Tl;dr - Marketing and sales is hard, and getting harder as technology expands and buyers become more sophisticated. A sales leads database - a paperback directory in the old days and a subscription database today - is one tool. Successful companies think of data as a larger topic including the quality and maintenance, integration of their own data with outside sources, and the marketing and sales playbooks to activate it.
The "Best" Sales Leads Database is Actually a Trick Question
There is no such thing. Marketing and sales data is a really complex topic that's often oversimplified by folks with a checklist mentality and siloed function KPIs.
Let's take a more realistic and holistic look at the question of sales lead databases, and data to support machinery marketing and capital equipment sales.
Who Could and Should Buy
If your company is part of the 99.97% whose products don't sell themselves, then you need active sales and marketing.
I write frequently about how to optimize both functions, and importantly how to integrate them. I also write about the importance of technology to provide a framework to increase effectiveness and efficiency.
Behind topics lies marketing and sales data. And data itself is a big topic. It can mean:
- lists of possible prospects in a sales leads database
- contact details in a CRM or in your email marketing database
- firmographic and technographic data that can be used to narrow your search for ideal prospects according to your ICP
- historical customer order and usage data (including the negative - e.g. lost)
- enrichment data that can be appended to your marketing database to provide a fuller understanding of leads
- data used for lead and deal scoring
- intent data
- marketing performance data
- sales activity data
In other words, "data" should be refined with specific and agreed terminology so that everyone in the company uses words that lead to a common understanding.
Nevertheless, there are many data types and sources, and a finite budget. So what kind of data is most important for capital equipment sales?
That's driven by the marketing and sales playbooks.
Data Spans the Customer Lifecycle
We need to think of data as being a thread woven throughout the buying journey and sales process - like technology.
Data should be used differently along the way. For example:
- unknown - identify target accounts that match our ideal customer profile (ICP) and key contacts for prospecting, target account sales plays and account-based marketing
- suspects - understand what actions they're taking that indicate they might be in market for our solutions, make sales assignments, and uncover affiliations that could be used for referrals or resonant case studies
- prospects - lead scoring based on contact and company attributes and activity (website visit/revisit/specific pages/chats/email opens, number of contacts from the company involved, etc.), time the hand-off between marketing and sales or between sales tiers
- deals/opportunities - aggregate inferred information that's consonant or dissonant with direct observations, build relationship maps for team selling, identify buying team members, gauge momentum
- customers - support ongoing sales like replacement parts, training, and service agreements, track key contacts retiring or moving to other companies (now higher likelihood suspects and prospects), measure usage and satisfaction for up & cross sell and to reduce losses
So the traditional sales leads database is an important tool for identifying unknown prospects, occasionally for rounding out the buying team for active deals, and sometimes for spotting influence roles in current customers.
But that's a small subset of the data required throughout the customer lifecycle.
Most companies don't think of it this way. They may have pieces of it in silos; they often despair of having what they need at certain points in the buying journey (because the tech stack, data quality, sales process and training don't support it); and the lack of integration between marketing, sales and customer success masks the cost of disparate data.
This is an important management mindset change. Until management owns the importance of high-quality data, including the tech stack to collect, manage, interpret and activitate it, companies will be selling with figurative hands tied behind their backs.
If you have marketing automation (website cookies, email tracking, etc.) you're already harvesting first-party intent data. If your team is trained to use it, that provides observations of activity across your digital properties that can be analyzed to draw inferences about what companies are active in the market, who's on the buying team, stage in buying journey, etc.
But that's only across your digital footprint - say one of two billion websites across the web.
Third-party intent data seeks to reveal what companies are doing everywhere else. It's a powerful concept.
But.....the devil is indeed in the details.
There are different collection methods that are technical and which you as a buyer need to understand because they impact quality, volume, and value of the data.
- Bidstream data relies on high-volume, low-accuracy, observations of the online ad market. While it covers the entire public web and purports to track activity down to a keyword level, it only provides information at the account level, and the data itself is built on tenuous assumptions and has inherent data privacy conflicts. If someone from a company simply happens to have been shown a certain ad, they interpret that as company buying interest and sell it even though users haven't agreed to have their data monetized.
- The publishing coop model is the aggregate set of observations of a few thousand publishers who share anonymous visitor activity with each other. In other words, of two billion websites, they monitor 4,000. It is at the account level only, relies on IP address resolution which is problematic anytime and particularly in a work-from-home world, and is built around "topic" taxonomies that deny the user any granular insight into the specific activity.
- Observations of public action are how IntentData.io collects data. Covering all the structured and unstructured data across the entire public internet, they observe demonstrable actions taken by people publically. This means that the data is based on actions (not just having content or an ad flashed in front of them) performed publically (for the whole world to see - addressing privacy concerns) and it is reported at the contact level with details on the action taken. The contact details make activating the data easier (job titles, for instance provide rich understanding of stage in buying journey and buying team) and the details on the action (key term, competitor, etc.) provide insights into the problem they may try to solve, etc.
This third party intent data has applications across the entire customer lifecycle, but it's not a silver bullet. Intent data is often thought of as a replacement for a sales leads database - a tool that will put a target on a company that's about to buy. That's unrealistic.
