thefunnel

Here’s one example how to think about measuring the success of a B-2-B web site. To start with you should first organize your metrics into different high level groups. The typical high level groups would be web acquisition, web engagement, and web conversion. The next step would be to define sub metrics for each group.  For your acquisition metrics should count and track the number of visits, unique visitors, returning visitors for different sources of referrals, such as organic search, paid search, marketing campaigns, and direct traffic. For engagement you should measure, visit depth (or page views), visit time (duration), visit returns, visits per visitor, bounce rate, exit rate, number of comments and content consumption (or form completion). In addition you must also be able to cross reference acquisition sources with the various engagements. For paid search, you might also want to drill even deeper down separating golden key words from, generic ones and the long tail words. This will enable you to scratch the surface in understanding ROI for your search investment. Finally, the web conversions also need to be divided into sub-groups. My suggestion is a four tier approach. Responses are the lowest value conversion events, where the visitor leaves a trace, but there’s no follow-up activity. Generic success events are things you want to count and put a value on. These could be certain page views (for example, example a new product), new names registered to the database, or sign-up for newsletters. The third tier is the selected few, most valuable success events. These could be contact requests, partner referrals, service requests or more valuable page views such as pricing views. And fourth are your key performance indicators such as a request for a quote or sales leads. Similar to web engagements, you should be able to trace the web conversions all the way back to the acquisition source. In addition since we’re talking about a conversion event, that normally requires a web form of some kind, you should also track completion rates or abandonment rates.

And finally you should be able to slice and dice this data based on the business segmentation (your web taxonomy). In addition you should also create a dash-board where you track the trending of all the different conversion percentages rather than the absolute numbers. If you then are also able to correlate this data to your web satisfaction data, measuring things such as overall satisfaction, would recommend, able to find, and a positive search / browsing experience, combined with how much money and effort you’re putting in, you’re much closer to understanding your real performance and business contribution of your web site.

Web analytics is hard and difficult! How hard and difficult can it be? It’s only data. Only data? – that’s exactly what the problem is. Unfortunately many companies get stuck, trying to fit their existing marketing metrics thinking into the area of web analytics, when to be successful, it requires a whole new approach, and thinking, and understanding in the limitations of the data based on how it’s captured and configured, in addition to an investment both in technology and in people.  But before even looking at what to invest in, companies first need to decide what they want to measure and why. Too often things are just measured for the sake of measurements, without knowing what it means for the business.  Even if the data in some cases is taken to the next step, and analyzed, it often stops short in providing decision makers with actionable managerial information. So the first step is to work backwards from the high level business objectives and overall metrics, to understand where web analytics can provide some insight. If, it for some reasons doesn’t, that’s also ok, but then companies needs to not waste resources pretending. Secondly various business metrics needs to be holistically aligned. This means segmenting the data and isolating key audiences, so that all measurement sources segment the data in a similar way, aligned with the business / customer segmentation. For example, lets say the web satisfaction data is segmented into large and small businesses, but visitors tracked on the site can’t be filtered with the same granularity, and maybe this is not at all the way the business segments their customers, would basically mean that the measurement system is more or less useless (for business intelligence purposes). Even if the segmentation is aligned too often various business data live in silos and can’t be easily correlated. Thirdly, the business needs to invest in technology and people, because unfortunately good information does not come free. Technology is easy to acquire, but extracting the value from it, typically needs some unique resources. And I’m not talking about the mainstream marketing person. What is required is someone with a combination of analytical skills, business analysis skills, marketing skills and domain expertise. A quite common mistake in many companies is to not invest enough in analytics resources versus technology. And last but not least, any limitations, either because of system limitations or the way the system is configured also needs to be considered, because web analytics is often instead of being perfect or the aboslute truth, more about the trending.

What is the value and how do you measure a B-2-B brand? A common theme in marketing literature is that the value of a brand can be expressed by two factors: Are customers willing to recommend you, and are they willing to pay a premium? Both factors are obviously good to know and have their own implications on your position and marketing / pricing strategy, but before you start to drill down deeper into the recommend factor, you really don’t have any actionable insight. To look what’s behind whether or not someone will recommend you, you have to look at individual brand attributes. Every brand is different, and you can go as deep or wide as possible, but for many B-2-B products it comes down to the following higher level categories: Price, Product quality, Quality of service and support / technical support, Knowledge of the sales organization / representative and product fit to the customer need. Each of these can be individually measured to further understand for what attribute a company is under-performing or performing well. But, that is not enough, because all customers are not equal and every attribute must be further segmented by various types  customer. At a minimum these are customers, non-customers and competitors customers. Depending on the company strategy existing customers might also have to be segmented into key accounts and others. In addition attributes needs to be segmented by product category. All this creates a challenge for marketers in regards to the qualified sample size, but since brand perceptions change slowly for many b-2-b products, it’s better to measure extensively less frequently, rather than do quick on the surface assessment that only provides artificial non-actionable benchmarks.