E-BOOK

THE KEY INGREDIENT TO FUELING FIRE STRATEGIES IN SALES & MARKETINGAn Inside Look At How Data Intelligence Is Creating Winning Revenue Strategies

SPONSORED BY

2

Building a great go-to-market strategy requires B2B companies to have deep insight into their target audience, what their pain points are and what information they need to accelerate purchase decisions. But creating targeted, resonant campaigns that drive revenue in today’s digital landscape is difficult due to the inundation of data that is available on prospective customers.

In the B2B marketplace, the data oftentimes used for account targeting isn’t up to the task. Research from Demand Gen Report shows that 83% of marketers agree old and outdated data is their biggest challenge, while 64% say that their biggest challenge is not having enough data on their current customers.

When data is off, salespeople can’t prospect, and marketers can’t segment and market appropriately. Bad data leads to rabbit holes and time spent searching for accounts that could be spent engaging with accounts. That same research from Demand Gen Report shows that more than half (51%) of marketers think their database strategy has room for significant improvement — only 13% say their strategy is fine-tuned and solid.

There are a lot of ways that data can go south, but often it’s because of the tension between accuracy and coverage. Organizations that provide data on businesses generally offer either highly accurate data across a small number of accounts, or they offer a variety of data across a high number of accounts with lower accuracy. A huge database means a lack of precision. High accuracy translates to a lack of coverage across your total addressable market.

This E-book looks at one solution to the conundrum of finding accurate data that offers scale. The following will detail the FIRE methodology, which provides a blueprint to pinpoint the best prospects. Points we will explore in this E-book include:

► A closer look at FIRE, including an examination of each of its steps – Fit, Intent, Recency and Engagement;

► How B2B businesses benefit from the FIRE methodology;

► How data intelligence impacts engagement; and

► How FIRE fits in the ABM stack.

INTRODUCTION

A CLOSER LOOK AT FIREThe FIRE methodology — an acronym for Fit, Intent, Recency and Engagement — is a new approach built to support the rapidly growing adoption and practice of account-based marketing (ABM). As modern marketing and sales teams focus on accounts, their buying groups and the individual needs of the people that make up these groups, they realize that they need the right data to fuel marketing and sales efforts. Ultimately, leveraging these four main data points can position B2B businesses to narrow down their focus to the right set of accounts — leading to marketing and sales plays that are easier to operationalize and execute.

Let’s break down each data point of the FIRE methodology.

3

FIT: FOCUSING ON THE ACCOUNTS THAT MATTERTo understand what “fit” is, picture a Venn diagram with TAM and ICP. Fit is where these two intersect.

The problem is that, if the data isn’t accurate for either, then you won’t see fit. To address this deficit, data models can look at closed-won accounts and use their commonalities to build a model. This helps to identify similar companies.

Another way to determine fit is to look at keywords. For instance, a sales team might have success with companies that sell “security software.”

Finally, hard filters can aid granular targeting. For example, if a company is unveiling a new product at a San Francisco conference that integrates with Salesforce, it can target companies in the area that use Salesforce and send them an email invite to the event.

Data can provide guidance in other ways. Sangram Vajre, Chief Evangelist and Co-Founder of Terminus, suggested the most salient aspect of EverString’s data is that it illustrates total market size. “You need to know if you want to grow at a certain rate and whether you even have enough of a market to go after,” he said. “That’s an important level from the board level down. It really back-ends to a lot of financials.”

4

INTENT: IDENTIFYING PROSPECTS IN THE BUYING JOURNEYIntent data comes in two forms: first-party and third-party. First-party intent data can be obtained through analyzing how people interact with content on a company’s website. It’s a lot easier to sell to someone that interacts with content on a company’s website, as visits to your website indicate interest and intent.

However, content marketing doesn’t work as well as it used to. Buyers are often wary of visiting your website because they don’t want to be pestered. Research from Demand Gen Report shows that 64% of respondents give more credence to peer reviews, user-generated content and third-party publications and analysts.

That raises a question: How do you find intent beyond visits to your website? How do you reach customers who research your competition instead of you?

Third-party intent data is obtained from analyzing prospective buyers’ web searches and how they interact on third-party websites. There are multiple intent data providers that can supplement a company’s first-party intent data with third-party intent data to gain a holistic view of customers’ buying intent.

“We look at intent using third-party websites,” said Kelvin Gee, Senior Director of Modern Marketing Business Transformation at Oracle. “Then we look at engagement. Ideally, if they’re scoring high on all three — fit, intent and engagement — we score them high.”

Another example can be found in EverString’s work with Bombora to monitor traffic within a broad network of publishers. Bombora indexes traffic based on content keywords and brands; if there’s a surge in intent in a related category, Bombora sends a notification.

5

RECENCY: TIMING IS EVERYTHING IN THE CUSTOMER JOURNEYFit and intent data is highly perishable. People are in market for a set time; and generally, when they’ve made a purchase, they’re no longer interested. That’s why it’s important to identify accounts that surge in intent within the past nine, 12 or 24 months, depending on the length of the average sales cycle.

