Marketing attribution systems: a quick look at the options

I’ve seen a lot of attribution vendors recently. If you're a regular reader here, you saw my reviews of Claritix (last week) and BrightFunnel (in December).  Last week caught up with Jeff Winsper of Black Ink, which I'll hopefully review before too long.  Bizible also popped up recently although I don’t recall the occasion; possibly something related to their interesting survey on “pipeline marketing” and attribution methods.

My rational brain knows that there’s probably no reason for this flurry of sightings beyond pure coincidence. But it’s human to see patterns where they don’t exist, so I did find myself wondering if attribution is becoming a hot topic. I can easily come up with a good story to explain it: marketing technology has reached a new maturity stage where the data needed for good attribution is now readily available, the cost of processing that data has fallen far enough to make it practical, and the need has reached a tipping point as the complexity of marketing has grown. So, clearly, 2016 will be The Year of Attribution (as Anna Bager and Joe Laszlo of the Internet Advertising Bureau have already suggested).

Or not. Sometimes random is just random. But now that this is on my mind, I've taken a look at the larger attribution landscape.  Quick searches for "attribution" on G2 Crowd and TrustRadius turned up lists of 29 and 17 vendors, respectively – neither including Brightfunnel or Claritix, incidentally.  A closer look found that 13 appeared on both sites, that each site listed several relevant vendors that the other missed, and that both sites listed multiple vendors that were not really relevant. For what it's worth, eight vendors of the 13 vendors listed on both sites were all bona fide attribution systems -- which I loosely define to mean they assign fractions of revenue to different marketing campaigns.  I wouldn't draw any grand conclusions from the differences in coverage on G2 Crowd and TrustRadius, except to offer the obvious advice to check both (and probably some of the other review sites or vendor landscapes) to assemble a reasonably complete set of options.

I've presented the vendors listed in the two review sites below, grouping them based on which site included them and whether I qualified them as relevant to a quest for an attribution vendor.  I've also added a few notes based on the closer look I took at each system in order to classify it.  The main questions I asked were:

• Does the system capture individual-level data, not just results by channel or campaign?  You need the individual data to know who saw which messages and who ended up making a purchase.  Those are the raw inputs needed for any attempt at estimating the impact of individual messages on the final result.  

• Does the system capture offline as well as online messages?  You need both to understand all influences on results.  This question disqualified a few vendors that look only at online interactions.  In practice, most vendors can incorporate whatever data you provide them, so if you have offline data, they can use it.  TV is a special case because marketers don't usually know whether a specific individual saw a particular TV message, so TV is incorporated into attribution models using more general correlations.

• How does the vendor do the attribution calculations?  Nearly all the vendors use what I've labeled an "algorithmic" approach, meaning they perform some sort of statistical analysis to estimate the attributed values.  The main alternative is a "fractional" method that applies user-assigned weights, typically based on position in the buying sequence and/or the channel that delivered the message.  The algorithmic approach is certainly preferred by most marketers, since it is based in actual data rather than marketers' (often inaccurate) assumptions.  But algorithmic methods need a lot of data, so B2B marketers often use fractional methods as a more practical alternative.  It's no accident that the only B2B specialist listed here, Bizible, is the only company that uses a fractional method, as do B2B specialists BrightFunnel and Claritix.  It's also important to note that the technical details of the algorithmic methods differ greatly from vendor to vendor, and of course each vendor is convinced that their method is by far the best approach.

• Does the vendor provide marketing mix models?  These resemble attribution except they work at the channel level and are not based on individual data.  Classic marketing mix models instead look at promotion expense by channel by market (usually a geographic region, sometimes a demographic or other segment) and find correlations over time between spending levels and sales.  Although mix models and algorithmic attribution use different techniques and data, several vendors do both and have connected them in some fashion.

• Does the vendor create optimal media plans? I'm defining these broadly to include any type of recommendation that uses the attribution model to suggest how users should reallocate their marketing spend at the channel or campaign level.  Systems may do this at different levels of detail, with different levels of sophistication in the optimization, and with different degrees of integration to media buying systems. 

Of course, there are plenty of other points that differentiate these systems.  But this list should be a useful starting point if you're considering a new attribution system -- as well as a reminder of the need to define your requirements and drill into the details before you make a final selection.

Attribution Systems

G2 Crowd and TrustRadius

• Abakus: individual data; online and offline; algorithmic; optimal media plans
• Bizible: individual data; online and offline; fractional; merges marketing automation plus CRM data; B2B
• C3 Metrics: individual data; online and TV; algorithmic; optimal media plans 
• Conversion Logic: individual data; online and TV; algorithmic;optimal media plans
• Convertro: individual data; online and offline; algorithmic; mix model; optimal media plans; owned by AOL
• MarketShare DecisionCloud: individual data; online and offline; algorithmic; mix models; optimal media plans; owned by Neustar
• Rakuten Attribution: individual data; online only; algorithmic; optimal media plans; formerly DC Storm, acquired by Rakuten marketing services agency in 2014
• Visual IQ: individual data; online and offline; algorithmic; optimal media plans

