A Product Executive’s Perspective On Unlocking B2B Data’s Latent Potential

By Mark Woollen, Chief Product Officer, Radius

Salesforce is a trailblazer in their space.

If you need proof, just look at the great legacy they’re building for themselves, particularly in promoting the cloud and democratizing access to software. We also saw their ever increasing breadth of solutions at this year’s Dreamforce event.

As always, Dreamforce 2016 left attendees buzzing on a variety of new products, perhaps none more notable than Artificial Intelligence (AI) and Predictive Analytics. But even bigger than those topics has been the major underlying theme around B2B data.

The growth of the Martech stack and explosion in data has led to most marketers sitting on a wealth of information in the cloud across their CRM and Marketing Automation (MAT). But the focus now is on <b>understanding the latent potential inside customer data.

Today, marketers are tasked with driving revenue and pipeline more than ever before. And the conversation increasingly needs to move away from the new tech you want to adopt to asking more fundamental customer data questions like:

What is the quality of my customer data?
How actionable is my data?
How quickly and efficiently can I monetize customer data?
How can I better understand past successes from my installed customer base?
What learnings can I apply from existing data to find new customers?

This overarching trend around pipeline growth primarily falls into two categories: driving net-new pipeline and increasing pipeline from existing customers. Chances are you have some new technologies and initiatives in mind that you would like to test out to meet your goals, but here’s what you need to be on the lookout for when considering them:

Predictive for the sake of predictive is not a viable approach

We’ve seen an increase in adoption of predictive over the past few years, but marketers need to be wary of the “smoke and mirrors” that many vendors are using to position their offerings.

Traditionally, “predictive 1.0” solutions have largely focused on their algorithms and models, even putting the impetus on ‘bake-offs’ rather than proof-of-concepts that factor in long-term goals. But while you can have amazing algorithms, your predictive solution of choice is only as good as the data driving those predictions.

So my suggestion is: Find a solution that has incredible data powering an amazing predictive engine.

Integrate your tech stack the right way

It’s no secret that the growing number of Martech solutions is throwing everyone’s tech stack for a loop. So if you’re in the process of integrating platforms across your tech stack, make sure you do it the right way.

Bringing together a tech stack is a ‘one-time-only’ event

Make sure to build out workflows and processes that are reputable and well thought-out. Also, check if your tech stack can integrate your solutions on an ongoing basis.

Data is great, but you need insights

According to a recent Demand Metric report, more than 80% of companies without effective demand gen blame data quality for ineffective marketing campaigns and sales flops. Yet, only 42% of companies with rich data are happy with their demand gen.

Data quality is a critical issue for marketers today, but it’s not enough to just have high-quality data. Marketers still need insights into their prospects, customers, and market to create more targeted campaigns that yield better results.
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