Knowing when an account demonstrated recent intent is crucial and allows sales and marketing teams to prioritize their outreach. For instance, if a target account had engaged very recently, both teams should be able to make outreach to that account a high priority since it could mean reaching that customer before a competitor.

Recency can also help identify timely cross-sell and upsell opportunities. For example, if potential customers searched for solutions with certain features, it can be a good opportunity to engage a customer regarding other offerings that have those features. Another example can be found in renewal deadlines. Around time for renewal, both teams can see whether existing customers are researching alternatives. This insight can be crucial to identifying current and prospective customers in market.

6

ENGAGEMENT: UNDERSTANDING BEHAVIOR TO BETTER COMMUNICATE WITH PROSPECTSA potential customer signals interest in a company in a myriad of ways. That engagement might include a visit to the brand’s website, attending an industry event or downloading a white paper. An increase in account engagement can signal a higher propensity to buy. Research from Demand Gen Report shows that more than a third (38%) of buyers can engage with five pieces of content or more before engaging with a salesperson.

Similarly, companies can measure account engagement in a variety of ways. For example, EverString can get a view of account interactions, and share them with their sales team, or with Engagio, an ABM-focused marketing orchestration tool.

7

8

The FIRE methodology has a positive impact on the marketing and sales efforts of a variety of B2B practitioners. Snowflake, an enterprise data warehouse company, was looking to build a clearer picture of what accounts are most urgent for the team to follow-up with. According to Daniel Day, the company’s Director of Account-Based Marketing, this insight became the inroads for building relationships with the sales organization.

“[FIRE data] is wrapped into our account intelligence initiatives,” said Day. “It’s the basis for everything we do. It really helps build a clearer picture of what the best fit accounts are for Snowflake and which accounts need our urgent attention.”

After being brought on to help lead its new ABM initiatives, Day and his team did some rudimentary analysis of what verticals Snowflake performed well in based on current customers and closed/won rates.

“We were looking for where to stick that stake in the ground and select 100 accounts to get the ball rolling,” said Day. “As we wanted to grow and scale, we’ve been able to bring [FIRE] on piece by piece.”

Day noted that his team started by collecting engagement data. “It was a clear indicator that the company knew our brand and was interested,” he added. Then, the team would look at that engagement over the last three months to uncover the recency component.

“We then pivoted to use historical account data along with EverString to make a model account fit score,” said Day. “Then we used intent data to fill in any possible gaps.”

“You have to have engagement and model fit insights in place for better account relationships,” said Day. “If you have people engaging with your brand that aren’t tied to the account, it’s difficult for account executives to have relationships with those individuals. That’s why they are called account executives and not lead executives. If an account doesn’t exist in your database, it’s impossible to be owned.”

Since joining the company 18 months ago, Day said his team has garnered $450 million in investments and has seen the sales team grow by 10X. He also said that the company has been able to close deals with Fortune 10 companies thanks to the level of insights his team can provide to sales reps.

B2B BUSINESSES PUTTING FIRE TO WORK

9

“With large enterprise companies, we’re dealing with roughly 500 people engaging with us,” said Day. “Our data lets us know where to focus our attention within that account to build the right relationship.”

The company now takes an “ABM lite” approach to 7,000 accounts in their database, along with a subsegment of the top 10 accounts for sales directors, totaling to 500. Those 500 accounts are engaged on a one-to-one fashion.

“That’s where we bring those FIRE insights full circle,” said Day. “We’ve been really able to scale ABM in tech that wasn’t possible before.”

SalesLoft, a sales engagement software company, is another brand seeing success with the FIRE methodology. The company wanted to understand what set valuable accounts apart from others when it initially adopted an account-based strategy.

However, the company discovered that their traditional processes for understanding accounts did not align with this new account-focused strategy. With the help of EverString, SalesLoft was able to better understand its existing customer base and identify the signals that encompass the FIRE methodology. This included data points such as:

► Recent technology investments;

► Raised capital; and

► Buying accounting systems.

After developing its machine learning and data model to prioritize SalesLoft prospects, the company was able to organize account lists into four alphabetical tiers — with A- and B-tier accounts showing signals with the highest propensity to buy. After one month, SalesLoft saw that it had achieved a win rate above 80% when pursuing companies that fell in the A and B tiers. This positioned the company’s marketing and sales team to properly allocate time and resources towards accounts more likely to close.

In another example, VersionOne, a company that creates agile management tools for enterprises, ran into the challenge of identifying target accounts when the company switched to ABM in early 2016. Inbound leads often didn’t meet sales’ ICPs and weren’t the company’s target buyers. Sales reps generally selected target accounts based on the ones they recognized rather than using data, which handicapped success.

The target selection process was also manual, time-consuming and based on little more than revenue ranges and location. VersionOne execs recall that they spent too much time researching the TAM and competitor presence. Since this was done manually, it took months.

