Data monetisation is no new creation. Since marketers began collating data of any form, they have looked for strategic methods to maximize its worth; what is questionable is how hard is this?
Most businesses are now aware that with big data, opportunism is not a solid long-term strategy, and as a result, employ structured data monetization plans to blend short term wins with long term data goals; confidently and precisely acting upon their data.
But in an evolving, mercurial data landscape there must always be room for review and improvement of current methods – so ask yourself:
1. Do you have the data that other companies would want to buy? Where is it – do you really know what you have?
Put simply, data products are designed to help third parties better meet consumer needs; creating incremental revenue, and driving new revenue. To match requirements, data providers must be able to access, then identify the data they have which is useful to third parties, and enable a monetisation programme.
But big data identification is a continuous process, due to data’s vast, varied and rapid nature. Before selling to third parties, providers must repeatedly assess and ask themselves what they really have of value, and consider what they don’t to create an initial knowledge base.
To be certain of what data, you really have (and know where it is!), data audits are integral. Firstly, identify the space between what is considered known, and what is actually known. Join the dots and create a ‘sandpit’, ideally linked to a consumer profile/audience (what you want to know), for data analysts to draw from. This provides insight for CRM’s and helps highlight and exploit unknown data. Assets you already have can also be uncovered and utilised.
2. Can you access the data required and present it in a structured format, and in a timely manner? How well packaged are your data bundles?
Effectively monetized data fulfills a need, a potentially valuable gap in information. After an audit, you’ll know what data you have, or not. Ask; does this data match the requirement gaps of third party organisations? If not, do you know what data is required? Can you access it? And can it be packed appealingly in good time?
Data provider Acxiom assesses this using a ‘three legged stool strategy’ or three-sided approach (ADA), which examines Application (credible uses for data which will appeal to 3rd parties), Data (knowing what data is required to enable the application), and Access (can you get the data you need, practically, legally, commerically?).
Application: Is your proposed data application credible? Is it commercially viable, serving a consumer need with clear ROI? Does it appeal to third parties?
Data: What data is needed for your data bundles? Do you have enough data to support your product, sourced within your organisation, or available externally? If not, can you create it?
Access: Data may need to be accessed in real time and whether it is or not, no matter how much data you have – you must be able to separate the signal from the noise, to locate the valuable proportion? Know how you will receive data and comply with privacy regulations, especially if collaborating with other data sources.
Appealing, monetisable data products must address all this to be credible, valuable and complete.
3. Do you know how much your data is worth? Will people buy it? Are you the only supplier?
Data audits establish if you are missing worthy data. If you do not possess all the necessary information, third parties will look elsewhere – to your competitors – for the resources they need. Your data products will only be as good as your data team, so ensure you employ the best data scientists you can.
And if this is still not enough to create the monetisable product needed, consider collaborating with others to collate more uniquely specific data products, of increased value.
Data insights and product solutions sold to third party organisations must be based on an understanding of their target audience, and marketing needs. So to achieve significant results, worthy data products should be developed in parallel with a business plan and sales strategy.
4. Have you considered how you manage preferences when you collect data? Rich data has limitations without the right permissions.
Trusted data use is transparent to all. Consumers should know exactly how their information is being used, and be given the choice to opt in or out. Equally, organisations must know what regulation states they can, and cannot do. Know what limitations are in place to maximise data use – there is no point creating a data product if half the information turns out to be unusable.
Returning to the ‘ADA’ approach, consider your data requirements and how you will access it. Be aware of the permissions involved at all stages. If you plan to collaborate with data sources external to your organisation, ensure the data you need is commercially available and correct (data integrity), and adhere to responsible use. The trust you have (from consumers and third parties) in shared data is crucial – privacy must never be compromised.
5. Explore unfamiliar industries; could your data be more powerful in someone else’s hands? Could the value of your data be even greater when combined with 3rd party data?
Don’t just consider one outlet for your data products! Collated information may be of infinitely more value to other parties, or more accurately meet data needs through responsible collaboration.
Montetisation often involves joint ventures and cooperation to expand revenue streams, and typically occurs through one of three methods:
Inside-In: Marketers seek data assets ‘hidden’ or ‘languishing’ within the organisation & use them to drive revenue through a creative application.
Outside-In: Marketers collaborate to use outside third party data to advance internal strategies.
Inside-out: Where internal (inside) data, can be of value to external brands, 3rd parties and marketers.
Asking yourself these questions as part of any monetisation framework allows resources to be confirmed, collated, stretched and put to most profitable use for the most appropriate party, benefiting all involved in the process, from data providers, to third party organisations, to consumers themselves.
