Oh How the Tables Have Turned: What B2C Marketers Can Learn From B2B

This co-authored byline was originally published on CMSWire by Sean Zinsmeister, Vice President of Product Marketing at Infer, and Adrian Chang, Director of Customer Marketing at Oracle Marketing Cloud.

Business-to-business (B2B) sales and marketing are entirely different from business-to-consumer (B2C) tactics — or that’s the general assumption.

One is relatively low volume and high budget, with lengthy, consultative selling processes and lots of personal relationships at play, while the other usually means huge volumes and low price points, with fast, direct sales processes.

B2C relies on consumers, transaction events, impulse buys and coupons. B2B is all about prospects, “journeys” and reaching people through content in context.

But more and more, we’re seeing these two universes converge.

Although we often perceive B2B companies as one step behind their B2C counterparts when it comes to adopting the latest sales, marketing and advertising techniques, that is quickly changing. B2C marketers have naturally excelled at bringing as many people as possible into the top of the funnel, but B2B companies have perfected the use of intelligence and personalization to move multi-stakeholder buying committees through non-linear customer journeys.

Today, many B2C businesses are finding that at a certain threshold, the price points and buying cycles of a considered consumer purchase are beginning to look almost like a B2B deal.

In this environment, B2C companies are turning the tables and adopting the latest B2B marketing approaches and technologies, to great benefit.

4 Ways B2C Can Benefit From a B2B Martech Stack

1. Adopt Hyper-Segmentation for a Better Understanding of the Ideal Customer

Most B2C technologies focus on point-in-time transactions across a massive prospect universe. But they aren’t great at capturing detailed profile and activity data on each of those customers.

While they ingest plenty of basic demographic signals from search patterns and lifestyle purchase history, much of that information is anonymous. Anonymization makes it challenging for marketers to understand and analyze the data, since they can’t link consumer patterns with internal data from custom forms or other identifiable insights.

B2B systems, on the other hand, track a complex web of detailed attributes and match activities to purchases across each customer at every stage in the buying journey. They are well-equipped to help marketers segment their total addressable market deeply and precisely across all types of customer signals and behaviors.

A consumer tech or financial services company might want to segment its customers based on their travel profiles (many of which overlap), or a home rental agency might consider not only what types of properties a consumer is interested in, but also what city they live in or which industry they work for.

B2B predictive platforms can deliver valuable insight into these situations by producing easy-to-understand customer predictions that inform the segmentation process so marketers can determine which profiles should receive high-touch, personalized outreach verses low-cost, low-touch campaigns.

2. Automate Customer Journeys to Reach Buyers at Every Stage

As opposed to the point-and-click purchases of the consumer world, in the B2B model, buying processes tend to be more intricate and involve multiple people. B2B marketing automation platforms support a more complicated funnel that helps marketers plan, prioritize and execute campaigns knowing that certain interactions are bigger influencers to winning a customer than others.

Think about the evolution fast-food chains like McDonald’s have undergone over the past few decades. Rather than organizing their prep staff to assemble Big Macs and other menu items based on a rough estimate of what most people usually order and when lunch and dinner rushes occur, most of these businesses have switched to made-for-you systems in which food is assembled as it is ordered for better quality.

B2C companies are poised for a similar transition away from one-size-fits-all marketing as they adopt B2B martech systems to better orchestrate end-to-end, dynamic automation across their many channels and segments. Predictive technologies inform these tailored workflows by showing which assets, campaigns or channels reach a company’s best-fit customers or profiles.

3. Increase Personalization to Boost Advertising ROI

While B2C marketing can rely heavily on brand advertising to push decision makers over the line, in B2B, brand awareness might help get a vendor into consideration, but won’t close the deal alone. Because B2B systems collect all kinds of customer intelligence, they do an excellent job of helping marketers go beyond general brand-building through profiling, predictive modeling and personalization at scale.

B2B martech stacks bring better data and richer profiles to the task of executing hyper-segmentation strategies, workflow automation and dynamic content — all of which combine to deliver both higher short-term conversions and greater customer loyalty over the long-term.

