Dynamics 365 Customer Insights Data - Simply Explained

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What if one customer shows up in three systems and each one tells a completely different story?

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Sales, season, open deal, support, season, complaint, marketing, see someone who clicked an email last week,

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"Oh, but that same person has already bought from you twice. Nobody looking at a single screen can see the whole picture."

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I'm Mirko Peters from M365FM, and this knowledge nugget puts that picture back together in plain English.

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You'll see why scattered customer data causes real headaches, and why companies need one shared view before they can treat customers like people,

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instead of rows, in separate lists.

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Why scattered customer data creates bad decisions?

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Imagine you run a company that sells equipment to other businesses.

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Your sales team uses Dynamics 365 sales. They see contacts, accounts, calls, meetings, open opportunities, quotes, and notes from past conversations.

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That helps the salesperson prepare for a call, but it only shows work inside the sales system.

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Your support team handles work somewhere else. They see service cases, broken products, questions, complaints, and the conversations needed to solve a problem.

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An agent knows a customer contacted support three times this month, yet that history never shows up on the sales record. Marketing has its own set of information.

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They know who opened an email, clicked a link, filled in a form, visited a page, or signed up for an event.

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A marketer sees strong interest in a new product, while the support agent sees a customer frustrated with the product they already own.

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Here's the simple analogy. Think of it like an office building with separate rooms. Each room has its own window AU.

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Sales sees one view, support sees another, and marketing sees a third.

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Nobody has the whole picture until you open the doors between them. That's what a shared customer file does.

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Oh, it opens the doors. Nobody is doing anything wrong. The information simply lives in separate places, built for separate jobs.

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Sales needs to manage deals, service needs to solve cases, marketing needs to understand engagement, order sit in a different system, and website activity lives in an analytics tool.

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That split creates a strange situation. Here's an example. Take a customer named Alex Morgan.

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In the sales system, Alex has an account record with an old work email and an active opportunity.

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Over in the support system, Alex shows up under a personal email with two recent cases.

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And on the website, Alex signed up for a webinar using only a first name, last name, and phone number.

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Those records all belong to the same person, but without a way to connect them, the company sees three different Alex Morgan's.

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Sales prepares a friendly pitch, marketing sends a promotion for an upgrade, and support asks Alex to repeat the same details from the last call.

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From Alex's side, it feels disorganized. You've seen this yourself or you contact a company about an issue, explain it clearly, then receive a cheerful email asking whether you'd like to buy more.

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The company has the information, but its teams just don't share the same view of it.

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Bad data links create smaller problems too. A duplicate record leads to duplicate emails and old email address leaves the right person out of a message,

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and a missing connection between an order and a service case hides the reason a customer stopped buying.

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Then people start exporting lists into spreadsheets, marketing creates one list, sales creates another, service keeps its own report.

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Before long, teams debate whose list is correct while the customer waits for someone to understand the full situation.

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This affects decisions every single day. A sales manager chases customers who recently raised serious issues,

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a marketer sends a discount to someone who already purchased that full price.

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And a service leader doesn't know a caller is one of the company's largest customers.

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But the answer isn't to force every team into one app AU. Each team still needs tools built for its job.

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What they need is one shared customer file that can connect the separate records.

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That way, every team starts with the same person, not a different version of that person,

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or what customer inside's data actually is.

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So what joins those different customer records together, without forcing every department to use the same app,

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the answer is Dynamics 365 customer inside's data, Microsoft's customer data platform often called a CDP.

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With the simplest definition, a CDP pulls customer data from every system you use into one place,

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connects records that belong to the same person, and builds a lasting profile your business tools can work with.

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Picture it like a big filing room. Every department sends folders into that room,

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sales, sense, contact details, and deal history. Support sense case records,

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your website sends signups and page visits, and your order system sends purchases and returns.

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On their own, those folders might use different labels, different formats, even different customer numbers.

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Customer insights, data sorts through them all, and puts together one customer file from the records that belong together.

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That file gives you what people call a 360 degree view. You hear that phrase a lot, so here's what it means.

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A 360 degree view just means you can look at one customer and see more than one type of interaction.

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Who they are, what they bought, when they called support, and how they engaged with you online.

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It's a fuller picture, not magic.

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Customer data normally falls into a few common types.

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Demographic data describes the person, name, address, job title, company.

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Transactional data records an exchange, a purchase, return, payment, reservation, or subscription.

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Behavioral data captures actions like opening an email, visiting a product page, filling in a form,

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using an app, or registering for an event.

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Customer insights, data can also bring in operational data like inventory or service information,

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plus data from connected devices, Internet of Things or IoT devices.

