Tl;dr - Marketing data has moved well past contact management. Yet that's where many industrial companies stop - with valuable corporate data decaying in various silos around the company. Once collected into a single source of truth, cleaned and maintained, sales and marketing data can power sophisticated and effective marketing and sales plays. Here's how to build a full marketing data stack, how to maintain it, and how to use it.
Sales & Marketing Data is an Enterprise Asset
Many industrial companies underestimate the value of the sales & marketing data.
On the one hand, they'd be appropriately horrified if an employee left with the list and carried their customer list and related data to a competitor.
On the other hand, they often fail to use the data for even simple applications like email marketing, they neglect routing database hygiene and maintenance, and miss opportunities to leverage data to shorten sell cycles, win more deals and uncover more projects.
In many cases, there is no CRM or marketing database that provides a "single source of truth." Often sales reps have contacts in their Outlook .ost files and LinkedIn. Customer service may have some in an ERP where parts orders are handled an in their own .ost files, and machine project contacts are often in spreadsheets and project files.
In the buyer-driven world of integrated sales and marketing that's a big miss. Often middle market manufacturers have 2-10K "known" contacts, but 2-3X that number if they consolidate data sources. Much of the contact data misses important fields including associated company, phone numbers, job titles, and email addresses.
But even if all those are pulled together, cleaned, and complete....it's still just a list. An accurate list is a prerequisite of course, but just a first step. Sales and marketing data includes far more than just the lead, contact, and account lists.
Just as boards and executive teams are now including cybersecurity as a mission-critical component of company strategy, marketing data's growing strategic enterprise value elevates its importance as well.
What is a Marketing Data Stack?
A marketing data stack is the full complement of marketing data (and sales data too typically) that a company needs to optimize its integrated sales and marketing effort on digital platforms. It includes the data the company collects regarding leads, contacts, companies, and deals along with the data on related engagements and behaviors associated with all of those. It also includes outside data such as a sales leads database and intent data, and the tools required for maintaining and leveraging the data.
Interestingly many companies have many of these, but in silos, and often without an understanding of what their disorganized data costs them, or the best practices for maintenance of the revenue growth data stack.
What Are the Elements of a Proper Marketing Data Stack?
A full marketing data stack is built on more than one marketing data source. It typically includes the following elements:
Known First-Party Data - These are the contacts, leads, accounts, opportunities and all related activities that you accumulate and track. In other words, it's your:
- CRM and marketing database, with data that's collected through trade shows and other in-person activities
- industrial inbound marketing data collected by your marketing automation
- sales activities collected by CRM and sales force automation (including emails sent, phone calls, meetings, email opens and clicks, page visits, document engagement, form completions and more)
Simply pulling this together into a single source of truth, establishing some basic best practices and maintenance processes, and teaching the team how to read and interpret the data is a huge opportunity for many companies.
Anonymous First-Party Data - Typically industrial websites only convert 2-3% of traffic into known contacts. And those visitors often use multiple devices - so if they convert from their desktop, but take further action from a mobile device it may not be immediately associated with the same person. That means there's lots of activity that occurs on a typical website with anonymous users. There are tools available to deanonymize some of it (at least resolve the IP address to understand what company someone's from) that can be helpful. If you have a new known contact lead from a target account and see a corresponding surge of activity from various users from the same account, that indicates a degree of engagement that's significant for your sales team.
Second-Party Data - Many manufacturers have industry data sources. Marketing through trade journals, promotion to trade show registrants, etc. are examples. 2nd party data is simply another company's 1st party data which they have permission to share and monetize. If you contract for a campaign via an electronic trade journal and receive a list of contact leads, that's 2nd party data. 2nd party data has inherent value as in the above example, but it also can offer a significant signal boost to first-party data.
Third-Party Intent Data - Intent data is a complex beast (learn more through this video and here.) There are lots of versions and variations, and most of the data providers focus exclusively on tech companies. So it's important to understand the details of intent data you're considering. The premise of intent data is to understand who might be in market for your products or services at any given time so you know where to focus marketing and sales resources.
Sales Lead Databases - Most companies subscribe to one or more sales leads databases. Examples include LinkedIn Sales Navigator, LeadIQ, Seamless.ai, ZoomInfo, Uplead, etc. The databases support searches according to company parameters (size, geography, industry) and key terms, and contacts by company, job title and other factors. These databases are used to identify prospecting target companies and relevant contacts, and to identify additional likely members of the buying team for complex sales. Most have CRM & marketing automation integrations to make importing contacts and accounts simple and fast. Data accuracy varies significantly.
Enrichment Data - This data helps to complete a contact and company profile. For instance, if a contact converts on your site using their work email address, most marketing automation will be able to create the associated company (from the domain) and may fill in some standard data (address, annual revenue, ownership type, etc.) But you may need more in order to score the lead. You'll certainly need more contacts in order to sell the account. Enrichment data is automatically added to your CRM/marketing automation single source of truth database according to rules that you establish within your automation tools. This saves reps time spent manually researching and updating data.
