First-year customers are at the highest risk for not renewing. That’s a problem because If you can keep a customer for about three years, there’s a good chance you’ll keep them for the next 10 years or longer. 

That is where you get that lifetime value. 

A retained customer is about five times more profitable than bringing in a newly-acquired 

customer and retained revenue are worth even more. 

So Why Are First-Year Customers the Hardest to Keep? 

There are a few reasons why first-year customers leave. Under many circumstances, customers may have unusually high expectations in terms of how much value they’re going to get in a single year. For some products, it takes some time for value to build. 

And at the end of the day, it always comes down to value.

Value is like beauty. It truly is in the eye of the beholder. 

If there’s one thing that will drive you absolutely crazy, it’s when you know, and you can empirically prove to a customer, that they’re getting tremendous value from their investment and they still cancel.

On the other side of that coin, you may actually have a customer who, you know, is not getting great value and they consistently renew. 

You can certainly improve your value proposition by ensuring that the whole onboarding process is nice for the customer. You only get one opportunity to make a good first impression. It’s worth it, they’ll contribute more to the bottom line than if they were only around for one year because they were oversold and over-promised something that didn’t deliver them value. 

Customer retention should be determined by both qualitative and quantitative data. Curiosity should drive you to try and understand at what point you might lose a customer, and your answer should come from data—or all you’re doing is guessing.

Roles Involved in Retaining Customers (It’s Not Just the Sales Team) 

Many times sales representatives talk to their customers 30 to 60 days before the renewal date, and the customer indicates they have no plans to renew. Then when you ask why, they will say, “because I’m just not getting the usage that I expected, I’m not getting the value that I expected.”

By this point, it’s likely too late to retain the customer. The salesperson might explain that the customer leaving was from lack of value. In reality, however, it was the lack of engagement. 

Companies expect so much from sales teams and they manage many accounts. There are two other roles that can support customers long before the sales team calls them to renew. 

1. Customer Engagement Managers answer questions and reach out to customers to make sure things are going okay. It’s all about monitoring the customer’s usage throughout the year. When training is necessary, absolutely provide that type of training. 
2. Marketing Team/Partners can develop any number of different campaigns that provide support and answer common questions. 

How to Talk the Talk and Walk the Walk 

You want to talk to customers. You want to understand why they renew and why they don’t. And, sometimes you learn more from the customers who don’t. 

The goal of these conversations is to attempt to prove and improve the value proposition. 

At the end of the day, you have to ask yourself, does the customer feel like they are getting a good return on their investment? And the onus is on you to prove that to them. 

Don’t expect them to invest the time to prove it to themselves. Sometimes they know it, sometimes they don’t. Equally important, is when there is a situation where a customer is not getting commensurate value for their investment, then you must utilize intervention methods.

A Case Study in Revenue Retention 

By Joe Douress

When I was in the legal field, this particular business was growing quickly. Our company helped law firms find clients. It was all happening at a time when we were making the transformation from print to web. 

Early on we were at a 25% growth rate and we maintained that over a four to five-year time horizon. 

But here’s what’s interesting: we were bringing in up to 6,000 new customers a year.

Those 6,000 customers on an average contract value were in the $2,500 to $3,000 range, but on an annual basis, we were bringing in anywhere from $17 million to $21 million a year in new business. 

When we started looking at the second-year retention rate, the retained-revenue rate, we were all shocked to see that it was only about 52%.

Meanwhile, the business as a whole had a retained-revenue rate closer to 90%, but we had first-year customers that we were losing at a high rate. 

We were trying to sell too much, perhaps too soon. So when we looked at the data. 

What we learned was that, if you sold a subscription between $2,400 – $3,000, we had a better chance of retaining that revenue because the customer felt what they were getting in return was commensurate with what they’re paying for the service.

But it was at this time where we were also developing lots of new products that law firms were intrigued by and eager to purchase. We had a sales team that was eager to go out and not only sell a basic subscription but also an enhanced subscription. 

It almost was impossible for us to actually deliver enough value in that first year to justify that investment the second year. 

It was difficult to renew it in the second year because the price was too high and the value we were delivering was not enough for that law firm to say, “yeah, we’re going to go for it a second year.”

A couple of things we did to solve the problem was first to communicate the situation to the sales team. 

In addition, we carved off a small group of four customer support representatives whose focus went from just providing general customer support to focusing only on these first-year customers. We called it the priority services team. 

Their goal was to prove that the law firm was receiving tangible value and a return on investment. As a result, We saw the retained revenue rates increase the first year by five points. 

Now that may not sound like a lot when you’re going from 50% to 55%, but if you consider that the revenue is in the $16 to $20 million range, a five-point improvement in retention is real money. 

The post Why Revenue Retention Is Hardest During the First Year (And How to Get Customers to Renew) appeared first on Pragmatic Institute.

Even though data professionals have the capabilities to execute high-quality analytics and business solutions, they’re not speaking the same language as some of the business stakeholders, and they can’t seem to get across this divide to communicate insights. The problem is there’s no translator between these two groups. 

The Pragmatic Data Analysis Model addresses this problem in a tangible way by giving teams a process that can lead them to success.

Here are some questions we answered during a recent webinar on how to best implement the model.

> > Learn more about the Pragmatic Data Analysis Model and watch the webinar replay

How is the Pragmatic Data Analysis Model different than other data models? 

There are many models that focus on analyzing and streamlining data. The Pragmatic Data Analysis Model is different because it helps organizations handle communicating, defining and presenting data to stakeholders. 

What makes this model uniquely helpful is the third step—refine. It is the reality check that helps teams avoid spending valuable time and resources working on projects that aren’t useful. It’s a different way of thinking about the problem. 

This model helps you confirm that what you’re doing when you analyze is actually relevant. There are tons of brilliant data scientists and analysts with impressive projects that go to waste because at the end they weren’t presented in a way that the executive team or management could take action.

When this happens, the data professional didn’t solve a business problem, they solved some other problem. It may be an interesting project, but they didn’t actually move the business forward. This model helps teams connect their ideas and projects to solutions for the business rather than simply providing an efficient way of analyzing the data. 

Who owns the “define” and “refine” steps, and does an analyst have the ability to push back? 

Analysts have fantastic minds and organizations should be utilizing their full potential, so absolutely, they should have the ability and permission to push back on defining and refining the question. 

Getting that buy-in from both the management and the data professionals will always make the end result more productive. A shared, negotiated reality for the scope of the project is critical to success. 

The define and refine stage is for you to be honest, and it needs to be implemented in the organization and understood as part of a process that both sides of the chasm are embracing. This needs to be part of the culture. We’re going to define this problem together. As the data professional, it’s my responsibility to quantify your problem and bring it back to you. 

How would you promote the Pragmatic Data Analysis Model internally? 

Businesses realize that if you ask a well-defined question, you’re going to get a usable answer much faster. Then, that kind of efficiency within an organization gets noticed, and in some ways, it will promote itself. 

If your company is working on a vague, imprecise and poorly-defined question, it’s going to be an unusable answer. Once you practice the model, the efficiency with which you can execute it in the business or in the data becomes highly efficient. 

You promote it by emphasizing it’s not about slowing down the process, but improving it and making it faster. 

What kind of tools work best with this model? 

The Pragmatic Data Analysis Model works with any tool. Companies can even solve the business problem by doing a pivot table in Google Sheets. 

Whose responsibility is it to make the “present” step understandable? 

It is absolutely incumbent upon the person who is responsible for the analysis to be able to explain the results of the analysis.

If the answer to the question is long and difficult to understand by the stakeholders, then it means there is a gap in understanding. It could be an unclear question or ambiguity on how the answer applies to the business. Sometimes, it could be the data is not sufficient to support a clear conclusion. 

