Data-Driven Work Cultures: Kristopher Lazzaretti of FMCG Direct by Deluxe On How To Effectively Leverage Data To Take Your Company To The Next Level

An Interview With Pierre Brunelle

Invest in data literacy. It’s hard to ask a team to put data at the center of everything they do without having a basic skill set to read, write and speak data. Team members who don’t understand data — who are afraid of or intimidated by data and analytics — will never embrace data-driven business processes.

As part of our series about “How To Effectively Leverage Data To Take Your Company To The Next Level”, I had the pleasure of interviewing Kristopher Lazzaretti.

Kristopher Lazzaretti is executive vice president of FMCG Direct by Deluxe, a data-driven, omni-channel marketing services provider. As a developer of campaign strategy and test design, Kristopher oversees the delivery of integrated, cross-channel marketing solutions using analytics, data and technology. Kristopher received his Bachelor of Arts, with highest honors, from Princeton University.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

In ways big and small, data-driven decision making has been a part of my life since I was a child. My grandfather owned a large equipment repair business, serving primarily agricultural the community, and my grandmother kept the records. I have many fond memories sitting with her at the kitchen table, papers neatly organized into stacks, with the manual Victor “adding machine” to her right. Today, we would call what she did FP&A. Then, it was called “keeping the books.” I was fascinated by her careful notes and calculations of money in and out, who could pay and who couldn’t, what new equipment we could afford and what would have to wait, what revenue we might expect this year given the weather and the crops, and how that was the same or different from years past. While we might not think of these as data-driven decisions, they were informed business strategies anchored in data and my grandfather ran a better business because of it. In college, I became interested in the mechanics and math of decision making: socially, cognitively and neurologically. I studied psychology, with heavy doses of economics and neuroscience, and I wrote my senior thesis on the decisions “consumers” make when they pick a mate. (If I had been smarter, I would have realized there’s more money in using data to help people find love than in using data to help people find checking accounts or home goods). After college, I found myself in management consulting focused on financial services — and when client needs and personal interests came together, I partnered up with a few colleagues to build a data-driven marketing practice inside the consultancy. A decade later, we joined Deluxe, a company on an incredible path of transformation, and accelerated our mission to deliver uniquely powerful data-driven marketing outcomes to our clients.

Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lessons or ‘take aways’ you learned from that?

As mentioned, I should have been clever enough to realize that I was on to something my senior year of college when I was analyzing data around decisions individuals make in picking romantic partners. Match Group (the company behind match.com), Tinder, and other dating brands were founded three years after I graduated college and now has a market cap of around $30 billion. Dating pays dividends.

I think this missed connection in my mind, helps demonstrate that data — and data-driven decision making — is present everywhere in the world around us. While matters of the heart may never be truly predictable or explainable, data can play a role in improving the experience and increasing the probability of bringing the right people together. For me, the lesson is in all situations there is an element of decision making that can be enhanced by the collection and analysis of relevant data. If it doesn’t look like there is a place for data or data-driven decision making in a given circumstance, we should be skeptical, look harder, and push the envelope on what data can do for us.

Is there a particular book, podcast, or film that made a significant impact on you? Can you share a story or explain why it resonated with you so much?

As it relates to my professional passions, the film “A Beautiful Mind,” had a monumental impact on my life. As a reminder, it’s the true story of mathematician John Nash and his groundbreaking work in behavioral economics, his heartbreaking battles with mental illness, and his eventual triumph in winning the Nobel Prize. The film came out at an incredibly formative time for me as a person. I was a senior in high school and had just been accepted to Princeton University for the following fall term. Although I had never visited, Princeton was my dream school and I applied on blind faith. After having the good fortune of being admitted, I was voraciously consuming anything and everything I could about Princeton. A Beautiful Mind focused heavily on John Nash’s time at Princeton as a graduate student and professor, and a key part of the plot revolves around his work on formulas that explain cooperative decision making. This was a revelation for me. The idea that math (and data) could be used to explain and predict behavior and choice was totally novel to me. This is not a concept you stumble across growing up on the great plains of northeastern Colorado. My decision to study psychology, neuroscience and economics in school and to co-found a marketing company focused on utilizing data and advanced analytics to predict consumer and business decision making, all owes some credit to John Nash and A Beautiful Mind.

