This is What the Future of B2B Marketing Looks Like

By Dylan Hebb

Let’s talk building a zero-friction experience, the future of B2B marketing, and using machine learning to make data-driven decisions.

With speakers from Google (Susan Charles), LinkedIn (Richard Wong), Twitter (Andrea MacDonald), and Facebook (Bobby Hennessy), SocialEast is a one-day conference designed for people working in digital marketing, customer experience, analytics, and social media strategy.

Recently, I attended the SocialEast Digital Marketing conference in Halifax, Nova Scotia.

Social East conference for b2b marketing with Content Bloom

In this post, we’ll focus on a few of the talks that had very actionable information that can be applied to any business (along with some additional information).

Let’s get started.

Zero-Friction Future

Bobby Hennessy from Facebook talked about friction.

Zero-friction future in b2b marketing on customer experience discussion with Facebook
Bobby Hennessy, eCommerce Client Partner at Facebook

Friction is when we over complicate the customer experience to such a point that it becomes too difficult for the user to do business with us. This happens when there are too many interactions, the experience is not intuitive; and the interface isn’t user-friendly. This often results in a loss of business because the site doesn’t allow the user to seamlessly navigate and achieve their goals in a simple and direct way.

Did you know?

When polled, the audience estimated that it would take 5-10 clicks to complete a checkout on a given website. In reality, on average, it takes 22 clicks to complete checkout.

This means lost customers (many more than you’d think, too). A total of 61% of digital shoppers have stopped the process because checkout was too long, too complicated, too confusing, or didn’t function properly. Fewer clicks equals higher sales. This is why companies like Nike and Amazon have optimized their customer journey to be a maximum of 5-7 clicks to checkout.

Turns out, customers really care about speed in interaction design.

Why do customers drop off so rapidly?

Three pivotal consumer behaviors:

  • Expectations of speed
  • Availability of choice
  • Demand for convenience

Elite CX companies like Amazon are constantly becoming market disruptors and increasing the expectations in online eCommerce. For every 100 milliseconds of load time, Amazon is estimated to lose 1% of sales. This has led to Amazon becoming a dominant leader in the ability to identify online consumer behavior and pain points. In practice, this has been achieved through strategic testing, market research and intense focus on becoming the world’s most “customer-centric company“.

Simply, they make it easy to do business with them, they anticipate where the customer is going, and they let customers explain their behavior.

“I would define Amazon by our big ideas, which are customer centricity, putting the customer at the center of everything we do, [and] invention. We like to pioneer, we like to explore, we like to go down dark alleys and see what’s on the other side.”

Jeff Bezos, Amazon CEO

Customers expect speed on desktop or mobile. If a website takes over three seconds to load, a massive 57% of customers exit.

With the shift to mobile, the expectation of speed is no different. Faster load times lead to higher ad viewability, longer sessions, and lower bounce rates. Most importantly, this shift means that you need to stop ignoring mobile.

When Facebook went public in 2011, they made a bet on desktop which resulted in poor stock market performance. Because of that, they implemented a new rule internally.

Their rule was if you’re showcasing the user experience/UI designs in sprint reviews and/or demos, the primary view shown has to be mobile. Why? Mobile is harder. If you can figure out how to get customers to convert, engage, and enjoy the mobile experience, you can do it with desktop.

Chart of online cart abandonments and why carts are abandoned on the web.
Percentage of users who abandoned checkouts for given reasons.

One of the easiest ways to increase conversions on your site, especially for e-commerce, is by reducing friction in your checkout process. More specifically, using best practices in mobile design. This could include simple changes such as adding the proper keyboards for custom form fields (i.e. numbered keyboard for number fields).

What Does the Customer Want?

Customers want a streamlined process.  

The data shows that if you want customers to convert, shorten the path to purchase. We can’t continue to force users into creating accounts or funnel them through long, complicated checkout processes. (At the very least, streamline the login/account creation process), such as using social or google login.

Even when customers do convert, the customer experience shouldn’t end with the conversion.

You need a post-purchase strategy. One of the most effective ways to do this is to evolve the experience to have a conversational element. It’s not just about repeat business, it’s about post-purchase engagement, driving customer referrals, and brand equity.