If there is alignment between the requester and the analyst, then you’re much less likely to get the difficult-to-understand presentations. 

Before scheduling a presentation meeting, you should be able to explain the results in a tweet. If you can’t say it concisely, then you didn’t understand the results. 

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Want to learn how to connect your data analysis to a business problem by leveraging the Pragmatic Data Analysis Model? Register for Pragmatic Institute’s new course, Business-Driven Data Analysis.

The post [Q&A] A Data Model to Drive Business Outcomes With Critical Insights appeared first on Pragmatic Institute.

Yet companies continue to invest in AI projects. According to IDC, corporate spending on AI systems was over $50 billion in 2020, up from $37.5 billion in 2019. By 2024, investment is expected to reach $110 billion, based on IDC forecasts.

Companies risk failure of their AI projects if they do not approach AI projects differently.

What follows are several key reasons why companies do not achieve the expected return on their investments:

• Approach: Lack of clear strategy and understanding of what is achievable
• Design: Poor model design, data and governance issues and algorithm bias
• Implementation: Limited internal support for full implementation
• Trust: Lack of trust in the insights
• Action: Inability to translate insights to action

When designed well, AI systems can deliver valuable insights into your business operations and revolutionize your approach to driving value for your customers and shareholders.

However, if you don’t plan your AI projects holistically and take systemic action based on the insights, you can waste resources.

Consider this framework to secure ROI from your AI projects:

1. Align with Strategic Objectives – Before you invest in any AI projects, start with the business objectives. Work your way to the AI projects or use cases that provide insights to accelerate achieving the strategic objectives for the company. Ensure from the start that you are investing in the right AI projects for the company.
2. Design to Predict – Before you build out the analytics model, validate the approach(es) thoroughly so you don’t have to make costly course corrections down the road. Build the models to predict the outcomes you’re looking for. Plan early to build the visualization of the model insights for the audiences that need to take an action.
3. Drive Systemic Action – Insight without any action is a wasted effort. Plan on change management to secure buy-in from all levels within the organization. Refine business processes and align training, incentives, and performance management so all relevant operations in the business are aligned to act on the insights.

Now, let’s review each step of this framework.

1. Align with Strategic Objectives –prioritize use cases for progressive success

a) Quantify strategic and operational objectives: First define and “quantify” the strategic objectives and break that down to specific operational objectives. “Improve client retention” is a good strategic objective but not specific enough to operationalize, whereas “Improve client retention by 3 points to 78% by end of the year” is quantifiable.

Now, identify the operational objectives that enable you to achieve the strategic objective. “Improve customer satisfaction” could be one of the operational objectives to meet the strategic objective. While that is an operational objective, you can’t hold someone accountable and measure progress. “Improve NPS [Net Promoter Score] by 5 points by end of the year” is a respectable goal.

Apply the SMART approach (SMART goals are Specific, Measurable, Achievable, Realistic and anchored within a Time frame) or a similar approach to quantify the objective, identify owners, determine the baseline and set up specific targets over time.

b) Define Business Use Cases: Break down the operational objectives into potential projects or use cases. An example use case to achieve “Improve NPS (Net Promoter Score) by 5 points by end of the year” could be an AI-based project to identify clients that are likely to be a retention risk (dissatisfied clients). Improving NPS could be one of the projects to help improve retention. An AI model to identify clients that have a high probability to cancel would provide valuable insights. The customer success team can then utilize these insights to pay particular attention to the higher retention risk clients and take proactive steps to mitigate the risk of losing these clients.

c) Prioritize use cases based on ROI and “crawl-walk-run” approach: Develop an estimate of potential ROI, time to implement and the complexity for each of the use cases. Plot the use cases along a 2×2 matrix with value or ROI on one axis and time to implement on the other axis—highlighting the complexity of the use cases as indicated below.

A low-risk crawl-walk-run approach would mean starting with low or medium complexity use cases on the bottom left quadrant and moving to the top right approach as you begin securing the ROI that could fund subsequent projects.

Ensure you have representatives from all relevant departments and functional areas involved in the development, planning, prioritization, and execution of the use cases. Involving the right representatives every step of the process not only strengthens the alignment with the strategic objectives, but also begins the process of securing buy-in across the organization.

2. Design to Predict – build models to accurately predict desired outcomes

a) Develop a Proof of Concept (POC) to confirm approach: A common mistake is to embark on a large project to build an analytics model before determining if you have enough of the right data or predictors and the right analytical model to predict the desired outcomes.

Start by defining the current process, conduct interviews with the right departments and individuals that impact the outcomes and are impacted by the activities. Determine the types of analyses that could be applied to address this specific use case. Develop a POC (or multiple) to select the best analytical model(s) to predict outcomes.

In determining the data requirements, consider all of the following. Use all available data to develop and test the preliminary models to determine the best predictors and the best model.

• Data within organizational systems that are readily accessible (e.g., CRM, databases)
• Data within the organization that is not readily accessible (e.g., PDFs, pictures, surveys)
• Data outside the organization, or publicly available data (e.g., average income levels by zip code available from data published by the IRS)

Data quality is always a concern given the adage “garbage in, garbage out.” However, poor data quality is not always a deal breaker. For the POC, manual data cleansing and transformations can yield the results and validation necessary before moving on to the next stage.

Utilize the POC to determine the systemic changes that will be required to improve data quality on an ongoing basis. Often, minor changes to existing processes can result in new information that wasn’t originally available.

b) Build and train the analytics model: At the completion of the POC you should have a good idea of the best analytical model for your particular use case and the likely predictors. Now, build the model and apply the required data extraction, cleansing, and cataloguing approaches to prepare the data for the model.

Ensure the models are designed with attention to data and governance requirements while incorporating safeguards to avoid algorithm or design bias. Build and integrate the model into the production systems as appropriate.

c) Develop dashboards for visibility and actions: If you want to take an action based on insights from the analytical models, provide visibility of relevant data and insights to the specific audiences that need to take an action and/or be aware of the changes.

Define KPIs (Key Performance Indicators) that need to be monitored to verify that the actions have been executed based on the insights. Define target metrics for each KPI. Determine the KPI owners across the organization while ensuring the KPIs are disseminated to the right departments and individuals who can impact them.

For example, in order to improve NPS by 5 points by end of the year, the sales, customer success and technical support departments will need to mobilize. Potentially, they will have NPS and other operational targets to meet. Make sure they have visibility into the metrics and are held accountable to the targets so you can achieve the operational goals.

3. Drive Systemic Action–change behavior across the organization

a) Build awareness, secure buy-in and gain trust: While AI systems can generate valuable insights, these insights alone cannot drive value. For that, you need to ensure the right individuals take the right actions. If a customer success agent does not take steps to mitigate retention risk of a particular client—that the model indicates as “high probability to cancel”— the outcome will not change.

Before launching the analytics model to the organization, you need to gain the trust of the individuals. To gain trust:

• Build awareness of the issue being addressed and why is it critical: for the individuals and for the organization
• Outline the actions being taken to address the issue
• Describe how AI fits into the solution and why it is the best approach, while clarifying any misconceptions

b) Refine business processes: Once you’ve done that, define what needs to change in order to achieve the objectives. Knowing which clients are likely to cancel is valuable, now make sure the relevant people are reducing that risk.

To that end, define the required changes to the existing processes and communicate to all relevant departments and individuals. When a client who’s at risk to cancel calls technical support, the team needs to prioritize differently and take particular actions and investments to earn or retain the business. Also, the customer success department may need to proactively understand the client’s concerns and respond accordingly.

c) Align organization to drive change: To drive action from a systemic perspective, you may need to update existing training or provide new training to the relevant employees. Align incentives and performance management systems to focus on high-risk clients.