For example, Dell leverages the same marketing automation platform to power both the B2C and B2B sides of its business. As a result, its marketers have a better understanding of their ideal customers and can draw these prospects into more intimate, personal conversations with the brand.

4. Use Full-Funnel Insights to Drive Engagement

With all of the apps launched during the recent martech explosion, integration causes major headaches, leaving dangerous blind spots between some go-to-market workflows. It doesn’t help that many B2C interactions happen offline, compounding the challenge of collecting and interpreting data across the entire customer funnel.

The good news is B2B systems generally “play together” better than B2C tools because they offer more open APIs, and were made from the get-go to deliver end-to-end insights to a sales audience.

As opposed to fueling the sales and marketing divide by keeping people siloed in different B2C systems, seamlessly integrated B2B platforms can bring all of the functions together. For instance, predictive platforms can learn from historical sales data to model what a good customer looks like, and apply that intelligence to the top of the funnel, which then cascades through marketing automation programs and all the way down the funnel to help customer success teams load-balance customers.

B2C companies using this type of intelligence throughout their business will grow consumers’ loyalty, increase retention and find new ways to delight people.

It’s Time for B2C to Flip the Funnel

Over five years ago, Joseph Jaffe started a B2C conversation about “flipping the funnel” (a term which has since been hijacked and distorted by the ABM community), yet few B2C companies have fully adopted the mindset. In comparison, B2B marketers inherently follow Jaffe’s approach, because it is built into the very way they sell.

As more noise infiltrates a consumer’s purchase decision, it’s becoming harder for B2C marketers to drive conversions with mass-messaging. It is time for B2C to take a cue from B2B and finally double down on one-to-one relationships.

AI 101, Part II: How to Deal with Data Preparation

This article was originally published on MarTech Series by Sean Zinsmeister, Vice President of Product Marketing at Infer.

My first post in this series covered what marketers and sales leaders need to know about the four main phases of building predictive models. The second of these steps – data preparation – tends to be the least understood part of AI and predictive analytics in marketing. In this next post, I’ll dig deeper into key considerations surrounding this process, namely related to data volume and data quality. When my company introduces our predictive platform to companies, two of the biggest concerns we hear are: (1) Do I have enough data? and (2) Is my data “clean” enough?

Read This: AI 101, Part I: What You Need to Know about Predictive Models

HOW MUCH DATA IS NEEDED FOR MACHINE LEARNING?

There’s a rule of thumb for how much data you need in order to be successful with a predictive model, and the most important number is the amount of positive signals or “good” examples there are in your data set. In the case of historical customer data for lead or account scoring, this would be how many total opportunities or closed/won deals you have in your CRM database.

Of course, these positive signals exist among other negatives. Make sure your positive is defined as a relatively significant achievement in the pipeline. For example, the creation of an opportunity is a meaningless milestone if it happens for every single free trial that comes in. Instead, consider going further down the funnel to find a tougher hurdle that really points to lead quality. infographic clean

Predictions will be most accurate when you have around 400 to 500 of these positive results. In that range, they can be randomized and split into two proportions (60% and 40%) for model comparison. If you have fewer than a hundred examples to test your model over, your results won’t be quite as precise as you might want (until you add more data over time and refresh the model).infographic via Sean Zinsmeister

HOW AI SOLVES THE DATA HYGIENE PROBLEM

The truth is that no business has perfect quality, complete data, but that’s okay. Modern data preparation techniques are built to work around that very problem, so there’s no need to delay AI initiatives while you wade through cumbersome data clean-up projects. If you do, you’ll just leave revenue opportunities on the table. By matching whatever limited lead data you have with hundreds of external signals from the web, predictive platforms like Infer can build a complete picture of each prospect or customer. In fact, our algorithms can produce lead scores with nothing more than a company name or an email address. That’s thanks to advanced data science approaches like Natural Language Processing (NLP), which can bridge gaps in your data by looking for patterns in the web crawls, performing title normalization and doing spam analysis on form input.

