With the growing pressure to scale pipeline and revenue, the best companies are at the cutting edge of using data to drive decision making. That makes an impeccable data strategy the lifeblood of your go-to-market strategy.
Looking through the sales and marketing lens in particular, our most recent meetup, Building a Data Strategy for Operational Efficiency, was dedicated to learning how operations leaders are using data to align their go-to-market organizations around the right data opportunities and how this has helped them drive operational efficiency to grow their businesses faster.
Defining Operational Efficiency
Simply put, operational efficiency means automating tasks - ultimately shifting people's ability to focus more time on high-value work rather than wasting time on cyclical (and unrewarding) low-value tasks. This also carries through to improved culture, which leads to stable businesses with happy customers.
Castelán breaks this process down into three steps: (1) automating grunt work so that people can focus on more challenging problems, (2) delivering data quickly and transparently without overwhelming the end user to keep the flow of information agile, and (3) matching the right resources to the right tasks. This requires being purposeful about prioritization of time and being intentional about rejecting certain tasks that aren't worth it. For every organization this will materialize differently based on business objectives, but a good measure of success for operational efficiency is revenue per headcount.
Using Data to Achieve Efficiencies
Data is a bridge between systems and systems. An effective data strategy will be responsible for distributing information so that teams can talk to each other and always be operating in real-time. For example, campaign data needs to be transferred from marketing to sales so that reps have the right context to follow up with leads. Without the right data foundation, this communication between marketing and sales breaks down.
Pitta uses data to achieve efficiencies in three ways:
People and Process: Removing tedious tasks and using a tool to set up automations and kick off program steps automatically. For example, Wordpress post, Marketo flows, or CRM triggers.
Go-to-Market: Prioritizing the right accounts so time is spent on the highest-value prospects. For example, planning sales territories by the distribution of high-value accounts and enriching data on top prospects or choosing conferences based on target account attendance.
Products: Putting the right content in front of the right person at the right time based on buying stage. For example, BrightTALK's Ada which recommends content and will ultimately answer questions based on the knowledge on BrightTALK.
Data doesn't come without its challenges, most commonly siloed databases prevent us from getting all the data sources in one view. That inherently creates a roadblock to looking across data and understanding what data you may have and what data is missing. Tools like DataFox act as a front end query tool, combining first and third party data to provide a comprehensive data view. Then, there's the issue of what type of data needs prioritization. Account-based approaches have been driving revenue and aligning sales and marketing but ultimately key data points like contact info will always remain important. Finding the right mix of data tools and a balance to data distribution are two key steps in providing the right context at the right times without data overload.
"Last week we had 45,000 calls that made 6 million changes to our database, so everything is happening in real-time and its important to understand how we can use this data to be more relevant in our outreach initatives." - David Pitta, Chief Marketing Officer at BrightTALK
Data is an excellent resource, but can also be really crippling if not deployed correctly. To get it right, it's about relevance and your ability to be more informed and confident in your decision making process. Am I eliminating grunt work? Is what I'm doing making your life easier? While it's important to get feedback and measure internal success, it's equally important to improve the customer experience with timely, relevant content. Are you progressing people through the customer journey? Do you understand customer velocity and conversion? Your data strategy should account for operational efficiencies for internal teams as well as customers, ultimately driving revenue efficiency.
With the speed at which data becomes stale, your data strategy is an ongoing project. It's therefore important to build a tactical plan and implement a quality foundation. Castelán highlights some of the key steps so continuous maintenance and updates are hassle free:
Protect Your Points of Entry: Identify how data is entering your systems and make sure you stop any bleeding first and foremost.
Clean Existing Data: Get a comprehensive look at what data you have and the quality of that data by updating and deduping records.
Data Governance: Establish rules of governance, deciding which employees and/or vendors have authority over your data.
Add New Data Sets: Understand current data scope and evaluate data vendors, making sure to verify for coverage and accuracy.
Integrate Across Systems: Establish your data strategy as the single source of truth, making sure data integrates across systems and teams.
With a quality data strategy at the heart of your organization, not only can you drive operational efficiency but also informed business decision making to achieve revenue efficiency.