Are you working on any new, exciting projects now? How do you think that might help people?

We have many exciting projects in the shop right now. To name a few, we are developing an integrated marketing solution that helps mortgage lenders compete in a rising rate environment and consumers get the best possible deal on their next mortgage. We are building a solution that uses technology and data to help financial institutions reach historically underserved communities. Also, we are working to improve the process to personalize e-commerce and telco marketing to make it more relevant to recipients. The value of these projects to both our clients and consumers is clear — none are zero sum — and that’s what makes each so exciting.

Thank you for all that. Let’s now turn to the main focus of our discussion about empowering organizations to be more “data-driven.” My work centers on the value of data visualization and data collaboration at all levels of an organization. So I’m particularly interested in this topic. For the benefit of our readers, can you help explain what exactly it means to be data-driven? On a practical level, what does it look like to use data to make decisions?

Like most simple but transformational concepts, what it means to be “data-driven” is a topic of spirited debate. At its simplest level, it means giving data, and the insights derived from that data, a “seat at the table” — a voice — in strategic decisions. From here, opinions diverge on how loud this voice should be. While some practitioners believe data should (almost) fully replace human experience and intuition, I see it a bit differently.

I believe data, and data-driven decision making, becomes most valuable when it’s used as a tool to augment and improve experience and intuition. The relative ordering of data, experience and intuition will vary by situation and use-case. However, using data and quantitative models without the uniquely valuable benefit of human experience can lead to undesirable or even irresponsible outcomes. For example, it could result in making a suboptimal business investment (an outcome on the relatively benign side of things) to limiting credit based on gender, race or other protected factors (an outcome on the more malignant side). Being data driven doesn’t mean discarding experience or intuition, but instead thinking about ways data can help limit blind spots of human cognition and accelerate the identification of insights and patterns that might be missed.

It’s easy to talk about data and data-driven decision making as an academic concept, but in practice, it can feel much more natural — almost invisible.

One of my favorite applications of data-driven decision making is IBM’s use of its tool, “Watson” to guide health care professionals in making better clinical decisions. IBM has trained Watson using millions of pieces of data — patient histories, disease symptoms, illness progressions, patient outcomes — to recognize patterns and suggest diagnoses. Critically, Watson doesn’t replace doctors. Instead, doctors engage with Watson to provide critical inputs on specific patient cases and it utilizes those inputs in combination with its deep data sets to suggest ranges of possible diagnoses. Closer to home, I feel the impact of data-driven business processes daily.

Deluxe data-driven marketing is, as its name suggests, a business oriented around partnering with clients to build uniquely effective marketing audiences and experiences using data.

Our process of building marketing audiences and experiences is not too different from the process IBM used to build Watson. We have assembled some of the industry’s deepest and widest marketing data lakes — “pools” of business and consumer names with thousands of individual characteristics and behaviors attached. We have also built one of the industry’s most comprehensive libraries recording the decisions businesses and consumers have made after receiving marketing.

By marrying together our distinctive data lakes and library of business and consumer decisions, we can build predictive models to understand the capacity of a household to spend, their interest in spending on a particular product or service, and their propensity to choose our clients’ brands.

IBM Watson and Deluxe data-driven marketing are both very sophisticated uses of data in business processes. However, the world is full of examples where data drives more invisible types of decision making. For example, need an umbrella today? Check your (data-driven) weather app.

Which companies can most benefit from tools that empower data collaboration?

Although I’m sure it sounds cliché, every business — large or small — can benefit from adopting a data-driven mindset. As the story about my grandparents suggests, every business creates a body of data in its day-to-day operating rhythms. What separates a data-driven organization from one that is less focused on it is the recognition that data (both your own and the data others may be willing to share with you) has value. In fact, the dedication to organizing that data in a usable fashion (the hardest, most time consuming part), patience to truly listen to what the data saying without forcing the result, and commitment to measure the outcomes of your decisions. Some businesses are much further along the maturity curve in these steps. However, data-driven decision making does not always require math PhDs and super computers. More than anything, data-driven decision making requires a commitment to the practice and understanding of underlying tools that empower it.

We’d love to hear about your experiences using data to drive decisions. In your experience, how has data analytics and data collaboration helped improve operations, processes, and customer experiences? We’d love to hear some stories if possible.

