Data Strategy Best Practices to Drive Value & Personalization

Data Strategy Best Practices to Drive Value & Personalization

October 21, 2019

Data is everywhere. We all have to deal with it, irrespective of our industry or sector. Every customer or donor transaction generates a data point. Simply collecting these data points cannot help you achieve personalization or derive value from your data. Data strategy best practices, when tailored to the needs of your organization, can deliver great value.

Data can serve many purposes. It can deliver insights, help you personalize your outreach, increase conversion rates, and deliver greater ROI. Furthermore, people are doing great things with data. Using “Data for Good” is becoming a growing phenomenon. So, the question is how can data help you?

Tom Monahan, Founder and Managing Partner of Norton Street Capital, recently shared insights on leveraging data to drive value and personalization at WE Prosper Summit 2019.

Read on to see how you can meet your organization’s goals through applying these data strategy best practices. 

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We’re All in the Business of Persuasion

Think of the last time you changed your mind about something. The process usually involves learning some news or receiving new data. You’ve learned something new that has led you to reconsider what you know. This means data is persuasive.

We’re all in the business of changing people’s minds. This could mean that they need to be convinced to give to your organization or to purchase your brand’s items. Your data strategy can help you accomplish this.

Data is like Oil

Clive Humby, British mathematician said, “Data is the new oil…” This means that data cannot offer value in its raw form. Like oil, it needs to be refined to deliver value. Furthermore, like oil, more value is accrued by the users of data than the owners of it.

So, how do you refine your data to ensure that it delivers value? Applying the core principles of strategy can help you derive valuable and actionable insights.

Core Principles of Strategy

At any organization, the overall strategy needs to be:

  1. Focused- If you find something that works specifically for your organization, pay attention to it and develop it to a point of exclusion of competing elements.
  2. Distinctive- Every player in the market cannot have the same strategy.  So, by understanding where you stand out and taking advantage of your position is ideal.
  3. Authentic- The way you stand out has to be true to your organization’s story. Your position should be organic. Furthermore, aiming for fairness is not always appropriate for your strategy. It’s better to stay out of fair fights and enter markets where you have a singular, unfair advantage over your competitors.
  4. Iterative- A strategy will do you no good if it is static. As much as you want to have a core mission and vision, your organization has to be flexible enough to accommodate what is contextual and relevant.
  5. Sustainable- Competition is forever. Although your strategy needs to be iterative, it also needs to be sustainable in the long run. If your strategy becomes plateaus, your organization could be headed for trouble.
  6. Valuable- Ultimately, it comes down to offering real value to your customer or donor. You need to offer a solution that makes a difference in your customer’s life. Similarly, as a nonprofit, you need to offer a solution so that your donor can make a difference.

These core principles are universal and can be applied to your data management. Thus, here are a few data strategy best practices to help you fulfill your fundraising or marketing goals.

Top 5 Data Strategy Best Practices

1. Sourcing

Don’t simply focus on what you can do, but on areas where you can excel. Many companies can claim that they have unique data. While that may be true, does your data have broad applications?

If the data you collect is accurate and informative but very specific to your industry and even more so to your organization, it offers no value to others. This means that your data cannot be an added source in your revenue stream. Similarly, when you source data from other entities, you have to ensure that it serves the broader purpose of your organization and not just a single department.

2. Staging and Structure

Save, combine, and protect data in interesting ways. Any of these could prove to be advantageous.

For example, WealthEngine goes beyond industry standards when it comes to data protection. Our regularly audited SOC 2 Type II certification sets us apart from other wealth data analytics solutions. This certification ensures all of our users that no one can misuse or hack the data they are compiling.

3. Analytics

Your data strategy could lie in combining your unique data with other data points. By doing so, you may be able to predict something that no one else can. While your data may be proprietary and well-suited to deliver insights to your organization, you can strengthen this even further with third-party data.

For instance, you may want to run a direct mail campaign. Your team already has a list of prospects and their addresses. You can make your campaign much more effective through personalization. Your team can add demographic and lifestyle data on these contacts through a wealth screening. Now, you are working with a broader picture of who these people are and what makes them tick. It is easier for you to deliver more impactful messages to them.

4. Value Creation

Your data strategy must create unique value. It is better to avoid broad, obvious approaches if you want your approach to be sustainable. Similarly, avoiding gimmicks to provide real value to your customers and donors will increase their LTV.

You could stand out by offering convenience, unique/intuitive bundling, economic benefits, or even emotional benefits. For instance, let’s say you are a university. Typically, you will have various departments collect data for their specific purpose. Admissions has data on incoming students, while alumni relations has data on graduates. Similarly, various academic departments have data on current students. These databases can often be silos. By unifying your database and creating a cohesive picture of all students and alumni, you will identify missed opportunities for cross-departmental collaboration.

5. Value Extraction

How do you get a fair return on the value you create? You begin by defining what fair means to you.  Any value return must benefit your customers, employees, and investors. You cannot choose to please only one of these parties as they all have opposing needs. Therefore, balance is key in your data strategy.

Finally, keep customer permissions in mind. There is a thin line between valuable and creepy.

3 Key Data Strategy Takeaways

  1. Your data strategy cannot just be to collect lots of it. Volume does not equal value. Data is like oil- it has to be refined and processed correctly to be valuable.
  2. Your data can be unique, but it has to have broad applications. Moreover, you can add value by offering unique insights or predicting something that no one else can.
  3. Ensure that you get a fair return on your data. Return could mean both the price that a third party pays for your insights as well as how much your data-strategy can support your overall organizational goals.
    For example, Starbucks and McDonalds are both data-driven QSRs, they both use data differently. But it pays off for each one because it serves each of their business strategies. McDonald’s uses data to increase speed while Starbucks uses it to create a curated cafe experience with personalized offerings.

Interested in learning more about Tom’s talk on data strategy? Catch a recap of his session from WE Prosper Summit 2019.

Watch Now–>

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