I’ve been in the marketing and advertising world for 19 years. In that time I’ve helped to pioneer emerging monetization models for apps, widgets and native ads. But it’s only in the last year that I’ve been embedded in data-as-a-service (DaaS) models.
I have to say I am thankful. It has been an eye-opening, career-altering experience. I have learned a tonne and my entire perspective on how marketing and advertising create value for brands has evolved. As a result, so has the way I consider investment opportunities in data companies.
Based on what I’ve learned, here are 10 things you need to know before investing in data:
1. Data has a common currency. In the mobile world (and it’s all mobile now), mobile IDs (IDFA for iOS, AAID for Android) are what the market trades in. Yet, many times I’ll talk to a well-known company and it’s ‘Yeah, we have data and we’d love to monetize it,’ but then they don’t have any unique criteria anchored by device ID. The point is, It doesn’t matter if you have billions of rows of data, or hundreds of millions of X this, or Y that. If the core currency isn’t native to how the money flows, there’s little value in it.
2. Data has to be tradeable. This is interesting . . . let’s say you have another type of data currency besides mobile ID, like cookies, or emails. These are valuable, but to work in conjunction with location (it’s all going location), they need to convert from one to the other. Email companies like LiveIntent are great, but to work with location data they first need to hash email to device ID. The same is true for mobile web companies, etc. From an investor’s standpoint, this could signal either risk (i.e. an extra step into the trading process), or opportunity (i.e. a trade solution that makes things happen more efficiently).
3. Data collection is the wild west. Data rights and ownership is critical. To belabour my earlier point, If you don’t own device ID , or something you can convert to device ID, you better have a partner, or vendor, who can. And they better be able to help you actually do something with it because dusty data is valueless data. Which brings me to my next point . . .
4. Parking data doesn’t equal monetizing it. Everyone likes to park data (on MediaMath, Tradedesk, Krux, LiveRamp etc. — all great companies). We all see the announcements about data partnerships that imply massive value waiting to be unleashed via revenue sharing or some such. But that does not equate to a) data actually being traded, or b) anyone making money. Net-net, some companies are making money by parking data but a lot of others seem to be waiting for something to happen. Innovators that find a way to activate more parked data, sooner, are going to be well-positioned in the market.
5. DaaS is where it’s at. Much of the trade market still thinks in terms of CPM and revenue sharing but I’m not putting much into those kinds of solutions these days. Why? When you do a data licensing deal, you know you’re getting 12 months of guaranteed revenue. You know when you’re getting it, and you know you’ve got a partner that you can grow a relationship with. CPM and revenue share are fine but they just don’t have the same kind of predictability as DaaS, and predictability is something we all crave as investors.
6. Dwell time is important. CPI, ARPDAU, Retention . . . all important indicators, but I’m starting to believe that dwell time is the best stat to tell us about consumer intent based on real behaviors. Paired with location data, it is pure marketing and advertising gold. If you know I spent 2 minutes in your store trying on a pair of Nikes, and 20 minutes trying out the Adidas while simultaneously Instagramming a pic of the shoes to my wife, you know what what my consumer intent is, period. Before I invest in a data company, I want to know that they have a real handle on dwell time — a critical indicator of a data company’s success, IMO.
7. Marketers are getting smarter about data really, really fast. When I first started with Unacast, the conversation about how to use data was all about retargeting. Then it morphed into a conversation about attribution, then data modelling and (currently) verification. Moore’s Law is incapable of keeping up with how fast marketer’s understanding of data is evolving. A lot of this is powered by rapid iterations of programmatic advertising, martech and analytics tools. Not surprisingly, billions of investment capital is flowing to these sectors
8. There’s more data partnership opportunities than competition. Sometime in the last year or so, a light went on over the marketplace and everybody started to understand that cooperation (e.g. ‘second-party’ data swaps) is where it’s at. How come? Data sharing helps brands get better at creating personalized engagements for their own customers and instantly scale target markets for brands with similar audiences. It makes sense, so it had to happen. This whole arena is about to be democratized and there’s a great deal of money to be made.
9. Data is a two-sided market with lots of buyer profiles. To make a data sale, companies need to engage with both commercial and product people. To drive usage (and thereby monetize), they need to connect with agencies and brands. Doing these things quickly and well is part of what made both BlueKai and now MOAT successful. Finding new ways to excel in this diverse market by fast-tracking the sales process is something I think we’ll see more investment in moving-forward.
10. Data companies need to stay true to their identity. Staying the course as a data company is critical, otherwise, you risk confusing an already complex and quickly evolving market. As an anecdotal example, at Unacast, our brand is clearly built on proximity data. Maybe we could have secured more revenue to-date by vacillating from that identity, but establishing a clear presence in this space is essential. As an investor, I am always looking for companies dedicated to moving in a straight and steady line rather than running around wildly looking for the quick buck.
These are a few of the thoughts I’d offer to anyone who is considering investing in data.