TITLE NORMALIZATION

Anyone who has sold into IT or the sales and marketing industry knows that job titles are all over the place (or sometimes not included in the data at all). Title normalization techniques tend to be especially important for lead fit models because you need to know that “Marketing Director” might be equivalent to “demand gen lead,” or that “IBM” and “International Business Machines” are the same company. NLP essentially splits out each word that exists across all of your records and uses an algorithm to assess related patterns and find the words that show up most often in positive outcomes for a particular data set.

SPAM ANALYSIS

Another sophisticated feature to look for is spam analysis – something that’s often used in consumer search algorithms like Google. By analyzing the number of capitalized characters and key input for a name, company, title or email, you can assess the likelihood that each data point is a legitimate input. For example, the way a person’s fingers traveled across the keyboard (i.e. the number of row switches, etc.) often indicates whether their entry is legitimate. An email like asdf@ggg.com doesn’t travel very far and is probably not a real address. Machine-learning can perform these checks on every single record, regardless of whether or not it matches a known website domain.

As you can imagine, NLP alone can help you immediately improve your data hygiene. That’s why, instead of doing months of data cleansing first in hopes of being able to get better intelligence, later on, it’s smarter to get your predictive and AI initiatives started now, with the data you have. There’s no sense in spending time and money augmenting fields and cleaning up data that isn’t helpful for your models anyway. Rather, use machine-learning to figure out what your most important data points actually are, and then focus your data cleanup efforts there as needed.

It’s so important to understand common data science methodologies like these as you move forward, even if you never intend to work with the algorithms yourself. This knowledge will help you spot any flaws, unrealistic expectations, assumptions, and missing pieces in predictive and AI solutions so that you can thoughtfully evaluate them. In my next post, I’ll expand further on basic model types and more problems sales and marketing teams can solve with data.

Getting Started with Machine Learning: 3 Things Marketers Need to Know

This article was originally published on CMSWire by Sean Zinsmeister, Vice President of Product Marketing at Infer.

The buzz surrounding machine learning and artificial intelligence (AI) in the consumer world has rapidly bled over into the enterprise.

Much of this hype stems from the new consumer trends that hint at the possibilities of AI, such as self-driving cars and intelligent voice-first products like Amazon’s Alexa and Apple’s Siri.

At the same time, mainstream cloud adoption and ever-increasing computing power in the form of new solutions like Google Spanner are accelerating the development, accuracy and speed of AI’s underlying foundations, from data availability and spam detection, to machine learning, predictive analytics and natural language processing.

So it should come as no surprise that sales and marketing leaders are questioning what all this means for their departments and companies.

At a basic level, AI is about replacing human function with computers. Without using machine learning, people simply couldn’t sort through the plethora of data that’s out there without making costly errors.

Today’s machines have proven they can successfully process, understand, translate and interpret that data — parsing its meaning into visual and actionable outputs. That said, in AI’s current state, what we’re talking about is computers taking over very simple, mundane tasks as opposed to complicated, nuanced actions.

Machine Learning Already Exists in the Enterprise

Several machine learning and AI related breakthroughs are already having an impact on business outcomes, most notably predictive analytics, chatbots and natural language processing (NLP).

For example, predictive analytics is helping enterprises process their CRM and marketing automation system data, combine it with external signals from across the web, and determine where their highest revenue potential lies so they can double-down on the best bets.

Chatbots are more efficiently routing people to services and quickly answering customer questions 24/7, as well as creating new ways for business people to interact with their company’s data.

B2B enterprises and consumer companies alike are experimenting with these conversational user interfaces, which use NLP to recognize speech patterns, essentially mirror a human, and (hopefully) create better customer experiences. But using these technologies carries risks, and businesses in the US can learn plenty of lessons from mobile-first companies like WeChat out of China and India.

But in spite of the success stories, most companies are still wondering if they’re really ready for machine learning and AI. The truth is nearly any enterprise can benefit from infusing more data into its operations, especially if they watch out for common pitfalls.

