As the MECLABS Research Partnership analyst team, my colleagues and I speak with professionals who attend our events (like the next month’s MarketingSherpa Email Summit in Las Vegas), purchase our publications, and want more information about how MECLABS can help grow their business. So every day we hear about the challenges they’re facing.
One issue that surfaces all too often is optimizing databases: When you have a database of thousands upon thousands of names, how do you help your team easily and effectively prioritize who to contact? Nearly every company I talk to does some kind of lead scoring, but rarely do those lead scores align with their database in a way that allows their sales teams to determine – at a glance – which prospects are the right fit at the right time.
This hit way too close to home. Here at MECLABS, my team was struggling with the same issue. Through events, publications, and general inquiry, we add hundreds of interested potential partner inquiries to our database every few weeks, sometimes even thousands. We have an ace IT team that has set up platforms so we can quickly identify who fits our Ideal Partner Profile, and we’d contact them as soon after they express interest in our Research Partnership program. We are very well aware of the importance of timeliness for marketers who are struggling to optimize their sales and marketing funnels. And we’d follow up based on the next action that was associated with their file.
But it took Brooke Bower, our data-analysis whiz, to help our team look at our database from a new perspective, one that would help us get the highest return on our time by focusing on the most promising potential partners, as opposed to merely the most urgent.
What we realized was missing was a comprehensive at-a-glance snapshot that basically shows us the key factors that define a successful research-partnership engagement:
- If the individual making the inquiry is a decision maker or an influencer
- How many events the individual, and his team, have attended and publications they’ve purchased compiled in an easily sortable list
- Their organization’s firmographic details – such as revenue, marketing budget, sales cycle and size
We enlisted the IT department to add fields to our existing platform to bring together these details into a single “opportunity grade” that would be applied to each potential partner’s account. (The concept of an “opportunity grade” was recommended to us by Dr. Flint McGlaughlin, Managing Director and CEO of MECLABS.) The higher the grade, the better fit for a long-term, strategic research partnership.
Within just a few days, through the teamwork of IT, marketing and sales, we have sorted our database so that it reveals to us that “opportunity grade” for each partner. It wasn’t rocket science, just taking the time to ask the hard questions (thanks Brooke), and look at what we do from a fresh perspective, to give IT the parameters they needed to bring it all together. This is a project that will never be completed, of course. We’re going to continually work with Brooke to analyze what qualities make up our most qualified research partners and make sure our database can easily and accurately help us identify them.
Great results happen when people and departments with different skill sets take time to put their minds together — in this case it was Brooke’s data savvy combined with my hands-on experience talking to potential Research Partners about their challenges.
I’d really like to hear about your experiences in building a database that helps you engage more efficiently and effectively. I welcome you to share them in the comments.