Two More Surveys Confirm that Most Marketers Don't Track ROI

The Sales Lead Management Association and Velos Group published their annual lead management practices survey last week. (Read it here; free registration required.) The survy had a relatively small sample (just over 140 responses) and was weighted towards smaller companies (80% had fewer than 25 sales reps). But it still provides some insight into how many companies actually do business.

The key finding from a marketing measurement viewpoint was that 62.5% of respondents do not track ROI on marketing programs. This is not especially surprising; in fact, it’s better than the 76% reporting they do not use ROI another, larger study released last week by Lenskold Group. (Click here for the Lenskold study.) But it’s still bad news.

Perhaps even more distressing is that just 19.3% of the respondents listed their inability to track ROI as a major sales lead management concern. Subtracting those from the 62.5%, this means that more than 40% were not particularly concerned about their failure to track ROI. It MIGHT also mean that many of those 40% actually could track ROI if they wanted to, although it’s more likely that most don’t have the capability but don’t consider that a problem.

The other findings from the survey also generally confirm that dismal state of the art, at least among smaller firms.

- More than half the respondents (55.5%) said they do not qualify their marketing inquiries before sending them to sales. This implies a huge waste of time by salespeople who then do the qualifications themselves, or, more likely, cherry pick the leads that look superficially promising and ignore the rest. Unless a company has managed to staff its sales team with clairvoyants, this is a guarantee that it will discard some good leads and spend more than it should on some bad ones.

- One-third (33.8%) don’t use sales automation or customer relationship management systems. Again, this is fundamental efficiency-killer. The survey also found that companies using these systems were not terribly satisfied with the results: 54% rated their satisfaction at 5 or less on a scale of 1 to 10. Maybe the problem is with the software itself, but I suspect the issue is lack of training and other supporting investments.

- Half had no formal sales forecasting process (27.2%) or used Excel only (23.8%). Again, this shows very immature sales management at these companies.

I must say I find these results quite sad, given how long these tools have been available and how well their benefits are established.

But perhaps it’s best to adopt the more positive attitude of the survey authors and see this as an opportunity. As they put it, respondents “have a lot of room for improvement in their sales and marketing best practices. By spending time and resources in this critical business area, companies will be able to increase sales, allocate marketing resources more efficiently and will be able to forecast their sales more accurately. All of which will help them survive these difficult economic times.”

Whopper Freak-Out Wins Ad Effectiveness Award

I received a mailing with the agenda for the Association of National Advertisers' Marketing Accountability and Effectiveness Conference in New York on June 2. This looks like a good event, covering all the usual-but-useful bases: proving the value of marketing (Enterprise Rent-a-Car), earning a place at the "C-suite" table (panel led by Ernst & Young), advanced analytics (VG Corporation) and media optimization (Citizens Bank).

But my favorite is an "EFFIE" Award for Burger King, for its "Whopper Freak-out" campaign, which "explored deprivation to see what would happen if America's most beloved burger was removed from the menu forever without any announcement." Since I avoid both television and Burger King, this was news to me, but I gather a bunch of TV commercials were involved. Interesting.

Synonyms for "To Make Use Of"

This isn't really a blog post but I couldn't find another easy way to put the image below on the Web. It's from www.visualthesaurus.com, which is certainly something I'm happy to publicize a bit because it is indeed useful. This specific map addresses a problem I've had for years, which is finding a word to convey "making use of something". Typically I want to use "exploit" but that sounds rather harsh. This map has a number of alternatives.

Marketing Measurement Book Includes Free Online Forms

I'm not usually quite so self-promotional but suppose it's reasonable to announce final publication of my long-promised book The Marketing Measurement Toolkit. It's a step-by-step tutorial on the process of building a marketing measurement system, from initial project definition through deployment. The idea was to move beyond the theories (important as they are) to help people with the practical details. You can order from the publisher at www.racombooks.com.

My favorite feature of the book (especially since they didn't put my picture on the cover) is a collection of forms and scorecards that help people to organize their project and assess risk factors. I've put these online here where anyone can download them. Obviously they make more sense in the context of the book, but even without that I think they'll provide useful checklists at different project stages.

Here, for example, is an extract from the Analytics Readiness Scorecard in chapter 8. The extract covers only Response Measurement, while the full scorecard includes similar sections on Segmentation Models, Predictive Models, Marketing Mix Models, Simulation Models and Optimization Models. The idea is to figure out which types of analytics your company can build with its current resources, or, looking at it slightly differently, which resources it must add to do the analytics you want. Users enter a 1-5 score for the existing and needed columns, and the system then calculates a gap. This isn't intended to provide much more than conventional wisdom, but a big, well-organized pile of conventional wisdom can be very useful.

