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The Value of Prioritizing Customer Data Quality

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Your buyer database is the inspiration for the work that you just do. Whether you’re constructing cross-channel buyer journeys, A/B testing email messaging, or utilizing AI-based insights to energy selections, you’re seemingly leveraging buyer attributes, insights, and engagement historical past to energy your workflows.

Although buyer knowledge has change into extra out there, many development groups nonetheless battle with features of knowledge high quality administration reminiscent of knowledge consistency throughout techniques, identification decision, and knowledge planning, typically counting on engineering for help. In truth, a 2018 Harvard Business Review study discovered that solely 3% of surveyed corporations had acceptable knowledge high quality requirements.

Growth groups with sound knowledge high quality administration processes, then again, are in a position to make data-driven selections with extra confidence and launch campaigns with better effectivity. This put up will stroll by why buyer knowledge high quality issues, the parts of buyer knowledge high quality you have to be specializing in, and the way a Customer Data Platform (CDP) might help you simplify knowledge high quality administration.

Why Data Quality Matters

In at the moment’s customer-driven period, personalised buyer experiences have change into the purchasers’ expectation. With the releases of Apple’s App Tracking Transparency and legislations reminiscent of GDPR/CCPA, clients have additionally change into extra conscious of the worth they’re gaining from exchanging their knowledge for personalised experiences. For entrepreneurs, this introduces a problem — a mistargeted message can lead clients not solely to disengage but in addition to lose belief in your model.

As buyer knowledge is the foundational asset with which you make focusing on selections, a scarcity of knowledge high quality at first of the info pipeline may end up in inaccurate messaging all through your campaigns. Additionally, when you’re leveraging ML predictive intelligence to drive your focusing on, the standard of suggestions generated will solely be pretty much as good because the buyer database that your fashions are educated on.

Even when knowledge high quality points are caught, engineering groups typically have to be known as upon to treatment errors with one-off transformations, consistency checks throughout instruments, and implementation updates, pulling them away from core improvement.

With a system in place for knowledge high quality administration, nonetheless, you’re in a position to ship focused campaigns quicker and with extra confidence. For any paid campaigns, the elevated focusing on accuracy will make your campaigns extra cost-efficient.

What does a high-quality buyer database seem like?

When managing buyer knowledge high quality, listed here are just a few traits to concentrate on:

Consistency, Accuracy, and Compliance

As buyer occasions, attributes, and insights are collected from completely different digital touchpoints and cloud feeds, knowledge factors are sometimes formatted in another way. For instance, a instrument could also be carried out to gather a person’s first identify out of your iOS app as `firstName`, however carried out to gather the primary identify from Android as `First_name`. Such inconsistencies could make it tough to construct viewers segments and activate campaigns inside that instrument.

To keep buyer knowledge high quality, it’s necessary to make sure that buyer knowledge factors are carried out persistently throughout channels.

Accuracy and completeness of knowledge can also be vital. If the info factors connected to every buyer profile will not be appropriate or up-to-date, there will likely be a detrimental affect on downstream campaigns, evaluation, and modeling.

Furthermore, that you must ensure that the info you’ve collected is finished so in compliance with all rules. mParticle and Iterable have instruments to assist keep compliance like Consent Management and SMS Opt-Out, respectively.


Teams throughout the group from advertising and marketing, product, analytics, engineering, to help, all must leverage buyer knowledge to make selections. Even probably the most strong buyer database will not be value a lot if key stakeholders can’t entry it when they should. When entry to knowledge is democratized, a number of groups are in a position to work off the identical, high-quality knowledge set, and make strategic selections.

It’s additionally necessary that development groups are enabled to make updates to their knowledge pipeline, reminiscent of altering how audiences are constructed or which occasions can be found in every instrument, with out having to request engineering help. This knowledge independence permits you to get campaigns to market a lot quicker and permits engineering to remain centered on core improvement.

Single View of the Customer

Growth groups want insights on buyer pursuits and engagement historical past to tell strategic selections. Without cross-channel knowledge unified to single buyer profiles, you’re left stitching knowledge throughout techniques and marrying recognized and nameless profiles manually—a laboursome and inefficient course of.

Often, groups battle to construct high-quality buyer profiles for 2 causes: knowledge is inaccessible or exists in silos throughout varied techniques, and/or occasions and key actions reminiscent of cart abandonment can’t be reconciled to particular person person profiles.

To activate your buyer knowledge for advertising and marketing initiatives and help compliance with GDPR and CCPA, it’s necessary to have a system in place that permits you to unify cross-channel knowledge to single buyer profiles, management how recognized and nameless profiles are merged, and enrich these profiles with engagement occasions.

Using mParticle’s IDSync together with Iterable’s nested knowledge construction and segmentation capabilities retains your buyer profiles organized for better affect in your personalization methods.

Data Schema Validation

As your buyer knowledge set grows in complexity (extra occasions, extra channels), it may be extraordinarily tough to catch mis-logged occasions as they’re collected in actual time. Although your crew might have established an information plan that catalogs the info factors you expect to gather, knowledge can simply be carried out inconsistently throughout channels or ‘fat-fingered’ when being logged.

Simplify knowledge high quality administration by having a monitoring system, like mParticle’s Data Master, in place that allows you to flag knowledge plan violations as your knowledge is collected. As errors come up, you’ll be capable of examine the problem and work together with your engineers to get it resolved.

Connecting With Iterable

For any customer-first group, the purpose of gathering knowledge is utilizing it to ship higher buyer experiences. While the techniques used to ingest and validate buyer knowledge are vital, equally necessary are the instruments used to activate that knowledge and ship partaking experiences.

mParticle’s Event and Audience integrations with Iterable allow you to ship high-quality buyer knowledge to Iterable by way of a user-friendly connection, the place it may then be used to energy personalised experiences throughout channels. Additionally, mParticle’s Iterable Feed integration permits you to ahead engagement occasions throughout email, in-app message, sms, and push channels from Iterable again into mParticle, the place they’re tied to centralized mParticle buyer profiles to maintain your knowledge constant, organized, and actionable.

The limiting issue of any data-driven marketing campaign would be the high quality of knowledge used to energy that marketing campaign. With mParticle and Iterable, you’re in a position to automate knowledge high quality safety, management how knowledge is forwarded to Iterable with out developer help, and use your knowledge to ship higher experiences to your clients.

For extra, dive deeper into the Iterable and mParticle integration.

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