Customer success managers and their teams must focus attention on high-risk clients to affect the outcome. To impact change, you may need to train the customer success agents to deal with high-risk clients and potentially carve out specific financial incentives to retain them.

The framework will help improve your odds of success by translating insights into sustained, relevant actions. Refine organizational processes and behavior with a robust management system in place to ensure the organization is aligned to achieve its goals. Only then can companies expect to secure the ROI from their AI investments.

[1] Based on a survey of more than 3,000 company managers about their AI spend conducted by MIT Sloan Management Review and Boston Consulting Group.

[2] Forbes article “Companies Will Spend $50 Billion On Artificial Intelligence This Year With Little To Show For It”, Oct 20, 2020

* * *

This is the first installment in Pragmatic Institute‘s series of white papers by Sciata President Harish Krishnamurthy on ensuring ROI from artificial intelligence, transforming insights into action, and driving a cultural change in how your organization leverages data. Learn how Pragmatic can train your data team to deliver critical insights that power business outcomes.

Sciata is the trusted Data Science and Process Automation partner that delivers certainty of results and insights to help you develop and maintain a competitive industry advantage. Sciata brings more than a decade of experience working with Fortune 100 clients, delivering time to value by providing on-demand specialists, project capabilities, and managed service offerings to accelerate change.

The post Making the Leap from AI Investments to Business Results appeared first on Pragmatic Institute.

Often, these projects never identified the business problem to solve with data, created a hypothesis to test or verified that the necessary data was available. What’s more, there are persistent communication gaps between business executives and data scientists and data analysts, who aren’t speaking the same language. Stakeholders don’t understand the data or what questions it can answer, while data teams don’t understand what the business wants. 

Organizations waste time, money and effort as a result of these disconnects. They also miss out on valuable opportunities to apply data analysis toward business needs. 

That’s why Pragmatic Institute developed an approach not just for data analysis, but for businesses to define a problem and solve it with data—tackling problems that are actually going to be tangible and add value within the business. Phase by phase, let’s walk through the Pragmatic Data Analysis Model: a proven, optimized and repeatable approach for any data project or toolset.

Editor’s note: What follows is a transcript of the first section of recent Data Chat “Applying Data Analysis to Strategy: A Proven Approach.” It has been lightly edited and condensed for clarity. Watch the full webinar below.

Define: Focus on the specific business problem you want to solve with data

The first step is defining the business problem. One of the big problems with a lot of businesses is they want to hit the ground running, “Let’s dive in and get to it.” Oftentimes, you skip this really important step, “Let’s really understand and define what it is we’re trying to accomplish.” 

In data, it’s absolutely critical to really deconstruct what the business is trying to understand about itself and the goals it is trying to lay out, and then bring it back into the actual data. I’ve seen time and time again the business is skipping the step of defining: What is the business problem? What are the hypotheses you’re trying to prove or disprove in this initial step? 

They’re diving right into the analysis instead of really trying to define what it is and how that is going to affect the business goals and getting buy-in. If you’re not getting buy-in from the business stakeholders, then it really doesn’t matter what you’re analyzing or what you’re going to come up with.

You’re not setting the right parameters. You’re not setting the right expectations with the people who are going to have to do something with the results of whatever analysis you’re about to embark on.

Prepare: Explore the available data and the most useful methods

The second step is preparing for that. So once you’ve defined it, then you’ve got to really dive in. Anybody who’s done any extensive data analysis knows that once you start really getting into the data, things start to change.

“You’re getting into data diligence to start framing all of these pieces  so that you can rethink what’s actually possible and feasible with the data itself.” 

You start finding that, “I’m missing this piece.” Or, “these databases don’t connect,” or “I need to supplement this external piece of data.” There’s a million different things that could go wrong. Once you get into the data itself, that is either going to change the cost of it, the time of it, or what the potential outputs of it are.

So once you get into preparing this, which means some part of the analysis, it doesn’t mean that you’re completing the analysis, but you’re getting into data diligence to really start framing what all of these pieces are going to be so that you can rethink what’s actually possible and feasible with the data itself. 

Once you’ve done that, then you need to rethink how you define the business problem and all of the hypotheses. You need to prepare to go back and refine that with the stakeholders.

Refine: Revise questions and expectations as necessary

The next step is to go back to the stakeholders and refine those statements. This takes some courage. 

This is something that data scientists and analysts are not being taught how to do. You need to actually go back to the sponsor of this project and say, “You asked for this, and we can’t deliver this with this data in this timeframe with these parameters. With the data that we have, if you want that, these are the things that need to be added to it, or we can deliver this or that with these changes in parameters.” This is extremely important. This is a step that doesn’t happen in most organizations.

This is why you get things called “scope creep” or a failed data project. Because it goes from a three-month project to a six-month project to a nine-month project to a 12-month project, only to deliver something that the stakeholder didn’t want and didn’t care about. All because you never went back and refined it after you went deep into the data to really understand what’s possible, feasible, and go back to the stakeholders to refine and adjust the parameters of the project.

This is incredibly important in making sure you’re setting yourself up for success right out of the gates before you even get in and start analyzing the data. 

Analyze: Build models to find actionable insights

When it comes down to being precise about what you’re trying to do, you can think about the difference between predictive analytics and prescriptive analytics. Predictive analytics, which is so common in data science, is building a model that can predict what people will do in the future, or that can predict what a human would do in the same situation.

It’s an extremely important task. It gets an enormous amount of attention. But most of the time, when a person comes to you with a business problem, they’re not asking for just a prediction.

They want to say, “Tell me what to do.” And that’s a fundamentally different question that requires a different kind of data and a different kind of analysis. And this leads into the “analyze” phase. 

Most of the time, when people get a problem, they want to start jumping immediately to the models: “I’m going to build a neural network to do this. I’m going to build some machine learning algorithms to do that.” Those are very good for predictive models or correlational models. But when you’re trying to come up with a business strategy and telling people, “This is what you do as a result,” you’re going to need to simplify it to something that can be easily interpreted and put into action.

“Simple methods can sometimes be the best. ‘Analyze’ means choosing a method that is going to be most likely to give you these interpretable actionable insights.”

Two themes came up constantly as we’ve prepared this course, Business-Driven Data Analysis, for Pragmatic. As this model got developed, the idea was that we are looking for actionable insights. Your analysis needs to tell you what to do next. We’re also looking for a good return on investment. We have to show that this is going to be the best way to reach a particular outcome.

In terms of analysis, it actually often involves keeping it a lot simpler. I have worked on projects where the best insights from our data were from a spreadsheet join. It was an organization that just simply never joined the data tables for their clients with the data tables from their donors. Once we joined those two, we were able to help them identify donors with high potential. They were a nonprofit. So that was really important.

Another one, it was just a pivot table where we said, “This is where you’re getting your donors.” And they had never done that. So these simple methods can sometimes be the best. “Analyze” means choosing a method that is going to be most likely to give you these interpretable actionable insights. It’s not necessarily the standard, less usual suspects in data science. 

Present: Communicate actionable insights and next steps to stakeholders

The fifth step in this model is “present. This is where you have to focus on actually telling the stakeholders what your actionable insights are.

I know that most analysts are not comfortable with that. But the whole point of conducting the project in the first place was to find out what to do next. Analysts need to be able to say: “This is the business solution.”

You had a business problem—not a data problem, a business problem. You are using data to help provide a business solution, which says, for instance, focus on this particular market, promote this item more, drop this location. Any one of those would be important for presenting as clearly as possible: the business problem, the data used to answer it, and the business solution that can be justified by the data.