Analytics Readiness Scorecard
Response Measurement	existing	needed	gap	comment
source captured directly			0
contact history available			0
response survey available			0
pre/post analysis possible			0
test/control possible			0
multi-variate test possible			0
total	0	0	0

So, by all means, check out the forms and, if you're so inclined, purchase the book. Any comments are more than welcome.

Rate This Neutral: Scout Labs Social Media Monitoring is Definitely Cool, Possibly Accurate

Sure I like flashing lights and buzzers: what technologist doesn’t? And if a product has all that plus a low price, it’s darn near irresistible. So I was quite excited when I saw Scout Labs, a very nicely packaged social media monitoring tool that combines automated search, sentiment identification, importance ranking, trend reporting, alerts, bookmarking, and collaboration for under $300 per month. What’s not to like?

A couple things, it turns out. But let’s look at the good stuff first. Scout Labs’ combines three of the five social media measures I proposed last week (tracking mentions, identifying mentioners, measuring influence, understanding sentiment and measuring impact). Specifically, it searches blogs, news feeds, video and photo sites, Twitter and some social network sites (although not yet the big ones); provides influence measures; and classifies blog posts by sentiment. It doesn’t attempt to identify mentioners (i.e., track multiple posts by the same individual), or to measure the impact of an item on its audience. But three out of five is pretty good.

More important, the things that Scout Labs does, it does well. The search feature lets users specify multiple terms and whether each term is required, relevant or excluded. Once a search is defined, the system will automatically scan the top 12 million blogs for qualified entries, rate their sentiments as positive, negative or neutral, and show them in a list. Each item on the list shows the blog headline and phrases with the search terms highlighted. A side box shows common words in all the entries, ranked by frequency. This by itself gives a quick view of what’s being said about the search target.

Users can drill into the listed items to see the full entry, details about where it came from, how many external links attach to the item and its source, and the sentiment rating. They can manually revise the rating, bookmark the item with keywords, attach a note for discussion, and email a link with a system-generated summary and the user’s own comments to anyone the user chooses. The system currently uses the link counts as an influence measure, and can rank the items by influence or date. Scout Labs is working to upgrade its influence metric by integrating Web traffic data and a measure of the source’s relevance to the search topic.

But there’s more. The system can prepare graphs showing trends in volume, sentiment, and share of total blog mentions. Graphs can compare statistics for up to four different searches. Users can specify the date ranges to report on, currently going back up to three months and soon extending to six months.

Things are a little less exciting once you move beyond the blogosphere. The system will list search results for photo sites, video sites and Twitter, but doesn’t offer sentiment tracking or graphs. Scout Labs is working on adding sentiment tracking to Twitter comments. I guess it's not fair to ask them to measure sentiment for photos or videos.

As to pricing, the smallest Scout Labs plan allows five saved searches for $99 per month, although the company thinks expects most businesses will take plans for 25 or more searches, which start at $249 per month. There are no limits on the number of users or search hits in any plan and the system continuously updates the results of the saved searches.

So far so good. There's a free 30 day trial, so I set up two test searches in Scout Labs, each for a demand generation software vendor I track closely. The system found many of the posts I expected, and it was definitely fun and convenient to dig into them. If I worked at one of those firms, I would gladly pay $249 per month for this.

But then I ran the same searchs in IceRocket, a free tool that also does searches of blogs and other sources. IceRocket found nearly twice as many hits during the same time period, and they looked legitimate. Ouch. But Scout Labs acknowledges that its 12 million blogs don’t cover the entire blogsphere (over 100 million blogs, last I heard), and it does let you add feeds if one you want is missing. Plus IceRocket doesn’t support saved searches or do any of the other cool stuff. So I’m a little worried about coverage but still willing to pay Scout Labs’ fee.

Next I took a closer look at the sentiment ratings in the Scout Labs results. I didn’t expect them to be perfect, but was seriously disappointed. On one search, 32 of 39 items were labeled as neutral. Some of those were actually pretty positive, but, as Scout Labs explains in a recent blog post, they try to be conservative by labeling items as neutral unless the tone is clear. Fair enough. But the seven positive items were all pretty much neutral too. For example, several were help wanted postings that simply specified experience with the products in question. There were no items classified as negative, although one or two of the posts arguably could have been.

In the blog post I just mentioned, Scout Labs offers a detailed discussion of its sentiment rating technique. The gist is that they don’t just count “happy” and “sad” words, but semantically analyze each entry to understand which words relate to the search topic. Sounds good in theory. They also say their automated ratings agree with college-educated humans about 75% of the time. In comparison, they say, college-educated humans agree with each other about 85% of the time. (Clearly they are not talking about married couples.)

