these use cases:

Automating repetitive tasks

AI-powered automation can take over repetitive tasks such as data entry, crafting emails, appointment scheduling, and customer service inquiries, freeing up employees to focus on more high-value activities. AI tools like SmartWriter.ai and Lavender can be connected to your CRM or LinkedIn to help you craft more personalized emails for your outreach. 

There are so many generative AI tools available, and even technologies such as Grammarly are available to help sales teams in that first critical touch with prospects. 

Exceed.ai can help schedule meetings with prospects identified as qualified—through a multitude of channels. 

Improving customer engagement

AI can be used to personalize interactions with customers, for example, by recommending products or services that align with their interests or needs. This can lead to increased customer satisfaction and loyalty, resulting in repeat business.

Intelligent technologies such as Drift can learn from simple chat conversations and sales process automations, which can help create robust CRM records on customers. This could include pages visited, questions asked, and more. Marketing and sales teams can better align by having visible, actionable data received through conversational AI. 

AI-powered email technologies such as rasa.io and Faveeo are able to customize email sends based not only on known and implied interests from form fills and purchase history, but can also automate subsequent sends based on historical email opens and clicks–without additional list building, data remediation, and workflow building. 

Tools like Uniphore can record and analyze sales calls to determine where a sales representative resonated with a customer or prospect, or conversely, where the deal was lost. Great sales teams will use technologies such as this to continually work to improve their pitch and delivery, and also better understand what customers need.

Conversica has an automated AI sales assistant that conducts conversations with leads, further qualifying them before they talk to a rep.

Identifying new revenue opportunities

AI-powered analytics can be used to identify new revenue opportunities by analyzing patterns in customer behavior and identifying trends in sales data. This can help companies make more informed decisions about which products or services to focus on.

Crayon is a competitive intelligence platform that can act in real time to share data immediately with sales teams, with the ability to detect both anomalies as well as big opportunities.

Optimizing pricing

AI can be used to analyze data on customer behavior, costs, and competitors to help companies optimize their pricing strategies. This can lead to increased revenue without sacrificing profitability.

Tools like Remesh can crowdsource and consolidate customer feedback on product, price, and more.

Enhancing lead generation and sales forecasting

AI models can help companies to identify the best leads and forecast the likelihood of a lead converting into a customer. By doing so, the sales team can prioritize their efforts, focus on high-probability leads and make better revenue predictions.

Data modeling tools such as Squark can predict churn risk and lifetime value with existing customer data. By pinpointing signals more intelligently, sales can be focused on the right customer at the right time. 

Technologies such as Rev can help build your ICP by integrating current processes and systems with external company and industry data to help forecast and prioritize. 

The common thread among all of these benefits is that these tools relieve some of the time sales teams spend drowning in data so they can focus on the actionable information discovered through the data. 

Making happier humans in the loop

We often hear the worries of AI taking our jobs, but without human oversight in these AI outputs, your customers and prospects will not be easily fooled.

An AI tool can scale faster and help us generate emails, catchy subject lines, and more. But even the best-written AI-generated emails still need human oversight to ensure the LinkedIn data it pulled in, or the CRM notes you have are still up-to-date. Even the best data analysis needs a human to look it over to check for inaccuracies or biases in the training set. 

Most importantly, the right AI technology allows more time for us to be more human. It lets us engage with our customers and prospects, make stronger connections, and invest time in creating the right product or service for our audience. 

At the Marketing AI Institute, we talk at length about the future being Marketer + Machine. AI is here, and we as sales teams, marketers, and business leaders can embrace AI and learn how it can augment our work, or we can ignore it and get passed up by competitors. 

What steps can you take today? 

• Think about the tasks you do each day, week, or month. Are they repetitive? Are they data-driven? Are they making a prediction? Document this so you can see some places where AI can help you immediately. 
• Estimate the hours you spend on the tasks you listed above. What’s the biggest opportunity for you to save time? What task has the biggest potential to impact revenue if it could be scaled faster? 
• Visit the Marketing AI Institute website for resources. We have a free Intro to AI class, and then things scale up to a Piloting AI course series, an in-person event (MAICON), a book, our AI Academy for Marketers, and more. There is so much FREE content on the site to help get started. 

It’s important we remember that AI is not a magic solution nor a one-size-fits-all technology. I’ve been in the marketing industry for a few decades now and I’m slowly integrating more intelligent technology into what I’m doing. It’s a slow build for me, but I’ve seen immediate returns on time and scalability in my processes and set aside time each month to reevaluate what I could be doing smarter. 

Identifying the right areas where AI can bring the most value and having a data-driven culture and infrastructure is crucial for the successful implementation of AI. 

The post AI’s Potential to Help B2B Companies with Revenue Goals appeared first on Demandbase.