Global retailers are showing that omnichannel is no longer a strategy you experiment with but the way modern retail operates. The most profitable brands have built unified retail systems that link stores, websites, apps, and CRMs into one shared network that manages all core retail data.

With that foundation in place, every team works with the same information. Fulfillment becomes faster, personalization improves, and customer loyalty grows.

This article explains how unified commerce strategies turn omnichannel from a theory into measurable profit, with clear steps any eCommerce and retail team can apply.

Key takeaways

• Profit in 2026 will come from connected systems, not more channels, and retailers that link data, stock, and teams see real financial impact.
• Measuring omnichannel performance requires one shared view of the customer across online and in-store interactions.
• Small, connected actions (from unified loyalty to digital receipts) create fast ROI and build the foundation for sustainable growth.

The reality of omnichannel in 2026

Most retailers already have the right tools in place: a website, an app, a CRM, and a POS system. What they often lack is connection. Each tool works, but rarely together.

That gap is now too large to ignore. Customers expect stores and websites to recognize them as the same person. They want to check stock online, pick up in store, and receive offers based on everything they have bought before.

Global retailers from Europe to the Middle East now treat their physical stores as digital engines: fulfillment centers, data capture points, and loyalty drivers that feed back into online systems.

AI and unified data make this possible. They allow retailers to connect every interaction, automate decisions, and deliver consistent experiences across all touchpoints. When data moves freely, even complex operations run smoothly and profitably.

Also read:
How to Unify Offline and Online Data for Omnichannel Retail Personalization

Five key eCommerce upgrades that increase profit

1. Unify stock visibility

A unified stock system removes the disconnect between stores and online inventory. Integrating an Order Management System (OMS) allows real-time syncing, so every channel reflects accurate availability. Adding live estimated arrival times, such as “Arrives by 5 PM tomorrow,” gives shoppers confidence to complete their purchase.

Retailers adopting advanced OMS systems are already seeing shorter delivery windows and higher conversion rates thanks to live ETA visibility and intelligent routing that automatically chooses the fastest or most efficient fulfillment point. This accuracy also cuts refund requests caused by overselling or incorrect stock levels.

2. Ship-from-store and BOPIS/BORIS

Using physical stores as fulfillment points changes how they support the sales process. When customers can buy online and pick up or return in store, both convenience and traffic increase. This model is known as BOPIS (Buy Online, Pick Up In Store) and BORIS (Buy Online, Return In Store) – two of the most effective ways to connect digital and physical retail.

Automating routing rules helps select the store best positioned to handle each order based on proximity and stock availability. Some leading retailers now fulfill most of their online orders directly from stores, which lowers last-mile delivery costs, speeds up fulfillment, and drives more visits to physical locations.

3. Cross-channel loyalty program

Loyalty systems are most effective when they operate across every channel. One shared loyalty wallet in both the app and POS lets customers earn and redeem rewards no matter where they shop.

These programs are now evolving into unified identity systems. Whether someone shops online or in-store, a single account records every interaction, making personalization more accurate and lifetime value easier to track. A Customer Data Platform (CDP) helps pull these interactions into one profile so offers can be triggered automatically based on behavior such as how recently they purchased or how often they return. This leads to more frequent repeat purchases and stronger long-term retention.

4. Unified digital receipts and customer timeline

Replacing paper receipts with digital versions sent through WhatsApp or email makes each transaction part of a connected customer record. When all purchases are linked to a single account, brands can view complete buying patterns and suggest relevant add-ons at the right time.

Digital receipts are becoming more than a convenience. They are key data points linking store activity to Customer Relationship Management (CRM) profiles and ensuring service continuity across all channels. This creates stronger trust, smoother support, and automated reactivation campaigns that feel timely and relevant.

5. AI shopping assistant

Conversational AI tools can now act as real-time shopping guides. Adding an assistant to product pages or chat apps helps customers get instant answers about size, fit, or availability and can suggest complementary items.

Retailers are seeing strong engagement when chat-based shopping tools blend human assistance with AI logic, combining service, discovery, and purchase in one seamless experience. This approach is part of a broader trend known as conversational commerce, where dialogue-driven interactions replace static browsing and help shoppers make faster, more confident decisions.

