The Many Use Cases of AI for Marketing -...

The Many Use Cases ofAI for Marketing

The Many Use Cases of AI for Marketing

Table of ContentsThe Many Use Cases of AI for Marketing ................................................................. Finding the Right Customers .......................................................................................

Your Funnel ............................................................................................................... Customer Discovery and Targeting ...............................................................................

Your Ideal Customer Profile (ICP) ..................................................................................

Predictive Lead Scoring ...............................................................................................

Predictive Model Applications ......................................................................................

Micro-Segmentation and Clustering ..............................................................................

Finding New Look-Alike Customers ...............................................................................

Conclusion ................................................................................................................

About Mintigo ............................................................................................................

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PageThe Many Use Cases of AI for Marketing 3

Artificial intelligence (AI) enables marketers using a predictive marketing platform to address many strategic questions. It allows marketers to build a self-served, predictive model, where each model is essentially a scientific tool addressing a marketing question. Have a look at examples of the many benefits of leveraging AI for marketing:

Discover the ideal customer profile (ICP)

Score and rank accounts based on their likelihood to convert

Enhance CRM data with predictive intelligence

Identify net new accounts and buyers for a specific product

Identify content gaps for specific audiences

Segment customers and prospects based on indicators

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Upsell and cross-sell more effectively based on intent data

Determine which product bundles to offer to which prospects

Determine channels to use to engage with a specific prospect?

Discover the expected conversion rates of prospects in each group

An AI for marketing platform can address all those uncertainties and many more in a matter of hours (both building and executing a predictive model). The good news is that you don’t need to be a data scientist to build those models.

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Optimize your marketing spendingPredictive marketing allows you to focus your resources and spending on the right prospects. Instead of using mass-market targeting and wasting 80% of your budget on no-value prospects who are never going to buy from you, now you can focus your entire budget on the Pareto Principle prospects—the 20% most likely to buy, reducing your spending and increasing your business.

Increase conversion rate across your funnelThe predictive marketing platform allows you to build models to optimize any conversion stage in your funnel. You can build a model to optimize marketing qualified leads (MQL) to sales-ready leads (SRL) conversion; you can build a model to optimize SRL to opportunity conversion, and so on. In that way, your entire funnel is optimized to maximize conversion rate.

Optimize your customer engagementPredictive marketing helps marketers and sales reps choose the best message, marketing asset, most creative, and even optimal product to engage a prospect.It offers actionable insights that deliver consistent, contextual, and personalized experiences that have impact no matter which channel and stage in the funnel the customer occupies.

Predictive marketing is much more than providing recommendations. It allows you to execute your marketing strategy in a scientific way with substantial business impact. Here’s a preview of its common use cases:

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Cross-sell and upsell throughout yourcustomer basePredictive marketing enhances your marketing and sales efforts to be more customer- centric, and cross-selling is a core component of this strategy. When launching a new product or feature, or when you have a hefty house list spread across different products and services, you can use predictive marketing to match prospects to the products that he or she is most likely to buy.

The insights provided by predictive marketing enable you to:

Enhance every potential customer engagement point;

Get to know your customers better; and

Have an integrated view of both your existing and potential customers.

Having this information at hand, cross-selling and upselling throughout your customer base has a greater chance of success than ever.

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Segment your leads and grow customer value (increase average selling price [ASP])The predictive marketing platform allows you to leverage data-driven insights and transform your marketing and sales campaigns into efforts that resonate best with existing and potential customers. Predictive segmentation works by:

Taking any lead;

Enriching it with additional data sourced from the web; and

Adding it to one of your product segments with its tailored, relevant marketing and sales efforts.

A personalized approach throughout the sales cycle ensures not only a larger customer base but also customer retention and brand loyalty.

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Grow your market and find new prospectsPredictive marketing helps marketing and sales expand markets, geographically or even into a new vertical. By using existing data, predictive marketing allows you to find new targets in that new market as well. Predictive analytics can help you:

Find the most powerful indicators that describe valuable customers in your existing market;

Identify new customers with the same characteristics in your new market; and

Help you identify and seize new markets, save time on strategic analysis, andcreate a go-to- market strategy faster.

