Learn how analytics is the key to a differentiated customer experience program with Dun & Bradstreet

To be a top company a data-driven mindset combined with a test and learn culture are practically requirements. But this approach is about more than just your financial bottom line, it can improve nearly every facet of your business, especially the Customer Experience. Dun & Bradstreet has long been a top player providing commercial data, analytics, and insights for businesses. As the purveyor of all things data, they know how to incorporate insights and analysis in innovative ways to drive customer loyalty.

In the latest in our series of "Customer Experience Visionaries", Rachel Richter, VP of Customer Insights at Dun and Bradstreet, joins us to talk about bringing together quantitative and qualitative data to improve customer retention, creating a data-driven culture, and corporate social responsibility.

On entering a career focused on analytics and insights, and landing with Dun & Bradstreet:

the best analytics will not have impact unless you have proven relationships and ways to bring them to market

I consider myself a little bit of a right brain and left brain thinker. Growing up I really wanted to merge my love of writing and creativity with my love of math. I saw analytics as a path to doing that. One of my biggest beliefs is that the best analytics will not have impact unless you have proven relationships and ways to bring them to market using partnerships throughout the company that you work for. I've worked across financial services, consumer packaged goods, and media for quite a while. The next stage in the journey to join Dun & Bradstreet was such an incredible opportunity! Being at a data and analytics company, where you are ensuring internal decision making is data-inspired, is what really got me excited about Dun & Bradstreet as a company.

On business to consumer (B2C) versus Business to Business (B2B) work:

A mentor said to me a long time ago, "There are a lot of ways where B2C is more simple. You're dealing in a one to one relationship, so it's very easy to get a lot of knowledge about that consumer, be very prescriptive and know how to understand and influence them."
I love B2B, honestly, I love it because it's a challenge. It's this one-to-many and you have to determine how each individual ultimately influences their respective company as a current customer or prospective customer across the buyer’s journey. My team and I find that a great challenge to work on. So thinking about an end user, an influencer, a decision maker, an executive, they all have different roles. Their companies have different locations, whether they're headquarters, whether they have buying authority. There are foundational analytics that you can apply, but when you're getting down to how you influence a decision, I would say B2B customer experience is more complex.

On demonstrating value to your company:

find those data-inspired leaders that will partner deeply with you so you can measure the end-to-end impact

I always say start small to prove the value. There's two things: build your foundation and prove your value. Part of proving your value is to find those data-inspired leaders that will partner deeply with you so you can measure the end-to-end impact. One of the key ingredients, in terms of growing your team and being able to work on more strategic, high impact opportunities, is unlocking a few critical business relationships. Find a specific use case where you can apply the insights and demonstrate the impact.

On starting small and establishing impact:

When we started building our team, we were very focused initially on what we call attrition (or retention) risk models. Those models tell us who's most likely to leave Dun & Bradstreet, drop a product or drop one of our lines of businesses. We tried to socialize that all around the company with sales leaders, marketing leaders, and nobody was ready for it. It just wasn't clicking for people, and they didn't trust the analytics at that point in the journey.

Shortly after, my team and I moved to Marketing, and I was now reporting to the CMO. Fortunately, our CMO was incredibly data-inspired, and said to me, "Rachel, front-end and back-end, I want every marketing campaign that we launch to start with your models and end with real-time measurement. I want you to tell me the goals that I should hold my team accountable for, what we should deliver and how we should measure our business impact as a marketing function."

It's not just about building the best analytics or the best trigger or the best model. It's knowing that you have a business partner on the other side that will hold their teams accountable

Once analytics became ingrained in Marketing, the appetite grew cross-functionally. We started to take on even bigger projects, such as how do we best assign our customers to sales channels based on their risk and opportunity. Our sales leaders jumped on board. It snowballed further from there. The more we proved that impact, the more business partners wanted to work with us to the point that we now get to be incredibly choosy. It's not just about building the best analytics or the best trigger or the best model. It's knowing that you have a business partner on the other side that will hold their teams accountable for using it and provide the closed-loop feedback to understand impact.