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A company selling connected machines might want to look at machine activity alongside customer history.

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A service team could see that a customer called a "bout a problem"

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while the operational data shows the machine also reported an error.

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That context changes the conversation.

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Notice I'm saying customer insights data, not customer insights journeys.

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They're both under the customer insights name, but they handle different jobs.

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Data brings information together, prepares profiles, and finds useful patterns.

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Journeys uses those profiles and groups of customers to send emails, texts, and other communications.

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One side, organizes and understands the customer data.

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The other side uses that understanding when talking to people.

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You can picture data as the records room and journeys as the communications desk.

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Now, a lot of people confuse this with Dynamics 365 sales.

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Dynamics 365 sales helps a salesperson manage relationships and sales work by tracking leads,

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opportunities, activities, and account conversations, giving the sales team a place to do their job.

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Customer insights data takes a wider view.

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It can take information from Dynamics 365 sales, but it can also connect info from service systems,

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websites, order systems, data stores, and other approved sources.

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It doesn't replace sales.

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It adds the surrounding customer context that sales alone might not have.

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Behind the scenes, customer insights data runs on Microsoft Azure, meaning Microsoft provides

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the cloud foundation for storing, processing, and protecting the data.

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The product also includes tools for privacy, security, and governance.

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Governance is just the set of rules that controls who can access data and how the company can use it.

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A customer profile carries personal information, so those rules need to be part of the design

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from the start.

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Once you understand customer insights data as the place where separate customer records

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become one usable profile, the practical next question is, how do all those records get

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into the same workspace?

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Building the unified customer profile before customer insights.

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Data can connect anything.

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You create an environment.

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Think of an environment as a controlled workspace for this customer data work, giving your

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team one place to bring data in, prepare it, connect it, and manage who can work with it.

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Inside that workspace, you start by adding data sources.

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If you already use Dynamics 365, Microsoft Dataverse often supplies sales or service records, as

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Dataverse is the data store behind many Dynamics 365 and Power Platform apps.

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Customer insights data can also connect to Azure Data Lake, Fabric One Lake, and Azure

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Synapse Analytics.

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Those names sound technical, but the simple idea is that companies often keep large amounts

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of data in cloud storage and reporting systems.

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Power query connectors give you another way in.

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Power query is Microsoft's tool for bringing in data and shaping it before you use it, and

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it helps when the data sits in a database, a business app, or another connected source that

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doesn't look exactly like your Dynamics 365 records.

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Each source arrives as tables.

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A table is just a structured list, like a spreadsheet with columns and rows.

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One table might list customers, another might list orders, a third might hold service cases,

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and a fourth might record website signups.

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The hard part is that these tables rarely use the same names.

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One system might call a column email address, another calls it email, and a third stores

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a customer number with no email at all.

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No numbers may include country codes in one source and leave them out in another and names

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might arrive with different spelling, spacing, or titles.

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Customer insights.

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Data needs help understanding what each field means.

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This step is called mapping, and you tell the system which incoming columns contain a

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person's name, email address, phone number, customer number, address, or other details

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that can help identify them.

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You also map the business records connected to people.

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An order table needs to connect purchases to the right customer, and a case table needs

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to connect service history to the same person.

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Without those relationships, you have lists of events, but no reliable way to see whose

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events they are.

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Now, you might think matching records means looking for identical email addresses and joining

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everything automatically.

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Sometimes it does, but real customer data needs more care than that.

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Two people can share a household email address, a customer can change jobs and get a new work

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email, a small spelling difference can describe the same person, while two people with the

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same name can be completely unrelated.

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Customer insights.

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Data lets you set matching conditions.

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You might match records based on an email address, combine name, phone number, and postal

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details, or use a customer number if your business has one.

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The goal is to connect records only when there's enough evidence they belong to the same

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person.

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Then comes duplicate removal.

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Duplicates are repeated versions of the same record inside a source table.

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Maybe a web form created two entries after someone clicked submit twice, or a sales import

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added in existing contact again.

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Removing duplicates helps stop one person from looking like several people before the

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wider matching work even starts.

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After the system matches records and removes duplicates, it unifies the information into

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a profile.

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People often call this a golden record, but that phrase can sound grand.

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So let's use a simpler meaning.

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It's the best combined customer profile the system can build from the records you approved.

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It might bring together a current email from one source, and account link from another,

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order history from a third and service records from a fourth.

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It doesn't erase the original systems.

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Sales still keeps it sales records, your order system still owns the orders, and supports

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still works from its cases.

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Customer insights.