With scandiweb’s Chat to Buy solution, retailers can take this even further by enabling customers to complete purchases directly within chat. Clients using this approach have seen higher conversion rates, increased average order value, five times longer customer engagement, and a meaningful share of previously inactive customers returning to buy again.

Read more:
Case Study: Conversational Commerce Brings 31% of Churned Customers Back

Store quick wins for fast ROI

Small actions inside the store can quickly improve both revenue and customer engagement. These steps require minimal setup but deliver visible returns within weeks, like increasing average transaction value and repeat visits.

Sending a short “thank-you” message on WhatsApp after each sale keeps the interaction personal and useful. Adding product care tips or a quick how-to helps build trust without feeling like another promotion.

Handing out “surprise-me” incentive cards to customers who have not purchased in a while is another simple way to reactivate interest. A small mystery discount is often enough to bring them back.

Place QR codes around the store that let visitors check stock, redeem loyalty rewards, or view related products. Each scan connects in-store behavior to digital profiles, making future offers more precise.

Finally, offer customers the option to receive digital receipts through WhatsApp at checkout. This makes it easy for them to keep records and gives your team a direct, trackable communication channel for future campaigns.

Making omnichannel measurable

Omnichannel success is only real when it can be measured. The goal is to see every sale, pickup, and return as part of one connected customer journey. That requires tracking performance across both digital and physical touchpoints, using data that flows in both directions.

What to track

To measure whether your omnichannel setup is working, focus on the metrics that show how customers move between channels and where value is created:

• Cross-channel redemption, such as how many in-store purchases come from customers who first interacted online
• Pickup footfall conversion, showing how many online orders lead to store visits and extra sales
• Repeat purchase rate per unified customer ID, to measure loyalty beyond a single channel
• Time to fulfillment from store versus warehouse, to identify the most efficient delivery paths.

How to measure

Combine your order management and CRM systems, so data from every channel ends up in one place. Tag offline actions, including digital receipts and QR scans, with customer IDs to make each purchase traceable and connected to the same profile.

Leading retailers go a step further by adding loyalty data to this setup and viewing everything in a single dashboard. This helps them see not just where sales happen, but how each channel influences profit and customer behavior overall.

Common blockers and how to fix them

Even well-equipped retailers struggle to make omnichannel work in practice. The issues usually come down to systems, goals, and people.

Disconnected systems

When POS, ERP, and OMS tools operate separately, information gets lost and customers notice the gaps. Connecting these systems should be the first step in any omnichannel plan. Start with real-time stock and order syncing, so every team works with the same data.

Misaligned KPIs

Online and in-store teams often chase different targets, which creates internal competition instead of collaboration. Set shared metrics such as pickup revenue, customer lifetime value, or fulfillment speed. When both teams benefit from the same results, coordination improves automatically.

Read more:
How to Avoid Internal Sales Cannibalization Between Online & Offline

Change resistance

Resistance usually comes from habits, not technology. People support new processes when they understand how it makes their work easier or more rewarding. Focus on clear incentives, such as faster service or simpler sales tracking.

Tie performance rewards to real omnichannel results, for example, acknowledging store staff who fulfill online orders or turn pickups into extra sales. Keep training simple and centered on daily tasks.

Conclusion

Omnichannel profitability is now a proven outcome. Retailers that have unified their systems and teams are already seeing stronger growth, steadier margins, and clear improvements in customer retention.

The goal is not to appear in every channel, but to connect the right ones through shared data and coordinated operations. The retailers who understand where their customers are, and act when they engage, are the ones turning connection into profit.

Want to make omnichannel profitable for your business? Contact us, and we’ll map out the steps needed to unify your data and bring your eCommerce and retail operations into one revenue-driving system.

The post Omnichannel in 2026: How Connected Retail Will Drive Profitability appeared first on scandiweb.

Salesforce Data Cloud is a Customer Data Platform (CDP) designed to bring together fragmented customer interactions across various touchpoints. Its primary mission is to create a comprehensive, unified view of each customer, enabling businesses to deliver more personalized and effective experiences.

At the core of this unification is identity resolution—Data Cloud’s capability to accurately identify individual customers across different systems. This function distinguishes unique persons despite varying identifiers, incomplete data, or minor discrepancies between records.

