Every organisation that relies on extensive supporter databases aspires – or should aspire – to achieve an effective single supporter view. In other words, to have all incoming data held on a single accurate, reliable and up to date master database from which all marketing and analytical aspects of their work can be run. This is the much sought-after 360° supporter view: simple in principle – very different in practice.
Why is this so hard to achieve? Barely a day passes without the emergence of a new fundraising data source. Traditional databases and flat files have been joined by a whole range of digital data sources – many of which stream information directly to a database without any human intervention.
SSV – challenges
So the challenge is to deliver that 360º supporter view across all data streams and holdings. But in reality the increasing complexity of data means that unless the organisation has recently reviewed, redesigned and re-specified its database systems, this view is almost certainly constricted and unclear.
At a time when compliance with regulatory requirements such as GDPR is paramount, organisations simply cannot afford to run the risks inherent in using inaccurate and/or out of date supporter data, behaviours and permissions.
Methods & Solutions
The over-arching concept of the Single Supporter View is the same the world over: we want to combine all the data we have into one place.
There’s value to be found in every database across your organisation – from volunteers to service users, to campaigners to donors. Information that can provide insights into the behaviour of individuals and identify opportunities to create a single view and single ID with a single view of consent.
This is a crucial data infrastructure for customer- or supporter-focused Next Best Action-driven marketing (rather than Next Best Ask – because effective fundraising isn’t always about asking for a donation). The graphic above demonstrates how an SSV can establish the foundation for a Nurture, Activate, Develop approach to supporter development, based on the comprehensive supporter knowledge.
It’s probable that any initial attempt to create an effective SSV from scratch will quickly run into complications: different techniques and processes of combining data will yield varying levels of accuracy and completeness. Data sources will inevitably be of varying quality, breadth and accuracy, and updated at different intervals. The challenge is to design the best way to relate and combine these sources together while prioritising those that should take precedence.
There is often a further requirement to supplement the single supporter view with additional data – as well as then updating the entire resulting system reliably and quickly. Future-proofing has to be mandatory, enabling previously separate individual records to be brought together as new data provides the means to link them.
The Wood for Trees approach
When combining separate databases, Wood for Trees deploys an in-depth methodology to provide the best possible matching of like-for-like records. Our approach for offline data features:
- A minimum of 12 different matching algorithms
- Algorithms applied to all available data, current and historical
- Matches identified even if records don’t comprehensively match
- Matching process extended beyond name and address to maximise robustness
Using these techniques we achieve a higher number of matches for records because there are multiple ways in which a match can be identified.
For web or online data, further challenges need to be overcome. Usually there’s an initial period after a digital contact is made when an individual cannot be identified from their digital footprint (such as their IP or MAC address).
However, the same individual may subsequently submit additional information as part of a separate transaction – signing up to a newsletter or giving an online gift, for example. The database system should be capable of tying what was previously anonymous data to an existing supporter, helping to group a range of web behaviour characteristics with the other offline data held against that supporter, with the correct data processing permissions in place from the data subject.
The same principle applies in the offline environment where the addition of a new piece of information allows the system to join two or more separate existing records.
Once the common identifying keys have been created they can be applied across all records to create a definitive master record. A set of defined business rules allows data from each contributing gross record to be combined, aggregated and promoted, thus creating the best master record to represent the entire net population of supporters.
This master record is continually evaluated through the same business rules upon each data refresh within the single supporter view. As data quality on one source improves, or the range of data on a particular record populates further, the business rules creating the master take advantage of this ever-growing set of information to ensure the master is kept up to date.
The database and the business rules governing its structure and content are effectively ‘learning’ every time new data is added.
Results, benefits and GDPR
The resulting database provides a single, robust foundation for marketing and analysis activities, combining behaviour from differing data sources and bringing together duplicate records within and across systems. The true behaviour of all supporters is more accurately reflected, with the ultimate benefit of focusing overall fundraising performance and delivering improved ROI and performance against KPIs.
It’s important to note (and this is of critical relevance to GDPR) that a properly structured and operating SSV will enable an organisation to combine potentially differing consents and permissions across varying originating sources. This means that the master level can deliver a vital overview of the methods available to communicate with supporters across multiple touchpoints.
When linked with a Permissions Management Platform this ensures that the correct permissions have been obtained from the individual data subject to allow their data to be processed for a specific purpose. It can also assist with Subject Access Requests and Right to Be Forgotten requests.
Outside fundraising the concept of a Single Supporter View is more commonly referred to as a Single Citizen (or Customer) View. It’s a prerequisite that individuals’ personal information consents can be tracked and confirmed throughout the usage chain, including among service users outside fundraising. Whatever you call it, if you’re not sure about your preparedness, try answering the following questions:
- Is all the information you have about an individual held in one SSV (or SCV)?
- Does your SSV match individuals on all possible variables?
- Can you receive and record consents and opt-outs across every channel used to communicate with your organisation?
- Is the date, the channel and the statement being consented (or opted-out of) all always recorded?
- Multiple and possibly contradictory consents can exist for an individual: are you able to generate rules to decide whether specific marketing activities such as profiling would be permissible with that information?
- If an individual does not consent to their data being used, can you prevent its use to derive segmentation or propensity scores (for example)?
- If an individual wishes to alter their preferences, can you easily access their single customer view and act on instructions to copy, transfer, amend or delete their personal data?
- If you delete an individual’s personal data, for example in response to a RtBF request, is it removed from both the SSV and from all upstream systems that have fed the SSV?
- After deleting personal data is it still possible to record transactional data for inclusion in sales totals (for example)?
A good SSV is a massive asset to any organisation – but it needs to be specified and built with care, with the full spectrum of incoming data streams and possible end user requirements in the forefront of planning right from the outset.
Get it wrong, and an organisation could be faced with years of rebuilding, stunted development because of ineffective marketing data, and at worse, substantial fines for misusing data.
However, the right SSV, with full consent tracking and editing options built in, will prove (and pay for) itself many times over in the challenging years ahead.