Nearly 90% of B2B companies say data quality is important to their sales and marketing teams. In an interview with Demand Gen Report, Adnan Zijadic, Principal Analyst at Gartner, shared his thoughts on the rise of data intelligence, its role in ABM and how to maximize your data intelligence solutions.
Demand Gen Report: ABM is growing and becoming much more of a focus area within B2B. Do you feel like that’s causing B2B organizations to put more thought and investment into their data efforts?
Adnan Zijadic: I do think it’s a piece of the puzzle, but I don’t think it’s solely attributed to account-based marketing. I think that data veracity is important for ABM efforts because you want to target and personalize your outreach and engage in a meaningful way with a potential prospect or even an existing client.
There’s an overwhelming amount of data that just flows into the company’s CRM, across different channels and even different lines of business, so the data still sits in siloes and making sense of it all continues to be a very daunting task. I do think that more organizations are becoming aware of this and, hence, putting more effort into their data initiatives to provide a better customer experience and engage prospects in a more meaningful way.
Whether it’s an existing client or a prospect, it’s important to not just blast them with a generic outreach, which clearly doesn’t work in today’s day and age. But more importantly, you need to get that single view of the customer or prospect, whether you’re doing ABM or not.
DGR: Are you seeing any other market dynamics that are driving businesses to make data a bigger priority?
Zijadic: I do think there are several things, but that they’re tied to what is now considered at our organization as the business’ customer experience effort. It used to be only a business unit priority, but now it’s a C-level priority to ensure that the customer experience resonates across the organization.
I think that data is crucial to providing a personalized experience. When tailoring a product or service for a prospect or a customer, it’s important to have the right data in place for both — whether that data comes from the actual customer or prospect themselves, or from your employees internally in order to help shape those efforts.
When I say AI, that can include anything as an umbrella of AI. It could be machine learning or natural language processing, which is dependent on clean data in order to extract and provide meaningful insights or dialogue. An employee can use that internally or it can be used externally for your prospects and customers. Let me give you an example: if you look at virtual assistants, sales teams can use some of this data to provide more personalized outreach to clients, especially if those clients interacted with a virtual assistant on a customer-facing website, such as requesting information, or just asking questions pertaining to certain products.
DGR: What are some of the main recommendations you’re giving to B2B companies looking to improve their data intelligence capabilities for their sales teams?
Zijadic: I usually try to understand their needs before I really suggest anything. Sometimes, their problems are due to process, not really so much the data intelligence solution itself being used. For instance, they may not even have a data governance strategy in place. If somebody’s looking for a data intelligence solution or capability for their sales team, maybe they are having difficulty funding the data or, in general, getting much accurate data into the system. Other times, it’s due to poor data governance policies.
If it really is a technological recommendation, I try to ensure that they understand that one solution or one data intelligence solution will not solve all of their data needs on prospects or customers, and it won’t really give them a 360-degree view if that’s what they are looking for. You usually need a combination of multiple data intelligence solutions. An example could be using LinkedIn with, let’s say, DiscoverOrg, because while the two solutions are complementary, they’re not really meant to replace one another.
When it comes to data intelligence solutions, I usually suggest that their sales teams are always engaged in social channels, especially LinkedIn. That’s where prospects come to engage their peers for answers and consume content, so you want to really start engaging at the point where they are in the learning phase of the buying cycle.
DGR: In terms of technology and platforms, do you think companies are going to need other solutions for managing all these external data sources? We hear talk of a network of record and customer data platforms, but is there an approach to data infrastructure that you think is greater given these trends?
Zijadic: Yes, I do think there will be a need for other solutions, and I think the primary driver for that is because there’s a lack of trust and transparency in these external data sources. Buyers and sellers alike are overwhelmed with the sheer amount of data out there. I think there will be a need to rein that in so that we are dealing with more accurate and trustworthy data. The other factors influencing this are privacy regulations and more recently, an increase in the amount of data breaches.
DGR: What questions would you recommend B2B buyers ask data intelligence vendors when they are considering investing in a new solution?
Zijadic: I always say besides the obvious one where they do the demo about the gaps it can fill in their existing records, they should ask them about their roadmaps and their investments in AI-enabled technology. This is because AI has the ability to shorten the research cycle and automate admin-related tasks that are taking up a sales rep time. We at Gartner predict that by 2022, B2B sales organizations using AI-based data intelligence solutions for prospecting will cut the amount of time spent on prospecting by up to 50%. There are machine-learning and natural language processing mechanisms that can actually do that automatically behind the scenes, curating structured and unstructured data sources to provide contextualized market intelligence to sales reps, so that they can spend more time selling and less time researching.
The other thing is they should ask them about some existing partnerships they may have with data intelligence providers. This ensures that the vendor is investing in themselves, but it can also help potential clients eliminate contracting with other vendors who may have data sets you can use. One example off the top of my head is that Bombora partners with some of the existing data intelligence solutions, and sometimes you could avoid contracting with both if a data intelligence solution is already partnered with them. You just have to determine the scope of partnership and how much data they may have within that partnership.