Monetize Data as a Strategic Asset and Profit Center

Monetize Data as a Strategic Asset and Profit Center

I recently facilitated a roundtable with senior executives where we discussed how companies can leverage Data Monetization initiatives to help meet strategic goals and move IT (and data) to be a strategic asset and profit center.  The following is a recap of our discussions on what Data Monetization means, how to get started, and how to evolve a company’s Data Monetization practices.

What is Data Monetization?

Everyone is doing some form of internally-focused Data Monetization every day – gaining insights from data and making decisions that directly result in increased revenue and profits.  For many companies, these efforts are ad-hoc, nascent, and difficult to make actionable and sustainable.  Traditionally, the term Data Monetization was associated with data aggregation companies selling someone else’s data.  Usually this entailed companies sending their proprietary data to the industry-specific data vendor, such as Experian, and receiving data back for a fee.  Often, this data was associated with industry-specific benchmarking information, for an additional fee of course, where the company was responsible for gaining their own insights and determining how to make the data actionable.

Innovations and advancements in data management and analytics has led to an evolutionary leap of Data Monetization possibilities that is enabling companies to drive significant monetary value themselves with less risk and cost while getting to market quicker with their own unique solutions.

We discussed many of these possibilities that can be categorized into the following four principal areas: 1) creating client value (or new revenue streams) by delivering client-specific, actionable data based upon a larger view across your client base (think proactive trends and predictive analytics that are actionable for your client); 2) uncovering unique competitive intelligence that enable actions before your peers by associating information across functions or across industries; 3) providing clean data sets back to clients or non-industry companies based upon your unique domain knowledge and enrichment capabilities (think data management as a service); and 4) sustainable customer event interventions (continuously improving conversions and attrition rates).

Illustrative Use Cases

Let’s use ABC, Inc., a B2B supply chain management company, to demonstrate the first three possibilities.

  1. Use Case #1 – Actionable Data: Providing a new service (aka new revenue stream) to their clients that will predict (based upon every client in their industry) when they should pre-order materials vs. waiting for a costly breakdown.
  2. Use Case #2 – Unique competitive intelligence: Being able to determine leading indicators of a supply shortage and proactively purchasing at current for your clients vs. elevated costs.
  3. Use Case #3 – DMaaS (Data Management as a Service): Being able to provide the client’s data back to them with enriched fields based upon ABC’s unique domain knowledge and ability to source data from multiple clients. This information would typically be used by the client for their own insights but differs from traditional services that only contain industry-genericized data.

For the last Use Case, let’s use a B2C company that provides online advice services for their customers:

  1. Use Case #4 – Customer event interventions: An evolutionary initiative that starts with identifying the key events throughout the lifecycle of a customer where there are significant drop offs. Next, root causes are then identified, and these reasons are associated with a model to predict which customer will drop off at each event.  Once identified, the customer can then be provided additional support to move them along.

How do I get started with a data monetization project?

In our informal poll, the room was split 50/50 between their Data Monetization maturity.  In our travels (and maybe its biased because they are speaking with us), we have found a larger percentage of companies at the nascent or “foundational Data Monetization” stages.  This was discussed as an evolutionary process that must be adopted at the executive level to be sustainable.

For those companies considering Data Monetization projects, the following are a few thoughts on how to get started:

  1. Find a Business-sponsor. It is rare that an IT-sponsored Data Monetization initiative will take root in an organization.  This is directly related to the need for the organization to adopt the change.  A great sponsor would be someone that has “skin in the game” and would directly solve a burning need.
  2. Conduct a “threshold ROI” analysis. Data Monetization projects are often difficult to estimate on the return-side.  Since this is usually a key indicator to obtain buy-in, determine the minimum threshold change in your metric that would be required to breakeven and obtain buy-in. If plausible, your peers will accept the level of risk and ambiguity.  For example, if your goal is to increase retention and you only need an additional 1% to breakeven, and 5% is possible, that may be enough to communicate the value.
  3. Start small and validate assumptions. Data initiatives often have many sceptics since the conclusions are usually associative and not direct.  For the initial project, the goal is to prove your assumptions.  A completely functioning product should not be your goal.  To this end, think of creating a temporary solution that gets you to market quickly and allows you to prove the concept.  Success begets success (and more funding to do it right)!

For those companies that have already had Data Monetization success, the following are a few thoughts on how to evolve and sustain:

  1. Encourage the analytically curious. Questions are the lifeblood of any Data Monetization evolution. Without those to ask questions and more importantly, make actionable the results, the organization will have a challenging time adopting a data-driven culture.  Don’t just think of those internally, but those externally such as clients and partners (and the sales and client support personnel that service them).
  2. Plan for organizational change. Like any other project, to make a Data Monetization initiative successful, those affected will need to change their thinking and behavior. What you expect the organization to do is as important as the result, without which the result will be difficult to measure and sustain.
  3. Govern, responsibly. As your company evolves, you will receive numerous ideas from your army of analytically curious. These initiatives will need an objective measure by which to prioritize. This is where a cross-functional team comes in with the authority to recommend (and fund) the next projects.  The ‘responsibly’ qualifier is meant so teams do not forget the foundational Data Monetization concepts that got them to this point.

These views are derived from executing business strategies, sharing knowledge, and evolving data management and intelligence practices alongside CxOs, in-house IT staff, and business users. Contact me here to help sort out your data monetization opportunities.