3 Requirements for Successful AI

1. Start with a business problem

To start with, ask yourself questions like: How well is my business utilizing data to make decisions? Do we have a problem that better data and accurate insights can solve?

Rather than just inventing a problem to have, look towards the business practitioners who are succeeding with machine learning and AI today. Find out what solutions they’re using, and you’ll uncover communities of innovators that have similar market problems.

The real differentiation among today’s emerging vendors is in how well they address customers’ most important business needs, so choose a platform that’s built for your specific use case and will be able to scale and adapt as your business grows.

2. Optimize your technology stack

Few companies embarking on the AI journey recognize the importance of setting up their systems for success — both from a cost and performance perspective. Rather than thinking strategically about which tools they need in their technology stack, teams are too often distracted by shiny new objects and go on a spending spree only to find themselves with a bunch of fragmented tools (many of which end up as shelfware).

The truth is less can sometimes be more, especially when it comes to a scalable stack that’s architected to leverage AI for greater efficiency and effectiveness.

Another consideration is data storage. It can get very expensive, especially when you’re trying to pull in data to, say, a CRM system from a variety of different sources across and beyond your business. Some vendor’s data fees are in line with hardware storage costs from the ’90s, and can even cost more than your annual license.

Of course, you can’t succeed at AI without data, and even small companies have customer data spread across applications. Unfortunately, most of today’s automation systems weren’t architected to scale to the volumes of data that are now typical for midmarket, high growth companies. Get creative from the get-go and find strategic ways to more affordably connect all of the data you’ll need for your AI initiatives.

3. Make systems smarter through integrations

One of the best ways to address the stack efficiency challenge is to find an AI platform with an open architecture that can serve as your company’s system of intelligence.

When it comes to marketing technology in particular, recent studies suggest only 21 percent of companies use a single-vendor suite, while nearly half go with a best-of-breed approach. The integration of these multi-vendor stacks is key to their ability to deliver value.

Your system of intelligence should seamlessly connect with things like your system of record (CRM) and system of engagement (marketing automation), as well as any other business applications the company uses — and then leverage that data to make accurate and actionable predictions.

The result is you’ll be able to quickly and easily infuse predictive analytics into your business’ decision making without disrupting current workflows or adding more complexity to daily activities. Once you’ve gathered all of your data in one place, you can increase the intelligence of all your systems, thanks to the smart outputs you’ll get from your machine learning and predictive initiatives.

The Road Towards Self-Driving Business

Once enterprises address these considerations, they are well on their way to reaping the rewards of AI.

Yet these technologies will never be a silver bullet for every business woe. The industry is still at the early stages of its journey towards disruptive machine and data-guided business models.

We still has a long way to go when it comes to shifting routine enterprise work away from humans and towards machines. And we will always need the expertise and intuition of people to make AI successful, but I anticipate a bright future ahead.

In the next five to 10 years, we’ll see the business world’s version of self-driving cars (‘self-driving’ enterprise software) make headway in functions like sales, marketing, finance and human resources.

As computers successfully transform unstructured data from disparate systems into actionable intelligence, AI will upend the manual, rules-based workflows of traditional systems of record — causing vendors and early adopters alike to re-imagine what true automation should look like.

 

AI 101, Part I: What You Need to Know about Predictive Models

This article was originally published on MarTech Series by Sean Zinsmeister, Vice President of Product Marketing at Infer.

FOUR STEPS TO START BUILDING PREDICTIVE MODELS

While predictive analytics and AI are big topics in the sales and marketing profession these days, it can feel daunting when you’re trying to figure out how to get started with these data-dependent solutions. Although most marketers probably won’t actually be building any data models themselves, it’s vital that the next wave of go-to-market professionals develop a solid understanding of how to solve business problems using data. In this series of articles, I’ll break down key concepts surrounding these technologies piece-by-piece, and provide a helpful look under the hood of predictive modeling.

