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Enterprises deal with an ever growing number of datasets today. Data is more abundant than ever in the form of financial reports, survey details, behavioral data etc. If managed carefully, we have information on everything from anonymous ad impressions to known client purchases, product usage, and customer service. This data can be leveraged to develop highly effective personalized user experiences.
In this article, we will learn what Customer Data Platforms are and how they help an enterprise. We will discuss the role of identity resolution in customer data platforms. We will also dive into users of customer data platforms, application areas, benefits and shortcomings.
Imagine a scenario when one is looking for a dress online. We visit websites of different brands and enter our preferences to make the best choice for ourselves. Once we zero in on the right dress, we provide the details on the website and shop for that dress. The website in this case captures our behavioral data based on our activity on the website. This could include how many other dresses we viewed, what query terms and filters we used, how many times we visited the website before making the final purchase etc. The website would also capture our geographical information and other details. By doing this, the business can learn more about how each customer interacts with them.
Customer data is the sum of all this information - provided explicitly by the customer or one that they leave behind when they use our products and services and engage in online and offline business interactions.
It is challenging for businesses to offer consistent customer experiences across a variety of channels and consumer devices because this data is typically held in silos, whether organizational or technological.
This leads to a strong need to combine information from first, second, and third-party sources to create customer profiles. This sources are a combination of transactional systems, CRMs and DMPs, website and e-commerce behavioral data, web forms, emailand social media activity, and more.
A Customer Data Platform (CDP) is a piece of software that integrates informationf rom several tools to provide a single, central customer database that contains information on all touch points and actions with our services and products. This database may be segmented in different ways to provide granular marketing and to understand customers better.
A customer data platform enables no code digital marketing and a marketer is the main user of the CDP. Marketing teams use the CDP to identify the needs of their customers and develop channel specific strategies for different groups of customers.
The most effective way to do this isthrough example. Let's say a business wants to comprehend its clientsbetter. Data from touch points such as Facebook, the businesswebsite, email, and any other locations a client might engage withthe company would be collected via their CDP. All those data pointswill be gathered by the CDP, which will then combine them into asingle, simple-to-understand consumer profile and make it accessibleto other systems that might require it, such as the Facebookadvertisements platform.
By using segmentation, the businesscan better understand its audience and develop more specializedmarketing strategies. The business may quickly build an advertisingaudience based on all website visitors who have visited a particularpage as well as users of the live chat feature. Alternatively,companies could rapidly segment and view information about sitevisitors who abandoned their shopping carts.
Identity resolution is the process of combining various datasets to produce a single and cohesive consumer identity. Identity Resolution in CDP seeks to gain a comprehensive understanding of a customer's experiences with a brand across all channels. This is essential for utilizing datacomplexity as a springboard for great customer service.
Real-timecustomized messages can be sent to clients based on their uniquepreferences thanks to identity resolution. For instance, a financialcompany that has a comprehensive understanding of a certain customercan determine the kind of product or service she is seeking. As aresult, the business can send him contextually customized offersthrough his preferred channels. Such focused marketing initiativesboost online conversions, which in turn fosters brand loyalty amongconsumers.
While retail,travel & hospitality, e-commerce, and other sectors were amongthe early adopters of identity resolution technology to improvecustomer data management outcomes, it is a useful technology for anyconsumer-focused and customer-centric sector that wants to get readyfor a world where customers increasingly expect to be recognized andtreated to personalized and contextual journeys in anever-more-complex environment.
Following are someof the core functionalities of the identity resolution system thathelp marketers create real-time identifiers for their customers.
Deduplication,(probabilistic and deterministic) matching, hashing or anonymizing,and suppression* processes start once all the data is in onelocation, often on the vendor's identity resolution system or on thecustomer data platform (CDP). The result is the establishment of apersistent (changes with any change in the client's use of thedevice, channel, platform, or address) and real-time personalizedprofile for each customer.
The procedure ofintegrating every internet and online customer data that is availableinto a single system. The keys to successful data onboarding arespeed, accuracy, and security.
The core conceptof an identity graph is to further enhance the PII (personallyidentifiable information) obtained in the customer profile with theadditional external channel, device, or behavioral data that canoperate as digital IDs. Proprietary identity graph architectures varyacross companies. Online surveys, event attendance, cookie and IPdata, device data, mobile advertising IDs, and municipal data that isin the public domain (such as voter data or data on a home orautomobile ownership) could all be examples of third-party data. Theresult is a comprehensive customer identity that is built from bothowned and external data sources and can be used to guide campaigndesign and customer experience.