* * * 

To learn the Pragmatic Data Analysis Model in depth and get hands-on practice with real-world business scenarios, sign up for our new course Business-Driven Data Analysis. The course is also available as private training for data teams.

The post Empowering Data Teams to Solve Business Problems with the Pragmatic Data Analysis Model  appeared first on Pragmatic Institute.

Some businesses have a single person responsible for product while others have an entire product team complete with a chief product officer, a vice president of product, two directors and three product managers. 

As an executive recruiter focused on product management for software start-ups, I talk with product leaders all day about their vision and the challenges they face while scaling organizations. 

Then, I help these companies strategically build out their product teams.

In this article, I want to teach you how to strategically think about building balanced product teams. 

The Two Types of Product Professionals   

Typically, product professionals can be broken down into two types of people: those who are market-facing and those who are technical. 

A market-facing product professional is attuned to the market. They tap into clients, prospects, the sales team and general market observations. They are usually forward-thinking and create the overall strategy and vision for the product. 

The technical product professional works closely with engineering, development and design to define and help implement the technical steps needed to bring the product’s vision to life. 

While there is often overlap between the two types, people in product usually gravitate toward one side. An ideal product team, however, has an even blend of market-facing and technical product people. 

What Happens if a Team Doesn’t Have Balance? 

Answer: You could fail to read the market properly, which could result in too much emphasis on feature building rather than addressing the real needs of the market, which could lead to building the wrong product. 

According to a study, 49 percent of the product managers surveyed stated that the biggest challenge they face is the inability to carry out appropriate market research that validates whether the market needs the product they are building.

Conversely, there could be too much emphasis on the vision without a concrete execution plan. 

Either scenario can frustrate the team and those around it, possibly reducing morale and increasing turnover. 

How to Create Balance 

Good product leaders mitigate these risks by first analyzing their own strengths and weaknesses and those of the team and then filling in the gaps accordingly.

For example, if a company has a director of product with a deep technical background but little experience on the market-facing side, adding a product marketing manager might be a good counterbalance. 

On the flip side, if the company has a chief product officer with a deep technical background, it may want to bring on a few product folks who are more strategic and market-facing. 

While this sounds straightforward, building the right product team may require some trial and error. Inevitably, a team will evolve as its team members grow and progress in their careers. 

Prepare to Pivot

Your role may also change. For example, you could have a technical background but move into a strategic role over time. 

Similarly, the composition of your team may change as the company scales and the market matures. 

What you need today could look different from what you need in 6 or 12 months. Finding the harmonic balance between market-facing and technical product professionals is the ideal state, not a goal that can be achieved overnight. 

For software start-ups, the biggest hurdle in building the right team is a lack of resources. Maybe a company needs two product managers, but it only has the resources to hire one because it is waiting for the next round of funding. It is best to play on the strengths of others to help bring the product to the next level. 

“Amalgam Rx”: A Balanced Team Ready for Anything

One of my clients, Amalgam Rx, is a small and growing healthcare software company. Amalgam’s scrum team is cross-functional with leads from each discipline. For example, client leads manage Amalgam’s clients, a development lead is over-engineering, a project lead is the scrum master, etc. 

The client lead is 100% customer-facing. They listen to the customer to determine Desired Outcomes (i.e., the “why,” not the “what”) and share that as a possible Opportunity (problem to be solved) with development and UX.  Development creates Solutions to problems.  UX does both: they capture “whys” and create UI Solutions. Amalgam uses Teresa Torres’ “Opportunity Solution Tree” to keep Desired Outcomes, Opportunities, and Solutions clear and distinct. This is helpful because clients often approach them in the reverse order, starting with, “I want you to build this for me.”

An example of how they’ve maintained balance across their team is illustrated in a quick, six-month transition from being a scrappy start-up, focusing on getting a proof of concept (POC) into a market, to a small growth company managing many possible opportunities. 

“We switched from Development engaging directly with client users to optimize the ‘whats’ of the POC, to engaging with clients using our new cross-functional way of working (WoW). A key moment came when our Client Manager explained our WoW to our #1 client. Instead of pushing back, the client saw it as a step forward and requested a copy of the process. They now actively support its use among their users” – Jay Butterbrodt, Head of Product at Amalgam Rx

Achieving a company’s long-term goals might take time but focus on a well-balanced Product team is one of the surest ways to get there. 

The post Building the Right Product Starts with Building the Right Product Team appeared first on Pragmatic Institute.

The good news is you don’t need a rockstar on your team to design incredible solutions and products.

What you need is a culture that rewards innovation and a process that inspires divergent thinking.

A Process That Consistently Yields Brilliant Results

Divergent thinking is a pathway for innovation. It forces teams to think differently about a project—whether they’re designing a new product, identifying solutions, or coming up with a new name for a rebrand. Divergent thinking pushes teams outside of their lanes and into different mediums to find inspiration for products and solutions.

It’s also a reproducible process. And the process beats out talent every time. 

At our agency, we discovered a scalable, reproducible process that inspires creative thinking and has become our sure-fire method to yield more innovative results. Our team doesn’t have to wait for inspiration to strike. Instead, our process fast-tracks brainstorming by generating the inspiration they need to come up with brilliant ideas and better solutions.

No need to keep waiting for a unicorn team member who’ll come up with the most inspired ideas. A divergent-thinking process can create the inspiration and innovation your team needs to produce better outcomes faster––and this process starts with one thing you might not expect.

Why Process Should Begin With Culture

As soon as they announced their merger, the leaders of CORHIO and Current Health—two leading organizations in the health information exchange industry—hired a marketing firm to work on their rebrand and renaming process.

But the agency’s work resulted in a name and identity that CORHIO and Current Health felt didn’t represent the organization.

With time running out, they reached out to our agency as a last-ditch effort.

Instead of going straight into digging up spiffy new names, we dove into their organizational culture. If we didn’t build on culture, we knew our work would be wasted. We’d end up offering a one-sided rebranding solution (one that could be slapped onto any other organization in the same vertical) instead of a name and identity that uniquely aligned with the brand and set it up for sustained success.

Phase 1: Crafting an Aligned Culture

The challenge was, CORHIO and Current Health didn’t have an internal organizational culture. They had two separate cultures that needed to merge together—or be completely reconstructed. 

Picture a newly blended family, with members on both sides wondering, “Are we going to move into their house? Or are they moving into ours? Do we have to learn a new set of household rules? Or do they have to learn ours?”

This is something product people encounter often––mergers and acquisitions suddenly force two teams together and often mean brands, products and services also need to merge. If the team leading this effort isn’t brought into the vision and guided by a set of principles, it can lead to serious performance complications down the road. 

With this in mind, we knew we’d need to start with the culture-building process––crafting a shared culture that both sides could buy into and that would align with the brand to drive it forward. 

We start this phase by leading them through a process that helped them shape and define a new mission, audience and purpose that everyone agreed on. Through in-depth workshops, we helped the team operationalize their values into clear, behavior-based statements that inform their employees’ daily actions and decisions.

Our framework for this process is built on the five pillars of brand.

The 5 Pillars of Brand

We believe brand is best understood as the sum of five parts:

• Culture: who you are—the convictions, values, and behaviors defining your brand
• Story: what you say—the brand and marketing narrative communicating who you are to your audience
• Service: what you do—simply put, the product you sell or the service you provide
• Experience: how you feel—the physical or digital touchpoints of your brand
• Identity: how you look—the visual and aesthetic qualities of your brand that your audience sees first

While identity tends to be the pillar most people associate with brand, the pillar of culture is what most determines and sustains success. 

