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6 Things We Learned From 2020

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What 2020 Taught Us (1)

2020 has been… Well, it’s been a bit of a dumpster fire. We kicked the year off with Australia literally on fire and somehow managed to continue to spiral. Even the 2020 Word of the Year award didn’t go as expected. The Oxford English Dictionary selected dozens. Doomscrolling, blursday, quarantini, and covidiots all made the cut.

There have been some good things, too. Unfortunately, the only thing coming to mind right now is Tiger King. Was that good, though? 

Moving on.

Because everything has changed so much this year, the learning curve for marketers has been steep! Let’s take a look at what 2020 taught us.

Real-Time Data Matters

In years past, revenue planning looked like structuring a top-down model and a bottoms-up model based on years worth of trended data. This year, predictive models broke because 2020’s data looked nothing like prior years’ data.

To understand what was happening day-to-day, particularly at the outset of the pandemic, we recommended using very short reporting cycles and shifting the focus to understanding what’s happening “today” rather than trying to predict months or a year’s worth of activity. We recommended looking at a much shorter time range during revenue planning to predict what will happen in the next year and develop multiple models for best, most-likely, and worst-case scenarios.

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Why? 

Buyer behavior has significantly changed. Companies slashed budgets, reevaluated, and began reinvesting, but they weren’t purchasing what they had planned in 2019. Companies were more focused on enabling remote productivity for their workforce and creative digital marketing tactics to (hopefully) supplement some of their historic in-person events.

We also don’t know if things will get back to normal (ever) or even what will happen in the next few months. The Pfizer vaccine has already been subject to supply chain issues, and upwards of 15% of Americans are planning on refusing the vaccine when it becomes available.

Because buyer behavior will continue to change unpredictably for the foreseeable future, real-time data is the best way to gauge what is and isn’t working right now.

CDPs Are Becoming Normal But...

In a recent Gartner survey, 43% of marketers said they currently have a Customer Data Platform (CDP), with a total of 79% of marketers planning to have a CDP live in the next three years. 

A CDP is a centralized location of customer data that is cleaned and unified to provide a complete picture of how any given organization (or person) is interacting with your company.

CDP has been a hot term in 2020, but we’re seeing a lot of misunderstanding around what they do, the resources needed to stand them up,  what’s required to maintain a CDP, and who should manage them. 

In the B2C sector, companies have figured out how to get a lot out of their CDPs. They centralize their eCommerce data and web activity, ad activity, and organic social activity, then augment their data with demographic information to better understand their ideal customer profile. It’s not uncommon for the CDP to live in IT, although some marketing teams have invested in data scientists.

Centralizing CRM and marketing data in a data warehouse or data lake is not a new concept for B2B marketers. However, the volume of marketing applications in enterprise B2B organizations (some estimates list as many as 91 cloud-based applications on average) makes integrating and translating the data complicated. 

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There’s also the matter of getting the data back out of a CDP. It’s not realistic to expect every marketer on your team to be well versed in Tableau or Power BI. Not all of them are able to interpret the data into actionable insights. It’s also not realistic to expect your business intelligence team to understand your marketing business problems and goals. 

And yet, only 23% of marketing leaders list investing in analytics skillsets a priority. 

To turn a 9-12 month project into a three-month project (or fewer), we recommend going with a B2B marketing CDP service (like us) that already has built out integrations, algorithms, reports, and dashboards and can push clean data back into your primary systems. We’re also very supportive of hiring a talented analyst to get the most out of your data.

It’s Time to Rethink the Funnel

Conceptually, the demand generation funnel made a lot of sense. We knew people through a mental progression before purchasing a product, and it made sense to measure those points.

Unfortunately, the funnel doesn’t account for people bouncing around stages (the buyer journey is not linear!) or that most B2B companies have sales cycles that involve buyer committees rather than a single point of contact.

Considering these factors, should it even be a funnel?

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We need to look at multiple layers of information, including account-level engagement scoring and individual engagement scoring. We should also be looking at campaign attribution from the point of name acquisition through Closed-Won to help understand campaign effectiveness.

Maximize Your First-Party Data

Once the General Data Protection Regulation European Union law went into effect, we knew it was only a matter of time before other countries follow suit. Sure enough, the California Privacy Act made us glad that we put opt-in tracking in place for our entire database and didn’t focus solely on Europe.

It’s going to be harder and harder to purchase reliable third-party data and cold market to ideal customer profiles. As a consumer, I’m happy about this change. As a marketer, I’m worried. Expectations are trending toward shorter and shorter forms (if any at all for long-form content anymore), and remote employment means it’s hard to infer information without data collection. 

It looks like people will remain anonymous longer, which means we have less opportunity to influence a buying decision before buyers lock down their preferred vendor list.

Ensure you’re using progressive form fill capabilities, get your cookie policies ironed out, and ensure you have explicit opt-in tracking for all of your customer data. Also, do what you can to know what’s required of you if someone wants their information deleted (including what qualifies as identifying information).

The Rise of Revenue Ops

Back in 2019, we interviewed a marketer who thought combining marketing and sales operations functions made a lot of sense from an alignment perspective. Since then, we’ve seen a sharp increase in revenue operations departments across B2B organizations.

Revenue operations often includes the duties formerly performed by marketing operations, sales operations, and customer success operations. They are responsible for analytics, technical stack management, process improvement, and enablement. Working as one collective department, they are less likely to be biased toward any one team or negatively impact one revenue team with system or process changes made for another department.

RevOps Org Structure

What we’ve seen work best in these organizations is a leader that is at least on par with the sales, marketing, and customer success leadership teams. Some larger organizations go so far as to have department heads report to the Chief Revenue Officer, so each arm of the revenue workforce is on even ground.

Having the VP of Revenue Operations report up to a CFO or COO gives the impression they aren’t part of the revenue team. Having them report to sales or marketing pretty much guarantees you’ll see a biased workload.

Having Data <> Having Insights

B2B marketers don’t suffer from a shortage of data. We’re buried in it. The problem is we’re underinvesting in our data strategy, or we’ve hired people who don’t know what to do with what we have. 

According to the data, it’s usually a combination of the two.

I remember listening to a lecture, and the speaker made it absolutely clear that knowledge does not equal wisdom. Knowledge is knowing a collection of facts. Wisdom is having the experience and judgment necessary to make the best decisions possible.

It’s not enough to know key stats like month over month web traffic or marketing qualified lead volume compared to goal. A wise analyst knows how to take those metrics and figure out why they are the way they are. They’ll figure out which levers to pull to change the outcome next quarter.

Meeting an executive’s reporting requirement shouldn’t be the goal of your data strategy. Figuring out how to grow more pipeline faster should be your end goal.

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