Wikipedia states that;
"A Single Customer View is an aggregated, consistent and holistic representation of the data known by an organisation about its customers."
and for all person related platforms be it a Single Customer View (#SCV), Customer Data Platform (#CDP), BI platform or other, they all face the challenge of matching customer data. If your analysts and systems know that ‘Dave Smith’, ‘David Smyth’,
‘dman@genericem.com‘, and ‘jagbox12’ are all the same person, your insight about and interactions with David will be more informed. There is more information about Single Customer Views in an excellent article by Christopher Ratcliff available on Econsultancy. However the purpose of this article is to highlight the importance of customer data matching and how SMEs can justify adding the capability without having to commit to an enterprise class platform.
Objective
Many organisations can justify a single customer view through their business case for a sophisticated platform that will deliver a single customer view as part of the offering. Some CDP, CRM and Marketing Automation platforms do include the capability to match customer data. However, many SMEs have valid reasons why they struggle to build a business case to buy or subscribe to an enterprise platform, for example;
They are happy with their overall system capability but want to be able to match their customer data and identify individuals across products and interactions to feed into existing functions like Analytics, Campaign Management and Compliance or in-house SCV.
They have talented IT teams that can design, build and integrate tactical and strategic solutions but wish to integrate expert services and API’s for specialist functions like Customer Data Matching.
ROI projections are too long or volatile.
For these organisations, can adding Customer Data Matching capability deliver enough of the objective of a Single Customer View to be justified as a tactical and/or strategic investment?
Benefits of Customer Data Matching
You have to match customer data if you want a single view of a customer and many of the benefits of customer data matching have been understood for a long time.
Pre-digital channels
Any organisation with multiple systems supporting multiple products can drive efficiency through a consolidated understanding of a customer’s complete portfolio and interaction history. Until the explosion in digital channels, many of the benefits of customer data matching that were measured were based upon operational and marketing savings.
In 1990, Australia established a Data Matching Agency within its Department of Social Security which lead to net savings of $100M p.a. directly attributable to data matching across 5 departments with a benefit: cost ratio of 5:1. This was predominantly through a reduction in payments claimed/given.
For most sectors, preventing wastage of materials and effort within a DM campaign by using Data Cleansing and Matching was the most frequently stated benefit. With matching focused on name and address, significant savings were gained from deduping within and across mailing campaigns. This also prevented presenting conflicting offers to the same individual. Although, the 9% - 14% saving typically identified is still valid today, DM usually represents a much smaller % of most marketing budgets.
The growth of digital channels and consumer expectation
With the increased number and immediacy of digital channels, matching has become faster and more sophisticated as have the benefits. Most of the measured benefits published come from studies of the enterprise platforms mentioned previously (CDP, MDM, Omni-Channel CRM etc.), but is it fair to say that many of the benefits would be diluted or dissolved without customer data matching?
In 1990 ‘Loyalty Effect’ by Harvard Business School Press stated “It costs 5 to 10 times as much to win a new customer as it does to keep a customer, and a 5% increase in customer loyalty can translate into a 75% increase in profitability.” and this was increased to 95% when including ecommerce
In 2016 Deloitte & Touche looked at MDM and Single Customer Views in Banking and stated “The following are some sample benefits that have been achieved by Banks in recent years and can be used in building the business case”. The benefits ranged from $38m over 3 years to $26m p.a.
The Aberdeen Group Inc. claim that companies with the strongest omni-channel customer engagement strategies retain an average of 89% of their customers, as compared to 33% for companies with weak omni-channel strategies.
In 2016 Gartner stated that “through 2017, CRM leaders who avoid MDM will derive erroneous results that annoy customers, resulting in a 25 percent reduction in potential revenue gains”.
When you consider all of the above, it is clear that a significant part of the savings and increased revenues can be directly attributed to good quality and right time data matching. Just by being able to link all of a person’s products and interactions, analytics can be improved, each conversation can be more relevant and the risk of doing stupid things is reduced.
Obviously intelligent use of matched data alone will not deliver all of the benefits of an enterprise class platform, but equally a significant proportion of these benefits are achievable without the implementation of such a platform.
Customer data matching and GDPR
For a long time customer data matching been an element of ‘Know Your Customer’ (#KYC) and ‘Data Protection Act’ (#DPA) compliance but now #GDPR increases the pressure on organizations to be able to match their customer data and/or create a Single Customer View. Articles 15, 16, 17, 18, 20 and 21 of the GDPR all imply the ability to locate and update all records relating to a single individual upon their request.
Solution - Matching Capability.
So if customer data matching delivers real benefits and together with a few simple processes can meet many organisations’ immediate requirements, what solution should an SME (#SME) choose? There are a lot of decent matching solutions available so what should an organisation look out for?
The right solution will depend upon requirement, budget and timescales and there is no ‘one size fits all’ option. However, some of the rationale behind the design of our new matching software-as-a-service will highlight some areas that we feel should be considered;
Matching is a specialty. A secure service that has its own development roadmap and can be easily integrated via secure APIs into any platform / transaction is preferable to matching only being available as part of a much larger platform with multiple functions. Some of the most powerful platforms recognise that matching is a specialty and support the concept of a single person but do not provide the capability to match and key the data.
Real-time should be an optional capability. It might not be needed straight away but at some point it will be for at least digital and customer service channels. The matching algorithms used and keys allocated should be the same for real-time as they are for batch processes i.e. the same instantiation.
First time Implementation or single use should be possible within minutes, hours or a couple of days at most. Longer implementation timescales can indicate a higher risk to business cases.
Appending a unique person identifier to the data should be a capability.
Postal address or postcode should not be a pre-requisite for matching. In the past, some matching solutions required at least a postcode or address to work.
Cloud based must be an option. The cloud giants are experts in data transfer security and regularly update their mechanisms e.g. to transfer data to and from Azure, you don’t need to open outbound ports, it is already in use in thousands of organisations and is signed off by data protection officers worldwide. Older or bespoke security is less likely to be reactive to new threats.
Manual data movement to and from a desktop is inherently high risk. Automated and locked down data movement via proven market leading secure and flexible services reduces risk.
Matching processes are resource hungry. Cloud scalability only when you need it can remove a high water mark in compute resource for the rest of the solution(s).
Encryption at rest and in-flight should be mandatory.
Article 25 of the GDPR calls for solutions with data protection by design and by default. This should be demonstrable.
Conclusion
The use of decent matching software can deliver many of the business benefits discussed and when SMEs are considering their customer data management roadmap they should bear in mind;
You don’t have to buy or subscribe to an enterprise platform to realise many key benefits of a single customer view.
It is often best to ‘buy’ expert components like matching unless it is core to your business model. The time to design, build, maintain and improve a matching solution will other not be cost effective or a key focus area.
About SingleCustomerViews Limited
SingleCustomerViews limited are experts in Single Customer Views and BI solutions. MatchFast is a secure matching micro service that can scale for all sizes of data and budget and can be implemented within hours. Encrypted at all times, batch and real-time variants ensure that data can be matched when needed across all channels.
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