Culture cascades into everything a brand does. Like glue binding the brand together, it frames the other pillars into alignment and stabilizes them into a single entity that makes an authentic promise and consistently delivers on it.

Doing this culture work first allowed us to see rebranding options we may not have otherwise seen. It moved us beyond one-dimensional thinking and out of CORHIO and Current Health’s vertical so we could approach the problem from other points of view.

With the first phase of internal alignment done, now we could tap into their culture to find the solutions to their rebrand.

Phase 2: A Naming Process That Wins Every Time

Three, two, one, go! For two minutes, our team huddled over brightly colored stacks of sticky notes, furiously scribbling down whatever nouns came to mind—any nouns, whether or not they related to CORHIO and Current Health.

At the end of two minutes, we posted our notes on the whiteboard. Then we went for another two-minute round, this time with verbs. In the following few rounds, we wrote down emotions, places, and animals––yes, animals. 

The point of these rounds of exercises wasn’t to come up with words that we’d actually try to use for a name. The purpose was to push ourselves out of our normal lanes in pursuit of completely different perspectives and concepts.

The next step in this divergent-thinking process was to look for patterns and group all our notes into the categories that appeared, such as care, movement, weaving, and clarity. With these groupings, we started brainstorming actual names, drawing inspiration from the sticky notes or whatever trains of thought the divergent-thinking exercise had opened up.

By the end of the session, we had identified two solid names. We mocked up two sets of logos, crafted accompanying narratives, and presented them to CORHIO/Current Health. Their team instantly fell in love with one of the naming options.

Then their legal department rejected it. It was too similar to another healthcare organization’s name. That’s when we realized just how challenging it was going to be to come up with a new name in healthcare, one of the largest industries in the U.S. Nearly every name imaginable is already taken. (Think we’re wrong? Try coming up with a good name for a healthcare company and Google it to see if it already exists.)

So, we went back to our divergent-thinking naming process. After several more naming sessions, we developed an original name in the health industry: Contexture. We crafted a clear narrative and visual identity that reflected the brand and aligned with the culture. The CORHIO/Current Health team loved it, and it sailed smoothly through the legal review.

The Twin Tactics of Culture and Divergent Thinking

This divergent-thinking naming process has been successful with every one of our clients. It wins out every time against the work of other agencies because it’s detailed, thorough, and gets to the heart of a brand’s culture.

Imagine your team needs to create an emotional connection within an app’s user experience. You can use divergent thinking to get your team out of your normal lane and draw on other mediums to find solutions and inspiration. Have your team address the problem as if they were designing a greeting card, a first-class in-flight menu, or a landing page for a diaper service. 

Divergent-thinking processes help teams approach problems from completely different angles, helping them land on solutions they might have otherwise missed. Process beats out talent every time.

Some people might wonder if divergent-thinking processes are risky. Could they lead a team off-brand? Sure. They might. But that’s where culture comes in. 

When aligned to reflect the brand, culture acts like a guardrail keeping creative teams on target. An aligned culture ensures that teams have the freedom to innovate and attack problems from different angles while staying on-brand and contributing to the larger brand narrative.

If you can tap into the twin tactics of culture and divergent thinking, you’ll be able to deliver on a promise that none of your competitors can match.

The post Process Beats Talent: A Case Study on Divergent Thinking, Company Culture, and Branding appeared first on Pragmatic Institute.

This scenario will sound familiar to anyone who’s tried to build a marketing automation system, or has transitioned a traditional marketing group from research-based targeted marketing to localized database marketing.

My data analytics company was acquired by an email marketing firm that was trying to become a data-driven marketing company. Shortly after, we started transitioning from a services/agency model to a full product company. Part of that plan was to build a targeted marketing automation engine.

I joined the project after it was already in progress. The customers with whom we chose to build a pilot program were a few large chain restaurants. They did mostly TV advertising and had started doing digital ads, but they were not yet effectively targeting. The first few customers would get a big benefit: a very customized approach to how the software and service would be set up to suit their needs.

The CMO (and project sponsor) at the first client was a seasoned researcher and very proud of their segmentation work based on custom psychographic clustering work. The mandate to our team was to take the clusters they were using for TV advertising and use those as the basis for the one-to-one digital targeting program. This would allow them to align their traditional campaigns with their digital campaigns.

Nice and easy, right?

The project team took this mandate literally and dove straight in. The data team’s objective became dissecting the composition of existing psychographic clusters and assigning each of the 2 million members in the client’s customer database to a cluster. This would enable the client to market to the individuals based on the previously determined media segments.

Because psychographic clusters are proprietary, this was no an easy task, and it ended up being extremely expensive. Originally estimated at over $2 per record, the clusters were eventually brought down to under 30¢ per record. Still, with 2 million records, this was a major investment for the client in new specialty data purchases.

As anyone who has worked with psychographic clusters could have predicted, when the segment identifiers were applied to individual people in the database, the limitations of this approach became painfully evident. Serving up an ad on TV and getting some of the audience wrong is low risk. However, when you direct message the “wealthy suburban dads” cluster only to find that 5% are women, 17% are not fathers and 12% are below average income, etc…the errors are not forgiving.

What’s more, the kinds of campaigns that were meaningful for mass media translated poorly to one-to-one messaging campaigns, so even once the database was fully segmented per the directive, the marketing execution wasn’t useful.

The project was an expensive disaster. And the most painful part is that we actually had rich behavioral and preference data for most of the 2 million customers in the database, which told us about their prior eating patterns, where they lived, who they dined with, what occasions they celebrated at the restaurant, how old they were, and they were willing to tell us more.

Eventually the project and the product was realigned around the behavioral data, but the missteps could have been avoided if the business problem had been clearly identified and refined around the available data and budgets at the beginning. The real business goal was to target customers using the best available—and most actionable—data in a way that would be most likely to alter their behavior in the company’s favor (e.g., increase purchase frequency and/or the check average).

If the project team had implemented the Pragmatic Data Analysis Model and asked the right questions from the outset, they may have avoided the pitfalls of a business leader who confused research acumen for data/analytics understanding, and a data/analysis team that was more interested in diving in than really understanding the intricacies of the business problem they were trying to solve.

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Want to learn how to connect your data analysis to business problems for greater impact by leveraging the Pragmatic Data Analysis Model? Register for Pragmatic Institute’s new course, Business-Driven Data Analysis.

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During the webinar, Steve:

1. Discusses how to identify the key elements of a successful competitive analysis program
2. Offer tips for optimizing your CRM, your customer relationship management platform
3. Explains how to minimize the garbage so that you have accurate win-loss stats
4. Explores why you need competitive intelligence as an ongoing record of truth for multiple reasons

Watch the Full Webinar

The Risks of Underinvesting in Competitive Intelligence

Competitive analysis programs are typically shacks. In other words, your program is under funded, it’s underappreciated, it’s undervalued. But, this area of the business should be a palace.

Competitive intelligence is where companies should be investing more because the risks of under investing are:

• You may have an inferior product
• There may be product-related concerns or opportunities you aren’t discovering in win/loss interviews or from the sales team
• There may be a competitor who will consistently outperform your company
• You may lose revenue

Many competitive analysis programs they’ll track win/loss, but they don’t track sales cycles.If your sales cycle varies by competitor, you might have a problem with one of your competitors. Which means you want to do some additional training to help reduce that sales cycle.

But most of all, and this is the biggest problem with under-investing in competitive analysis: You’re. Flying. Blind.

A mantra that I’ve just taken away from my Pragmatic training from years ago is, “Your opinion, although interesting, is irrelevant.”

We want to maintain a living record of truth for both product planning and sales enablement.

On the product management side of the house, we need to know where to make product investments.

