Why B2B marketing analytics fail: how to put it right

B2B marketing analytics is a powerful tool. However, it can be complex to master, and businesses often run into problems that lead to B2B analytics projects failing, as Carl Mortimer at MarCloud explains

 

Marketing analytics in the B2B arena are very useful tools, enabling businesses to gain a clear understanding of which initiatives or tactics are helping with the sales pipeline.

 

Yet they are complex and many marketing professionals struggle to master them. The result is that time and again we see significant B2B marketing analytics projects fail.

 

Experience shows there are some common threads to these problems. These include the failure to understand datasets, difficulties with setting things up, and data that does not conform with the company’s definition of things like MQLs or SQLs.

 

In this article we deal with each of these in turn.

 

1. Not understanding datasets

Some B2B marketing tools are far more powerful than standard reports and dashboards, being able to pull data from various sources. You are likely to see engagement metrics that are not otherwise available.

 

Datasets underpin everything, and you should be able to mix and match on the same dashboard. Within a dataset your solution should give you the ability to decide which objects and fields you want included. You then link these objects together using a common field or data point, such as a primary key. That way, you can associate each record correctly, such as tying prospects to the correct contact to see engagement for that contact. This should be the same for any dataset within your dashboard.

 

2. Problems with setup

Your software vendor, or their partner, may have provided you with collateral on how to set up their systems at a very basic level. But there are often a few areas within implementation that are not discussed, leading to errors.

 

To avoid such pitfalls you need to have a good understanding of how data works within marketing analytics. The reason is you will need to set your own schedules for when data is updated. Without this, dashboards usually appear to be ‘stuck’ at a point in time, which can lead to its own issues without care. If you schedule datasets to run too frequently and one dataset is dependent on another, this can lead to the dashboard showing inaccurate data.

 

While these areas are not always easy to fathom in solution guides, you do need to consider them – ideally with a partner to help navigate the complexities. This is critical to ensuring problems do not arise in the future.

 

3. Data does not match the required definitions

The more advanced B2B marketing software dashboards are designed with default definitions in mind. If your business definitions differ, then your dashboards may be skewed. A pipeline dashboard, for example, should be showing you the funnel of visitors through to closed-won opportunities and how long it takes on average to move them through each stage.

 

For marketing-qualified leads (MQLs), the standard logic in your solution may be that if a prospect is assigned to a sales user, then they must be an MQL. However, if you have a different way of measuring MQLs or someone could be multiple MQLs over the lifespan of the record you hold for them in CRM, then that component becomes inaccurate.

 

Yes, it is a little complicated, but understanding that these are default definitions will go a long way towards helping you know why something is not adding up the way it should. It will tell you when it’s time to start making amendments to existing dashboards.

 

Each of these points need careful consideration, as the more advanced and effective B2B marketing analytics require greater involvement than simply generating reports and dashboards. It is not a set of tasks that should be taken on lightly, but with the right support it is possible build a highly sophisticated, responsive B2B marketing analytics function.

 

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Carl Mortimer is Senior Lead Consultant at MarCloud

 

Main image courtesy of iStockPhoto.com