Over the course of the last nine months we have discussed many theories, however, for these to work properly it is imperative that the issue of data hygiene and data quality is addressed.
Without this you run the real risk of alienating both customers and prospects and losing much more than the cost of a campaign. With marketing budgets needing to cover many disciplines, resource allocated to data analysis is a wise choice (and can generate high returns for your company). But it can be entirely wasted if the data being used has not been cleaned or checked for accuracy and transformed into a working format.
This is something I have witnessed only recently; a highly successful blue-chip company was at one point sending out over 20 mailings to the same customer. How was this possible, you ask?
Well whenever a company registered for a new piece of work a new account was opened to fulfil company procedures. Unfortunately this data was then transferred through to marketing who did not spend time addressing the quality of the data and looking at duplications.
You can easily see the waste of budget simply on the duplication. Far harder to measure though is the extent of the damage around brand value and the irritation this causes to the customer. For some customers a repeated mistake like this could be the final straw, ending your business relationship.
To the uninitiated, these may seem simple problems to solve, however when you dig a little deeper it becomes clear that the tasks are highly complex.
At the first stage, there are a number of address management systems that are built to deal with the basic address integrity. These systems all use a compressed version of PAF (the Royal Mail’s database of delivery points) which can create real headaches for B2B marketers as it lacks the integrity to recognise most business names, particularly in the small business sector.
Poor results here will lead to potentially serious impacts upon all subsequent data manipulation and analysis stages.
Over the last few years, Information Arts has spent time developing an address management environment called Streetwise, which is designed only for business data. Inputting all client data in here before running it through PAF has allowed us to double recognition rates with company names as it is able to identify these, even when they are embedded in any position of the file.
Using systems that have been developed especially for business data will help eradicate some of the issues, but this can only be seen as a downstream fix. To truly solve the problem, a data strategy must be adopted across the whole of the business, aligning all departments and touch points in the business around a common vision ultimately creating a true customer centric approach.
Data strategy and all that it entails is actually the key issue for most organisations. To start to develop and adopt a data strategy, you must first gain the attention of the board of directors as their support is critical to secure investment.
Information Arts has achieved this by developing a Data Impact Model that calculates the monetary value loss to the organisation resulting from poor quality data.
Having got their attention, the vision and importance needs to be communicated through the organisation and engrained at every level.
Thereafter a process must be instigated to understand what information is required by the organisation to support relationship marketing to both customers and prospects. At a simple level this is likely to include a contract renewal date and current supplier.
When you have identified these, make sure that every customer touch point is aligned to collect this information and in a consistent manner. For example, on the website you might be asking how old the company is. Whilst asking for a date of incorporation at the call centre. Such confusion is common and it will take money and time to sort out ‘downstream’.
Personnel ‘data performance’ measurement is important. Aligning data culture to job descriptions will keep personnel focused on how important collection of data is to the company.
Once this structure is in place it is essential that it is reviewed at scheduled intervals throughout the year. This will then maintain the strategy and keep the focus on the importance throughout all departments of the company.
Fundamentally data culture has to be internally owned, however working with experts who have track record in this area will help to ensure that you get it right first time.
Overall, companies must change their mindsets that data is a commodity. Unfortunately this is still the case for many organisations, but ultimately this way of thinking will be detrimental to all marketing activity and everything else the company does.