A more appropriate understanding is to see the data as a set of signals. Each individual signal (a specific person taking a specific action that meets criteria you identify by key term and competitor) may be of interest, but can also be an accurate signal that's not indicative of intent. In aggregate (the progression of an individual's activity and the sum with other individuals' activity in the same company) can provide context that, once analyzed by a person or some technology, can help to refine the picture of a company's buying initiatives.
Intent data is also abused. In many companies with poorly integrated marketing and sales it's combined with tools like syndicated content to help marketing hit their MQL (marketing qualified lead) numbers with "leads" that have little value to sales.
Intent data is powerful, and it can be used in many nuanced ways throughout prospecting, marketing, sales and customer success. But it requires creativity and a resource commitment. It's not a layup.
There are endless varieties of specialty data. You may not need any, but some may help.
- perhaps you want to know what companies import significant quantities of a specific ingredient commodity that your machinery processes uniquely well
- if you sell products that integrate particularly well, or poorly, with specific ERP systems, technographics might help identify who's running them
- maybe political donations as a proxy for policy engagement can help you identify the strongest prospects in your space
- trade associations in parallel spaces might help to forge partnerships, as those in target market industries could offer opportunities for thought leadership. Quickly identifying the groups and key contacts would help
- often "event" data is used to identify common opportunity triggers. A common example is zoning activity related to new factory construction
- tradeshow and event registrants/attendees
These are merely examples. There are data sources for nearly any data set you might need.
Data Quality and Maintenance
An axiom of marketing and sales data is that it's far easier to accumulate than it is to organize. Data quality is a ubiquitous problem. Companies that recognize it may quantify the deleterious impact on operations. But many companies simply ignore it.
This is never solved, but rather is continuously managed with parallel approaches.
First, be clear what data you're collecting, and for what specific purpose. If you sell to companies in the pharmaceutical industry exclusively, then don't add companies or contacts beyond that.
Second, be organized and process-focused in collecting data. If you provide salespeople access to sales leads databases, establish guidelines so that imported companies and contacts match your ICP, and that company/contact associations are created. Many CRM systems simplify creating new contacts by easily, or even automatically adding emails directly from the mail client. That can be helpful, but training should help ensure it's used for business contacts and not the dentist appointment reminder email.
Third, use tools to run periodic data maintenance tasks such as deduping, updating ownership, fixing telephone formats and name capitalization, etc. An appropriate technology stack includes this capability (for instance HubSpot's OpsHub.)
It's also important to track and manage data subscription costs and renewals. It's easy to add data sources for specific projects and campaigns, and then forget to refresh or cancel when complete. This is a good example of marketing operations and sales operations responsibility.
The Full Data Stack
A sales leads database is normally the first, and often the last place companies seek external data. It's an important piece, but just one in a complete data stack.
The data stack should be built like the tech stack - designed and constructed in a logical way.
A proper marketing and sales data stack for an industrial manufacturer will include:
- first party data - CRM and marketing automation data built on a common database. Used for prospecting, marketing, email, customer service, etc. This includes not only inputted properties (e.g. first name) but also observed properties (pages they've visited, emails they've opened, links they've clicked, etc.)
- second party data - data acquired normally from industry trade publishers, often through digital marketing and lead gen products that help you reach their lists and then convert leads
- third party intent data - to identify companies and contacts that are taking action that appears to indicate they're working on a project for which your products/services may be a solution
- third party enrichment data - appending the likely contacts on a buying team, verifying email addresses, adding firmographic information to use in refining the lead/project score
- competitive data - a continuous curated stream of competitive intelligence helps to guide marketing and sales approaches
- sales leads database - source of searchable prospects. Common sources include LinkedIn Sales Navigator, ZoomInfo, UpLead, LeadIQ, and countless others. Building preconfigured lists according to your ICP and buyer personas will help your sales team prospect efficiently and improve data hygiene. This will also support team selling and relationship mapping in complex capital equipment sales
- technology to automatically enrich/append data - it's a waste of time (and an opportunity for data entry errors) for your salespeople to manually add the likely contacts who, based on job title, will be members of the buying team as they add a deal to the pipeline. This can be automated along with appending other enrichment data
- technology to interpret the data and trigger actions - with all these data sources it's impossible for marketing or sales teams to track every data point. But there's almost always one bit of data, understood in the context of others, that represents an inflection point. That could be a higher-level job title visiting the site after many of their staffers, opening a quote email from months ago, or engaging with a competitor. So technology needs to monitor data for specific actions, and constellations of data points, which reasonably hint at something changing. And then it needs to trigger appropriate marketing (often automatic) actions like paid ads and emails offering appropriate sales enablement content, and sales (prompted or automatic) actions like triggering an outbound sales cadence or creating a phone call task
- technology to maintain the data - and as noted above, the task of maintaining data quality and hygiene is complex and can't be left to chance
Industrial Sales and Manufacturing Marketing Data
Strong revenue growth teams market, have inbound sales and active outbound sales.
A sales leads database is a logical first step to support outbound sales. It's today's version of the manufacturers' guides that you may have used early in your career.
And there's no reason that you have to move beyond simply subscribing your team to LinkedIn Sales Navigator or other comparable tools, and integrating them with your database.
But buyer expectations are moving quickly. When you take a very limited approach to data, you should recognize the existing data quality challenges which will be aggravated, and understand the progression of data maturity that your company is electing not to travel to really refine marketing and sales, improve results and deliver great experiences for buyers and for your team.