The company turned to EverString to replace that inefficient process with a more data-driven approach. By employing EverString’s solution, VersionOne was able to see an 88% boost in engagement metrics and engaged the best-fit accounts to grow its business.

Another example can be found in EverString’s internal usage of the FIRE methodology. With this new approach, roughly 87% of EverString’s pipeline is generated from companies that have a high fit and a high intent. While the company would have gotten most of those accounts anyway, some would have gone to the competition.

EverString also sees that when sales development passes leads to the sales team, those leads convert into opportunities. The company also boasts shortened sales cycles and higher average selling prices. Over the last two quarters, EverString’s reps have hit quota 100% of the time. Conversion percentages have also risen. Putting a personalized message in front of target customers has increased open rates, click rates, downloads and repeat clicks.

HOW DATA INTELLIGENCE IMPACTS ENGAGEMENTEnvision another Venn diagram. One circle is Intent data. The other is Fit data. There is an opening to an ABM strategy where the two overlap.

One key benefit of this approach is that marketing can prioritize its outreach. Some 80% of top-performing companies are likely to use some type of lead scoring, according to research from the Aberdeen Group. However, experts note that this level of insight can bring a new focus into account priorities, enabling companies to focus on engaging the accounts with the highest propensity to buy.

“There’s usually some other level of prioritization that needs to occur to figure out who to go after first and who to go after in different ways. So, think of it as putting prospects into A, B and C tiers,” said Kristina McMillan, VP of Research at TOPO. “Once you break them you can say, ‘A are the crème de la crème; we’re going to white glove, roll out the red carpet and give them the star treatment.’ That means we’re going to spend more on campaigns. That means we’re going to give them high-value offers; we’re going to invite them to dinners. They get a different level of attention.”

For example, Oracle uses an ABC structure and bases it on how the accounts score on fit, intent and engagement, according to Gee. As the company presented at the recent TOPO Summit, strategic “Tier-A” accounts received the most time and effort in marketing. This includes getting specific into account stakeholder’s personal interests to build out in-depth personas that can guide messaging.

In contrast, the B’s will get less, but still some meaningful campaigns and offers. As you go down the list, there will be more one-to-many types of approaches. “That means not just the money, but the way we spend the time,” McMillan said.

10

HOW FIRE FITS IN THE ABM STACKThe task of selling would be much easier if it was possible to identify prospects who were likely to buy and were at a stage of their buying journey where they were open to contact from sales.

Sales automation platforms have offered a means to narrow the potential targets and to provide actionable data. However, one element that has been missing is documentation of the overlap between fit and intent.

In EverString’s case, implementing FIRE was a seven-step process that included:

1. Starting with a CRM and marketing core with Salesforce and Marketo;

2. Filling that core with best-in-class data that’s dynamically updated, instead of static lists;

3. Adding a sales automation platform. In this case, it was SalesLoft, which became indispensable after it added ABM features;

4. Tracking account-level engagement with Engagio;

5. Prioritizing high-fit accounts with intent data. For this, EverString used intent data from Bombora; and

6. Using ABM ads. Terminus, in this example, helped the company target high-fit accounts with display ad messaging.

7. Complete visibility. Each sales rep prioritizes outreach using their FIRE dashboard.

This process enabled EverString to find and target only the most qualified accounts with precision.

11

CONCLUSIONThere are several elements that have fallen into place to allow for better B2B targeting. The amount of data continues to increase every year – some 2.5 quintillion bytes of data are produced every day. AI has also emerged as a helpful and effective means to sort and make sense of such data.

A mature marketing stack also means that sales and marketing departments have real-time visibility that allows them to act quickly. Taken together, this means that salespeople are better armed than they’ve ever been to reach the right prospects at the right moment.

Use cases and research shows that leveraging fit, intent, recency and engagement data positions B2B business to prioritize marketing and sales efforts on the accounts that are in market and have the highest propensity to buy. Ultimately, the FIRE methodology is a surefire way for businesses to drive revenue by having the right data and insights to have 1:1 conversations with these future customers.

12

Demand Gen Report is a targeted online publication that uncovers the strategies and solutions that help companies better align their sales and marketing organizations, and ultimately, drive growth. A key component of the publication’s editorial coverage focuses on the sales and marketing automation tools that enable companies to better measure and manage their multi-channel demand generation efforts.

EverString helps B2B sales, marketing, and operations teams build pipeline, prioritize prospects, and streamline operations. EverString’s marketing and sales intelligence software puts the power of artificial intelligence (AI), applied data science, and the most reliable data directly into the hands of marketing, sales and operations teams. With EverString, users are able to quickly expand and prioritize their pipeline, gain insight into relevant prospects, and directly access data with the highest accuracy and coverage. EverString is backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, IDG Ventures, and Lakestar. For more information, visit www.everstring.com.

650.425.3937 • contact@everstring.com

201.257.8528 • info@demandgenreport.com