Where do we have a gap in comparison to other platforms? It might be a feature gap, a product usability gap or pricing gap.

On the sales enablement side, you want to equip your sales team and channel partners with accurate information. The worst thing you can ever do is provide bad Intel to your sales team. They’re going to look bad. You’re going to look bad. We need accurate information so we can increase our win rates and also shorten our sales cycles.

Those are the overall goals of a competitive analysis program.

#1: Configure your customer relationship management platform to capture accurate win-loss stats.

For CRM’s, it’s garbage-in, garbage-out, and there are ways that you can configure your CRM so that you can have 98-99% accurate competitive intelligence.

In terms of win-loss statistics, the first thing I want you to do is to create a pick list of competitors in your CRM (not free-form fields). You may compete against multiple competitors in a deal, so you want to list all known major and minor competitors.

For that rare instance where you’re not going up against a competitor, you may want to have an “unknown category.” Maybe it’s a competitor that’s brand new, that’s not on the list.

Now here’s the important part; require the sales team to select at least one option when an opportunity is forecasted at a specific percentage based on what makes sense for your organization.

In other words, once the opportunity is forecasted it might start at 10 percent, and then when it hits 30 percent it is fully qualified.

To qualify for the opportunity, you should also ask about what other vendors they’re considering.

What I mean by technical enforcement, is let’s say hypothetically 30 percent is your qualification criteria. The salesperson tries to save the opportunity at 30 percent or higher. It won’t let you, it’ll have a pop-up message requiring you to enter this mandatory field.

In other words, this pick list, checking at least one box on this picklist is technical enforcement. It’s a mandatory field. The salesperson can’t save the opportunity until they’ve selected one or more competitors.

This is a big step toward reducing the garbage that you get in your CRM. So make sure you have technical enforcement and a pick list, no free form fields.

The next tip here, make sure if a competitor is no longer active, mark them as closed, basically a cold case, you’re not worrying about them anymore.

You also need a picklist for reasons why you win and lose, do not make this a free form field.

Your picklists are going to vary by your organization and your products, but here are some things that you might want to consider:

• Product functionality or maybe superior product performance or usability
• Integrations with the third party
• Pricing concerns
• Personal connections (the salesperson is a friend or old colleague of the prospect)
• Contact has stopped responding
• Prospect is out of business

We want to generate win-loss reports, but we also want to audit those reports.

#2 Generate quarterly and annual win-loss reports that are accurate.

We want to export quarterly closed opportunities. Then, look at the descriptions for the wins and the losses.

There are two ways you can track wins and losses: dollars or deals (many organizations mistakenly look at deals).

I like to track win-loss stats by both. But if you just want to pick one, go dollars, your win-loss by dollars is in my view more important than your win-loss by deals..

Don’t just do it globally, you definitely want global stats, but you also want regional stats. So look at your win-loss stats by your different teams because it can help from a training perspective and then from a product planning perspective.

#3 Aggregate Competitive Intelligence.

Have a living record of truth, and update it often.

What we do at CyberEdge is we create a spreadsheet called a feature comparison matrix (FCM).It’s where we track things that matter to us. We could track software, components, competitor components, key features, performance stats and anything else we think might be appropriate.

The spreadsheet is for internal use for the product management, product marketing and engineers. It’s appropriate to update this spreadsheet when you or your competitors have a product change. It’s also appropriate to log both public and non-public intelligence.

#4 Conduct periodic customer win-loss calls.

Don’t just talk to the losses, talk to the wins too. There are things you can learn on both sides. I know this is something that pragmatism emphasizes, and this is mission-critical.

You’re going to have a lot of data, but we want qualitative data too. So, interview the customers that chose you over your major competitors and chose your major competitors over you.

Interview a minimum of five wins and losses at the minimum. It’s anecdotal data, so preferably 10 wins and 10 losses per competitor.

Repeat this quarterly, but at the very least annually. You can also choose an outside vendor, but if you do, make sure they have technical expertise.

#5 Create impactful competitor battle cards

Here are some of the things that you might want to include in a typical battle card.

You want to have some key themes or key takeaways. I recommend three because people seem to remember things in threes.

Challenge the sales team to memorize these three things, so that they’ll have the info right when they need it. You might want to have key capabilities and a comparison table.

If your solution or your competitor solution has many different components then consider a components table. A list of your major advantages and also your competitor’s advantages.

We want to equip your sales team and channel partners with information related to your competitor’s advantages. But don’t just state the advantages, give them some responses.

You might want to say that’s on our product roadmap and it’ll be coming out next quarter or if your competitors can integrate with a specific platform, show them ways that you can also.

If your salesperson is the first one into an account, help them set traps in their questions. Questions that your salesperson can ask your prospect that inform them of shortfalls of their opponent, such as they don’t have this feature, capability, etc.

This is all strictly for your salespeople, you don’t want to inform customers about your disadvantages and shortcomings.

Reinforce key themes and key takeaways, challenge your salespeople to commit them to memory. Then update your battle cards when your competitors update their stuff and remember that intelligence from the battle card is pulled from that spreadsheet. What we call a feature comparison matrix.

Don’t just focus on your advantages, address your competitors’ advantages.

#6 Conduct regular sales training sessions

Provide training against each major competitor to every internal salesperson and channel salesperson once per quarter.

You can’t train them enough. If you’re doing it through a webinar platform, record it and make sure it’s available to them, but provide that training. Teach your audience how to plant those landmines that I just talked about.

Steve Piper is a Pragmatic Institute alum. He has more than 20 years of product marketing, product management experience.

He is the co-founder and CEO of CyberEdge, a high-tech marketing agency. CyberEdge works with companies to do competitive analysis, content development, market research reports, custom books and eBooks and we work on a project basis or retainer.

“I also have an undergrad degree, master’s degree and over a dozen technical certifications, but I can tell you there’s no course that I’ve ever taken that has been more impactful to my career than what I learned at Pragmatic Institute.”

The post 6 Key Elements of a Successful Competitive Intelligence Program appeared first on Pragmatic Institute.

It’s a question I get often from aspiring analysts, and a good one—they see that providing value to the organization is indispensable to their desired role, and that crunching the data is just a means to the end. And after all, one can only focus on growing in so many areas…what’s the better knowledge investment?  

To answer this question, I’d like to (appropriately enough) cite data from research advisory Gartner: by 2022, 90% of corporate strategies will explicitly mention analytics as an essential competency. 

Data is now a core lever to creating business value. Craig Mundie, senior advisor to the CEO at Microsoft, put it like this: “Data are becoming the new raw material of business.”  

Mundie’s declaration echoes that of British mathematician Clive Humby, who stated that “data is the new oil.” As more experiences become digitized, more data is collected. But that data is only useful when it’s been “refined:” not only cleaned and transformed, but placed into its proper business context.   

“Data is only useful when it’s been ‘refined:’ not only cleaned and transformed, but placed into its proper business context. “

This close pairing of data and business means that analysts can comfortably grow their technical proficiency and business acumen at once, particularly in how they provision their tools (in particular, when choosing between open source and proprietary solutions), and how they work cross-functionally to connect business value to data. 

Ultimately, an analyst can only communicate business value insofar as they have a technical command; but this technical command is of little value outside its business environment to begin with. 

You Are a Customer of Technology 

As an analyst, you’re often seen as a supplier of reports, insights and so forth. But it’s worth remembering that to make those deliveries, you’re a customer yourself – of data technologies like spreadsheets, databases and business intelligence tools. 

You may not be responsible for budgeting, selecting or deploying these applications. But how you deliver your products and services is undoubtedly shaped by the business models of the vendors whose tools you use. You can unpack the consequences through a combination of technical and business skills.   