But if the vast majority of items are neutral, that’s less useful than it sounds. Remember the basic statistics: if 80% of the items are neutral, then a system that blindly ranks everything as neutral will be correct 80% of the time. The ratings that really count are the positives and negatives, and I wonder how often a human would agree with those ratings in Scout Labs. I’d want to look at that much more closely before deciding whether to rely on Scout Labs' results.

I'd still pay for Scout Labs for the convenience of the searches, statistics and collaboration tools. As I say, it's a very nice interface. I might even find on closer examination that the sentiment ratings are useful even if they’re only somewhat accurate: after all, they might still get a trend right and call up useful samples. But much as I like the bells and whistles, I’m not as enthusiastic about Scout Labs as when I started.

Vizu Measures the Brand Impact of Online Ads with Just One Question

I wrote last week about a general framework for measuring the marketing impact of social media. This proposed a general hierarchy of:

1. tracking mentions
2. identifying mentioners
3. measuring influence
4. understanding sentiment
5. measuring impact

As with all marketing measurement, the hardest task is the last one: measuring impact. This requires connecting the messages that people receive with their actual subsequent behavior, and hopefully establishing a causal relationship between the two. The fundamental problem is the separation between those two events: unless the message and purchase are part of the same interaction, you need some way to link the two events to the same person. A second problem is the difficulty of isolating the impact of a single event from all the other events that could influence someone’s behavior.

These problems are especially acute for brand advertising, which pretty much by definition is not connected with an immediate purchase. Brand advertisers have long dealt with this by imagining buyers moving through a sequence of stages before they make the actual purchase. A typical set of stages is awareness, interest, knowledge, trial (the first actual purchase) and regular use (repeat purchases).

Even though these stages exist only inside the customer’s head, they can be measured through surveys. So can more detailed attitudes towards a product such as feelings about value or specific attributes. For both types of measurement, marketers can define at least a loose connection between the survey results and eventual product purchases. Although the resulting predictions are far from precise, they offer a way to measure subtle factors, such as the impact of different advertising messages, that techniques based on actual purchases cannot.

The Internet is uniquely well suited for this type of survey-based analysis, since people can be asked the questions immediately after seeing an advertisement. One vendor that does this is Factor TG, which I wrote about last year (click here for the post.) Another, which I mentioned last week, is Vizu .

What makes Vizu different from other online brand advertising surveys is that each Vizu survey asks just one question. The question itself changes with each survey, and is based on the specific goal for the particular campaign. Thus, one survey might ask about awareness, while another might ask about purchase intentions. Vizu asks its question to a small sample of people who saw an advertisement and also to a control group of people who were shown something else. It assumes that the difference in answers between the two groups is the result of seeing the advertisement itself.

Although asking a single question may seem like a fairly trivial approach, it actually has some profound implications. The most important one is that it greatly increases response rate: Vizu founder Dan Beltramo told me participation can be upwards of 3 percent, compared with tenths or hundredths of a percent for longer traditional surveys.

This in turn means statistically significant survey results become available much sooner, giving marketers quick answers and letting them watch trends over relatively short time periods. It also provides significant results for much smaller ad campaigns or for panels within larger campaigns. This lets marketers compare results from different Web sites and for different versions of an ad, allowing them to fine tune their media selections and messages ways that traditional surveys cannot.

Another benefit of simplicity is lower costs. Vizu can charge just $5,000 to $10,000 per campaign, allowing marketers to use it on a regular basis rather than only for special projects. Vizu also has little impact on the performance of the Web sites running the surveys, reducing cost from the site owner's perspective.

The disadvantage of asking just one question is that you get just one answer. This prevents detailed analysis of results by audience segments, or exploration of how an ad affects multiple brand attributes. Vizu actually does provide a little information about the impact of frequency, drawn from cookies that track how often a given person has been exposed to a particular advertisement. Vizu also tracks where the person saw the ad, allowing some inferences about respondents based on the demographics of the host sites. Mostly, however, Vizu argues that a single answer is a good thing in itself because it keeps everyone involved focused on the ad campaign’s primary objective.

According to Beltramo, Vizu’s main customers are online ad networks and site publishers, who use the Vizu results as a way to show their accountability to ad agencies and brand advertisers. Some agencies and advertisers also contract with the firm directly.

What, you may be asking, has all this to do with social media measurement? Vizu’s approach applies not just to display advertising but also to social media projects such as downloadable widgets and micro sites.

