In the first article in this series – aimed at start-ups – I recommended a focus on the quality of data rather than the quantity. By this I didn’t only mean quality in the sense of accuracy and standardisation. I stressed the importance of a data strategy that was closely aligned to business goals. As you build a business you have to begin with a clear emphasis on replicating your prime target market in your data set.
As a company grows, of course, the marketing journey begins to get messier. Paraphrasing Mike Tyson: “No plan survives a punch in the face.” No marketing database remains in a static or pristine condition after a few campaigns or simply the passing of time. You have to work hard to convert a foundation into a scalable process based on real results.
So, if the emphasis for new companies is on strategic direction, detailed planning, initial data acquisition and building a data platform, the four principal drivers for more established, but growing companies are:
- Testing, analytics and insight – what works in practice?
- Prioritisation – resource and budget allocation
- Process – configuring the resources for effectiveness and efficiency
- Scale – rolling out to maximise marketing impact
Let’s look at all four in the logical sequence listed above.
- Testing and analytics
Testing and analysis, the process of quantifying what works and what doesn’t across a range of marketing variables, was in danger of becoming a lost art in the earlier stages of the digital age – at least in campaign mode. And it’s still a danger in badly run marketing organisations. The low transactional costs of email, and an emphasis on speed-to-market at the expense of targeted, relevant messages, meant that many companies fell into the trap of skipping testing and going straight into campaign roll-out.
Fortunately, this ‘suck it and see’ syndrome was overtaken by three factors. Firstly, the tools now available for real-time analytics have been adopted very widely so the ‘takes time to test’ argument no longer applies. Secondly, while transactional costs of email remain low, the costs of setting up a functioning marketing technology stack are comparatively high. In order to take advantage of the stack – from marketing automation and data integration platforms through to Business Intelligence tools – and return a proper ROI, you have to market more smartly. Finally, the growth of ‘inbound marketing’ makes it an absolute requirement to analyse and identify inbound enquiries.
In other words, although you still see ‘spray and pray’ email, it’s largely in businesses where there is a disconnect between capital expenditure spent on martech tools and campaign transaction costs.
Obviously, there are many variables you should test, measure and understand. But let’s focus on how testing and analytics can positively impact data acquisition and enhancement. Here are the points you need to evaluate after looking at the results of both campaigns and web inquiries:
- Market Coverage Where is engagement coming from? Are the better results coming from the sectors (geography, size, industry, demographics) that you expected and reflect the assumptions on which you built your initial data set?
- Data source It’s probable that you will be relying on a number of third party databases, each providing different assets. Analyse them not just in terms of accuracy, but also how their particular attributes contribute to your requirements.
- Contacts As with market coverage, you want to assess which contact profiles perform better. Then drill a little deeper to see how closely the personas match the contact definitions.
- Accuracy Is the data accurate enough? Or not fit for purpose – too many bounce backs, poor match rates with incoming responses, records that simply shouldn’t be there?
- What matters most? Profile or behaviour? This is crucial. What are the key attributes that seem to drive engagement and response? And what is most important to you? In B2B especially, a positive engagement score with a low potential market segment may mean you have misaligned content and personas with the data. Data insight in this case will inform, but may not be able to correct the problem unless the other factors are fixed. It’s important here to incorporate feedback from all parts of the revenue team (sales and marketing) and remember that, in the end, it is the ultimate revenue number that counts. Sometimes, breadth of coverage may outweigh depth.
HOW DO WE TURN THIS INSIGHT INTO ACTION?
- Prioritisation
Now you know, at least to some degree, what is working and what isn’t, you can prioritise your resources and budget around improving and enhancing your data. Here is a simple chart to help you score your wish list. Obviously, the scores are hypothetical – impact severity will change from case to case. You may have a highly accurate database, but limited coverage, or vice versa. The solutions cited are just examples – multiple fixes may be needed.
This method will enable you to begin a holistic data remediation plan that tackles the most serious business issues first. And keep you focused on measuring improvement. But you will also be able to see how solutions can deal with more than one problem. Let’s ‘reverse engineer’ the list above:
It’s up to you how complex you want to make this. You can score based on a quantifiable as well as a subjective view of business impact – and quantify the remediation as well.
- Process Implementation
A database is like a garden – it needs continual attention. But once a plan has been established, a marketing cadence is rolling out and the right tools and team have been put in place, it is essential that a clear and defined process is marked out and followed. It’s got beyond the stage of ad hoc manual intervention. Then manage by the metrics. Generally, it will be the components rather than the process that needs changing over time.
- Scale
Scaling a database operation for a growing company is hard. All the stages above must be layered over sound business strategy. Crucial factors include:
- Strategy and direction
- Choosing your technology stack
- Vendor selection – tools and data
- Data integration
- Team deployment – who does what
- Measurement and management
But for getting the right data and keeping it right, then focus on three key questions:
- What do I need at a very granular level based on actual results across the marketing continuum?
- What data do I already have, what do I need to buy and what should I build?
- What do I need to do to keep it fit for purpose?
The right answers to these points will lead to successful marketing.
Have an opinion on this article? Please join in the discussion: the GMA is a community of data driven marketers and YOUR opinion counts.
Read also:
Deciphering big data: business analytics versus business intelligence