0 Advertisers Must Help Marketers To Build Relationships

Today’s New York Times reports that cable TV networks are balking at an eBay-built auction site to sell their advertising (“For Cable TV, No Interest in Selling Ads The eBay Way”, page C3, The New York Times, April 6, 2007). The networks’ justification is that many ads are now sold as part of larger packages, rather than simply on price. The article quotes Cabletelevision Advertising Bureau President Sean Cunningham as saying, “The grand majority is about idea-driven packages that have got multiple consumer touch points.”

Mr. Cunningham has access to more information than I do, but I somehow doubt that the “grand majority” of cable TV ads are part of multi-touch point packages. The very fact that, according to the article, the online exchange was sponsored by large advertisers including Hewlett-Packard, Home Depot and Intel who committed up to $50 million, suggests that those advertisers felt many of their purchases could be made outside such packages. Even an amateur cynic would suspect the networks’ real concern is that an auction would result in lower prices.

The article also mentions efforts by Google and DoubleClick to create online exchanges for advertising purchases, and suggests these may also face resistance from media companies. (Although the article doesn’t mention it, other reports have stated that Google is indeed having trouble gaining cooperation from radio stations.)

The obvious story line here is “new technology tries to reduce costs and the old guard resists”. But maybe things aren’t so simple. After all, integrated, multi-touch point marketing is a Good Thing in my personal universe, and I think in the customer experience management world in general. If an auction model does prevent more sophisticated marketing programs from happening, perhaps it really is a bad idea. Even though the core concept of the Customer Experience Matrix (delivering the highest value message possible for each communication opportunity) sounds very auction-like, it requires more information about those opportunities than would be available in a typical auction situation. If I may pontificate a bit (and who’s to stop me?), markets in general are only as good as the information known to their participants—so if closer buyer/seller relationships make information more accessible, they may actually result in a more efficient use of resources than a freer but less informed auction.

This doesn’t mean auctions are useless. Certainly eBay, Priceline and various price comparison sites work well for consumers who making one-time purchases (not necessarily of commodities) primarily on price. Even in the advertising world, price-driven purchases can work for campaigns that don’t require on-going relationships with customers (say, viewers of a particular TV show) or media (say, cross-promotion on TV, radio, Web and print venues with a common owner).

There’s a general theory in here somewhere, that the value of a communication opportunity can be increased by having more information available but only if the information is usable. In an advertising situation, it’s the medium owner (TV station, Web site, newspaper, list owner, etc.) who must provide the information and the advertiser who must be able to use it. For internal situations, the medium owner and advertiser are the same company but the separation between exposing and exploiting information is still relevant.. The general theory would also need to account for the fact that the same communication opportunity can have more value within the context of a relationship, or even within a particular contact stream, than outside of that context. This means creating a relationship actually creates a more valuable communication opportunity—a sort of “sweat equity” if you will—although this value only exists for the relationship owner.

Media owners will naturally want to share in the value created by their advertisers’ relationships. Their negotiating position is weak since no alternative buyer can benefit from the relationship (and thus, alternative buyers will not pay a premium for the same contact opportunity.) What media owners can do is to negotiate up front when they are giving advertisers the opportunity to start building the relationship. Really clever media owners would go a step further and make it easier for advertisers to build relationships, both by making more information available and by helping advertisers learn how to make use of it.

None of this really justifies the cable TV networks’ refusal to participate in advertising auctions. Advertisers who have relationship-based programs would not use the auction-based ad exchange even if it existed. Advertisers who can be more flexible in their purchases are the ones who stand to gain. If the cable networks reject the auction approach because they think they can make more money selling ads the old way, that’s their privilege. But I would respectfully suggest that they put their efforts into adding value to their media instead of the defending the status quo. Advertisers have too many other contact opportunities to pay a premium where none is justified.

