thefunnel

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.

One way to measure social media is through using NPS as a metric. Think about it. Social media is all about customers having conversations about your brand, and the ultimate question is, would they recommend your product, service or brand, or do they fall into the category of talking negatively about you? So if you track the conversations, and develop a way of rating these conversations, you have a metric. The metric is about your brand, the communication channel just happen to be social media. Combine this with some other marketing best practices, such as unhappy customersare each likely to talk about you with 10 other customer, which has a compounding effect on the negative conversations about you. Example. Let’s say your promoters % is 80 and you detractor % is 20, leading to an NPS score of 60%, quite good. However, lets now imaging that all the detractors talk to ten of their friends, therefore amplifying the negative message to 200%, while only two of your promoters talk positively about you leading to a a score of 160%. Suddenly your real NPS score has turned into a negative 40%. Using this math, you need 8 out of 10 of your customers to be a promoter, and a maximum of 1 out of 10 to be a detractor to hit a NPS of 60%. This really shows the power of social media, and the importance of customer satisfaction, and the importance of participating (by at least listening) and helping to create positive conversations. It’s could also be a way to determine what your social media strategy should be. If for example you have a really high NPS score, you need to go all out and capitalize on this. If your NPS score is low, and although you can’t control social media,  you need to consider a different approach.