Featuring RK Maniyani, CTO of True Influence.
Companies are spending more than ever on data technologies and services, as evidenced by the recent Outlook for Data 2018 survey released by the Interactive Advertising Bureau’s Data Center for Excellence.
More than 80 percent of respondents said they plan to grow their investment in data tech this year. And that follows a boom year in 2017, when 60 percent of companies increased their spend on data and related services.
Smart investment in data tech is essential to successful B2B Account-based Marketing (ABM) and targeted advertising. Without quality user data, including third-party sources such as those available through True Influence’s InsightBASE service, your Marketing team will have an incomplete view of prospects’ purchase journeys. And without effective analysis and marketing automation systems, your messaging efforts may miss critical spikes in prospects’ intent to buy.
Note that I said “smart investment.” As with all surveys, the IAB report offers some great high-level insights on how most companies are approaching their data tech spending. But evaluating you own investment is data tech requires a closer look at the maturity of current solutions in key market segments.
Here’s a look at some of the survey’s findings, and how I think they map to current market realities.
Growing emphasis on investment measurability/accountability
About 59 percent of respondents in the IAB report said that measurability and accountability for marketing investments will be the most important factor driving their investment decisions in 2018. That’s up from 50 percent last year and second only to the fairly generic survey option of “customer demand.”
Obviously, accurate ROI measurements and accountability have been key issues for B2B marketing teams for years. (Our own Ken Stout discussed how ROI is key for building alignment between Sales and Marketing teams in a recent blog post.)
Just spending a lot of money to gather data is not enough. The real question for CMOs is exactly what kind of lift / benefit are we getting from our spend, and what exactly in the data is making that happen?
Performance on direct marketing and Google Adwords spend is not difficult to track. And current analytic tools do a good job measuring inbound content marketing performance for lead nurturing and direct response.
Tracking metrics for brand lift is tougher. Marketing Land has a solid primer on how behavioral data, such as length of video views and social sharing of content, can be coupled with survey and other data sources to develop Awareness, Interest and Desire metrics to track brand lift. For example, assets viewed and commenting rate can be paired with survey responses about the believability of an online ad to give you a quantifiable idea if your ad is delivering the message you want.
Growing volume / quality of first-party data
The percentage of survey respondents who said growing their first-party data sources dropped about 13 points in this year’s survey, down from 53 percent last year to 40 percent in the 2018 report.
This should be no surprise. Companies have been investing for years in various systems — Web sites, email, social, predictive — that collect huge amounts of data. So building the raw volume of first-party data should be a non-issue for most marketing organizations.
The challenge now, I think, are quality and actionability. Data quality is always going to the be an issue (in fact, I discussed these issues in my last post), and the answers there are as likley to be operational as much as they are technological.
New investment should target actionably. Companies have been spending on various marketing stack technologies that still are not fully integrated for analysis or action. Marrying these disparate systems and data stores will be a key investment area in 2018.
I’d also note that third-party data, which has been identified as essential to successful ABM by experts including Sirius Decisions, needs to be so closely married to first-party data that it is essentially treated the same by internal systems. Of course, there will be issues of onboarding and validation, but increasingly, data is data.
Cross-device audience recognition
About 52 percent of survey respondents say cross-device audience recognition is the industry development that will most demand their attention this year. This tops even the buzzword topics of Artificial Intelligence (41 percent) and blockchain (37 percent) in the IAB survey’s Hot Topics section.
Cross-device technology is maturing and is becoming commoditized, making it a key sector for new or second-wave investment. (For a detailed backgrounder, check out this article at Ad Exchanger.) A recent report indicates that almost 60 percent of online ad spend in the UK will use cross-device audience ID technology by 2020, compared to just a little over 25 percent in 2016.
Technology now extends beyond just smartphones and laptops to include smart TVs, wearable tech and any other internet-connected device where your company’s message may reach a prospect. A key benefit of cross-device audience recognition is managing exposure to ads. Particularly in B2B, you don’t want to show a key prospect the same high-funnel ad if they have already responded and moved along the purchase journey.
Historically, data tied to IP address, email addresses and client-side cookies have been disconnected. Cookies on the desktop can be problematic; on mobile devices they are next to useless for segmentation and other key marketing functions.
Device IDs are a leading component of cross-device audience recognition solutions. These IDs, such as IDFAs (Apple’s Advertising IDs) or Android IDs (aka Google Advertising ID), when coupled with email addresses used to log into apps, can create a reliable cross-device profile in the Deterministic model of cross-device recognition.
A second model, Probabilistic matching, employs algorithms to evaluate enormous volumes of data — including IP addresses and browsing patterns — to create a best guess at who’s looking at your messaging. (A fine-tuned version of Probabilistic matching is sometimes marketed as Device Fingerprinting.)
As you’d expect, Deterministic matching is broadly considered to be superior, since it is based on explicit data. But, like always, there are barriers, most notably the “walled gardens” of data that large platforms like Google and Facebook create around their own networks.
Probabilistic certainly is more scalable, given that is based on such huge volumes of data. And some Probabilistic solution providers claim accuracy of 70 percent or higher, and they tend to access a broader set of data. As always, the ultimate success of these solutions, either Probablistic or Deterministic, depends on the quality of data sets employed. And some companies are having success with hybrid solutions thats use both methods.
Better Reporting, Measurement and Attribution
Another attention-grabber for this year is reporting, measurement and attribution, with 49 percent of respondents saying it tops their topics to watch. Interestingly, this “hot topics” rating is down markedly from 2017’s 74 percent response; however, an emphasis on measuring and accountability will be an important motivator in 59 percent of initiatives this year.
So, this category has moved past buzz and into the action phase.
There’s really nothing new on the reporting or measurement tech front. Concerns in this area really speak to data quality issues.
Attribution, however, is a category where emerging tech will drive interest and investment. (Martech Advisor has a nice overview package about developments in the space.)
How do marketers determine if what they are spending on Google is driving conversions and sales, or is it a third-party data provider such as True Influence? This analysis is complicated because the same prospect may be touched via so many different channels. A 2017 AdRoll survey showed that 39 percent of companies employ attribution on their campaigns, but also that 70 percent of respondents struggle to act on their findings.
For ad spend, optimizing media mix is the key benefit of attribution. And of course, there are always misgivings about data coming back from platform providers.
Content Marketing is a little trickier — it’s more about understanding how various channels work together (cited as a key benefit of attribution by 64 percent in the AdRoll survey). Digital AdSpend grew more than any other channel as a direct result of attribution.
Data Security and Governance
This persistent headache for data pros is notable because the number of respondents who say it will be on their “hot topic” radar jumped dramatically, from 20 percent in 2017 to 34 percent this year.
I suspect this spike in interest can be attributed to new regulations such as the EU’s General Data Protection Regulation, and certainly companies need to be aware of these issues. Increased spending here will likely be on policy and internal control review.
Invest in Mature Tech
Overall, I suggest evaluating any investment this year by which data technologies are currently producing results. Data integration to drive actionability and cross-device audience recognition have enormous potential for ROI. And any smart investment in data tech and services should prove to be a boon to winning new customer and growing revenue.