Building an effective, end-to-end lead generation strategy is far from simple. But if there’s anything I’ve learned in more than a decade leading B2B sales teams, it’s that lead generation strategies live and die by the quality of the data they are built upon.
So many companies struggle to reach their potential for the simple reason that their databases are not in order. They’re running before they can walk. It doesn’t matter if you’re using the most advanced marketing technology solutions, whether you’re pursuing account-based marketing or focusing on leads – clean, accurate and complete data must be the first step to support your lead generation strategy.
To illustrate this point, I’d like to share with you how we at Leadspace optimized our own lead generation some time ago by building a strategy from the bottom up – starting with our databases.
Reassessing our lead generation strategy, from the bottom up
Our first step was to take a look at current account/territory assignment.
We realized that while we once had a coherent territory model, it had been iterated on and tacked onto so many times that it was more based on exception than rule. We saw instances of multiple reps owning the same account or subsidiaries of the same parent account, creating confusion and frustration with lead routing. For a significant portion of our accounts, revenue information was either incorrect or incomplete, leading to occasions in which enterprise accounts and leads were sent to Mid-Market reps, and vice versa.
We also weren’t entirely clear which of all the accounts in our database were the very best to strategically pursue – for both sales and marketing.
Luckily for us, we had all of the solutions we needed in-house. At the time, our “Leadspace for Leadspace” project, as we called it, was an exciting opportunity to test out our solutions on our own lead generation. In the process, we learned some valuable lessons about the centrality of data quality – lessons which, in turn, were subsequently applied to our data management solutions.
Every effective lead generation strategy starts with one thing: data
Our first step was to make sure we had all of our best accounts (our A’s and B’s) assigned to our sales team and outbound SDR team for prospecting.
So we first cleaned up our entire database with our own data management solution, and enriched that data with 80+ fields of data. Our enrichment didn’t make do with relatively superficial insights like job title or location – insights which often don’t translate into actionable intel. Instead, we obtained highly granular, human insights like job function, familiar technologies, company revenue, company headcount, site-level location, vertical, and various contacts that fit our ideal customer profile within each account.
Rather than relying upon one data source, we used the Leadspace Virtual Data Management Platform to compile and verify results from over a dozen data providers, as well as unstructured data, for the most relevant and accurate results. This ensured that outbound sales and marketing efforts would be directed at the best contacts, within accounts that were the most likely to convert. We also entered a sizable number of new contacts for pertinent accounts into our sales and marketing databases (in our case, Salesforce and Marketo).
Moving to account-based marketing
Now that our data was clean, accurate and up to date, we could turn things up a notch. Our next step was to further refine and structure our databases for account-based marketing.
Specifically, we conducted a thorough lead-to-account mapping/matching and site level matching process. This meant that each child account or subsidiary was associated with the correct owner, and that we had a single view for each account with each lead and contact rolling up to that account in Salesforce.
The results: no more calling into other reps accounts; highly accurate lead routing; and the ability to be more strategic with our application of ABM, particularly at Enterprise level.
This whole process also left our marketing team very happy — and greatly improved sales-marketing alignment in general — by solving the age old question of how to reconcile marketing’s traditional focus on leads with sales’ focus on accounts.
Of course, it’s crucial that this process can be regularly refreshed. Without real time or on-demand data enrichment processes in place, your database will inevitably degenerate again, no matter how well you cleaned it up in the first place. Keeping your data in order is a constant battle, one which can’t be solved with even the most accurate one-off enrichment. But it’s a battle which must be won to build a truly successful lead generation strategy.
Predictive analytics: One of the most powerful lead gen tools out there
As mentioned above, without high-quality data, implementing advanced sales/marketing technologies won’t deliver optimum results.
However, once your databases are in order, in my experience there are few more effective lead generation tools than predictive analytics.
Using our now clean, refined, highly actionable data, we then went out and built a predictive model which helped us score our existing database. The predictive model also identified thousands of other accounts and contacts in our white space that we could market to. We took the A/B accounts (those with greater than average lift) and assigned each to a sales rep. Less qualified accounts (C’s and D’s) were prioritized for nurturing in our marketing database.
Result: we now have an accurate view of all accounts that are likely to convert and are actively working through outbound efforts. This also meant we could be more equitable in territory/account assignment. In our case for example, each mid-market rep received roughly 100 A accounts, and 50 B accounts. These target accounts will be refreshed and updated quarterly.
The importance of subjective input – and avoiding “black boxes”
One of the most crucial questions to ask any predictive analytics vendor is whether they offer transparency, or operate off of a black box model. “Black box” refers to a predictive model which delivers you a set of results (ideal customer profile, lead/account scoring, etc.) but doesn’t let you see how they came to those conclusions. If you can’t “lift the hood” to look at the data and traits which informed the predictive model, you’re essentially working blind.
This means that even if the predictive model delivers results in the short-term, you won’t be able to learn anything from those results to apply in the future. A black-box solution, no matter how powerful in the immediate-term, isn’t of much strategic value.
But the biggest problem with this is that you won’t be able to tell if the model has mistranslated the data, or otherwise be able to correct or alter the model if necessary.
For us, this would have been a major problem, as we were looking to move into new verticals. A black box model would end up automatically giving low scores to all accounts and leads in those new verticals, since we hadn’t traditionally sold to them.
Simply building a model off of historical data will deliver a pretty blunt instrument, and won’t empower you to reach beyond your existing markets.
Leadspace’s predictive model is entirely transparent, so we didn’t have that problem. For example, part of our lead generation strategy at the time was to venture out of our core vertical and focus on two new verticals. Since we hadn’t been as effective at selling into those two new verticals in the past – purely because we hadn’t focused on them – our model would naturally score them low. We addressed this in the same way we do with our customers – by manually adjusting our model so the result included accounts within these verticals.
Similarly, as we’d traditionally had a lot of success selling into SMB accounts (again – purely because that had been our major focus) the predictive model would have given SMB’s an unnaturally high score. Given our desire to move up-market we manually altered this attribute to ensure that our model A and B accounts would only be comprised of our new priority targets – i.e. primarily mid-market and enterprise companies.
Within a short time this razor-sharp, data-led process had yielded impressive results. Today, Leadspace counts 7 out of the world’s 10 top enterprise software companies among its customers, along with a steadily growing list of other enterprise and mid-market clients. Thinking back, it’s actually quite astonishing to think how we reached this stage quite so rapidly.
Yes, a great part of our success lies in the technology. Though I’m not exactly objective, I have been in sales and sales leadership for over 15 years and honestly can’t think of a time when I had a tool that allowed me to better implement a sales/marketing strategy.
That said, we would never have achieved this scale of success had we not prioritized data quality from the very start.
We know we’re focusing on the right accounts and contacts that have the highest propensity to convert. Territory assignment is fair and objective. We’ve eliminated overlap on accounts that have multiple sights or entities. Lead routing is much cleaner. We’ve discovered thousands of new contacts and accounts that we’ve scored, and loaded into the marketing database.
Image by sasint from Pixabay | CC0 Public Domain
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