Businesses need data to survive. And that has never been more true than it is today.
The first party data you collect and hold is an extremely valuable asset – as long as you are applying the right insight! But when it comes to acquiring new prospects and customers, why limit the scale and profitability of your campaigns? If you choose your data partner wisely, third party data can help you to find more of your best customers, drive more informed decisions and deliver ROI.
54% of business leaders asked believe third-party data is valuable for enhancing the data that they hold in their organization,
while a similar proportion – 56% – agree that they would benefit from even more of it!
Here are the 8 questions to ask when choosing your data provider
The devil is in the due diligence
So, it’s clear that third party data can help businesses to gain even more value from their marketing activity. But how do you find quality data?
As with any important purchase it is imperative to do thorough research. When it comes to buying marketing data you should apply strict due diligence before you select a supplier – or repent at leisure.
Here are some of the qualifying questions you should always ask when you are choosing your data provider:
Source and provenance
How is the data collected and what is the source? You should also ask for confirmation of the collection methods and audit trails to ensure the principles of the regulation have been meet and the data is being processed lawfully, fairly and in a transparent manner.
Your supplier should be able to provide you with the permission statement used at the point of collection.
Validation and Due Diligence processes
Ask for confirmation of the validation process. A provider with nothing to hide should be able to provide on request an outline of their due diligence process and the steps they take to ensure their data fully satisfies legislative requirements.
When was the last engagement?
Is the data accurate and up to date? Has it been screened against a reliable suppression file to remove deceased and Gone Away contacts to meet GDPR data quality requirements?
Check out their creds and ask peers for a recommendation or ask to speak to an existing customer of the supplier for a candid view.
Ask for some examples of the results and case studies – especially if you are using the data for acquisition campaigns.
Do they offer a trial?
If you are new to buying data or using a new supplier – ask if you can run a trial campaign to test the quality of the data.
If a supplier can’t answer these key questions…approach with caution. It is important to remember that at the heart of GDPR is transparency, accountability and the fundamental rights to process data. It is the minimum you should expect from a reputable data provider.
About REaD Group
REaD Group have been supportive of the GDPR from its inception and we are proud to say that our due diligence is the best and most thorough in the UK.
All contributors must pass our strict Data Compliance Due Diligence Audit and GDPR rules before REaD will accept the data. The audits for existing and prospective data contributors also include the following verifications and checks for compliance:
- Contributor’s legality, location and contact details
- Contributor’s Professional membership, accreditations and certifications (ICO, DMA, ISO) registration
- How contributors deal with enquiries, complaints, data subject access requests etc.
- Full Permission statement audit including audit of all permission statements, FPN and privacy policies served at point of data collection
- Details of how the permissioned data was originally captured and channel collection methods
- Asking suppliers to confirm information security practices and that data is processed in a manner that ensures appropriate security of personal data
The REaD compliance team also carry out 6 monthly audits on the data provided, requiring contributors to provide full details of when and how the data subject’s permission for their data to be passed onto a third party was obtained to ensure collection methods remain compliant and align to the principles of the regulation.
If you’d like to know more about quality marketing data contact us today!
By Scott Logie, MD, Insight at REaD Group
Propensity. It’s a funny word, and one we throw around a lot in marketing circles but rarely in everyday use. The propensity to donate to a certain charity, the propensity for a customer to lapse, the propensity to make a repeat purchase. We can even build propensity models to try and predict these outcomes.
However, you wouldn’t ordinarily hear someone say that they have a propensity to eat chocolate late at night (as I do). Or perhaps that their dog has a propensity to wolf down her food (you can be sure mine does) or even that their wife has a propensity to buy more shoes than could be worn in a lifetime (…no comment).
What is propensity?
So why is the term used so frequently in marketing? I suppose it satisfies a need we have for wanting to know what our customers are likely to do next. When we talk about propensity, what we really mean is the inclination for someone to do one thing more than any other.
For the purposes of marketing we’re looking for people who are more likely to do the thing we want – or don’t want – them to do.
As with most things in life, this comes down to probability and the likelihood of something happening. So, let’s say that when I stay in London, I buy chocolate two nights out of three when I’m heading back to my hotel. This would make the likelihood of me doing this the next time I’m in London two thirds. It may be that I don’t fancy chocolate so much when the weather is hot, and this likelihood can be increased or decreased accordingly depending on the weather. There may be any number of variables that will affect the probability (but more often that not, I’m going home with a chocolate bar).
But can my propensity to buy late night chocolate help to predict if other people will?
Well yes – if you know they’re Scottish (and are therefore always craving sugar) or that they can never say no to a Snickers after having a few pints…or are convinced that going for a run the next morning justifies the eating of said Snickers. With the right data to understand the driving factors behind the decision, we can predict the likelihood of others doing so.
Individual vs Group
This can be approached in two ways. First of all, at an individual level – what is the probability that I buy some chocolate tonight? And then secondly – in a certain group, who is most likely to buy chocolate later tonight? Having this insight will help to sell more chocolate (or to combat diabetes if the data is used more responsibly).