As a data-driven marketing and analytics agency, we have a unique vantage point in seeing and measuring the power of data driven decisions and value of data collaboration between agency and client.

We ingest hundreds of independent data feeds monthly into our “massively-multi-sourced data lakes.” These feeds capture general characteristics, as well as individual and household needs, attitudes and behaviors — e.g., what marketing channels a household prefers, what types of goods and services they buy, what they do for relaxation and recreation, and what media they consume. As a part of these data lakes, we also maintain one of the country’s largest repositories of consumer and business life events. In near real time, we assemble a comprehensive look at major household milestones: newly married or single, new and expecting parents, newly retired, new mover, and so forth. Using our audience resolution engines, we stitch these discrete feeds together into a unified, useable data fabric, which we further enhance with our own proprietary data attributes, such as Consumer Financial Insights and Consumer Spend.

However, a data lake, by itself, is not terribly actionable. On top of our data lakes sit our audience decision engines. The purpose of these engines is to algorithmically evaluate the data we’ve collected about each consumer or business to assess the relevance of the product or solution our client is promoting. The audience decision engines are generally built to assess three factors per household:

1. The capacity to spend on the product or solution being promoted.
2. The interest in spending in that particular category.
3. The propensity to choose our client for their next purchase.

Consumers or businesses with the capacity and interest to spend and the propensity to choose our client’s brand are likely to become part of the audience identified for the marketing experience. We use this approach successfully with many different types of clients, including retail banks, lenders, retailers, e-commerce companies and others.

For example, we recently partnered with a top 10 U.S. bank looking to grow the number of consumer households they serve. The most common “entry point” for a consumer to a bank is a checking account. Unfortunately, this particular bank was in a tough spot. They had been running a net negative household growth rate for several years and just begun a major effort to close and consolidate branches. We partnered with marketing and product teams to run the data-driven playbook discussed. Utilizing Deluxe’s massively multi-sourced data lakes and audience decision engines, we built two types of campaigns.

First, we developed periodic campaigns to stimulate demand for the bank’s checking products, targeted to households that were open to switching providers but not shopping at that moment. Secondly, we created weekly trigger campaigns targeting consumers who were actively in the market for a new checking account. The campaign deployed across marketing channels, including e-mail, social media, programmatic display (digital advertising banners) and direct mail.

During the annual marketing program, we exceeded leadership’s goals for net new household acquisition. Most importantly, we reversed the bank’s negative consumer household growth rate.

Bringing it full circle, this successful approach was anchored in data collaboration. We collaborated with underlying data partners to assemble powerful data lakes. The bank collaborated with us to leverage our data lakes and audience decision engines. We partnered with the bank to access their first-party customer data to tune our audience decision engines to their specific market segments. In sum, no one wins alone in today’s modern data ecosystem.

Has the shift towards becoming more data-driven been challenging for some teams or organizations from your vantage point? What are the challenges? How can organizations solve these challenges?

I believe that among most organizations there is now a general appreciation of the power of data in strategic decision making. Many organizations also appreciate the need to take real, meaningful steps to become more data-driven, even if the first step isn’t entirely clear.

There are generally two common pitfalls we see organizations make on their journeys to become more data driven. First is failing to invest in basic data literacy across the company and the second is the desire to go it alone. The great news is that both can be easily overcome.

First, investing in basic data literacy across the company is foundational. It’s impossible to put data at the center of everything you do without a common language and understanding. Data literacy does not mean that everyone in the company needs an advanced degree in statistics or econometrics. Basic elements of data literacy include understanding what data is, how it can be accurately collected and made ready for analysis, what common tools are used to understand, visualize and analyze data, where data can be misleading without the right guardrails, and what questions to ask when reviewing data and analytic outputs. There are rich resources available at little or no cost to organizations looking to improve data literacy, from free or almost free web series to more formal training, coursework and certification.