0 Channel-Specific Analytics Are Doomed: Doomed, I Tell You

Did you ever have one of those crazy dreams, not quite a nightmare, where unrelated things get mixed up together? I felt that way this morning when I was looking at the Web site for one of the mobile marketing systems and saw they had alliances with Web analytics vendors. That rang a bell, but it took a while for me to realize that I had been writing about consolidation in the Web marketing space separately from mobile marketing.

The confusion is compounded by my recent look at non-Web analytics system including ClickFox (which gathers interaction logs from call centers and other systems) and Skytide (which gathers all kinds of data; I haven’t written about it yet).

There’s an obvious connection between systems that gather interaction data and those that manage marketing messages. As the Omniture / TouchClarity hookup I mentioned yesterday illustrates, some of the vendors are themselves bringing the two together. It’s no surprise that this would happen for Web systems, which tend to be internally integrated but isolated from other media.

Of course, the Web should not be isolated, and the trend is in fact towards cross-channel integration. Does it make sense, then, for Web analytics vendors to integrate tightly with Web targeting systems? You can see why an analytics vendor would want to do it—as a revenue-generating line extension and a way to help clients who lack an existing targeting solution. But the vendors (and I’m sure Omniture recognizes this) must also make it easy to integrate their systems with any other targeting product. Otherwise, they risk losing sales to prospects who already have a targeting solution and don’t want to change it.

From a broader perspective, though, interaction data from many channels needs to be combined for marketers to do the best job of analysis and targeting. This can be done by physically copying the data into a traditional data warehouse or by using some sort of virtual or federated structure. What’s important is that data from many sources must come together into a single location, where it becomes accessible to many execution systems. In other words—am I beating a dead horse here? —you don’t want direct connections between single-channel source and execution systems, such as Web analytics to Web targeting.

This has technical implications. In the cross-channel scheme, the role of the analytics system is just to gather and reformat data so it can be presented to the central storage facility. The actual analysis would be done in the central system or by a cross-channel analysis system that draws from it. This means that products which combine data gathering and analysis, like current Web analytics systems, need to decouple those functions and build open interfaces to reconnect them. These interfaces would allow users to substitute other products on either side of the relationship. In addition, vendors with specialized data storage technologies might offer a storage component with interfaces at both ends, one to accept feeds from multiple data-gathering systems and the other to allow access by multiple analysis and targeting tools.

This is not an appealing proposition for many vendors. Breaking their systems into components opens them up to more competitors and risks each component appearing to be a commodity. It also eases switching costs, placing further pressure on prices. In general, as I’ve noted many times, vendors seek to expand their footprint and increase integration, not the other way around.

But vendors who specialize in systems for one channel will increasingly find themselves frozen out of multi-channel opportunities. There are already many products to provide multi-channel data store, analysis and targeting. Data gathering still tends to be channel-specific, but that won’t last as new channels become better understood.

In short, vendors who seek to remain channel specialists are likely to find their business shrinking over time. This may seem like bad news, but the sooner they begin to adjust to it, the better off they’ll ultimately be.

0 Acxiom Digital Buys Kefta for Web Page Personalization

It doesn’t take a crystal ball to foresee continuing consolidation among online marketing systems vendors. Today, email specialist Acxiom Digital announced it is purchasing Web targeting specialist Kefta. In February, the news was Web analytics vendor Omniture buying behavioral targeting vendor TouchClarity .

The Acxiom Digital / Kefta match up is somewhat more interesting because it combines email and Web channels. Omniture and TouchClarity were both primarily Web specialists. Of course, marketers are increasingly interested in synchronizing efforts in both online spheres, so we can expect more cross-channel acquisitions as vendors seek to meet this demand.

The Kefta acquisition is also interesting because Kefta targets with user-specified rules, rather than the self-adjusting statistical models used by TouchClarity, [X+1] and Certona . This suggests that what Acxiom Digital found really appealing was Kefta’s ability to render personalized Web pages, rather than its particular targeting technology. Much as I personally am fascinated by automated targeting solutions—and much as I suspect they can bring higher ROI than rules-based approaches—this probably reflects an accurate assessment that most marketers prefer the clarity of a rules-based approach to the mysteries of a self-adjusting model. Certainly the rules-based approach is closer to the targeting methods used in Acxiom Digital’s e-mail personalization system. So the two vendors are philosophically compatible, regardless of what it will take to integrate their technology.