Predictive intelligence enables you to answer strategic marketing questions, quantify and learn from the entire customer journey, and provide valuable experiences to customers in any channel at any point of the sales funnel. Predictive marketing is a holistic, customer-centric approach that leverages data science and produces results at scale, resulting in optimized efforts and substantial business growth.

Having a broad idea of what predictive marketing can do for you, continue reading as the next sections provide in-depth discussions of how it works.

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Finding The Right Customers

The Many Use Cases of AI for Marketing 7

While organizations have managed to increase the quantity of leads generated from various sources, the quality of leads continues to suffer. There is, unfortunately, a next-to-nothing chance that many of those B2B leads are ever going to buy anything from you, because these leads include students, technology enthusiasts, and companies that simply don’t fit the profile and won’t become buyers.

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Finding The Right Customers


On the other hand, potential customers that have not engaged with your organization, and consequently not part of the leads list, might have more chance of becoming customers. But discovering them in the most effective and efficient way is another task that marketing and sales, already swamped with large amount of leads, might no longer be able to handle.

Why does it matter that not all leads are qualified? Basically, the sales development teams are overwhelmed with bad leads and have precious little time to find the needles in the haystack.

What’s the fix? Using AI, predictive analytics and machine learning to scan your marketing database to identify those precious, high-likelihood customers based on a robust set of indicators. Predictive marketing can give you a 360-degree profile of each contact and account in your database or leads list, and it predicts their likelihood to convert.

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Your Funnel


One of the most popular frameworks depicting the sales and marketing funnel was introduced by the advisory firm SiriusDecisions. Called the Demand Waterfall®, the framework defines a shared view between marketing and sales of the lead management process.

This framework aims to create a common way for sales and marketing to develop a strong pipeline by creating shared definitions of various stages in the funnel. Many of these terms are now widely used in the B2B space, for example marketing qualified leads (MQLs) or sales qualified leads (SQLs).

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Your Funnel


The goals of the Demand Waterfall® framework are to:

Enable creation of a shared responsibility between marketing and sales to the revenue generation process; and

Provide organizations a tool to measure and track the conversion rate from one step to the other, helping them meet or exceed expectations.

When you know the share of leads that move from MQL to SQL and then to opportunity and closed-won deal, you can do this calculation backwards. Plug in your sales forecast and figure out how many MQLs marketing must bring to the table. Demand-generation marketers then plan campaigns and programs that are meant to drive conversions through the funnel aiming at meeting or exceeding these performance goals.

If it’s so easy, why do some companies miss their sales targets? Converting MQLs to SQLs is the challenge. According to Brian Carroll, author of Lead Generation for the Complex Sale, 95% of leads coming from your website are not ready to speak with sales1. Similar research by SiriusDecisions reports that about 98% of MQLs never result in closed business2. When so few leads are turning into business, it follows that bringing in more leads is not the solution. Sales reps are looking for qualified leads. Consequently, bringing in more leads that are 90-98% unqualified or unfit does not improve their performance. More of the same leads results in more sales time and effort being wasted on leads that are not likely to convert. Reps spend more time and effort on calls and other activities with a low likelihood to result in revenue.

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Customer Discovery And Targeting


The power of AI in your predictive marketing platform lies in its ability to use the massive data being generated every day in every medium, make sense of it, and come up with insights through robust science-based models. Predictive intelligence uses this robust data to create marketing indicators that form part of the models, which result in powerful predictions about who is likely to buy.

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Customer Discovery And Targeting


Let’s say you already have the data gathered from various sources, including your marketing automation platform or CRM. Predictive marketing then does this:

Takes the data and attempts to match it with what is available in the platform;

Creates marketing indicators;

Combines these newly-identified marketing indicators with all the potential buying signals from the exogenous data; and

Uses predictive analytic machines to do all the number crunching and statistical analyses for you.

This predictive modeling process is where the magic happens. By using data science methods such as machine learning, the predictive model:

Analyzes all the data;

Determines the best marketing strategies to get the highest ROI; and

Reveals the data points that matter and have the highest parallel to your customer base.