This doesn’t just apply to chocolate. The same principle applies when selling a product, asking for donations, encouraging someone to renew their car insurance or book an all-inclusive holiday. The trick is finding the people who have a propensity to do these things more than others.
On an individual basis we can use past behaviour (of a person and of others) to work out how each person is likely to interact with a company next – often called next best action. This involves looking at past behaviour, current status (their last purchase – when this was and how much they paid) comparing their behaviour to lookalike customers – and also considering what we would like to sell!
By taking all of these things into consideration we can construct a model for each person that scores all the probable decisions and chooses the ones that are most likely to happen. This can also be weighted accordingly to areas that are most profitable.
There is a slightly different outcome at group level, but it is a very similar approach. A score is applied to everyone and we then select those with the highest propensity to do that thing.
For instance, if we are organising an event we want to make sure we choose those who are most likely to take part in that event – whether that be high propensity to run an Iron Man, eat an excessive amount of chocolate or to bungee jump from a skyscraper.
Using the right data
Whichever approach is taken, it is essential to have enough data and the right data in order to build the models. The necessary data should exist inside your business – in the form of data you have on existing customers and past behaviour and outcomes (such as who lapsed and why). It can also be useful to incorporate external data – such as online activity, social media and general activity outside your business.
Ensuring that we are harnessing the right data and applying it to our customers and to the outcomes we are interested in will ultimately increase our propensity to make better decisions. Which is what we all want to do, surely? Perhaps I need to remember that the next time I get peckish after a few beers!
It has been one hell of a year – and we are beyond delighted to now be multi award winning in 2019! And to be shortlisted for three categories in the DataIQ Awards is the icing on the cake.
But, ultimately, the real winner is data.
With all the hype surrounding data – it’s the new oil, gold, best thing since sliced bread, etc – it is easy to lose sight of the real impact of using it intelligently. Well managed, maintained and respected data will drive better strategic decisions and deliver tangible value to businesses on a day to day basis.
To be honest some of it isn’t that sexy but has the potential to transform businesses and achieve outstanding results – and win awards!!!
Data quality is one of the bedrocks of good data management and an obvious first step towards getting the most value from data. The old adage of “rubbish in, rubbish out” has never been more relevant – or potentially costly. In these highly regulated times, all businesses need to be confident that their data is clean, accurate and complete (and compliant!). And with the technology available to manage data quality more efficiently and securely advancing almost daily, there really is no excuse.
It can be as ‘simple’ as understanding your customers better. If you know who your best customers are, what they like, and how you should be communicating with them, then your messages to them will be more personal and positively received (not just personalised). The right analytics can transform strategy and with dramatic results. And deeper insight projects can completely transform business structures and promote new and more productive ways of working – as the award-winning project we have delivered with Marie Curie UK, The Big SHIFT, so aptly demonstrates.
Using high quality third party data, from a credible supplier, can have a dramatic impact on marketing strategy – particularly when it comes to acquisition. If you understand who you best customers are (see actionable insight!) third party data can help you find more of them and engage with them in the right way – using the right channel, message and offer – to ensure more successful outcomes. As our client Titan has very successfully demonstrated.
A new marketing mix
We believe there is a new marketing mix in town – data, creativity and technology.
The companies that use the data they have to make informed decisions that drive both creativity and personalisation – and choose the right technology to put the consumer at the heart of everything they do – are in the best position to win.
As our multi-award winning year goes to show, get the balance of these right and the sky is the limit!
We are absolutely delighted and hugely proud that REaD Group and Marie Curie have won the Best Use of Insight Award at National Fundraising Awards!
Hosted at the stunning and historic venue, The Brewery, and organised by the Institute of Fundraising, the National Fundraising Awards celebrate the very best in fundraising and are a unique opportunity to showcase excellence in the Charity fundraising sector.
It is a genuine honour that REaD Group and Marie Curie have been recognised in these prestigious awards – a reflection of the power and impact of this data and insight driven project, the Big SHIFT.
Marie Curie delivers nursing and care services across the UK, with people interacting with it through many touchpoints, from hospices to shops. The Big SHIFT project aims to model the effect of these services on fundraising income. A team of senior analysts was established, both internal and external, comprising Steve White from Marie Curie, Scott Logie and Diane Dao from REaD Group, Colin Stewart from Caversham Analytics and Andrew Lockett, Natural Data Insight. An example of successful collaboration and team work.
A carefully constructed methodology enabled the creation of a baseline fundraising landscape onto which Marie Curie-specific variables were overlaid. Analysis of these ‘layers’ generated predictive models that were then mapped, highlighting areas where fundraising performance is above or below expected levels, in the context of service provision in the locality. A strategically powerful tool, it has already delivered surprising and extremely effective insights.
Well done to everyone involved – a fantastic example of data and insight in action!
For the full list of winners: http://www.nationalfundraisingawards.org.uk/and-the-winner-is