Customers want and need your focus. Think about it, a whopping 89% of consumers expect companies to respond within 24 hours or less.

It’s time to start a conversation with your customers. They expect and demand speed, choice, and convenience. Can you blame them?

The Future of B2B Marketing: Trends for the Contrarian Marketer

Richard Wong from LinkedIn gave contrarian takes on B2B marketing trends and metrics for 2020.

Richard Wong of LinkedIn discussing B2B trends such as the subprime data crisis, using direct data and overall how to use data analytics to measure your marketing.
Richard Wong, Lead, High Growth and Agency Program

I’m going to focus on three of the trends discussed, including:

  • Subprime data crisis
  • Signal to noise ratio
  • Why ROI is an overrated metric

Subprime Data Crisis

Similar to the economic crisis of 2008, LinkedIn senior executives have been warning B2B marketers that they are falling victim to bad third-party data.

“It’s eerily similar to what’s going in the marketing industry: we’re taking fake data, bundling it with real data, and selling it as big data.

Peter Weinberg, LinkedIn

In the current marketing landscape, there is so much focus on personalization and retargeting, but a lot less discussion on ad fraud. So much so, that, according to Richard, it is estimated that 80% of the entire digital marketing industry is built off bad data.

Even though massive amounts of fake traffic are driving decisions, companies are still putting money into it, while trying to get cleaner data.

The answer? Shorten the supply chain.

Build data sets that you can rely on. This means direct data.

The less hands the data goes through, the shorter the supply chain, the better the data.

Signal to Noise Ratio

The signal to noise ratio is the notion that more data universally equates to more decisions.

Research finds that this assumption is untrue and that more data does not result in additional decisions being made, but instead ultimately leaves marketers in a state of analysis paralysis where decisions become cluttered.

This trend is all about moving away from the signal to noise, and rather than thinking about big data, we focus on long data.

According to Richard Wong, when measuring campaigns, site performance, and user experience through analytics tools, we find that only 4% measure beyond 6 months. Why? Because most marketers are using their data to optimize and measure recent campaigns, rather than identifying long trends.

Companies are investing in big data but ignoring long data.

The lesson here is that adding more metrics is not the solution. Instead, we need to refocus and look at our current metrics expanded over a longer period of time. This can be done by reviewing and tracking metrics across 6-month, 12-month and 36-month periods. IPA has released 50+ years of studies based on this very idea.

Focus on the same metrics, over a long period of time.

Return on Investment = Overrated

ROI is one of the more overrated metrics to measure effectiveness.

ROI is an efficiency metric, not an effectiveness metric. This is due to a few main reasons:

  • Long data is very difficult to track accurately or especially quickly
  • One of the fastest ways to increase ROI is to cut all spending budgets
  • ROI trap (see image below)
Comparison of the ROI metric that is commonly used in marketing.

The flaw in ROI as an effectiveness metric is that it will always favor tactics that don’t cost or make a lot of profit. Some campaigns can also take several “touches” before a customer is receptive to the message, making ROI even more difficult to measure. Therefore, it should be used as a secondary metric only. 

Instead, chase excess share of voice (ESOV). The most underrated metric in marketing.

ESOV is the share of voice of your brand vs. your market share.

  • How much of the category are you reaching?
  • How much of the category do you own?
ESOV analysis is the share of voice vs the share of market.

In order to grow, you need to reach more customers than you currently have. This will measure how much you’re speaking vs. your competitors.

If you speak more, your brand will grow.

Using Machine Learning to Make Better Marketing Decisions

Michael Racioppo, Digital Director of NextHome talked artificial intelligence (AI) and machine learning (ML).

Michael Racioppo, Digital Director of NextHome discussing artificial intelligence and machine learning used in b2b marketing.
Michael Racioppo, Digital Director of NextHome

AI and ML are two buzz words we often hear in present-day marketing. But what exactly do they mean and in what way are they used?

There’s no doubt that we can automate many tasks that underlie the digital marketing industry; ones that can undoubtedly be valuable. However, we still need a human element and perspective to apply most of these models.