In other words, the model identifies the set of data indicators that make your actual customers unique compared to all the leads or prospects in your database. The output of this analysis is a picture or profile of your ideal customer, the target you want to replicate. It is a more complete set of data indicators that can be used to predict the probability of a lead to convert.

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Your Ideal Customer Profile (ICP)


Knowing whether an account or prospect is likely to become a customer saves time and energy for both your marketing and sales efforts. The ICP, or ideal customer profile as previously described, becomes the barometer for predictively scoring accounts and prospects.

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Your Ideal Customer Profile (ICP)


By evaluating every prospect in your database as well as those inbound, any data indicators shared by the prospect with the ideal customer is identified by the predictive model. This results in a predictive score disclosing how similar the prospect is to your customers. The main point is, predictive scoring:

Shows you how closely matched each lead is to this ideal customer profile, your target audience;

Gives you a 360-degree profile of each lead in your database; and

Identifies the leads with the highest proclivity to buy.

The magic of AI is the continuous, real-time analysis of attributes of the highest value customers applied to discover prospects like them in your lead database.

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Predictive LeadScoring



What if you based your process of finding customers on the true profile of your ideal customers? Suppose it helped you target the prospects most likely to become buyers for each of your products? And what if the lead score could guide us to the best nurture path for each customer, instead of the other way around—using nurturing to simply gather behavioral data for scoring?

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Predictive Lead Scoring


Predictive lead scoring is an apparent and immediate use case of predictive analytics. Lead scoring as a concept is meant to be predictive because it is a methodology that ranks leads to determine their viability, their sales readiness, and likelihood to close.

Two main factors differentiate traditional lead scoring and predictive lead scoring:

Predictive lead scoring relies on a robust dataset that contains, in addition to demographic and behavioral data, data around online activity, technologies used and much more.

Predictive lead scoring uses predictive algorithms that are grounded in science.

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So instead of building a lead scoring model based on rules of thumb, gut-feel, and guesswork, build one based on proven scientific and statistical methods that predict who is most likely to buy. A model that:

Uses data science (i.e. statistical analysis, look-alike modeling, machine learning, etc.);

Identifies both the data within your customer database and external data signals harvested from all over the web; and

Ensures that all data owns a high correlation to converting prospects into customers.

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Predictive Lead Scoring


This process is called predictive modeling, and the output of this process is a well-defined, ideal prospect profile that provides a more comprehensive picture of leads with the highest propensity to buy. This profile should be the target that you base your lead scoring against.

Considering this, “predictive lead scoring” can be defined as a methodology for ranking leads to determine sales readiness by using predictive modeling and other data science techniques to discover the most accurate and relevant data points for which to score.

What’s implicit in this definition is the requisite access to data not generally captured within typical CRM or marketing automation systems. While the data that resides in a CRM or marketing automation tool is important to use in predictive models, it only divulges, as previously described, a limited view of the profile of your true buyers.

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Predictive Modeling Applications


You can accomplish several goals using predictive analytics and AI in your marketing efforts, including:

Uncovering hidden gems in your database: Once you identify your ideal customer profile, you can predic-tively score your entire prospect database to find any low-hanging fruit—these are the untouched or recycled leads that look like your customers but didn’t make it all the way through the sales funnel.

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Predictive Modeling Applications


Better funnel efficiency and lead velocity: By helping your sales reps more accurately identify the higher quality inbound leads, they can prioritize lead follow-up more efficiently and start working the leads most likely to respond. With predictive scoring, marketing can provide sales with a better idea of the identity of the best bets. Where should they be spending their time? Where should they be going the extra mile, because they have a higher chance of converting the lead?

Data-driven persona development and fine-tuned segmentation: Knowing your ideal customer profile and the data elements that are part of its DNA, product and content marketers can develop richer personas for better messaging. At the same time, marketing operations can more effectively segment lists for targeted campaigns, relevant nurture tracks, and a more personalized customer experience.