The Growth of Open Source 

As a burgeoning data analyst, have you used R or Python? What about PostgreSQL or SQLite? If you’ve worked with any of these, then you’ve worked with open source software. 

According to software company and open source pioneer Red Hat, open source is “designed to be publicly accessible—anyone can see, modify, and distribute the code as they see fit.” This open source software is free – compare this to proprietary, “closed source” programs like Tableau or SAS.  

In the past, many organizations were hesitant to adopt open source for fears of security breaches, intellectual piracy, and so forth. In fact, Microsoft famously waged a crusade against open source for many years – former CEO Steve Balmer in 2001 even went so far as to compare the open source operating system Linux to a “cancer.” Flash forward a decade later, and current CEO Satya Nadella put up a slide declaring “Microsoft ♥ Linux.” 

What changed?  

As Red Hat (which coincidentally offers Linux-based products and services) goes on to explain in its definition of open source, open source is “often cheaper, more flexible, and has more longevity than its proprietary peers because it is developed by communities rather than a single author or company.” 

Microsoft and others came to appreciate the rapid execution and iteration on open source projects, as product lifecycles have continued to decline during the digital era.  

The distributed, flexible and asynchronous nature by which open source is often built only became more of a benefit during the coronavirus pandemic, as teams had to adopt similar methods of collaboration. 

In fact, cloud-based code management provider GitHub, where many open source projects are hosted, found a 40% year-over-year growth in open source project creation between April 2019 and April 2020. GitHub holds the code for many of the most popular open source analytics packages, such as R’s ggplot2 and Python’s pandas. (GitHub, as it turns out, has been owned by Microsoft since 2018.)  

All that said, there can be some amount of risk for relying on a software that is not commercially supported and is maintained by volunteers, who may not have any succession plan in place for their project. I myself have run into bugs within open source packages that caused incorrect results. Fortunately, I caught them before I used the results in a presentation. 

Proprietary vs. Open Source: A Technical Choice with Business Impact 

What does any of this have to do with the technical versus business skills divide? You’re likely to work with many tools, from spreadsheets to databases to BI systems. Some of these may be open source, some proprietary. 

If you use open source, you’re likely to find one freely available package (likely out of tens of thousands) to kickstart whatever your use case may be. At the same time, that package may be in a beta state at best. Proprietary tools are likely to be more vetted, but at the expense of speed to delivery and variety of tools.   

Understanding the pros and cons of each framework, you’ll be more productive and anticipate possible technical roadblocks. Your stakeholders may not care about the licensing of the software you’re using to conduct your work – but they might care about turnaround time, project risk or cost. 

Is developing an intuition about open source versus proprietary tools a technical or business skill? In many ways, it’s both.  

The Data Is the Business 

Whether it’s a discussion about what software to adopt or how to design a product, technology has become so embedded into an organization that it can be hard to extricate the two. 

As discussed, data has become the raw material of business. But, like cash, good data doesn’t just grow on trees—someone needs to collect and analyze it with the needs of the business in mind. And that’s where you come in as an analyst.   

The burgeoning market for wearables technology provides a useful case study here. Say your organization wants to develop a smart wallet (And yes, there is actually such a thing… look it up!). While the wallet may have some aesthetic appeal, its main value driver is the data that is collected and used to personalize the customer experience.  

“As a data analyst, you can help both gather the data requirements of the product and evaluate its potential profitability.” 

As a data analyst on this smart wallet project, you might work with user experience researchers and product managers to design and map the user’s journey: how they will use the wallet, and where will the benefit be? What additional features could help? 

Importantly, you’ll also have to figure out what sort of data needs to be collected, and how it will be transmitted. You may work with engineers or product designers to develop that last part. From there, you might work with finance to determine pricing and feasibility of adding various requested features.  

What’s inherently powering the business value in this example is data—without the data, the smart wallet wouldn’t offer much added value over a regular one. As a data analyst, you can help both gather the data requirements of the product and evaluate its potential profitability. 

You stand to be of tremendous value on such a cross-functional team where no one can succeed unless by leading with data. It’s hard-pressed in such a situation to parse technical from business value here.  

Technical Expertise Requires Clear Communication (and Vice Versa)  

Here’s another way that it’s hard to untie these skills: as an analyst, the better you know the ins and outs of your methods, the more you can spell out what the results can and can’t tell your stakeholders.  

Inferential statistics provides some great examples here: for example, a FiveThirtyEight journalist found that even very few professional scientists could translate the meaning of a p-value into everyday terms. 

Yet scientists and business practitioners alike often rely on this measure alone for making decisions. As an analyst, you would never let your business partners act solely based on some key performance indicator (KPI) that no one can explain; why should it be any different for you?  

By taking the effort to deeply understand the mechanics of a subject like inferential statistics, you as an analyst are in a better position to communicate the right findings in their proper context to your audience. For example, in my book Advancing into Analytics I provide two ways to communicate the same findings to an audience:  

Imagine you were a research analyst at a bank reporting the results of this study on home prices to management. These managers wouldn’t know where to start conducting a t-test if their careers depended on it—but their careers do depend on making smart decisions from that analysis, so you want to make it as intelligible as possible. Which statement do you think will be more helpful? 

• “We rejected the null that there is no difference in the average sale price of homes with or without air conditioning at p < 0.05.” 
• “With 95% confidence, the average sale price of homes with air conditioning is approximately between $21,200 and $30,800 higher than those without.” 

Same results, communicated with different metrics—and it’s only with technical knowledge that such a distinction between what these metrics mean can be made. At the same time, framing this distinction in the right terms requires communication skills as well. You could, like so many scientists in the FiveThirtyEight article, know all about p-values but be unable to explain their value to a general audience.  

Analysts require an aptitude to deliver what an audience will really care about in a medium that engages them. The deeper your technical skills run, the better you’re able to interpret the results. And the deeper your business skills, the more capable you are to put these into a relevant context. It’s hard to get far in one without the other.   

The Two Lungs of Analytics 

To close on my reflections of the interoperability of technical and business skills, I’d like to draw an analogy between learning analytics and, of all things, weightlifting. Weight machines are used to isolate and strengthen a certain muscle group. Working in isolation, one muscle group at a time, has its benefits for the same reasons it’s good to stick to one task at a time instead of multitasking. But any trainer will tell you that such isolated motions are unlikely to be encountered in the real world; free weights offer an alternative that more realistically approaches everyday weight strength needs.  

The same goes for analytics skills. Isolating technical and business skills for the strength of growing each has its benefits. But when you get to a job, they’ll become so intertwined that it will be hard to tell them apart. Don’t sweat too much on separating out your skill set; instead, look at the ways that technical and business skills are like two lungs for today’s analyst, working together at all times.  

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Want to build up your business acumen, communicate more effectively with stakeholders, and advance your career in data? Register for Pragmatic Institute’s new course, Business-Driven Data Analysis.

The post Should Data Analysts Gain Technical or Business Skills … Why Not Both? appeared first on Pragmatic Institute.

How should data professionals craft a narrative around their findings to contribute more strategically? In a recent episode of Data Chats—a PragmaticLive podcast by Pragmatic Institute and The Data Incubator—host Chris Richardson is joined by Christopher Laubenthal. Christopher is a data and visualization consultant for Lockton Companies, information designer, data visualization designer, podcaster and the creator of EdWise. Listen to the conversation in full or read the write-up below.

(This interview has been edited and condensed for clarity.)

Q: Tell me a little bit about how storytelling plays a role in your data.

Storytelling is my everything because it starts with the premise. My job is to parse a story for the data. Once we start to get the results back, I’m beginning to think about how those results relate to the context. How do these results relate to the challenges, the problems and the prompt? And I start to think, how can I compellingly make this story come alive?

