So you want to sell data?
Just when you thought we could not get any more data hungry, AI usage explodes across nearly every use case and user persona imaginable. If the scope is everything, do we need data on everything? It would seem so. Demand for data is only exceeded by executive expectations to increase profits by gathering it, buying it, and increasingly selling it. Data monetization has now become an intriguing business opportunity for most companies. Companies that most would never imagine becoming data providers have become critical dependencies to seemingly unrelated businesses. Farm tractor manufacturers are selling soil condition data. Satellite communication companies are selling retail parking lot traffic data. Thermarator companies selling aggregate illness data. And we are only touching the surface of the useful (and scary) data correlations that AI/ML will find as more diverse data becomes available.
I have been on both the buy and sell side of data monetization with industry leaders for three decades. I now advise companies. My primary data strategy was always simple. What data will help me effectively and efficiently understand and influence consumer behavior for my retail, brand, and media companies? Thinking back, even before the recent AI and data explosion, the data I was able to leverage and correlate was crazy as it included: product purchasing, warehouse shipments, store visits, television viewing, media browsing, attitudinal surveys, tweets, reviews, comments, store locations, product labels, business profiles, consumer demographics, population stats, weather, sporting calendars, school schedules, and so much more. Net, this experience has given me an inside perspective and a few tips on data monetization that might be useful for you too. I hope you enjoy it.
If you are still reading this, you presumably have determined that you have data that you think is useful to others. But do you truly know how useful and why? You need to, and this leads to my first tip.
TIP #1 - Understand how your data will be used and valued – This seems obvious, right? Too many do not put themselves into the shoes of the data buyer, solution architect, and end users. Don’t gloss over this. You need to know your client’s use cases and nuances to properly design, differentiate, position, and price your data product.
If you struggled with the first tip, you probably need to understand your data buyer’s domains better. This is common. They are not you. If your data buyer’s business was just like yours (i.e. competitive), I doubt you would be selling them your data. So…
TIP #2 - Learn your customer’s domains – You cannot do tip #1 very well without it. Plus, you would miss use case opportunities altogether or focus on use cases not worth solving. If your long-term P&L can financially support it, hire a domain expert for your team. If not, hire a domain consultant until you get enough client vertical penetration and feedback. I know a guy.
It may seem pretty obvious that you need to understand what and why clients would want your data. It’s less obvious that you should better understand how your clients will integrate your data into their other data, platforms, processes, and solutions. Anticipating and mitigating the challenges your clients will have when activating your data is a huge advantage that will help you close and renew more deals.
TIP #3 - Strive to simplify and accelerate your data usage – Your data is only one piece of your client’s puzzle. In most cases, your data will be integrated, enhanced, transformed, and analyzed with other data before it can be effectively utilized. You should strive to simplify and de-risk that if possible as it only takes one bad puzzle piece to end the game for everyone. This often means augmenting your data beforehand with reference data, digital ids, descriptive attributes, industry segmentations, calibration factors, etc. Doing this will often accelerate your client’s timelines, reduce implementation risks, and improve their ‘speed to value’. Yes, this will add to your costs, but you can pass on those costs at a price likely lower than your clients could get on their own.
For most of us, we are not the only game in town. You need to know going into this data venture how your data stacks up to your competitive peers, other data scenarios, and perhaps non-data alternatives. Only then can you properly position your relative strengths and weaknesses around your client’s needs.
TIP #4 - Overtly understand the relative value to other client alternatives - Rarely will your data be involved in apples-to-apples or price-per-pound comparisons. Some data alternatives will be bigger but less representative. Some will be tiny but designed to mirror the population (panels). Some behavioral. Some attitudinal. Some data is directly related to your use case. Some are just highly correlated to your use case. You need to know your data solution role and be ready to quantify your relative differences, whether it be speed (faster profits), coverage (broader value), quality (mistake cost avoidance), confidence (decisiveness), risk (business continuity), etc.
Not all your data ‘monetization’ comes via Account Receivables. Your data monetization value can also come from internal use cases, including enterprise-wide cost synergies, broader process efficiencies, core product enhancements, data vendor bartering, data dependency stickiness, risk avoidance, etc.
TIP #5 - Determine non-revenue ways to ‘monetize’ your data – Ideally, your external data revenue more than justifies your data monetization investment. That said, you should still look for and take credit for other ways to ‘monetize’ your efforts. Can your base business leverage this data? Can they utilize any of the platform technology, computing, or analytics capabilities? Does this data have value for your partners or suppliers and have potential bartering or stickiness value? In many cases, the efforts required for your new data monetization business were already desired by your core business but never got prioritized on its own. Whatever it is, put a value on it and bake that into your data monetization investment thesis.
Before you build your data monetization platform, processes, and partnerships, you need to know that you are managing a formal product, not some internal capability. That means you need to decide upfront how ‘flexible’ your product is to client whims, wishes, and demands. Are you a one-size-fits-all solution? Configurable to popular desires? Open to full customization for a price? Whatever you decide, you need to have a stance and invest accordingly.
TIP #6 - Be overt on your data product’s capabilities – Firmly decide upfront if you want the lower and more predictable costs of a simpler product or if you are willing to spend considerably more upfront on a more sophisticated product. I do not suggest that you adjust as you go to client demand and feedback. I know some may not agree with my stance on this, but hear me out. Data monetization as a ‘side business’ needs to be more predictable from a cost and resource standpoint. It’s often positioned as an easy way to make money off the exhaust of your base business. The problem is when this side gig’s scope creep or excessive support demands take resources away from the core business. Companies that sell data solutions as their main gig are far more patient with these learning pains and cost overruns. Non-strategic side gigs that cause trouble sometimes get killed without much thought. That said, adapting to client feedback is still critical and it is okay to change what you sell, but do it overtly and invest in your product capabilities, not higher support costs and custom one-offs.
Lastly, invest in your future growth and viability by overtly creating tangible value for the consumers and customers that directly or indirectly enable you to use the data that involves them.
TIP #7 - Nurture your data source dependencies – It is easy to overlook that most companies are not in full control of their first-party data. Most data being monetized involves other parties, most notably the consumers and clients that interact with the core products that generate the data being monetized. Yes, in most cases, those parties have legally or unknowingly agreed to let you use data that involves them. Times are changing, however. Government privacy regulations, AI publicity, highly publicized data leaks, and of course, the popularity of data monetization initiatives are creating higher awareness, expectations, and regulations. Consumers are increasingly cautious about the data they share, and if they are not, their government has them covered. Likewise, corporate lawyers and procurement experts are also becoming more conscious about data usage, value, and rights. Net, it is important that you consciously invest to strengthen the value proposition you offer to those dependent parties, ideally including the data being monetized to create logical co-dependencies and stickiness. This will not always be easy or free, but ignoring this could ultimately kill your data monetization business.
That is all for today. Hopefully, these thoughts were helpful and sparked your own ideas. Of course, I am available to help you navigate your unique situation. Just DM me.
Welcome to the data business!