The Digital Revenue Model and the Science of Marketing

Bharath Ram Srinivasan

According to a Gartner 2018-2019 CMO Spend Survey, marketing technology now accounts for 29 percent of total marketing budget and is expected to grow over the next few years. While this shows marketers’ continued commitment to using the latest and greatest innovations in technology, the reality is that the large majority of them still continue to leverage these technologies only as a vehicle to get their messages out to their customers. Some of them go a little further in leveraging technologies to learn about the accounts visiting their web properties, but very few have managed to successfully monetize from all of their digital traffic.

In the consumer marketing world, monetizing every visitor is not a new phenomenon. It’s something B2C companies like Amazon, eBay, Facebook and Google have done successfully for years. However, in the B2B world, monetization of digital traffic has never been a huge priority. While B2B companies have leveraged vendors and techniques to decipher which accounts have been visiting their website, from a sales prospecting standpoint there have not been very many attempts to orchestrate all the digital interactions into revenue.

The marketing tech stack for companies large and small today at a minimum include a marketing automation platform. Integrating such a platform with the company’s website and the company’s CRM platform enables the base infrastructure for revenue creation. With this basic integration, the marketing automation platform can add a tracker id (aka cookie) for every visitor to the company website. When the visitors navigate through the website they leave behind their digital footprint. Every visitor becomes a known contact once that visitor fills out their email ID on any web form.

This approach works in the scenario where the B2B website is getting good traffic and form conversions. Therein lies the challenge: Today, on average, we only see between 2 – 5 percent conversion rates for form conversions on B2B websites. At this rate, we will have to bring a very large number of visitors to the site over time to help increase the volume at the top end of the lead funnel.

Some companies have solved for this by creating a controlled access area for visitors on their site – making certain areas only accessible through a login. This allows the company to track any visitor who logs into their site to access those parts of the site. However, not many B2B visitors have warmed up to the idea, and therefore unless the visitors see some compelling benefits from the personalized web access, they are not going to log in. 

B2B marketers have been struggling to get over this huge challenge and fortunately for them, the digital technology evolution has matured beyond form fills. We now have products and tools available in the marketplace that can help identify potential buyers on the website before the visitors reveal themselves. The reverse IP lookup techniques have matured over the years and today, companies such as D&B and Demandbase have built a robust database of company IP addresses.

When a visitor lands on a company’s website these technologies now help the marketer understand the companies the visitors are originating from based on the IP address lookup. On average these technologies help the marketer discover 30 – 40 percent of all accounts visiting the company’s web properties, as opposed to knowing the accounts just from form fills.

The next step in the process for is to use the discovered account to identify relevant contacts to target within the account. This contact space has become highly commoditized, with a lot of vendors in the market claiming to have the highest quality of contacts. The B2B marketer can work with any of these data vendors to identify all potential contacts within their target set of accounts.

With GDPR now in place, however, this tactic may have to be modified to either limit it to the set of contacts within accounts where the company already has an opt-in or alternatively work with third-party media and publishing companies to target those accounts who have provided their consent to cookies and disclosed themselves on the company’s website.

Now that the B2B marketer has two key pieces of information: (1) Accounts who have shown interest in the company’s offerings and (2) Potential target contacts within those accounts, some of whom may have visited the company’s website, the next step is to target these accounts and contacts through a multitude of options available in the B2B marketing world today.

The easiest and most inexpensive means is still via traditional emails. Although there are several studies pointing to this becoming a declining market, especially with the privacy controls and email governance policies that different countries have adopted, personalized emails with relevant content is still the most effective tactic.

Having the right email nurture campaigns in place within your marketing automation platform that map to where the account and the contacts within the account are in their buyers' journey will yield the reveals marketers are seeking.

But while B2B marketers make every effort to make the content relevant to their target audience, it is virtually impossible to guess every piece of content that will work. This is where the B2B marketer should utilize the data science and machine learning techniques to serve up the best possible content to their audience that continues to engage their interest.