G2 Crowd only

• BlackInk: individual data; online and offline; algorithmic; provides customer, marketing & sales analytics 
• Kvantum Inc.: individual data; online and offline; algorithmic; mix models; optimal media plans
• Marketing Evolution:  individual data; online and offline; algorithmic; mix model; optimal media plans
• OptimaHub MediaAttribution  individual data; online and offline; attribution method not clear; data analytics agency with tag management, data collection, and analytics solutions

TrustRadius only

• Adometry: individual data; online and offline; algorithmic; mix models; optimal media plans; owned by Google
• ThinkVine: individual data; online and offline; algorithmic; mix models; optimal media plans; uses agent-based and other models
• Optimine:  individual data; online and offline; algorithmic; optimal media plans

Other Systems

G2 Crowd and TrustRadius

• AdGear Advertiser: full stack advertising platform inc. ad serving, bidding, exchange technology
• DialogTech: tracks inbound phone calls
• Google Analytics Premium: ad data analytics including algorithmic attribution
• Invoca  tracks inbound phone calls
• Telmetrics: tracks inbound phone calls

G2 Crowd only

• Adinton: Adwords bid optimization and attribution; uses Google Analytics for fractional attribution
• Blueshift Labs: real-time segmentation and content recommendations; individual data but apparently no attribution
• IBM Digital Analytics Impression Attribution: individual data; online only; shows influence (not clear has fractional or algorithmic attribution); based on Coremetrics
• LIVE: for clients of WPP group; does algorithmic attribution and optimization
• Marchex: tracks inbound phone calls
• Pathmatics: digital ad intelligence; apparently no attribution
• Sizmek: online ad management; provides attribution through alliance with Abakus
• Sparkfly: retail specialist; individual data; focus on connecting digital and POS data; campaign-level attribution but apparently not fractional or algorithmic
• Sylvan: financial services software; no marketing attribution 
• TagCommander: tag managemenet system; real-time marketing hub with individual profiles and cross-channel data; custom fractional attribution formulas
• TradeTracker: affiliate marketing network
• Zeta Interative ZX: digital marketing agency offering DMP, database, engagement and related attribution; mix of tech and services

VisualIQ Measures Marketing Impacts Across All Channels

Summary: VisualIQ combines customer-level transactions and contact history with traditional aggregate data to produce better marketing performance measurement. It hasn't solved the problem of identifying the same customer across channels, but it's trying.

I was going to start this post by writing that last-click attribution has recently come under fire, but the first Google hit on the topic brings up a study from 2007. So maybe the criticism isn’t particularly new. But the fact remains that, now more than ever, marketers are trying to measure the impact of all contacts on customer behavior.

Broadly speaking, the problem is attacked in two ways. One, most common among consumer goods manufacturers and others who do not sell directly to their customers, uses aggregated data in marketing mix models to find correlations between marketing efforts and total sales. The other, favored by banks, retailers, communications providers and others who do sell directly to known buyers, assesses the impact of each contact with specific individuals. Last-click attribution is a particular challenge for online marketers because they fall between these two situations: they can often identify their buyers but not trace their full contact history.

VisualIQ, founded in 2005 as Connexion.a, proposes to straddle these worlds by combining aggregate-level models with customer-specific contact history. They haven’t found a magic bullet: like everyone else, VisualIQ tracks online customers through cookies, with all the limits that implies. But VisualIQ strives to make the best use of what’s available by unifying data from as many online campaigns as possible, linking cookies with online transactions, and then linking online transactions to offline identities.

This approach offers some general advantages and two specific capabilities. The general advantages come from assembling all advertising and customer transaction information in one database. This allows VisualIQ to analyze campaign results, do whatever identity matching is possible, and to isolate the impact of source, contact frequency, demographics, location and other variables. VisualIQ, a hosted service, has invested heavily in technology to analyze massive data sets along such dimensions.

The first specific capability is relating pre-purchase contacts to actual purchases for individual customers, thus moving beyond last-click attribution. Although this is subject to the limits of cookie-based tracking, VisualIQ does what it can to build a unified identity by sharing the same cookie IDs across as many online channels as possible. The second capability is building mix models with data from actual customer contacts instead of market-level estimates or surveys. VisualIQ says it has found this yields more accurate results than traditional information.

This is all good stuff and VisualIQ has packaged it nicely in a tiered set of offerings. These range from campaign-level reporting to customer-based insights to predictive modeling and simulation, with prices for the simplest system starting as low as $5,000 to $10,000 per month. The company has had considerable success, counting major banks, retailers, and communications firms as clients. Note that these are all industries that sell to their customers directly.

But VisualIQ’s specific offerings are just part of the story. What’s really important is setting explicit goals of linking identities across channels and measuring cross-channel marketing impacts. These are arguably the core challenges in marketing measurement today. This focus has led VisualIQ to look for alternatives to cookies and to use existing methods to combine online and offline information for the same person.

The company is also seeking to make it easier to apply its results. Today, it basically generates reports that suggest better media allocations and advertising contents. But it is working to automatically feed those findings as rules into execution systems such as ad servers and ad exchanges. This brings marketers closer to the ultimate goal of self-optimizing programs. Other vendors are also pursuing self-optimization, but VisualIQ promises the advantage of decisions based on data from all channels rather than a single channel or, heaven forbid, just the last click.