Define the differences:

  • Artificial Intelligence is the development of computer systems tasked with performing functions that would normally require a human. This could include speech recognition, decision making, translation, etc. Essentially, its purpose is to mimic a person.
  • Machine Learning is giving technology a goal through the study of algorithms and statistical models in order to perform tasks without specific instructions while relying on patterns and inference instead.

“Think of it like this: Using machine learning is like having a billion interns working for you, not a single Einstein coming up with the perfect solution. You have to figure out how to use them, which requires assigning them tasks and translating their output into something useful. Without you, the interns would be lost.”

Ben Jones, Google

Through feeding more data and giving it more time to learn, machine learning enables systems to rapidly improve upon itself.

The expansion of these models into your business will help you adapt to your customer experience in 2020. Studies indicate that 57% of major executives believe CX is the area that will be most impacted by ML.

Practically speaking, there are many ways that it can be used in CX.

Examples could include:

  • Further personalization (such as recommendations) to guide the buyer journey
  • Automation, including 24/7 nearly-instantaneous customer service
  • Engagement with customers

Even though companies are beginning to invest more in personalization, 61% of consumers think that brands do a poor job of predicting their needs.

ML is being introduced into businesses in an effort to bridge the gap between customers’ needs and what is actually being offered.

This is being done by businesses that are currently using ML to segment different customer groups with their data. For all e-commerce businesses, segmenting customers is critical to gaining an in-depth understanding of their customers behavior.

Through data analysis tools like Google Analytics, Facebook Pixel, Adobe Analytics, etc. you can do some basic segmentation of customers. Think shopping cart exits, demographics, psychographics, etc. But through ML, you can utilize these segments and customer profiles to enhance the customer experience.

Additionally, we can use this data to identify who our customers are. In the example Michael provided, since NextHome is in the real estate business, he and his team identified the following affinity profile characteristics:

  • Users 24x more likely to be shopping for a coupe
  • Users 18x more likely to be looking at Italy trips
  • Users 6x more likely to be shopping for a tablet
  • Users 4x more likely to be working in healthcare

With this information, you can deliver dynamic experiences and advertisements through these characteristics. This looks like:

  • Changing your creative (image, video, blogging, ads) to match these verticals
  • Use advertisements with coupes in the driveway or an image of a customer using a tablet
  • Review customer journeys and mapping to personalize based on your users
  • Retargeting campaigns
  • A/B testing: create several different hero images for the web and online advertisements (multiple sizes, colors, images, video, multiple descriptions, multiple CTA)
    • ML will begin to provide insight into which ads, images, and media are performing the best
    • ML will begin to optimize, highlighting which ads perform the best and display those more frequently

The Takeaway:

  • Machine learning, advertising, and marketing go hand-in-hand
  • Data insights are critical, personalization is now essential, and customer experience is rapidly evolving through ML
  • Use your data, optimize your experience, and let these technologies help you get there

Wrapping Up

The online experience has never expanded faster than the speed it’s growing at today.

Customer expectations are growing. They want more, in less time, in an efficient way.

Luckily for you, most businesses are still pretty far behind the curve in terms of catching up on these best practices.

If I had to sum up the three main takeaways from this blog post, they would be:

  • Reduce friction: Make transactions easier. Focus on the customer journey. Start with one customer journey mapping exercise.
  • Effective metrics > Efficient metrics: Emphasize real, long data. Focus primarily on effectiveness metrics like excess share of voice (ESOV); don’t get too caught up in efficiency metrics like ROI.
  • Use your data: Creating a customer experience or running online advertising without customer insight is like walking through a maze blindfolded. Use your data to gain in-depth knowledge on your customers and then optimize your online customer experience and advertising through machine learning.

Last but not least, talk to your customers, collect feedback, and use that to provide value.

A company that focuses on real value when it comes to the customer experience always has the loudest voice in the room.

Are you interested in data machine learning and the future of B2B? In what way has customer feedback helped your business grow?

Reach out and let’s talk. Facebook | Twitter | LinkedIn

End of the conference party at the Stubborn Goat restaurant in Halifax
Customer Experience