Today we are looking into the challenge of creating the single customer view and how this challenge is linked to identity resolution.
The idea of personalising a service or a product in response to a customer’s need is as old as commerce itself, but in the past generation the internet, cloud computing and machine learning have given businesses an unprecedented opportunity to increase its reach and scope. When, in the noughties, Jill Dyché and Evan Levy tried to explain in a book (Customer Data Integration: A Single Version of the Truth, 2006) what customer data integration was like, this phrase had been already criticised for being a repackaging of previous notions such as Service-Oriented Architecture (SOA), Extraction, Transformation and Loading (ETL) or plain old Business Intelligence. But the criticism missed the point that the frontier of data analytics keeps shifting.
As the volume of customer interactions has surged over the past years, new processes and technologies have become available to retain and link customer data so that companies can engender trust in both prospects and customers and make better marketing decisions. However, at every turn, such technologies and processes, together with the jargon associated with them, have had a short lifespan or seemed insufficient to keep pace with the plethora of new data. Reaching a single customer view (SCV) has so far remained an elusive goal – chasing the proverbial unicorn – but at every turn the instruments available have become more sophisticated.
A SCV’s definition usually includes words like ‘holistic’, ‘accurate’ or ‘consistent’ to indicate that any successful effort to join the dots between various channels have to bridge the offline and online divide, break down data siloes, and improve data quality. For instance, in retail, this could mean bringing together e-commerce transactions, in-store purchases, contact information, browsing behaviour on the company website, call center interaction and email engagement. In this case a SCV would be the foundation of campaigns that do not target prospects who are unlikely to buy or have just bought the product you offer – such personalisation failures cost credibility – while instead cross-selling or upselling to customers who might be near the end of a subscription, have recently interacted with the social media or haven’t made any purchase recently. The more integrated the view of the customer, the higher the return on marketing investment. But the multiple points of entry of customer data pose real challenges to existing ways and systems with which businesses have worked so far.
Customer Relationship Management (CRM) software stores data from customers’ direct interactions and keep tracks of how far along they have moved into the sales funnel, but is limited to transactions and communications with the company, usually not capturing information from other multifarious sources, such as social media, the website or the company app. CRMs stitch together information across some channels, but not across all of them. They also lack the machine learning power to unify data that might belong to the same customer but presents some discrepancies, for instance a misspelled name or address. As a system used by the whole company, it is not particularly oriented to marketers need; for instance it does not provide further segmentation insights for personalisation.
Data warehouses, which ingest data for every area of the business, similarly lack capacity for identity resolution and, while useful for spotting patterns and trends, do not provide insights that marketers can action directly. Both CRMs and data warehouses are company-wide tools that are not specifically focused on marketing improvements.
Businesses also often still work with Data Management Platforms (DMPs), which are tools more oriented towards marketers needs and do provide segments that can be used for personalisation. However, a limitation of DMPs is that they are disproportionately reliant on third-party data and/or anonymous first-party data tags, such as cookies, device IDs or IP addresses. Since the storage and usage of personally identifiable information (PII) is strictly regulated, DMPs must anonymise any data that they aggregate in profiles. While DMPs can provide anonymised audience segments for specific campaigns, they do not provide the single customer view across all data points that some marketing actions call for.
Marketing Cloud Platforms fall short in this respect as well. The likes of Salesforce, Adobe or Oracle Marketing Clouds can be effective in delivering a message to an identified audience on specific channels, but their segmentation ability often relies on demographics characteristics (i.e. female, >40, UK-based) rather than on the last up-to-date registered behaviour. Also, they often constrain marketers into using a single vendor.
Last type of software that has entered the solution set to create a SCV are Customer Data Platforms (CDPs) offering a precision tool for marketers that can sit with these others and enhance them. Initially hard to differentiate from other platforms, since they were first introduced a few years ago CDPs have emerged as offering the closest approximation to a true Single Customer View that the industry has so far achieved. This is because it is a tool with one single purpose, precisely the SCV, and aimed at one single team, marketers. In comparison to the platforms above, CDPs ingest data in real-time, so are always up-to-date; they work primarily with first-party and PII data; they capture offline, online, and multi-channel data; and they use machine learning power to join the dots between customer data in different shapes (avoiding data duplication) and to churn out segments that can be activated in personalisation campaigns. CDPs often complement, rather than substitute, other tools. Their precise configuration in any company, unsurprisingly, varies according to the sector, the type of customer and the business goals. It is your decision what data you want to prioritise and which datasets you want to match. That’s why Teavaro has started offering CDP functionalities as part of our product suite. Activating data through Teavaro’s CDP makes the customer journey as smooth and exciting as possible.
No matter where you look, all types of solutions pop up in the market promising the best in market solutions for creating SCVs and data activation across devices, browsers and channels. All these promises can only be fulfilled, if the CDP solution is based on or paired up with a strong solution for Identity resolution and Identity Management like Teavaro FunnelConnect. Not being able to digitally identify your customers and visitors across different touchpoints and channels will take away every benefit your CDP Solution has to offer. It’s as simple as that: You have to know who you are dealing with, otherwise you can’t tailor your data activation on a one-to-one basis. If you want to learn more about our Identity Solutions and how they can benefit your digital marketing activities, please reach out! We are happy elaborate on your use cases and how we can help you achieve your goals.