Note that, if I’m correct that the real appeal of Kefta is in its page personalization technology, this supports my thesis about the value of Web testing vendors (see Are Multi-Variate Testing Systems Under-Priced?)

From a user’s viewpoint, the important question about the Kefta acquisition is how much easier it will make it for marketers to tightly integrate cross-channel customer experiences. Frankly, I wouldn’t expect much change any time soon. Judging by the results of other mergers, it will take quite a bit of effort—perhaps even a complete rewrite—to run both delivery systems from a common campaign manager. Moreover, the real challenge with integrating Web and email campaigns is identifying when a particular Web visitor is the same person as a known email account. This linking is done outside of the Web and email delivery systems, so merging two delivery systems into one won’t really make it any easier.

And let’s not forget that Web and email are not the only online channels. A truly integrated online marketing system must also include mobile. That consolidation has yet to begin.

0 Differences Among Mobile Marketing Systems

You may have thought from last Friday’s post that I had merely gathered the names of mobile marketing software vendors. Au contraire. That list was based on a close parsing of the relevant Web sites. (And if it’s on the Web, it might be true, right?) Now that I’ve had some time to sift through the materials I assembled, it’s possible to make some more precise observations about what differentiates the different systems.

1. Voting vs. Ad Serving: this seems to be the Great Divide. Of the seven products that seem to be serious marketing systems (as opposed to simple message blasters), four mention voting and related applications (sweepstakes, contests, etc.) and three mention mobile ad serving, but none mention both. I realize that ad serving is a pretty specialized skill. But I don’t see why it should conflict with voting and similar interactions, so perhaps some vendors do both and just don’t mention it. (Note: after posting this, I learned that at one of the ad serving vendors, Enpocket, indeed does both. The others may as well.) The voting vendors are Flytxt, Netcom Consulting, Kodime and MessageBuzz, while the ad serving vendors are Velti, Knotice and Enpocket. It’s also worth noting that Ad Infuse, which is not on my list of seven, is a mobile ad serving specialist.

2. Upload user content. User-provided content might be considered shorthand for all the Web 2.0 community features. You need not just to upload it, but provide a way for others to search for and view it. Only Kodime and Enpocket make a point of mentioning this—which, again, doesn’t mean others don’t do it.

3. Download paid content. This could be anything from simple ringtones to coupons to full e-commerce, so there is probably a broad range of functionality among the five vendors who mention it (Netcom Consulting, Kodime, MessageBuzz, Velti and Enpocket). If you’re interested in this, also look at Mobiqua, a mobile coupon and ticketing specialist.

4. Interactive dialogues. This could mean automated interactions (Kodime), connecting to live humans (Flytxt, Velti), or enabling user-to-user conversations (Enpocket). These are wildly different applications, so this category also takes more digging depending on your exact needs.

So much for differences. Everybody is hosted, although Velti seems to offer on-premise as an option. Everybody can broadcast messages, usually in multiple formats (SMS, MMS, video, games, Web pages, portals, etc.) and receive responses. All maintain some form of customer database to capture permissions and build profiles. Most explicitly mention campaign management and the rest probably have some kind of campaigns too. But Velti, Knotice, and Enpocket describe more sophisticated campaign administration, with workflow and targeting. Those are the same three that do ad serving, which make sense if you think about it.

If you’re looking for truly unique claims, Enpocket is still the only one I’ve seen that describes an automated scoring system to predict customer behavior, and Knotice is the only one positioning itself as multichannel in the sense of including Web and email as well as mobile.

Let me stress yet again that these are just impressions based on vendor Web sites. If you have a specific requirement, it’s definitely best to query all the vendors directly to see whether (and, more importantly, how) they support it.

