How to win in B2B with Artificial Intelligence

AI/Predictive Analytics

12 Min Read

By J. David Green

Today I am going to talk about how to win in B2B with artificial intelligence.

Few business assets are more valuable than data. Want proof? Look no further than the market caps of the top five companies in the world, according to Fortune:

Three of the five—Amazon, Alphabet (formerly Google), and Facebook—have used big data and data science to catapult above corporate giants like JP Morgan, ExxonMobil, Berkshire Hathaway, and Johnson & Johnson.

Amazon has a highly personalized recommendation engine. Google delivers relevant searches and relevant ads. Facebook provides relevant ads and a more relevant experience for users. Each of these companies yields unprecedented value as a direct result of the use of big data and data science. Indeed, without these advantages, these historic valuations would not be possible.

According to LinkedIn, Amazon has 174 employees with the words “data” and “science” in their title. Alphabet has 130, Facebook, 265.

Of course, there are, no doubt, linguists, engineers, database architects, database administrators, and other specialists supporting these efforts. No to mention consultants, agencies, and other suppliers.

The other two companies in the top five are investing in big data. Microsoft has one of the largest installed bases in the world, a huge source of B2B big data. The company has poured billions into its search business (Bing) and bought LinkedIn for $26.2B, their largest acquisition ever and one of the richest sources of B2B data in the world. Apple, which made its mark through the fusion of technology and design, now has the iPhone, iPad, iTunes, and the Apple Watch for gathering massive amount of data.

On LinkedIn, Apple has 105 employees with “data science” in their title while Microsoft has 235.

While not making the top five, IBM has Watson, and Salesforce has Einstein and a data portal called Lightning Data on their AppExchange. IBM has 441 people with “data science” in their title; Salesforce has 62.

To transform this data into actions, those companies are using machine learning, predictive analytics, natural language processing, and other programming techniques under the banner of artificial intelligence.

Most of us experience the benefits of artificial intelligence in our own shopping experiences, such as when we get a ride with Uber, find a movie on Netflix, or book a room on Airbnb. The success of B2B giants like Facebook and Google, however, should be a wakeup call to every B2B CEO in the world. Effective use of big data and data science is far more predictive of financial viability than many traditional balance sheet and operating income line items.

The trillion-dollar question is, “Where should B2B CEOs invest in artificial intelligence?

Get the AI white paper: “How to Win the Love of Your CEO”

The Most Compelling Use of Artificial Intelligence for B2B

Think of the most miraculous things that artificial intelligence might do to improve the world. I’ll bet improving the efficiency of B2B sales and marketing is way down the list.

That’s a shame.

There are a few advancements which help everyone very quickly. For example, the invention of writing, printing, phones, computers, and the internet all resulted in rapid advancements in civilization.

Advancements in B2B will have the same catalytic effect. Whether your company is researching a cure for a disease, trying to improve education, or developing ways to make the world a cleaner or safer place, there are businesses who can help your company do it better, faster, and cheaper.

Improving the ability of B2B buyers and sellers of complex solutions to find each other more efficiently will make the world a better place. Period.

Both the world and B2B sales and marketing could use some improvements.

Cracking the Mystery of B2B Sales and Marketing Innovation

You might be thinking that B2B Sales and Marketing, at least in the best companies, is not the biggest problem in the world. After all, B2B sales and marketing, in the best companies, would seem to have made enormous strides over the last ten years. In that time, marketing teams have embraced social media, content marketing, account-based marketing, marketing automation, retargeting, sales and marketing alignment, programmatic advertising, attribution models, and funnel metrics.

In fact, B2B companies have embraced marketing innovation for nearly 30 years.

For its part, Sales has adopted CRM, inside sales models, sales cadence software, social selling, lead enrichment, calendaring tools, virtual meeting tools, and contract automation tools. They’ve adopted selling styles like that found in The Challenger Sale. The list for both groups goes on and on.

For sales and marketing professionals, there is also more content—and high-quality content, at that—than any sales and marketing professional can consume. Knowledge in B2B may be growing even faster than technology.

