Few things can be more frustrating in B2B lead generation than the experience many in sales sometimes encounter – particularly SDRs.
You just want to do your thing: sell your product to as many of the right people as possible. You’re confident in what you’re selling, you have the right pitch – hell, you’ve been doing this for years, and you do it well.
Instead, you’re spending the vast majority of your time chasing prospects.
The frustration builds up. Why can’t marketing provide you with enough qualified leads? Why is your CRM such a godawful mess? How are you expected to meet your quotas when most of your time is wasted sifting through contacts? At times like this, B2B lead generation feels like eating soup with a fork.
The problem is only going to get worse (like eating soup with a… toothpick?). In today’s lead gen world, so much of the information you need is knocking around on disparate social media profiles, across multiple platforms. Turning to a data vendor for help won’t do the trick. In fact, it might only multiply the mess by importing yet more incomplete contacts and leads into your CRM.
So what are you going to do? Spend your entire day Facebook-stalking or trawling through LinkedIn? Might as well return to that bowl of soup.
B2B lead generation made so much simpler – and more effective
The SDR team at ObservePoint can definitely relate.
ObservePoint’s data quality management platform is designed to maximize return on marketing technology. Their truly excellent product was fueling rapid growth. But their SDR team was spending a full 80% of their time chasing contacts – meaning so much more potential was going to waste.
An Ideal Customer Profile builds on past customer and lead behavior and identified traits, to build a virtual model of a qualified prospect. (At Leadspace, we use a “semantic” model, which combines a machine-learning model with crucial human insights from your team.)
The idea is to know precisely which kinds of leads you’re looking to sell (or market) to before you’ve even begun fishing for net-new. The more specific you can be (and much of that relies on the sophistication of the platform you’re using), the better.
ObservePoint were able to build an extremely granular Ideal Customer Profile, with data fields ranging from seniority and department, to geographic location, expertise, technologies used, buying power, and so on.
ObservePoint’s SDRs were able to use that ICP to prospect for net new leads far more efficiently.
The results were stark. Using the Leadspace platform their team were able to find 35% more leads than before. SDR productivity rose by 30%.
In all, 5% of Bloomreach’s bookings in the first six months of adopting the platform came from the Leadspace platform – representing a 640% return on investment.
Read the full case study and discover how you could unlock the potential of predictive analytics for your business:
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