The second pitfall organizations face is a hesitation to engage outside partners. While there are certainly use cases where outside help is not needed, there are other situations where it makes sense to partner. The modern data ecosystem can be incredibly complex in manufacturing, distribution, pricing and marketing. Even for the largest companies, it’s not practical to maintain a fully-staffed, dedicated team to cultivate specialized internal and external data assets, build and apply the appropriate analytical tools (AI-powered or otherwise), and action on the outcome of those tools with speed and precision. Engaging a specialist, with capabilities purpose-built for the use case, can help. The right partnership can also rocket a company up the learning curve, unlock access to the most comprehensive data sets and advanced analytical tools, and help avoid costly mistakes. The partnerships can often be secured under a variety of financial models, including traditional consulting or agency arrangements and pay-for-performance or share-gain structures.

Ok. Thank you. Here is the primary question of our discussion. Based on your experience and success, what are “Five Ways a Company Can Effectively Leverage Data to Take It To The Next Level”? Please share a story or an example for each.

Here’s my checklist for success in using data to drive business outcomes.

1. Put data at the center of everything you do. Push hard on situations where there doesn’t appear to be a path for data to improve your decision making. For example, we partnered with a client who set annual goals for each sales team member based on the prior year’s performance. Sales leaders were stumped by the yo-yo performance of the team: one year exceed, one year miss. This happened repeatedly. While the process for setting annual sales goals was not entirely based on gut, numbers were anchored to the prior year’s performance and the process only considered a single factor. The solution to setting challenging, yet achievable targets, and driving more consistent year-over-year growth was a goal setting model informed by many different data points specific to each salesperson. The model involved:

• What’s the opportunity?
• What’s the conversion rate?
• How fast is the market growing?
• What is our brand recognition and market share?
• How are we priced and what are the competitive pressures?
• How valuable is each sale?

2. Invest in data literacy. It’s hard to ask a team to put data at the center of everything they do without having a basic skill set to read, write and speak data. Team members who don’t understand data — who are afraid of or intimidated by data and analytics — will never embrace data-driven business processes.

A simple observation from our work at Deluxe: Clients who invest in data literacy across have faster speed to market and better marketing results. Why? Product owners, marketing professionals, sales teams and shared services resources all start with a common understanding of data and analytics. This universal data language drives faster decisions, resulting in no more strings of meetings to educate stakeholders on methodology or metrics. It also leads to better business outcomes because everyone understands the marketing performance metrics and what the data suggests is needed to drive better future results.

3. Inventory and collect data available to you. Invest in technology necessary to make data useful. We find new clients undervalue and underappreciate available data. We partnered with a large retail bank looking to identify business owners within their consumer customer base. These customers are a natural audience for marketing small business products. Our suggestion for identifying this population was to first create a list of customers who applied for a financial product and listed “business owner” as their occupation. Next, we augmented this initial population with additional businesses identified using Deluxe’s business data lakes and identity resolution engines. Utilizing application information had not occurred to the client as a possible source of data intelligence and the timing was fortunate, as the bank had recently invested in a new marketing data infrastructure. A few lines of code later, this new stream of data was ready. In sum, businesses likely have more data available to them than known.

4. Identify gaps in your data and analytics capabilities and consider partners to help fill those areas. About five years ago, a client we partnered with experienced a change in leadership. The new leaders decided to internalize all data and analytics across all functional areas as a strategic advantage. While this can be a sound strategy in certain areas, the client did not account for the complexities and specialization required for massively scaled data sourcing and leading-edge, predictive analytics for marketing applications. The decision was costly because marketing programs underperformed almost immediately; eventually, they were discontinued altogether. The institution has grown slower than its peers ever since. In short, no one can win alone in the modern data ecosystem. The right partner can be a high-octane accelerant to success in data-driven decision making.

5. Measure outcomes of your data driven decision making. Measurement is probably one of the most important and often forgotten elements of data-driven decision making. Data-driven decision making thrives on a closed loop process: collect your data, analyze and interpret your data, make an informed decision, observe and measure the outcomes of your decision, add those outcome measurements to your training set, and repeat. More important, not measuring the performance of your data-driven decision-making leaves valuable data on the table. Time and again, our marketing clients who embrace measurement and approach it with consistency and rigor can optimize business processes faster and achieve longer term strategic objectives more consistently. They are also more respected by cross-functional peers and have greater organizational influence.

The name of this series is “Data-Driven Work Cultures”. Changing a culture is hard. What would you suggest is needed to change a work culture to become more Data Driven?