For marketers, great sources of content include HubSpot, Rand Fishkin (founder of SparkToro), Neil Patel (co-founder of Neil Patel Digital), Avinash Kaushik (an evangelist at Google), SiriusDecisions, Tim Riesterer and Erik Peterson at Corporate Visions, Clayton Christensen (Professor at Harvard Business School), Joe Pulizzi (founder of the Content Marketing Institute), Brian Solis (Principal Analyst at Altimeter), Jay Baer (Convince and Convert), David Meerman Scott (speaker, author), David Lewis (CEO of DemandGen), Mark Shaefer (consultant, author), Matt Heinz (the CEO of Heinz Marketing), Forrester Research, and Seth Godin (founder of altMBA, author, and more). The list is endless (and sorry for leaving out your favorite author).

The same is true for sales: Jill Konrath (speaker, author), Mahan Khalsa (Thought Leader at FranklinCovey, author), Randy Illig (Global Practice Leader at FranklinCovey, author), Barry Trailer and Jim Dickie (co-founders of CSO Insights), Trish Bertuzzi (CEO of The Bridge Group, Inc.), Jeff Thull (CEO of Prime Resource Group, author), Anneke Seley (founder and CEO of Reality Works Group, author), Jill Rowley (Chief Growth Officer at Marketo, Daniel Pink (author), Jeb Blount (CEO at SalesGravy, speaker, author), Brent Adamson and Matthew Dixon (co-authors of The Challenger Sale, Matthew is also Chief Product & Research Officer at Tethr). Again, the list goes on and on.

Resources include blogs, webinars, books, podcasts, videos, infographics, marketing events, and so on. With all these tools and all this knowledge, wouldn’t a breakthrough seem likely?

The Sad State of B2B Customer Acquisition

Despite these seeming advancements, even the best B2B companies are wasting massive amounts of money trying to find customers. For marketing, a few benchmarks shed light on the ineffectiveness of this approach.

For example, this benchmark report from WordStream includes conversion benchmarks for B2B companies doing paid search and display ads, stats based on 14,197 US WordStream customers:

Click-Through Rate (CTR)—the percentage of users who clicked on a paid search ad—2.41%. In other words, 97.59% of people didn’t click. For display ads, the click-through rate is much worse, just 0.46%. That is, 99.54% of people didn’t click the ad. No wonder they call it banner blindness.

Conversion Rate—the percent of those clicking a paid search ad, sharing their identity and converting into a lead—3.04%. That is, 96.96% of the visitors to your landing page leave without telling you who they are.

Conversion of Marketing Leads into Closed-Won deals1.54%. You would think, with all the upstream filtering, that the closing rate on marketing leads would be much higher, but it’s not. For that reason, the best companies use lead scoring to reduce the number of unqualified marketing leads sales receives. The best companies also have a dedicated team of inside sales people who just follow up and qualify scored marketing leads and then set up meetings for sales people.

What does that effort tell you about the quality of top-of-the-funnel marketing leads? It’s not a pretty picture.

Get the AI white paper: “How to Win the Love of Your CEO”

The Full Impact of Low Conversion on Revenue Production

The economic implications of low conversion rates extend beyond marketing. Those low yields on lead generation reduce sales productivity, too. Lower conversion rates at the top of the funnel mean fewer leads for sales. Fewer leads for sales mean more sales capacity spent prospecting. Today, according to B2B benchmarking giant, SiriusDecisions, marketing rarely sources 45% of the leads that sales needs to hit quota. In addition, the larger the organization, the lower the percentage of closed-won business.

As a result, salespeople spend 20% of their time prospecting, according to CSO Insights. If you multiply the sales budget by the 20% figure allocated by sales to prospecting, the dollar figure often surpasses the entire marketing budget.

It’s not just the time investment, however. Time spent looking for prospecting robs sales teams of revenue capacity, so the problem of lead conversion has significant lost-revenue implications.

Additional benchmarks shed light on this problem. In this report from Salesforce.com, just 1% of leads convert into opportunities (much lower than the Forrester benchmark above), and only 6% of opportunities convert into closed-won deals. A RAIN Group study of 472 companies with 10 or more salespeople found that sales teams won 47% of proposals. Across all opportunities, however, the win rate falls to 19%. A Marketo study found that just 22% of sales qualified leads convert into closed-won business. Of course, very few leads become sales opportunities or even sales qualified.

When you break out the prospecting function to a dedicated team who qualifies marketing leads and prospects on behalf of sales people, the conversion rate picture doesn’t get that much better. In this report by The Bridge Group, reps make an average of 45 dials per day, 9.1 attempts to reach a prospect, and have 5.1 quality conversations per day. For teams delivering meetings, that activity level results in 21 meetings set per month, with 11 converting into opportunities. For teams providing sales reps with opportunities (i.e., which are more qualified than meetings), the monthly average was 15 per month, with 10 converting.