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Data creates a connected profile for understanding the customer across those sources, while keeping

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track of where the information came from.

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That source trail matters.

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If someone asks where an address, phone number, or purchase detail came from, your team

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should be able to trace it back to the source system rather than treating the unified profile

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like a mystery box.

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A profile also becomes more useful when it includes activities over time.

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Customer insights.

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Data can connect activities from the tables you bring in and place them on a chronological

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timeline, a purchase on Monday, a support call on Thursday, a product page visit the following

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week.

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Each event adds context when you look at the customer's recent relationship with the company.

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Imagine a salesperson opening a profile before calling a customer.

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Instead of seeing only an opportunity in a few call notes, they could see a recent order,

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a service case that closed yesterday, and a recent event registration.

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The salesperson doesn't need to guess what might shape that conversation.

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The profile supplies the context.

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Of course, a connected profile still starts as information.

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The next step is turning that information into answers your teams can use when they decide

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who needs attention, what changed, and what action makes sense.

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Adding.

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Meaning.

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Measures, Enrichment, and Predictions.

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So you can build a unified customer profile and your team can see a cleaner view of each

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person.

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But here, Tim is the thing.

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A list of records still one hour, Tim, to answer every business question on its own.

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You still need simple answers, who bought most often, who has not been ordered recently.

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Which customer's contacted support several times this month?

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Which ones look likely to leave?

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Customer Insights.

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Data answers many of those questions with something called Measures.

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Let our comes breakdown what a measure actually is.

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A measure is a calculation built from data you already have.

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Think of it as one agreed upon number your team tracks together.

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No more asking everyone to calculate it manually in Excel every time.

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Let our to missay you need total purchases over the past year.

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Create a measure or the number of days since the customer outtms last order outtms another

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measure.

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You could count service cases, calculate average order value or track event attendance.

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The exact measure depends on what your business needs to understand.

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For a subscription business, a measure shows how long someone has been a customer and whether

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their usage has dropped.

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For a retailer, it shows total spending, recent returns, or the gap between purchases.

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Measures turn lots of separate activity into a clear answer, that saves time.

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But more importantly, it helps teams use the same definition.

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If sales calls someone a high value customer while marketing uses a different calculation,

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the customer experience splits again.

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A shared measure gives everyone one agreed way to look at the question.

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Now some customer profiles still have gaps.

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Your system may know a person outtms name and purchase history, but not their location,

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company details, or other helpful information.

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Customer Insights.

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Data can enrich profiles using approved Microsoft or partner data sources.

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And just means adding more information to data you already have.

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It outmishes not an excuse to collect every possible detail.

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Your company should only add information it can use responsibly, and only when it fits

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the permissions and rules around that customer data.

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Used carefully, enrichment fills in missing context.

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For example, a business selling to companies may want clearer company information alongside

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its sales context, that helps a salesperson understand who they are automore speaking with

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before the conversation starts.

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Then there are predictions.

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Your insider insights data includes ready-made AI models that estimate churn likelihood

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and expected revenue.

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Churn means a customer stops buying, cancels a subscription, or stops engaging with your

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company.

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Expected revenue estimates what a customer relationship brings in over time.

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These predictions look for patterns in the data, considering purchase history, recent

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activity, or changes in behavior depending on the model and the data you provide.

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The result is a signal that helps a team decide where to look first.

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It outmys not a verdict about a person.

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A churn score does not empty mean that customer will leave.

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It means the available data looks similar to patterns of customers who left before, people

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still need to check the context and use judgment.

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Imagine a customer whose buying activity suddenly stops, a prediction flags that customer

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as a possible churn risk.

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Before anyone sends a windback offer, the team checks the profile and finds an open service

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case.

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Now the right action is to solve the issue first, not send a sales email that ignores

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the problem.

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Some organizations build their own AI models and use wider analytics tools for questions unique

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to their business.

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A manufacturer studies product use alongside service history.

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A company with its own data science team tests a model built around the signals that matter

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to its business.

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Customer insights.

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Data can become part of that work, but it does note them to remove the need for sound

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data or human review.

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Here are what terms the key takeaway.

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Measure and tell you what happened.

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Enrichment adds context.

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Predictions point you toward patterns worth checking.

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An insight that stays inside a profile does note them a change anything.

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Teams need a way to use those answers when they decide who to contact what support to

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give and where customer information should go next.

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Segment in activation.

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Turning profiles into action.

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A customer profile becomes useful when you can act on it, that automates where segments

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come in.

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A segment is a safe group of customers chosen by rules you set.

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Those rules use profile details, activities, behaviors and measures from customer insights

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data.

