"Forrester reports that between 60 percent and 73 percent of all data within an enterprise goes unused" inc.com
Data is the currency of modern marketing and sales.
But what should a rapid growth company's marketing and sales data stack include? That's an important question that's often never asked or proactively answered. Instead the data stack is often built by chance as new martech tools are added.
The result is that data stacks are rarely designed and constructed - instead they kind of just congeal.
And, unsurprisingly, the resulting pool of goo may not fully deliver on the promise of a modern, integrated data stack that compliments the sales and marketing tech stack.
The good news is that it just takes some awareness and planning to trim, adjust and supplement the typical data stack so that it's more optimized for results.
An optimized data stack includes the following components.
Anonymous First Party data: Visitor identification helps to understand what companies are active across your digital footprint, even if individuals don't submit forms, chat, or otherwise engage. Most tools in this category are based on IP address resolution. Some are embedded (like within marketing automation) and others are standalone tools (like Leadforensics.) This is account level only.
Known First Party data: This is the information typically captured in marketing automation which includes email opens and clicks, social media engagements, page visits, form fills, and conversational marketing engagements. It's powerful and valuable...but it's limited to what's happening on your digital properties. This buyer level data.
Second Party Intent data: Publishers are a common source of this intent data. Leaders in the technology space include G2 and TechTarget. This data often provides software and technology category specific information which is indicative of both top and bottom of the funnel activity. Depending on the provider this may be account or contact level data.
Third party Intent data: While first party data explains what's happening on your properties, and second party data reveals what's happening on a publishers' own digital properties, third party data covers the broader web. Depending on the method used, that may be a limited range of other sites (for instance a few thousand publishers in a data coop) or the entire web. Most third party purchase intent data is at the account level only, although IntentData.io provides Contact Level™ Intent Data. Common sources of account level 3rd party intent include Bombora, Demandbase, and 6Sense.
Enrichment data: Enrichment data helps to build a more complete profile. Email addresses, phone numbers, mailing addresses, job titles, etc. can be added to contact records to provide the details necessary to execute robust omni-channel campaigns. Sources include Clearbit and DiscoverOrg.
Firmographic data: Ideal customer profiles (ICPs) often stipulate certain company attributes as prerequisites or desirable prior to selecting companies for targeting under account-based marketing or outbound sales initiatives. Firmographics help to fill in gaps in these critical fields. Sources include Owler and dun&bradstreet.
Technographic data: In many cases the ICP will also look at details of the software technology stack. Technographics capture this and can be used to (dis)qualify accounts according to various criteria. In addition to IntentData.io's Contextual™ technographic data, other sources include HG Insights and Datanyze.
Sales Channel data: A rarely used but hugely powerful type of second party data, sales channel data allows vendors to work hand in glove across their channel ecosystem by sharing activity signals for action and/or enablement and collaboration in both directions. (Dig deeper into how to use intent data with your channel.)
Contact databases: This is the first brick in the sales data stack. Once we know that an account is active, or meets our profile, we need to identify all the members of the likely buying team in order to "surround the account" for ABM or target account sales. Common sources include zoominfo, LeadIQ, Seamless.ai and LinkedIn Sales Navigator.
Having the right data is an important step. But it's not enough. It may not even be the most challenging aspect.
Unifying the data is often the biggest barrier, and orchestrating it the most complex requirement.
Ingesting, deduping, enriching, and associating data from each source is a challenge which most CRM and marketing automation can't handle. Yet, it's critical because managing the data in disparate silos inherently misses the actionable insight which is found in the intersection of data. It also precludes rich omni-channel orchestration.
To optimize a comprehensive marketing data stack really requires a CDP. Custom objects in CRM or marketing automation can help to visualize some aspects and trigger some actions, but fall short of full optimization.
These are simple examples. The range of capabilities expands with the range of integrated martech.
The CDP helps to unlock the full range or orchestration opportunities across the entire martech and sales tech stack, full customer lifecycles, and account priorities and stages in buying journey.
Using data with indirect sales channel is a big potential use case for big thinking, rapid growth companies with the requisite tech stack.
There's no denying that a full marketing and sales data stack is more expensive than many companies initially budget for "intent data." That's OK. Starting with one or two sources is common. Building the data management infrastructure, including a CDP, takes time and requires resources as well.
The key is to build a roadmap for the full marketing data stack, recognizing that it will be a gradual process.
The marketing data stack in aggregate offers lots of opportunity for personalization, segmentation, messaging and various marketing and sales movements.
It also creates rich opportunities to go further. Enhanced lead-scoring and even propensity to buy modeling are logical extensions of the aggregation of fully integrated insights.
This remains the "Holy Grail" of revenue growth. Imagine the power of resource allocation and accuracy of forecasting that you'd experience if you had reasonable predictive models that told you which accounts actually would buy, when, and which movements had to be executed when and with which member of the buying team to ensure that result.
Thata' a truly optimized sales and marketing data stack!