Culture is one of the few topics that has received more academic and practical attention than data and data-driven decision making. It’s also probably one of the least settled. There is still considerable debate around what culture is, how it improves organizational effectiveness, how it can be measured, how it changes over time, etc.

As a leadership team within Deluxe data-driven marketing, we have instituted formal and informal operating rhythms that govern how we talk to each other and our clients. We have both internal and external practices that support data-driven decision making.

Internally, we work to avoid what we call, “problem admiration.” That is, you show up at a meeting, put a problem on the table for the group to admire, and then stand around and hope someone can solve it.

Instead, we push each other for thoughtfulness and diligence. The expectation is that before any meeting, the owner or presenter will have completed the diligence, defined the decision, opportunity or problem, collected and analyzed data associated with the topic, and prepared options to move forward. The expectation is those options will be supported by data and shared with a data-centric view of likely outcomes. Presentation materials and analyses will be shared in advance and the team will commit to pre-reading them. We hold each other accountable to these expectations at every level of seniority, and the leadership team practices this methodology. If the data is not ready and the diligence has not been completed, the meeting does not happen.

This approach is successful because the leadership team uses it, and it has proven successful in advancing and growing the business and delivering client results.

The future of work has recently become very fluid. Based on your experience, how do you think the needs for data will evolve and change over the next five years?

More companies will adopt and advance decision frameworks anchored around data. Concurrently, we can reasonably expect that collection, organization, availability and amount of data available to companies will increase exponentially. A lot of that data, maybe even most of it, will be useless — all noise, no signal — and it will fall to employees of all levels to identify what’s useful and what’s not, to apply the right analytical lenses, and to distill actionable insights. Leadership teams will need to interrogate and pressure-test processes associates used to distill those insights and act on what’s reliable. The value of data literacy, as a basic qualification, will be more important than ever. Reading, writing, and speaking data will become an even more critical skill in driving business value.

Does your organization have any exciting goals for the near future? What challenges will you need to tackle to reach them? How do you think data analytics can best help you to achieve these goals?

At Deluxe, we are on an incredible path as an enterprise, namely the century-old check printer is now one of the nation’s largest payments and data companies. It’s truly a remarkable transformation.

Within Deluxe’s data-driven marketing business, we are also on an incredible journey. We recently unified and reorganized our marketing businesses, centralized our data assets, and launched major investments in modern, scalable data technology. Also, we have further solidified our dominant position in providing performance marketing to the financial services industry, and expanded our beachheads in new verticals, including e-commerce, retail, and telco.

Today, we are in the process of replatforming our business into the cloud (we expect to achieve this in less than 12 months, whereas most companies take years). Moreover, we are accelerating the speed we ingest data into our data lakes and enhancing our data product development and insight identification capabilities. In addition, Deluxe is turbo-charging audience decision engines and building next-gen technology for clients to interact and engage with our products and solutions on their own terms.

It’s important to note, this roadmap was not built on gut feelings. On a macro-level, our decision to pursue these strategic initiatives was fundamentally rooted in the data — e.g., analyses of existing and prospective customer feedback, quantitative assessments of market opportunity, modeling of expected value creation for clients and more. Experience played a role in guiding the exercise, to be sure, but the results of data-anchored diligence and careful analyses pointed the ship.

On a micro-level, thousands of data-driven decisions continue to inform the execution of strategies. In deciding which data sources to ingest into our data lakes, we analytically consider quality, coverage, pricing and performance for clients. In prioritizing data product development, we quantitatively evaluate the distinctiveness of the product in the market and the value and competitive advantage it will create for our clients.

In building what’s next in data driven marketing for our clients, we will continue following our playbook — a data-driven one — of analytically defining the problem, decision or opportunity, collecting and thoughtfully analyzing relevant data, and anchoring our decisions in those analytic outcomes. We will pivot when the data tells us to adjust. Also, we will continue investing in resources and technology to use data for better business decisions as a team and build better marketing experiences for clients.

How can our readers further follow your work?

Interested readers can learn more at deluxe.com and/or follow me on LinkedIn. The Deluxe data-driven marketing team regularly presents at industry conferences and publishes thought leadership about the collection and application of data and analytics in delivering uniquely powerful marketing experiences.

Thank you so much for sharing these important insights. We wish you continued success and good health!