I quite literally use the storytelling pyramid that a lot of us learned in school. Which is the current state, inciting incident, rising action, climax, falling action and new state. I also make sure that the story has clear application to strategies and problems that have existed in the company.

Q: How do you tell better stories with data, and why do you think that is a challenge for so many people in data science?

First, you have to have a focal point. But the challenge here is how do I pick what I think is the most important thing? And if I’m wrong, will they think I’m wrong all the time?

And so here’s the deal: Get over it. Because you could be.

You can’t keep giving them tables and saying, it’s not my job to provide insight, it’s not my job to bridge the context, because they’ll let you go. They’re in a bubble where they’re constantly having to make decisions. What they want more than anything is insight wrapped in a story. I would spend time hammering down their:

• Strategies
• Interests
• Focus

That becomes your secondary dataset.

You need to become the data person who is speaking their language and understands that strategy is at the heart of their decision-making process. You need to ask yourself, how can I use what they’ve told us about strategy? Be thoughtful about it, and then write down their responses, because guess what? You might spot something that they don’t know.

When was the last time the data person gave strategic feedback to the C-suite on a lack of management around strategic objectives?

Q: Communication is a two-way street between data analysts and the people they work with. If you have one-way communication, then you’re failing in some way. But it’s also hard because data analysts aren’t necessarily given strategic information. How would you recommend facilitating these conversations?

First, data professionals are amazing at doing the homework. If [the stakeholders] are not giving you the information, I assure you somewhere there is an internal document on the intranet for your company with the strategy—if not the minutiae of the specific quarterly goals, then at least a purpose, a vision, a mission, and some key indicators.

Second, go to your direct report and say, what we did was great for the past, but we want to give you insight into the future, which means adding in story and understanding so that we can make your job easier. And you can focus on the bigger picture issues and provide that much more insight into the system. But to do that, we need a special tool and it doesn’t cost us any money— it’s strategic documentation now.

We’re only going to use this information to embed a story and validation to this work. It doesn’t have to be the yearly review with the full board. It can be a regional thing.

Who’s going to tell the story better, the person who thinks they understand the data or the person who both wrote the data and did the analysis?

Now, the key here is to make sure that the discussion is about lifting them up. This is where you need to think strategically about what they can do if they had that out of the way. It’s okay to say things like, “Janet, what would you do if every quarter you didn’t lose four days to this prep cycle we’re on? What if we could be more active on the narrative development angle?”

Third, find champions in the building, because I’m trying to describe data science in a way that builds bridges instead of walls. And being a little bit vulnerable.

Q: I’d like to know your thoughts on vulnerability. Maybe you could share potential data failures or missed opportunities, or where you put yourself out there. 

Sometimes the direct pathway is indirect. Life is not a Sudoku puzzle with one answer. It’s a game of high stakes chess where we don’t know the other player.

The first thing I would say is when you begin to embrace vulnerability, innovation begins. You start to have connections that you didn’t before. Importantly, you start to get information about the problem that you didn’t have already. However, being vulnerable and opening up your design process leads to the chance for successes and failures.

If we’re going to be more than just numbers, our design has to be intuitive and that’s different based on audience. So, the more interactions you have with your audience and the more vulnerability you’re able to engender, the more feedback you’re going to get.

One example I have for you related to is this:

I was working at UKC as an event coordinator for continuing medical education. For every event there we had Likert scores. It was a fairly large amount of data. So on the left hand, you’d have individuals that we had who were public speakers, and the columns were different events. And then each box was a score.

I had additional data about those speakers. I knew what hospital they were from. I knew whether they were a man or a woman. I presented the analysis to our board, and it was interesting because we found that women were a full point less than the men speakers.

I’d been in the reviews and I had gone to every single one of those events. The women were no less amazing than the men. They were just as competent and had similar backgrounds. This leads one to believe that perhaps there was a bit of bias in the audience.

Guess what, when you’re dealing with doctors and nurses and pharmacists, implicit bias on the ranking of physicians in educational settings is not necessarily the biggest fire in the building. For me, being a young 20-something-year-old, it certainly felt like the biggest fire of the building.

That’s one of the challenges. When we talk about opportunities, the closer you get to strategy, the more you’re going to have a conflict of what you think is valuable and what they think is valuable.

You bridge that gap by respecting their point of view, fighting with them in an honorable way, passionately making your case about how this thing that you’ve identified will meet their needs, but then ultimately taking it on the chin. If they decide to go the other way, don’t be disgruntled about it.

If you want to play the game, you have to be okay when you lose.

Q: How do you suggest we have impassioned dialogue, but also be civil and productive about it at the same time?

The very first thing we must do is we must disengage our identity from data. We are more than just data. We are mothers, fathers, husbands, sisters, Christians, Muslims, Red Sox fans or Yankees, etc. We have all these different identities. The reason that feels so red hot at first (and we don’t say this out loud) is because we feel like they’re not saying that the data is bad; they’re saying we’re bad.

We should hear that the data is bad. First and foremost, we need to start to make peace with that process of being outside of something called “strategic risk consulting.” That’s step one.

Step two is to practice the conversation. Practice with a friend. I assure you, there will be a little bit of levity in the room. There will be a little bit of working through it, because you get to play the role of the person who challenges rather than the person who defends. That’s how you get better.

Here are some steps to keep a peaceful conversation:

• Strengthen yourself as a person
• Practice keeping the peace in the moment
• Use verbal strategies to keep the peace
• Prepare a pathway for success

What everyone wants to know is the pathway to success.

Visualize your development cycle and keep day counts. Here’s an example:

1. Here we had 26 projects.
2. This is what they look like.
3. This is what our development process looks like.
4. Let me walk you through the steps: 1, 2, 3, 4, etc.
5. This is where all the things are in that particular cycle.
6. Here’s the bottleneck.
7. Here’s why I think it’s a bottleneck.
8. Here’s the number of days: notice a week, two weeks, seven months…

Be able to go through steps with kindness in your heart. Realize that they need you and that’s why they hired you, but that doesn’t give you the right to make their decisions for them.

Set up a time to meet with them afterward in order to get more insight into the status of the company. That can make things a little easier.

For instance, you might find out that there’s a big merger coming, and they tell you the strategy surrounding that. At that point, when you bring them data information, you’re going to be a lot less offended by their decision if you have more insight into how that decision was made. Because imagine you didn’t have the merger knowledge.

Now you can say, “Hey I’m going to show you x information, just so you can see it. I know it doesn’t align with our immediate needs, but you should see it anyway for a frame of reference.” They’ll end up enjoying the information at that point, rather than becoming annoyed.

Q: If you were going to give two pieces of advice to a data analyst or a data team, what could they do tomorrow to get started or to improve?

Tip #1: Do your homework.

• Ask for the strategic documents, the goals, the accounting sheet
• Ask around for group meeting presentations
• Study the profile of presentations
• See if people are invoking the speech of all the documentations
• Understand the expectations of the audience

Tip #2: Have a conversation with leadership about your homework.

• Say things like, “Hey boss I would like us to level up our contextual understanding of leadership strategy, so that we can embed that strategy.”
• Pick the best person to talk to in the C-suite and say, “We want you to give us feedback on the profile that we’ve developed for the people we’re trying to serve and lift up.”

Q: Any recommendations for further reading?

These books:

• Obliquity: Why Our Goals Are Best Achieved Indirectly by John Kay
• Crucial Conversations Tools for Talking When Stakes Are High by Al Switzler, Kerry Patterson, and Joseph Grenny
• Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations by Scott Berinato 

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