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Now it's time to manually picking names from a spreadsheet.

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Instead, you ask a business question and turn that question into rules.

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For example, show customers who opened a support case in the past 30 days have an hour

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time and bought anything in the past 90 days and agreed to receive communications.

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That group updates as customer information changes.

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If someone makes a purchase, they no longer fit the rules and leave the segment.

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If another customer opens a new case and stops buying, they enter it.

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Your audience stays connected to the customer information rather than becoming an old exported

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list.

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This changes how teams work.

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A service team uses a segment to identify customers who need extra care after repeated

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issues.

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Sales focuses follow-up on customers with a recent product interest and a history of buying

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similar products.

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Marketing creates an audience for people who attended an event but have an ultimate ask

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for a sales call.

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The point is no TNT to send more messages.

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It outens to make the next action fit what the company already knows.

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A customer with a recent problem needs an honest service update.

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A customer who has researched a new product for weeks needs a helpful sales conversation.

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Treating both people the same ignores the information your teams worked to connect.

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Customer insights data can also send unified profiles, segments and other prepared data to

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connect to destinations.

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A destination is another approved tool that needs the information we will.

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Maybe a marketing platform, an analytics tool, or another business system where your team

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works with customers.

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For organizations using Dynamics 365 apps, the customer card add-in brings customer context

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into other Dynamics 365 experiences.

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Picture a sales or service employee opening a customer record in the app they already

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use.

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The customer card places wider customer context close to their day-to-day work so their

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OTM says no need to search across several systems before each conversation.

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Customer insights journeys also uses audiences from this data work.

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Journeys handles customer communications like email, text messages and other planned

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interactions.

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Customer insights data prepares the people and context.

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These uses that audience when the company decides to communicate.

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Keep those jobs separate in your mind.

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Data answers, "Oh, who is this person and what do we know?"

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"Oh, journeys answers."

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"Oh."

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What communication should happen next?

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"Oh."

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That separation helps teams avoid a common mistake, building a segment does not make

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automatically mean you should contact everyone in it.

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A segment identifies a group based on facts and rules.

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Someone still needs to decide whether contact makes sense, which channel fits, and whether

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the message respects the customer's current situation.

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One shared segment also reduces the usual spreadsheet problem.

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Marketing does no OTM types need one manually cleaned list, while sales uses another version

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and service keeps a third list hidden in a report.

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Teams work from the same customer rules even when each team takes a different action.

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The data can travel, but it needs limits.

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Do you privacy, security and the CDP difference?

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So the bigger your customer profile gets, the more responsibility you take on, and that

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responsibility starts with privacy and security.

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When you connect names, contact details, purchase records, support history and online activity

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together, you need clear rules for handling that information.

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Consent, privacy rules and access control all matter before you start connecting.

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Customer Insights

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Data includes privacy, security and management tools that work behind the scenes.

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Think of it like the security desk in an office building.

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In plain English, you set rules about who can view customer information and how you use

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it.

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You think about customer permissions before you collect data connected, build a segment

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or send it to another tool.

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Here's the thing.

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More data doesn't mean you can use it however you want.

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The data has to match the permissions, laws and internal rules for each customer.

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That work happens at the beginning, not after a campaign goes wrong or a customer complains.

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So how is the CDP different from other customer tools?

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Let's break it down.

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Let's compare two common examples.

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Think about a CRM system.

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A tool like Dynamics 365 Sales helps sales and service teams record and manage direct

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relationships with customers.

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Sales people use it to work with leads, opportunities, calls and accounts.

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CRM does one job well, while customer insights data fills in the gaps across marketing and

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service, customer insights.

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Data works across a wider set of approved sources.

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It connects records into lasting customer profiles that other business tools can use.

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Think of it as extending your CRM rather than replacing where a salesperson manages a

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deal or a service agent tracks the case.

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Now compare that to an anonymous advertising audience tool.

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Advertising tools often group unknown people for targeting.

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Other insights data focuses on lasting profiles built from customer information you can connect

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and manage.

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One tool works with anonymous audience groups.

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The other helps you understand known customers across their entire relationship with your

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company.

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That's the difference between targeting a crowd and understanding a person.

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And here's the thing that shared view only works when access stays controlled.

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Conclusion one customer file dynamics 365 customer insights.

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Data lets you act from a connected customer profile instead of separate sales, service, marketing

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and purchase lists.

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I'm Mirko Peters and this has been your knowledge nugget.

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Subscribe on your favorite podcast platform and share it with someone who still sees customer

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data as separate lists because the next customer conversation might depend on information

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sitting in a system nobody checked.