Vendors such as PathFactory and Uberflip were born out of this need for the marketers to maximize every click they get. For instance, when a recipient clicks on an email link where the traditional journey may have ended up with a gated landing page or asset, these new tools let the customers continue to binge on the content at their own pace and choice of topics.

This, in turn, allows the B2B marketer to hone in and understand their target’s topics of interest and engagement better than ever before. The machine learning engine in the backend enabled through these tools automatically eliminates the need for any guesswork by serving up different pieces of content based on certain inferred attributes of the customer and learnings from each engagement.

In addition, the B2B marketer should also aim to surround their target set of accounts through additional tactics, especially for those accounts where email is not an option. These tactics include traditional ad-based targeting specific to a given set of accounts, with the goal to bring those accounts back into the company’s digital properties.

Additionally, while some marketers have been cynical about B2B marketing through social media platforms, these new channels have offered an additional route to follow target accounts. The biggest value is found via social listening, where the marketer can gauge customer’s opinions and needs even before targeting them with relevant content.

When the visitors engaging through all of these different tactics come back into the company’s digital properties, the B2B marketer should have the right infrastructure in place to capture that engagement and tie it back to all the engagements the contact and the account has had overall in the recent past.

New digital infrastructure acknowledges the reality that reveals are not always form fills. Marketers are now able to measure overall engagement within a given account by tracking all the cookies associated to the account, some of which may have already converted and revealed in the traditional sense.

In this new model, the primary objective is the ability to track all of the account-level engagement and ensuring sales engages with the right contacts on the account once they hit a certain level of digital engagement. This threshold could be measured as a combination of the number of activities and the minutes engaged.

Marketers can also weigh different activities, providing a new scalable way to generate more revenue digitally. Empirically, we have seen 4x to 5x higher opportunity conversion for marketing leads generated through this digital process and handed off to sales.

Ideally, when sales received the lead they should be provided all of the relevant information that was used to qualify the lead in the first place, so that the salesperson reaching out can continue on the conversation with the account and the contact instead of starting afresh. Oftentimes when sales and marketing organizations do not have a seamless lead handoff process, critical information gets lost and decreases the likelihood of revenue creation down the road.

Therefore, it is in the best interest of the marketer and the salesperson to ensure the information exchange and feedback on future conversations are all well captured in the CRM system so that all of those data points can be leveraged for future prospecting into the same account as well as for other potential target accounts.

The idea of using digital engagement to generate leads is not new, but applying it to this new framework — i.e. starting with a web cookie, identifying the company, discovering the buying team of contacts, targeting the contacts, measuring engagement of the buying team and driving leads when the buying team’s engagement level hits a threshold — is wherein lies the difference.

The marketers needed to drive this type of digital model need to have a strong data orientation and the companies wanting to adopt this should have a solid digital infrastructure that can capture all of these engagement insights in real time and deliver it in a consumable manner for the marketer to make the decisions.

Artificial intelligence (AI) has already taken over some of the business areas. With the amount of data rigor that is required in driving the new digital revenue model, this area is ripe for further disruption through the application of AI. For the B2B marketers who have the right framework, tools and processes in place, the digital revenue model is going to be a fascinating area of growth, driving significant revenue for their businesses in the years to come. 

Bharath Ram Srinivasan is a Marketing Professional and Technologist with over 15 years of experience building and scaling companies from start-up to IPO and beyond. He is currently responsible for modernizing and building the marketing platforms and products at Hewlett Packard Enterprise and driving revenue digitally. Previously, he ran marketing operations and analytics at Nimble Storage. He is a speaker at global marketing conferences such as Marketing Nation Summit and MarTech. In his spare time he advises local start-ups in building their marketing engine. Bharath holds an M.B.A. from Kelley School of Business in Bloomington, Indiana and a B.S. in Electrical Engineering from BITS Pilani, India.

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