Following currentlaws that specify what "personal information" implies in aspecific area, sector, or period. This often means that everythingthat can be used to identify or link a specific person or householdis subject to compliance with privacy and data security laws.Solutions for resolving identity issues must apply to all in thecontext of your brand.
With all thedetails discussed above, it can be stated that the foundation of CDPsis a unified view of the customer, but a crucial component ofdeveloping that view is identifying customers' identities acrossvarious devices, platforms, channels, and locations to help resolvenumerous encounters coming from the same person. Recently, identityresolution has developed into its category of customer datamanagement solutions. Marketers can arrive at a probabilistic ordeterministic match of their customers across devices, platforms, andonline/offline channels, depending on the technology and methodologyemployed for identity resolution.
It is possible to perform intricate matching across millions of datapoints and records in real-time or near-real-time using identityresolution systems. Depending on the technology and data sets, theidentity resolution system can give either a probabilistic match or adeterministic match after collecting all the terrestrial, digital,and device data. In essence, the type of match refers to theconfidence degree with which the match is established, not merely thelinks between the data pieces.
Through estimation of the statistical likelihood that two identitiesbelong to the same consumer, profiles are matched using probabilisticmatching. Millions of anonymized or anonymous data points fromvarious digital sources, such as IP addresses, device types, browsertypes, operating systems, location data, wifi network types, surfingtimings, patterns, and other behavioral data, make up the so-called"identifiers" in reality. The set of attributes chosen formatching in each use case provides the reasoning or confidence inclassifying something as a probable match. A "foundational databank" comprising billions of data points that can be used forprobabilistic matching has been developed by several data providers.
If you identify certain connecting identifiers among several similarrecordings made on several devices, you can infer that the subject ismost likely the same. When modeling "look-alike" audiencesfor digital advertising segmentation and targeting, probabilisticmatching is a very effective tool.
Probabilistic matching would be a preferable option when the goal isto reach and scale. Reaching the best target segments, or those whoare most likely to respond or convert may be more important in thissituation than accuracy. This kind of matching is typically used byprogrammatic adtech platforms to identify the prospect categoriesthat are most likely to be profitable.
When usingdeterministic matching, client records are compared by looking forequality across identifiers such as hashed email addresses, phonenumbers, and usernames that have been logged in. It is better to usethis high-confidence method when first-party data is easilyaccessible. Personal information (PII) such as an email address, homeor work address, phone number, or credit card number, as well assign-on and log-ins, are typically included in this "first-party"data. Usually, there is no question as to who the person is. You canconclude with high confidence that this is undoubtedly the sameperson if they log into your website on a desktop computer and thenyour mobile app on a smartphone a few days later.
Deterministic matching approaches would be more appropriate whenmarketers frequently use first-party data and are more concerned withprecision (high confidence of a match) than size. Deterministicmatching is typically used for highly personalized martechapplications, such as upselling an insurance policy to knowncustomers, displaying specific website content to returning visitors,or tailoring offers to loyalty program members.
You may identifythis consumer in your physical stores based on information abouttheir activity in the virtual world. In this manner, you will have acomplete picture of your customer.
Due to thetransactional and qualitative data a CDP can collect, upselling andcross-selling can increase sales. You may, for instance, provideunique discount codes or suggest products based on the preferencesand lifestyles of your customers.
You can divideyour target audience into dozens of segments using a CDP. Utilizethis data to engage potential customers in your brand's activity onsocial media and attract their attention.
A CDP powers theadvertising networks you utilize in addition to collecting data. As aresult, you can develop successful retargeting ads and use look-alikemodeling to draw in new clients who share your present clients'tastes.
With the help ofpredictive data, you can continuously enhance the profiles of yourconsumers and the marketing messages you send to them. Examples ofpredictive data include the chance of purchase, churn, visit, andemail open.
Customer Data Platforms are the secretto knowing your consumers and their demands by enhancing the value ofthe data you get every day through your channels and platforms. Theycombine all the data you already have so you may get complete andaccurate information on your users. You may boost your marketing,sales, and even product creation with the aid of this new knowledge.
The software used by CDPs is intricate. One will offer you profound insights into yourcustomers if you use it properly. After that, you can use that information to enhance your product's marketing, sales, and otheraspects. A CDP can be the ideal solution for you if you're preparedto move forward with your understanding of your clients.