Business folks who are ready to get their feet wet with AI should first zoom out and learn the basics of predictive modeling – one of the underlying technologies that’s required in order to effectively replace human functions with machines. AI solutions use this advanced data science to process, understand, translate and interpret all the data that’s out there, and parse its meaning into visual and actionable outputs.

Let’s explore each of the four main phases of building predictive models:

1) DATA ACQUISITION

When it comes to building a predictive model, the first step is to gather all of your inputs or data sources. There’s no question that sales and marketing teams are acquiring a plethora of data. In many cases, however, marketers are collecting data that only they care about, and it might not be valuable, insightful or actionable for sales (and vice versa). Regardless, given the mass adoption of passive marketing channels, low-barrier free trials, etc., most businesses are gathering a lot of data about their prospects at scale – much of which can be used to inform smarter predictive models.

2) DATA PREPARATION

Before digging into all this data, it’s important to first step back and figure out what business problem you are trying to solve with AI, which will help you prioritize your data preparation tasks. The reality is that it’s very common for data to be incomplete and dirty (there’s no getting around the human error that comes with data entry), so data preparation is crucial to the future of AI. Your data should be properly cleaned up, if you will, so that you can normalize typical errors during the data acquisition phase, and ultimately produce a sound model. Only then, will predictive analytics answer your specific questions and drive the actions you want. Some common ways to prepare your data include enrichment (bringing in external signals to complement current records), spam analysis and title normalization – stay tuned for more on these techniques in my next post.

3) MODELING

Once you understand the machine-learning problem you want to solve for, the next step to building a model is to employ data science methodologies like classification or regression. Classification is also known as probability estimation, and it is used to predict which of a small set of classes each individual belongs to. For instance, you might ask “Among customers of Company X, who is most likely to respond to this offer?” There then would be two classes: “Will Not Respond” or Will Respond.”

On the other hand, regression (or value estimation) is used predict the numerical value of some variable for each individual. Looking at historical data, you might produce a model that estimates a particular variable specific to each individual, such as “How much will this customer use this service?” Both of these techniques, and many others, can deliver model outputs that drive powerful AI and predictive analytics use cases in sales and marketing.

4) OUTPUT

For example, sales teams can achieve major performance management improvements by using predictive models to improve the way they filter and prioritize both inbound leads and account-based outreach tactics. With score outputs that indicate which leads look most like the company’s ideal customer, sales can confidently focus their time on just those prospects that are likely to buy. In addition, teams can use AI to more thoughtfully route their leads – either to SDRs for outreach and development over time, to account executives for more aggressive follow-up, or to automated nurture programs – based on each lead’s potential value. Predictive behavior models can also alert sales when an old lead starts acting like a customer. By looking at engagement patterns in marketing automation and web analytics systems, you can determine when neglected leads are likely getting close to a conversion threshold. This helps reps find good qualified accounts and contacts that are “reawakening,” and then trigger data-driven workflows for more aggressive follow-up with just the right message at just the right time.

Another valuable use case for AI is to drive marketing efficiency. With the right customer intelligence, marketing teams can optimize conversions for the greatest possible funnel efficiency. And since predictive analytics outputs deliver immediate feedback on the quality of marketing campaigns, they can easily calculate key performance metrics in real-time rather than waiting for sales cycles to play out. Accurate predictions also add value when it comes to quantifying key marketing performance indicators like cost per good lead, average lead quality, pipeline-to-spend ratio, etc. By using these KPIs to look past traditional vanity metrics and identify top performing campaigns and content, marketers gain deeper insights into which programs attract the highest quality leads, drive larger deals, and accelerate deal velocity.

Each of these four steps to the predictive model build process is important to understand if you want your models to produce statistically accurate predictions, and these phases become increasingly mission-critical as AI takes over more and more everyday tasks from humans. No one wants to miss out on real revenue, just because AI made unnecessary mistakes when determining outbound prospecting lists or writing the content of sales outreach emails.