Even though Vizu can’t fully bridge the measurement gap between exposure and actual purchases, it does offer more insights than simply counting downloads, clickthroughs or traffic. In a world where so little measurement is available, every improvement is welcome.

Tools for Social Media Measurement

I was whining last week on my other blog about the lack of integrated solutions for social media analytics. No sooner had I written that, of course, than up popped several interesting solutions to prove me wrong. I plan to write soon about a couple of specific products, but will use this post to set a framework for evaluation.

I suppose I should start with a definition of “social media”. By this I simply mean any communication method that allow users to interact directly with each other, as opposed to a broadcast medium where only a few people can send messages. I’m not intending to be especially restrictive here – I’d include blogs, public forums, Facebook , Myspace, YouTube , Flickr , Twitter, LinkedIn, Plaxo and many others. These all provide a huge stream of public chatter that marketers can tap into both to monitor what is being said about their products and to proactively spread their preferred messages.

From a measurement perspective, I see several distinct functions. Today, these are largely served by separate point solutions. Integrated systems are beginning to emerge that combine at least a few. The ultimate integrated system would service them all. The functions are:

- tracking mentions. This is the simplest goal; it simply means uncovering and reporting on social media events that relate to your product, brand or company. The fundamental tool here is the keyword search. Many systems do these, and some even combine different social network sources to provide a consolidated report. Google Alerts is probably the best known, although it doesn’t do much with social media aside from blogs. BoardTracker and Linqia are more focused on social communities.

- identifying mentioners. Most social media comments are signed with a user ID of some sort, but the identity of the person behind that ID is often not clear. I haven’t actually seen tools that address this, but they probably exist. What’s needed is to look at whatever public profile is available, use that to find out other information about the person, and then in turn see if you can find that person in other social media. As a not-too-scary example, I recently saw a Twitter post that mentioned a vendor I follow. Checking out the poster's profile to see if she was worth “following”, I saw that she was from a small town where I used to live. Curious, I then found her in Linked In and discovered the company she worked for. Yes, this sounds uncomfortably like stalking, but it’s old news that the Internet is really good for that. What’s interesting here is the potential to help understand background of an individual and her social media profile. The steps that I took could easily be automated; indeed, products like ZoomInfo do something similar, although so far as I can tell they don't include social media other than blogs.

- measuring influence. Influence has two overlapping dimensions: the influence of an individual mentioner, and the influence of a particular event. The mentioner’s influence is related to the profile I just mentioned, but also to blog readership, “friends” and network members in various social platforms, authority as measured by links and recommendations, etc. Again, these statistics are available in a scattered fashion for individual social media, and it wouldn’t be hard to build a system to pull them together once you had linked the user IDs. Surely someone is out there doing this but I haven’t tripped over them. Maybe if I spent more time at the gym?

Measuring the influence of a particular event is actually easier. It is a matter of links, views, downloads, recommendations, ratings, etc. The statistics are often published along with the item itself. One possible tool is TrackUr, a low-cost product (from $18 to $197 per month) that scores Web sites based on “the number of backlinks pointing to a web site, the number of blog discussions, an estimate of traffic, and even the number of times the web site has discussed the phrase in the past.” Another that I suspect costs much more is Radian6, which “tracks comments, viewership, user engagement and other metrics, 24/7, so that you can clearly see the reach and affect[sic] each post has on the community.” It also can “uncover the influencers online by topic, based on user-determined formula weightings.”

- understanding sentiment. This is the domain of semantic analysis (that’s a pun, kind of), which is a long-established field with many players. One specialist applying its technology to real-time Web content is Hapax. Solutions integrated more closely with social media search include Crimson Hexagon and newly-launched Scout Labs Scout Labs is also a low-cost option, with plans starting at $99 per month and currently offering 30-day free trial.

- measuring impact. Ah, the bottom line: what did people exposed to the social media event actually do? Even the Web hasn’t yet reached the stage of universal behavior tracking that would really let you answer this, and I personally hope it never does. But one product that gets close is Tealium Social Media, which builds a list of Web URLs (both social media and regular online media) related to your product, checks which of those your Web site visitors had seen previously, and pops the results into Google Analytics so you can treat the Web events like any other visitor source. (See my earlier blog post on Tealium for details.) At the other end of the process, Vizu lets marketers embed a question in Web ads that asks about the brand attitudes, and compares this against answers of people who didn’t see the ad, thereby measuring the net impact of the ad itself. The vendor has embedded its questions in social media applications from vendors including Lotame (ads in social networks), AdNectar (social ‘gifting’) and Buddy Media (custom social applications). See their press release for details.