That’s 900 phone calls to get 10 to 11 sales opportunities each month, per rep.

Even with these incredibly low conversion rates, some of the B2B customers are not profitable, leave early, complain in social media forums and review sites, and make life miserable for front line employees.

Ugh!

The Scaling Problem in Sales and Marketing

Just so you know that the problem doesn’t just exist in old-school industries like heavy manufacturing and wholesaling, let’s peek at those darlings of innovation, the SaaS industry. Cross-functional resource allocation highlights just how much sales and marketing inefficiency is costing B2B customers and shareholders in that sector.

In this study by SaaS Capital, the percent of revenue allocated to functions like engineering, cost of goods, general and administrative, and customer support/success declines as a company grows, not sales and marketing.

“Sales and marketing do not scale. Spending in these areas is at least 30% across all revenue levels.”

While that study looks only at SaaS companies, those tend to be the most profitable and most innovative companies in the world. If they can’t do better than that, what hope do traditional industries have?

The Accepted Wisdom about Low Conversion

Industry leaders cite many factors for this poor performance. Sales methodology vendors believe improved sales training will make a big difference. Messaging consultants believe better messaging will win the day. Brand advocates believe a great brand advertising program will make a big difference. Sales and marketing technology vendors believe new technology is the answer.

These and other explanations clearly have merit in certain contexts. Anecdotes of success for these narrow remedies won’t solve the core problem. Identifying and dealing with root causes will.

Maybe it’s not about getting the right message into the right channel. Maybe it’s not about asking the right question at the right time. Maybe it’s not about name-recognition. Maybe it’s not about more technology. Of course, those things matter, but low conversion rates suggest something more fundamental is wrong.

What if these low conversion rates have more to do with the customer situation than your message, your sales and marketing channels, your brand recall, your sales skills, or your sales and marketing technologies? What if targeting could more precisely identify these situations?

The Real Reason Conversion Rates Are Low

There are many reasons most of the market won’t buy a B2B product or service. Some recently invested in an alternative solution. Others have a solution in place that is working sufficiently. Some can’t find what they need. Others tried a similar solution that didn’t work out and so are cautious. Others still have more pressing priorities, based upon their current situation.

Many lack the resources (money, management attention, capabilities) to make the solution work. No message or cadence, regardless of the method of contact, will change the reality on the ground. As a result, many look but very few buy.

Moreover, most of those who look have no authority to buy. Most of them don’t even influence big ticket items. In 2018, for example, there were nearly 400k employees at IBM. How many of those employees have any influence at all on purchases of even $5k, much less large commitments?

Between targeting the wrong accounts and soliciting those with no voice in decisions, is it any wonder conversion rates are low, and sales and marketing alignment is still a top challenge? The implication of this sober view of the conversion funnel is that something is fundamentally wrong with the ability of sales and marketing to target the right accounts.

Better targeting, then, is the killer application for artificial intelligence.

Unlocking the Potential of Artificial Intelligence for B2B Sales and Marketing

To use artificial intelligence to target accounts and people more accurately, you need the right data. In the conversations I have had with data scientists, they always volunteer that any predictions from an algorithm depend largely on the quality of the data.

Today, B2B sales and marketing professionals use a combination of these attributes to target new accounts:

What’s wrong with this picture?

  • Firmographics are not accurate, yet many sales leaders use firmographics (especially employment or revenue) to allocate sales resources and to route marketing leads. Sales and marketing rely on firmographics to identify account-based marketing targets. Sadly, those who use firmographics (a widespread practice) as the primary filter eliminate more accounts than all other targeting mechanisms.

 

  • Extrapolating the level and function based on a title is useful, but the story shouldn’t end there. People are more than their titles. They have varying levels of influence and competence, experience, tenure with their companies, and so on. These things matter. Moreover, the meaning of a title depends upon the context—the size of the company, the company’s core activities, the titles of others on the management team. Plus, contact data has a very high decay rate.
  • Intent data, which has come to dominate B2B marketing, has lots of false positives.

Obviously, relationships matter a lot. Today, however, relationships are largely the domain of salespeople networking. Where’s the scale?

Clearly, B2B companies need a new source of data. Such a source will require an entirely new way of thinking about both new and existing sources.

Get the AI white paper: “How to Win the Love of Your CEO”