thefunnel

This post might be a bit different to the ones than what I written before, but here’s a question for you all. In B-2-C, who is the stronger brand? The brand manufacturer itself, or the distributor of the brand (assuming it’s not the manufacturer)? There are plenty of articles about this topic (who has the power) and what tactics can be used to deal with it, but I thought this, my particular personal experience was interesting enough to bring up. At my work, I used to have a computer, brand X, that I was really happy with (it actually still works, but it’s a bit limited in speed and capacity). Then I got a new one, brand Y, because I needed an upgrade. Brand Y turns out to be a real disaster. Hard-drive failures, motherboard failing, sluggish, you name it, several times. Some of the software problems probably hasn’t anything to do with the computer itself, but every time I log on, what do I see. Not the operating system,or the network, or the virus checker, but yes, brand Y. So, brand Y is doomed. I will not recommend, actually, I’m an active detractor spreading the bad news, and I will never, ever, buy brand Y again (spending my own money), ever. And all this is confirmed by my organization, who has now signed up with brand Z. Anyway, I need a new personal computer (compatibility, need at least one non, you know), and it’s on sale at one of the distributors of brand Y. To my big surprise, I find myself considering the never, ever brand! Why? Because I trust the distributor, I trust their no questions ask return policy. In fact, I trust the distributor brand way more than the brand (Y) itself, go figure, to the point that I’m willing to take the risk, with my own money! And, I’m a marketing guy who should know better. I think this just shows the real power of the a brand, or  the brand of the carrier of a particular brand. How about that for a Porter’s Five Forces example? Or maybe brand Y was smart enough to even fool the most seasoned marketing guy. Only time and google  side wiki will tell. By all means, like to have some comments for a change.

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.