Sales enablement: By providing the context behind the predictive score and making the exogenous data for each lead transparent and available directly within the CRM, sales are armed with greater intelligence for each lead and understand why that lead is most likely going to buy. Discovering cross-sell or upsell opportunities: If you can create multiple predictive models (one for each of your product lines, for example), then you can analyze a lead against each of these models to discern which products the lead is likely to buy.

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Micro-Segmentation And Clustering


Knowing your ideal customer better through its ICP, including not only their behavioral and buyer intent, but also their level of engagement with your products across the sales cycle, enables you to tailor your campaigns, collaterals, messag-ing, and general approach to the account and prospect.

Market segmentation is not a new strategy. Dividing your market by subsets of customers sharing similar characteristics and needs is a practice done mainly when you have diverse products and a sizable market. This process is costly and re-lies on arduous market research to thrive.

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Micro-Segmentation And Clustering


Then comes a predictive marketing platform leveraging AI that equips you with richer data about your existing and potential customers and enables you to:

Custom-build your ideal customer profile per segment that you created for your products and outreach;

Have a specific predictive model per segment;

Match your product and marketing mix more closely to the needs of one or more segments.

Personalize your approach;

Do more than traditional segmentation bases such as age, gender, income, geographical area and buying behavior; and

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Leverage new data sets being produced today—information such as technologies in use, social influences, department sizes, firmographic data, and intent-based data that are more useful in the B2B space.

Knowing the profiles of your ideal customer at the granular level using intelligent predictive models gives your market segmentation effort a greater likelihood to achieve its overall goal of business growth—whether that is an increase in conversion rate, customer acquisition and retention, or cross-selling and upselling success.

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Finding New Look-Alike Customers


Another application of knowing your ICP is being able to attract the right new customers. After cleaning your prospect database, setting up your ideal customer profile for your inbound marketing efforts ensures that you attract quality leads who are likely to buy. This saves time and effort for sales because they are not reaching out to unqualified inbound leads.

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Finding New Look-Alike Customers


Moreover, let’s say for each predictive model you create, you have an ideal customer profile, so it only attracts customers reflecting the ideal attributes. Once you have this set up, you can then deliver the right offer, the right product, and the right content to the right prospect.

Having the ability to look for and engage with the right customers is an advantage to exploit. It streamlines processes and your database is far less likely to be populated with unqualified leads.

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Conclusion


AI for marketing drives greater success by both leveraging more robust data and delivering a more accurate model and consequent results. It applies the power of algorithms to prioritize leads and focus the time, money, and effort of marketing and sales on what’s most likely to drive more revenue.

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Conclusion


So rather than building your model on partial data using simple rules of thumb and delivering an inaccurate model, predictive marketing can:

Tell you what your ideal prospects look like;

Compare them to your actual customers’ characteristics; and

Show you how to build your lead scoring and marketing efforts on the data points that matter, so you no longer have to guess.

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With AI, marketers leverage past conversion data to predict future buyers. Using predictive scoring, you can:

Rank and score your contacts;

Determine which leads to approach;

Segment your leads; and

Create nurture programs with cogent messages for each.

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Conclusion


Predictive lead scoring is the “brain” behind your marketing automation platform. It allows you to score hundreds of thousands of leads instantly, using the scores immediately to make smarter decisions. With this application, you have an automated “air traffic controller” for your fresh incoming leads. This means you know which:

Leads to send sales;

Contacts to nurture; and

Leads to discard.

Delivering the right offer for the right product, and the right content to the right prospect, predictive marketing empowers you to scale your marketing and sales efforts, optimize customer engagement, and effectively improve your conversion rates and revenue.

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References:Caroll, Brian J. Lead Generation for the Complex Sale (2006).2 Gracy, Peter. 98% of Your MQL’s Will Never Result in Closed Business!

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About Mintigo


Mintigo changes the way people do Marketing and Sales by delivering intelligent customer engagement powered by AI. Mintigo’s AI platform helps marketing and sales teams find the right buyers faster with actionable insights and engage with them in the most optimal way - at the right time with the right offer. Enterprise and growing companies like Oracle, Red Hat, Datavail and CA Technologies work with Mintigo to leverage AI to drive more and bigger deals faster.

To learn more, please visit www.mintigo.com

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