Background on SharedBook
Zak: Can you start by giving some background about SharedBook?
Jeff: SharedBook is a small business that helps management consultants, professional trainers, and executive coaches meet the full spectrum of print, digital, and mobile demand for their training content while reducing the operational costs of delivering that content.
Zak: How did you get involved with SharedBook?
Jeff: I’ve worked in the Ed Tech space since 2000 and have always loved it. Most of my experience has been with stage two companies (organizations past the startup phase and focused on scaling their model) or startups incubated within other businesses. SharedBook is an example of the latter. It provided an opportunity to form a small, scrappy team and build a business nearly from scratch while benefiting from the maturity and experience of a parent company and seasoned board of directors.
Zak: What are your day to day responsibilities at SharedBook?
Jeff: As a small, early-stage business, we run a lean but closely-aligned sales and marketing team. My goal each day is to help our sales team increase engagement with target accounts and develop that engagement into revenue through a combination of inbound and account-based marketing practices. To make that happen at scale, we try to get as much leverage as we can from data and automation.
Data-Driven Sales & Marketing
Zak: How did you get interested in data-driven marketing?
Jeff: I spent my first five years in inside sales and field-based sales. Because I had done a lot of experimental research design in grad school, I had an analytic and data-centric approach to managing my territories. These aptitudes eventually got me drafted into sales operations and business intelligence roles.
In 2009, while working on my MBA, I discovered “Marketing Engineering” which is the application of advanced statistical methods to marketing problems. ” I was immediately hooked and knew that the marketing function offered an enormous opportunity to create value through data-driven insights and process optimization.
These days, I’m really excited by how business intelligence, sales operations, and marketing operations are coalescing into revenue operations. I think that alignment framework coupled with the proliferation of analytic tools is a powerful combination.
Zak: Could you talk about some of the sales and marketing technologies you have set up at SharedBook?
Jeff: For marketing automation, we initially considered Eloqua, Marketo, and HubSpot. By design, HubSpot has tailored itself to SMBs like SharedBook. So over time it’s become the cornerstone of our marketing and sales stack. We love the platform, the people, and the community that has developed around it.
Zak: How are you looking to improve sales and marketing by using data? What are you doing to make SharedBook’s team more efficient?
Jeff: As a small business, we don’t have the luxury of headcount that other businesses may have. So, as a substitute, we need to identify the critical paths in our marketing and sales processes, and leverage data and automation as much as possible to support and augment the efforts of our team.
Automating the flow of data is the foundation of these efforts. Specifically, we need to combine signals from different source systems into timely indicators of engagement with target accounts. As a small business, you want to avoid wasting precious time digging deep into APIs. This is a big reason for why Bedrock Data Fusion is so appealing: it’s a turnkey, easy solution for automating your data pipeline and giving data meaningful structure so you can apply the transformations you want without weeks or months of headache.
Data Transformation at SharedBook
Zak: Could you say more about why transforming data is so important at SharedBook?
Jeff: Like many other companies, we are trying to figure out how best to combine Inbound and ABM. One day, about six months into our first ABM implementation efforts, my COO asked me “Is ABM working?” While I could point to closed accounts and lead volumes, I knew what he really wanted to understand was whether we were successfully operationalizing ABM in a way that led to predictable results. What I needed to provide was a way for him, and our sales team, to see how engagement with our target accounts was rising or falling in as close to real time as possible.
While there are a number of great platforms to orchestrate and measure ABM efforts, the cost of adding more and more platforms quickly becomes prohibitive for a small business.
Additionally, while some CRMs offer machine-learning based company scoring, that can be tricky if you’re in an early stage enterprise. In general, a small business usually doesn’t have enough data to train their data model with reliable machine learning or A.I.
I knew we had the data to answer the question of whether our ABM efforts were working, and I knew we had the analytic savvy to process it. But we were missing a data pipeline. As we explored Fusion, we found we could quickly and easily bring data from our various cloud applications into a single data warehouse and transform it in ways that allowed to us answer our COO’s question.
Zak: How are you using your Fusion Warehouse?