On encouraging a test-and-learn culture:

First of all, test-and-learn is hard. Let's take an example for our Supply & Compliance line of business, which helps companies understand both supply chain and compliance risk. Using our propensity models, we identify the best prospects within our own customer base that we should be cross-selling this line of business to. Convincing the business partner to not go after a subset of the best prospects to prove that the analytics work is just a very, very hard thing. I think it's important to just acknowledge that. In most cases, if you hand a sales leader a list of high propensity targets, they're just going to go after all of them, and that's nobody's fault. However, we really do want to apply more of that rigor, because frankly a lot of analytics teams do get asked the question of what's your ROI. If you don't have that test and learn, it's very hard to show the impact of your work.

On learning, refining, and scaling your approach:

One of our Dun & Bradstreet solutions is called Visitor Intelligence (VI). It allows you understand the companies that are visiting your website. It does that by connecting the digital behavioral data to a DUNS number, our Dun & Bradstreet proprietary company identifier. So let’s think about this. To know in real time that a target prospect is consuming content on your website about a product that you want to sell them, how powerful is that? That's the behavioral aspect of analytics. The predictive analytics tell you “who” to target, but the behavioral data tells you “when” to target them.

What we started doing is building out triggers using the VI data, starting with one of our sales teams. If you start small and pilot with a sales leader or marketing campaign, you show the value of what you're bringing, and then you scale it. That's been an approach I will say over the last year or so, I have been pretty bullish about. I think in the end, that really helps get everyone's buy-in and ensures that you prove the value before you go full scale.
So now, with everything we build, we're doing phased approaches so that we can learn and scale. It's not even just learn and scale. It's learn, refine, and scale. A lot of the value in this approach is that there is a built-in learning opportunity. Sometimes you realize the analytic or the approach is not built exactly the right way or is not quite the right fit, and that is completely ok. That is the only way to make the analytics and those who build them smarter.

On unexpected sources of collaboration:

Where we found some interesting opportunities that may or may not apply to all business models is with our Dun & Bradstreet Worldwide Network. This is comprised of global partners that sell our data & products: one example would be Altares in France. We found some really interesting opportunities to teach and sell some of our analytics to partner markets, where my team and Dun & Bradstreet doesn't own or directly support the market. So that's been really cool and fun.

there are all kinds of ways you can pivot analytics and research

We also conduct an annual customer survey across that entire Worldwide Network, supported by both the partners and a few corporate teams. So it's kind of an untraditional model, but there are all kinds of ways you can pivot analytics and research. It doesn't always have to be a direct selling model for customers.

I will say though, on the voice of the customer and the customer research side, there's not a business unit in Dun & Bradstreet that we don't work with. I love that. So people may don’t think about my team working with legal, but we do partner with them on how to improve the contracting experience, which is an important touchpoint to customers.

On corporate social responsibility at Dun and Bradstreet:

Dun & Bradstreet over the past few years has really put focus on our corporate social responsibility program, including building relationships with non-profit organizations and supporting local communities

This is really close to my heart. Dun & Bradstreet over the past few years has really put focus on our corporate social responsibility program, including building relationships with non-profit organizations and supporting local communities. We have a Human Trafficking Risk Index and things that we do to help our customers and the world. Looking at this from the perspective of what my team can offer, I thought, “My goodness. How many non-profits would like to do some kind of survey, but they don't have a survey tool? They don't know how to analyze the data. They don't know how to design the survey. How many would like to understand who their best future donors might be based on the donors they have to-date in the local area? Get contact information on those donors? Who might want better training in Excel or PowerPoint or even a BI tool like Tableau?”
So a few years ago, we kicked off “Do Good Analytics” within my team. We've completed at least five or six big projects for various non-profits, including designing and programming donor satisfaction surveys, analyzing the current donor base and building donor propensity models. I think it's important to balance the work that's going to grow the bottom line with the work that fills your heart. It's important for motivation.