0 Deltalytics' Lloyd Merriam Comments on LTV

My friend Lloyd Merriam has left a thoughtful comment on last week's post about Lifetime Value. It's worth treating as a post of its own. Here's Lloyd:

I completely agree that customer lifetime value (LTV) is the single metric against which all strategic business decisions should be evaluated. Although non-trivial, determining the current value of a customer isn’t particularly challenging. Calculating future LTV – which, as you know, is what really matters – is neither simple nor straight forward. LTV is driven by lifetime duration (LTD) and future purchases. How much a customer is likely to spend (on average per purchase), how often they’ll do so, and for how long will together determine their LTV. To the extent that these may have been poorly estimated, the accuracy of any subsequent analyses will be compromised.

That huge challenge aside, what’s even more difficult is to qualify and quantify the relationship between discreet business decisions (primarily strategic but sometimes tactical) and their back-end results. In other words, assessing which business drivers had what direct and specific impact on LTV. For example, even if we can reasonably estimate that LTV has increased, say, 15% overall, how do we tie this increase back to a particular driver when, in fact, many may be at work? Was it our redesigned website, product line expansion, more restrictive (or liberal) returns policies, or new factory that is primarily responsible? Whether a particular strategic driver had a positive or negative affect is difficult enough to discern. Assigning its quantitative score is typically next to impossible.

Therefore, while it’s perfectly valid to assert that it’s the impact of a given business driver on customer lifetime value that’s most important, it’s just as important to recognize that leveraging this principle is exceedingly difficult due to the sheer complexity of the numerous interactions taking place – especially internal, but also external as well (e.g. the actions of competitors).

Our approach (which we call “Deltalytics”) is to periodically estimate the average customer lifetime duration, the average customer spend, and subsequently track their change over time to expose trends that will ultimately govern future business performance. If we know that both are increasing, for example, it’s safe to say that the business is trending upwards. The rate at which this is occurring can, of course, be used to make specific predictions about future growth (or decline, as the case may be).

But these two metrics are just the tip of the iceberg. Others that can and should be used to gauge business performance include:

(1) Rate of new customer acquisition (and, conversely, attrition)

(2) Customer distribution by recency (the greater the proportion of recent buyers, the better the business will perform)

(3) Average latency (the sooner customers place subsequent orders the better)

(4) Customer distribution by frequency (the higher the better, although not nearly as predictive as recency)

(5) Multi-buyer conversion rate (the percentage of 1X buyers who become multi-buyers)

(6) Customer re-order rate by recency (the ratio of repeat buyers as a function of their recency segment, e.g. <30 days, 30-60 days, etc.)

(7) Customer reactivation rate (customers flagged as having lapsed but eventually reordered)

(8) RF Delta (the change in population density over time at the intersection of recency and frequency)

Although quite useful in themselves, the greater utility in each of the above metrics lies in evaluating and forecasting their deltas over time. Change, and the rate thereof, is far more meaningful and insightful in this context than the more common “static” approaches to predictive analytics.

Getting back to business drivers (and measuring their impact on the bottom line, viz. LTV) one must concede that no single solution or approach can effectively gauge them all. At some point, a seat-of-the-pants determination must be made based upon relevant, albeit inherently incomplete, data. Tests can be conducted to measure the impact of, say, introducing a new product line or instituting wide scale changes in pricing. But even then, other contributing factors that cannot be controlled for, and are likely to cloud the results, must be acknowledged (such as a new website, outsourcing the call center, and so on).

In a perfect world, strategic changes would be implemented in a linear and mostly piecemeal fashion to ensure that consistent and reliable analyses can be made. Because this is so rarely possible, however, some compromises must be made in terms of measuring and forecasting the impact of such changes.

It is our position that an optimal way to approach the problem is to analyze trends amongst the aforementioned business performance measures – more specifically, their change (and rate thereof) over time, and subsequently tying these back, as best we can, to their underlying business drivers. This, unfortunately, is much easier said than done.

Lloyd Merriam
lmerriam@deltalytics.com