Jeff: There are two ways. First, because the Fusion Warehouse applies a standardized schema to data coming from different source systems, my team and I can use Power BI to perform ad hoc analysis on near real-time data without having to implement a lot of ETL ourselves.
Second, when we do want to apply additional transformations (as with the company scores), we can further process the Fusion Warehouse data in Amazon RDS using MySQL Workbench and surface insights from those transformations using Power BI.
Our company scoring model is an example of the second use. We start with three simple assumptions: 1) The more people who engage at a target account, the better; 2) the higher the engagement per person, the better; 3) the more recent the engagement, the better.
With these assumptions in mind, we weight and score the engagement behaviors of individuals at our target accounts, discount those scores for how much time has elapsed (awarding more recent engagements more value), and then aggregate those scores at the company level. Finally, we index the scores to put them on a consistent, intuitive scale of 0 - 100. This gives us a standardized, time-sensitive, and event-weighted measure of how engagement is building at each target account. As marketing is seeding interest at target accounts with campaigns, sales is able to see which accounts are most responsive and engage when that interest has reached a threshold they consider to be meaningful. And, our COO can see how our actions translate into movement at target accounts.
All of this hinges on being able to rein in the maelstrom of engagement signals our sales people get from various cloud applications into a timely, coherent “pulse” on the account. To do that, we must have reliable data we can trust to model, run regressions, and join tables across a lot of different data sources. And that’s where we see a lot of potential with Fusion — helping us get data out of our systems, giving it structure, cleaning formats, and then into a cloud data warehouse that anyone at SharedBook can perform this kind of analysis.
Zak: Can you say more about why near real-time data is so important?
Jeff: Because the fused data warehouse offers a virtually live data feed, I can stir the data up however I want. That level of freshness allows me to put the integrated data through the transformational model I was alluding to, and create rules or operations that increase sales and marketing’s likelihood to close. In this regard, Fusion works really well for a small business.
BI Tools & SaaS Applications
Zak: You mentioned you use MySQL Workbench for analysis. Where do you see business intelligence and the analytics space heading over the next few years?
Jeff: The proliferation of cloud applications and greater desire to use that data is undoubtedly a big reason for growth in the BI and analytics industry. Still, what each company needs to do with the data to derive value doesn’t always line up with the resources at their disposal. Business intelligence providers who can “democratize” BI by providing turnkey solutions will create a lot of value for SMBs.
I think Fusion is a great example. If a small business adopts Fusion today, they can internally bridge the data that Fusion exposes with their unique vision of how they want to crunch the data in order to improve the effectiveness of their marketing and sales efforts.
Zak: How does Fusion assist you with business intelligence?
Jeff: Fusion saves a lot of that ETL [Extract, Transform, Load] work. Any database engineer who’s had to do that work before is bound to see a ton of value. They know how complicated API integrations can get. Whereas with Fusion, we can just authenticate to our HubSpot portal and within a minute or two have a common schema and standardized data.
All this is really key for account-based reporting. If you use Salesforce, the model is based on leads rather than companies, so any account-based reporting becomes challenging. Your data’s structure falls apart. Fusion solves that by allowing you to roll up data by contacts and accounts.
Zak: How does using Fusion with business intelligence help to make SharedBook’s sales and marketing more efficient?
Jeff: Fusion helps us increase the value of our data by taking it from disparate source systems, standardizing it, and enabling insights and analysis via popular visualization tools.
HubSpot, for example, is a MAP/CRM at heart. It’s an incredible software for inbound or account-based marketing, but it doesn’t specialize in processing data or reporting. Fusion does. So with Fusion I can still have access to valuable HubSpot data and a sophisticated analytics tool like Power BI.
Zak: What are you hoping for your sales teams to achieve in a native BI tool?
Jeff: I’d like my sales team to see a live Power BI dashboard with data from Fusion that clearly shows how target account are engaging with our efforts. If, for example, they’re chasing 50 accounts, making sense of hundreds of signals per account spread out across time can be difficult. But if Fusion is on a 30 minute refresh cycle, we can distill all of that activity for them into meaningful, timely indicators they can act on. ♦
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