Moving from data collection to data monetization

Moving from data collection to data monetization

While gathering, refining and analyzing data has become a part of everyday business in all industries, several companies are still figuring out how to best turn that asset into a currency. 

Data monetization is a tricky topic, and sometimes it’s hard to see the impact of analytics on the bottom line. The media industry has obviously taken some first steps here and other areas are about to follow. 

A few months ago, we took the stage at the Data Innovation Summit 2017 in Stockholm together with Aller Media to talk about integrating data and analytics into the customer value proposition (you can watch our presentation here).

Both at the event and after we’ve had several discussions on how we see the shift from data collection, refinement and management to bottom line impact taking place and how we approach the question of monetization with our clients. 

Below I’ve outlined the main aspects of our data monetization approach as well as some of the main challenges our clients have experienced in their transformations.

BIGDATAPUMP Data Monetization Approach:

Rethinking operations

Using data to improve existing operations is the oldest trick in the analytics book as well as the easiest and quickest opportunity to act on, however this opportunity should not be limited to supply and manufacturing. 

Financial returns gained from addressed process inefficiencies are a no-brainer, whereas sales and service optimization might be a bit less self-evident. Better sales lead quality, pricing optimization, sales pipeline management and customer relationship management are all areas where big data is contributing to increased revenue across industries. 

However, the groundwork is often overlooked – identifying and locating the relevant data sources, standardizing the format and integrating it all into a one comprehensive 360° snapshot is crucial for efficient time management, lead prioritization and opportunity prediction that all translate, when properly executed, into increased revenue streams. 

Off-the-shelf CRM systems address the need for pipeline visibility and planning to some extent, but adding predictive analytics and machine learning to the mix takes the sales process to an entirely new level.

Rethinking products

Here’s where the monetization starts to get tricky. Integrating data into either product development or the final product offering provides an opportunity to stand apart from competition and to create a unique value proposition.

Easiest examples are the products that are already digital:

  1. Netflix recommends you movies and series based on what you’ve watched, and LinkedIn recommends “People You May Know” based on your extended network.
     
  2. Procter & Gamble use simulation analytics to design, iterate and again reiterate new products, monitor social media for real-time product comments and brand perception, gain feedback through focus groups and continuously adjust accordingly.
     
  3. Starbucks continuously customize the customer experience based on weather, local events, inventory and data from their 90 million weekly transactions.

The idea of analytics-driven product development is not new but it’s implementation is a task not many companies are ready to tackle – clear analytics strategy, restructured R&D processes and organization, heavy technology and capability investments as well as an extremely agile approach are critical.

Data as a standalone

Turning data into a standalone revenue stream is the most difficult approach for data monetization, and for the monetization to succeed three things are a must.

  1. Firstly, selling data requires an entirely new business model with adapted sales processes and skills, as the new product might be quite far from the company’s current product or service portfolio.
     
  2. Secondly, data is rarely usable as it is, but needs refining, formatting and often some level of preliminary analysis before it can be sent out to the customer.
     
  3. Thirdly, data sharing often requires the information to be integrated to some sort of a secure platform, portal or client-side reporting system that both serves the end user through certain data views and takes all necessary privacy concerns into consideration.

Here the key is collaboration – the information products should be developed closely together with customers to be able to efficiently address the three points above.

Data partnerships are a step towards an even more integrated approach where data is enriched with the partner’s data on both sides and used to rethink sales and marketing processes, products and customer experience.

The foundation

Companies can choose to monetize data through one or more approaches simultaneously, but it’s important to note that they place emphasis on different management functions – process leaders, sales leaders, product leaders or business-unit leaders. This is such a wide an important topic that it deserves a separate blog post, but I’ll leave it here as a reminder.

Often organizations feel like they are playing catch-up with competition when it comes to analytics efforts, which means that the management focuses on technology implementation and pilots at the expense of monetization targets.

However, we’ve noticed that the projects with prioritized, realistic and communicated targets are set, are delivering the greatest return on investment.

Prioritization is crucial – as the possibilities with data monetization are endless, the organization must be able to make a strategic decision on which opportunities, features and capabilities to focus on and in what order.

Technology investments does not only cover chosen hardware or software, but also the required analytical talent. Together with organizational changes, these lay the foundation for a competitive analytical organization that can efficiently leverage data in their operations, product development or as a separate revenue stream and see direct impact on their bottom line.

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Also take a look at the previous parts of this blog series: 

  1. Cloud analytics has changed the game and old tech players are about to be marginalized (Ville Suvanto 08/2017)
  2. Let’s Face the Fact: Cloud is More Secure Than On-premise (Matti Hidén 09/2017)