Firmographic Data - Firmographic data is about the company. It can include factors like locations, subsidiaries, annual revenue, number of employees, industry, etc. Much of this is included in sales leads databases and enrichment data, but certain industries have specialized data sources and criteria that are only available through other sources.
Sales Channel Data - Partners and channel ecosystems often involve other data - through a PRM or some data sharing (manual or automated.)
Additionally, there are related components that should be overlayed. These include the ICP (ideal customer profile), sales target accounts lists and/or account-based marketing lists, order history, contacts involved who participated in won or loss deals while working at other companies, competitive penetration and more.
Guidelines for Fully Leveraging the Sales & Marketing Data Stack
The first step is to start to think in terms of derivatives of 1st party data. For instance, the number of deals that you've opened with a contact and the % that have closed is an important factor in lead scoring. If your team is using CRM well, the background marketing data (deal histories) is there, and extracting insights from it is straightforward.
Further, it's helpful to think of the same single source of truth data set from multiple use case perspectives. These include:
Marketing - There are a huge number of potential uses ranging from the obvious, like email marketing, to more complex like:
- using marketing data to trigger workflows
- (re)assign salespeople
- deliver customized and personalized experiences to each visitor
- lead scoring
- creating data-driven marketing content
- providing alerts to the sales team of activity among target accounts, companies with open opportunities, and leads that suddenly seem more active
- A/B testing of campaigns and landing pages
- dynamic form content and comparison of chatbot vs. form conversions
These are obviously just examples.
Prospecting - Effective and efficient prospecting requires ready access to accurate data that's properly integrated with the CRM, sales force automation, and sales acceleration systems. That helps outbound sales work efficiently. Sales sequence results are important marketing data that are used to update sequences to boost effectiveness. Intent data can focus the prospecting efforts on active accounts.
Complex sales - With >10 buyers on the buying team, understanding who all the players are, what priorities and concerns they have, and even their degree of engagement is important. Data tools will help to identify the contacts and engage with them to observe how involved they are (emails and documents they open), the concerns they have (to guide sales enablement and coaching), and their priorities (what pages/content they spend time on your site reviewing.)
Opportunity qualification - There is a range of observed (number of people from a company on your site recently), researched (firmographic, seniority of contact), and collected (compelling reason to buy) data that is important to effective deal qualification. Some of this can be automatically added through enrichment automation. Other will be collected during conversations and can be automatically routed from discussion playbooks to the appropriate fields. All of it can be used to gate or advance deal qualification to improve forecast accuracy.
Sales enablement - Marketing data can be used to suggest sales tactics, questions, follow-up sequences, and even appropriate content to help salespeople improve effectiveness. These suggestions are based on observations of various data fields that are integrated to understand stage in buying journey, problem people are trying to solve, etc.
Prioritization - What leads? What target accounts? What opportunities should be prioritized for top reps? Immediate response? Management involvement? All those decisions hinge on marketing data, and can often be automated with scoring when the data is present and accurate.
Sales coaching/management - Sales and marketing data can be collected to inform the focus of sales coaching, and even suggest specific techniques. Metrics from conversion rates, sales cycles, and win rates are obvious ones. Tools like conversational AI which mines recorded sales conversations for insights are built on data and are hugely powerful. When sales managers know who to coach, when, and on what topics, data becomes a driver of efficiency and results.
You'll have many other potential use cases and applications for data. The point is that it's not simply contact management. That requires a big mindset shift because that's where most of us started with CRM.
Technology Required to Optimize the Marketing Data Stack
Let's be clear. This doesn't happen automatically. It doesn't happen easily. Data is inherently dirty. It deteriorates quickly. Salespeople move fast in prospecting and often add volumes of irrelevant data. Integrations are great when they simplify the flow of information, but often they end up cluttering the database with cruft.
Therefore building and maintaining a marketing data stack that supports revenue growth requires processes, mindset and technology.
- best practice expectations for sales teams - what data they add, what data they update, etc.
- data maintenance policies - plans and processes for standard data hygiene functions (dedupes, capitalization, etc.) as well as common scenarios (parent/child accounts, contact moves to another company, etc.)
- person/people/team responsible for data quality - to oversee and implement policy and process
- KPIs - intervals and expectations for data quality criteria with reporting against those
- tools with the capability of automating and improving data quality - AI is helping as are improved data lake tools. Often these can be used with minimal technical skill (depending on your CRM & marketing automation this may be built in or available as an add-on module)
- training for the sales team on how to use data and continuous coaching
When you see marketing data as a business asset and understand the mechanics of how it can improve marketing and sales performance, then you'll be ready to invest properly in the asset.
That requires understanding the full range of data to consider, the operational implications, and the talent and tools that your team will need to make it all work.