On innovative ways Dun & Bradstreet works:

An innovative thing that we've done is combining customer feedback with analytics to have a 360˚ view. When I was first approached a year after joining Dun & Bradstreet to lead internal analytics, I told the leadership team that I would do it only if I could keep the customer insights team as well. That was five years ago, and this has continued to become more and more of a trend since then. It’s not about silos of data – financial vs. firmographic vs. survey vs. vs. digital vs. predictive. It’s about what you can learn holistically about a company or a person when you’re able to combine all of that into a singular view. That’s no easy feat, and we are fortunate to have our own Dun & Bradstreet proprietary data & analytics to support this. However, this type of powerful insight is what is needed to singularly inform Marketing, Sales and Customer Success teams of the next best action to take to move a prospect along the buyer’s journey or prevent a current customer from leaving for a competitor.

On the impact of actionable feedback:

We have built an integrated view of account or customer health. At Dun & Bradstreet, we are very lucky in that our highest spending customers very rarely leave us entirely, but they sometimes decrease their spend. So one of the things we've built to enable customer-facing teams is this account health dashboard that combines what are we hearing from a survey perspective, so NPS and CSAT-type scores related to each experience, as well as the retention risk models that predict the level of risk.

You are fixing the customer relationship first and foremost, and as a result, the business impact will follow.

About a year ago, we launched this directly into our CRM using Qualtrics as the core functionality for the survey data. It's changed the game because once you have a handful of customer examples of somebody seeing that risk and changing course ahead of that customer leaving or declining in spend, it's such a win. That's exactly how this type of data should be used. You are fixing the customer relationship first and foremost, and as a result, the business impact will follow.

 On the future of analytics:

The role of analytics in the last 20 years has been about getting your foundation right, deciding which practices and areas make the most sense for the company at which you're working, but it has had a somewhat large manual component to it. Although, I think there's some willingness to consume, and teams have come a long way with using BI, data lakes and data warehouses to bring together their data, I still think analytics hasn't scaled the way it could.

the future is a mix of using technology as best as humanly possible and gaining the organization's real trust

I only see scalability becoming more and more critical going forward. And part of what I mean by scalability is all the buzzwords you hear. How do you go from predictive to prescriptive? How can we best leverage AI/machine learning to deliver real-time insights and triggers? In the last 20 years, when we've said things like "real time", most of the time it's not real time. Real-time could mean data that's updated weekly, data that's updated monthly, best case daily, but I think the future is a mix of using technology as best as humanly possible and gaining the organization's real trust. Real trust in data can fuel decision-making, and I think that's extremely exciting.
Part of that is democratizing the data, but I don't think we're yet at the place where we've enabled any business partners to get to the right answer to the question they're trying to ask, when they want to ask it. That’s an ongoing opportunity in fine-tuning how we leverage BI. I think there's going to be heavier use of analytics to drive strategy. That's where you're always going to need human intelligence. The human instinct will sometimes point you against what the data will tell you, but there are absolutely times when that's important, as well. So, I think we have to get to a mix of which use cases can the data drive and identify where we need that human intelligence going forward.

Lastly, reading recommendations:

it's really important to stay connected to the industry, but always remember that your company will likely require some kind of unique approach

I am a little bit less of a book person than I am a blog person, or an article person. There are a couple books that are traditional that I have always loved. I'm a big fan of Tom Davenport, as the founder of business analytics. I love all of his books. His classic Competing on Analytics, or his new one, The AI Advantage. Of course, I like Fred Reichheld as a champion and founder of NPS and his books, like The Ultimate Question and The Ultimate Question 2.0.

I'm more of a person that just reads the latest and digests it as much as I can humanly find time for to get different perspectives, and then form my own. I apply that to my work and my company based on what I've observed, what our current & future capabilities are and our business model. I think it's really important to stay connected to the industry, but always remember that your company will likely require some kind of unique approach. So being out there and making sure you understand trends is really important because it's easy to become outdated and be seen as archaic in the space of data and analytics, as it's changing so much. But I don't think there's one perfect book that I consistently lean on, and so that's really me. I'm more of a snippet article reader and following organizations & influencers that I respect and look up to.

Want to dive into Rachel's reads to create great customer experiences?

Qualtrics compiled a reading list based on recommendations from CX leaders like Rachel, you can download that here.