Hacking Content Marketing With Predictive Analytics

Content Summit is a 5-day virtual conference dedicated to sharing the most effective B2B content marketing strategies & tactics, which are delivered by a B2B marketing executive or thought leader. Our very own Sean Zinsmeister hosted a session on how content marketers can get the most out of their programs using predictive intelligence.

Watch the replay below:

Getting past the AI hype: How predictive analytics fuels conversion optimisation

This article was originally published on MarketingTech by Sean Zinsmeister, Vice President of Product Marketing at Infer.
These days, marketers can’t read about their profession without getting bombarded with wild claims about how AI is going to disrupt everything they do. And with the sales and marketing functions evolving so rapidly in recent years, marketers in particular must embrace an entrepreneurial spirit and constantly explore new technologies in order to give their team a competitive edge. That mindset shift, along with new consumer trends—such as self-driving cars and intelligent voice-first products like Amazon’s Alexa and Apple’s Siri—are bringing the possibilities of AI to the forefront of business-to-business marketing technology discussions.

But all of this begs the question, “Which AI claims are hype and which are reality?”

In order to know what a new technology like AI can bring to the table, it’s important to fully understand the problems you’re trying to solve. When it comes to the current state of AI solutions for marketing and sales, today’s reality is less futuristic robots or automating every single marketing workflow, and more about how data can answer one important baseline go-to-market question: who to sell and/or market to. There’s a wealth of intelligence that predictive analytics and machine learning bring to the task of answering these questions – and that’s the crux of where AI is delivering value today.

Sales performance management

Forward-thinking enterprise sales teams are saving tons of time by simply using predictive solutions to improve the way they filter and prioritise inbound leads. Companies with the “champagne” problem of an overwhelming volume of incoming prospects are using predictive analytics and AI to automatically research and qualify leads who looks like their company’s ideal customer. For example, Shoretel’s market development team found that predictive scores could tell them not just which prospects are the best fit, but also which ones are showing current buying behaviour. As a result, the telco leader’s sales reps can instantly understand who their best prospects are and determine where their time should be spent — insight which has resulted in 8X greater conversions. Now it takes just 12 calls to uncover one marketing-qualified lead (MQL) vs. the 100 calls it took before the company adopted predictive analytics.

Word on the Street: Predictive Advice from Infer Customers

Earlier this month, G2 Crowd sat down with Infer customers InsightSquared and Yesware to ask about how they use Infer’s predictive analytics and AI platform, what benefits they’ve seen, and their recommendations for other early adopters.

First up was Matthew Bellows, CEO of Yesware, a long-time customer of Infer. During the interview, he shared some helpful tidbits about how his sales team uses predictive to automatically qualify the best prospects from their high-volume lead flow, focus on the right accounts, and increase overall sales velocity and performance:

For more helpful advice from Yesware, check out this recent webinar with their director of demand gen, and learn how she was able to build a revenue-centric funnel with Infer. As a result, the company eliminated wasted sales efforts and won more deals.

G2 Crowd also chatted with Joe Chernov, VP of Marketing at InsightSquared, about how the company’s go-to-market teams use Infer to build alignment around the best accounts, and drive engagement as part of their account-based sales and marketing strategies.

Read our full snapshot to learn more about how InsightSquared is using Infer Predictive Scoring to make their marketing and sales machine much more efficient by identifying high-value leads and accounts, increasing conversions from top leads, and reducing overall cost-per-lead.

And for even more “word on the street” comments from other customers, browse Infer’s many reviews at G2 Crowd.

What is Account-Based Marketing And How Can You Leverage It? [Podcast]

Though account-based selling strategies are far from a new concept — sales teams have been using this approach for quite some time — the conversation around how to apply this to marketing has really gained steam for B2B marketers over the past year. Of course, it’s not hard to see why. ABM has the potential to open up new revenue channels, and when combined with predictive-driven tactics, this approach drives even higher conversion rates and larger average deal sizes.

In this episode of the Marketing School podcast, Neil Patel and Eric Siu talk about what account-based marketing (ABM) is, how to leverage it, and why Infer is one of their favorite solutions for finding their most valuable leads.