In this article, we’ll briefly cover key concepts of identity resolution within Salesforce Data Cloud, then dive straight into practical advice and real-world implementation insights. This guide will be particularly useful for anyone preparing to implement Data Cloud, managing identity resolution projects, or navigating common challenges—tips and insights we wish we’d had when starting out.

Also read:
The Complete Guide to Data Ingestion in Salesforce Data Cloud

How identity resolution handles customer data inconsistencies

Imagine a customer like Emily Swift who interacts with a business through multiple channels. In one instance, she uses the email emily.swift@gmail.com when purchasing at a physical store with her loyalty card. Later, she shops online using a different email, emily_swift85@outlook.com.

What makes Identity Resolution truly powerful is its ability to handle real-world data inconsistencies. For example, in the online store’s system, her name might be accidentally entered as “Emly Swift” (a simple typo). Or perhaps her address is recorded with slight variations: “123 Main Street, Apt 4B” versus “123 Main Street, Apartment 4B”. Fuzzy matching capabilities in Data Cloud can intelligently recognize these as variations of the same customer’s information.

This also helps with customers who have tricky surnames that cashiers often misspell.

While the emails and names might differ slightly, other details remain consistent: her core personal information, purchase history, and contact details. Identity Resolution in Data Cloud tackles this complexity. It uses smart matching rules based on Data Model Object (DMO) values to recognize that these seemingly different profiles belong to the same customer.

Identity Resolution in Data Cloud operates through rulesets—collections of matching rules that determine when records from different sources represent the same customer. These rules examine specific attributes to identify matches despite variations or inconsistencies.

When the system identifies matching profiles based on these rules, it creates a unified profile combining all customer data. Reconciliation rules then determine which value to keep when multiple sources have different data for the same attribute (for example, selecting which email address becomes primary in the unified record).

Why identity resolution matters for your business

Identity resolution offers substantial business value beyond its technical aspects. From a reporting and decision-making perspective, it directly impacts how organizations understand their customer base.

One of the most significant impacts is on customer count accuracy. Without proper identity resolution, businesses often face customer overcounting due to duplicates across systems. A single customer might appear as multiple individuals when their web session, email interactions, and in-store purchases are treated as separate entities.

This incorrect customer count affects other key business metrics, including:

• Average Revenue Per User (ARPU)
• Customer Acquisition Cost (CAC)
• Return on Marketing Investment (ROMI)
• Conversion rates across touchpoints.

This capability is crucial because without a unified customer view, businesses struggle to accurately:

• Calculate customer lifetime value (LTV)
• Measure customer retention
• Understand purchase history
• Personalize marketing and service offerings.

When decision-makers rely on data with artificially inflated customer counts, they might underestimate the true value of each relationship or misallocate resources. Identity resolution transforms fragmented data into reliable business intelligence, enabling more accurate forecasting and strategic planning.

Now that we’ve covered the basics, let’s move on to practical steps.

Preparing your data for identity resolution

Data Preparation is crucial when working with Identity Resolution. When you’re setting up Salesforce Data Cloud, you’ll be connecting data from different systems that capture and store information in their own unique ways.

Important: Successful Identity Resolution requires understanding all the ways customers interact with your business and thinking through potential challenges in matching their information.

Data from different sources rarely comes ready to use. You’ll often need to clean and reshape the information before you can start matching records. This means working with data from your CRM, online store, point-of-sale systems, and other tools to make sure everything lines up correctly.

Making data consistent with standardization and harmonization

Data standardization and harmonization might sound similar, but they’re not quite the same. Standardization focuses on converting data to a consistent format, while harmonization goes deeper, ensuring data from different sources can work together seamlessly.

Data harmonization is a big part of any Data Cloud setup. When you’re connecting data from multiple sources, you’ll quickly discover that the same information can look wildly different across systems.

Take country names as a perfect example. Your web orders might use “US”, while an offline tool prints “The United States”. Dive into customer-entered data, and you’ll find even more variations: ‘The US’, ‘USA’, ‘United States of America’, ‘The United States’, ‘usa’. To a human, these all mean the same thing. To a computer matching system, they’re completely different.

Important: Converting data points to a consistent format is crucial for accurate identity resolution and creating meaningful customer insights.

When it comes to data transformation, you have two main approaches:

1. External data transformation. Cloud platforms like AWS Glue or Azure Data Factory offer powerful data pipeline tools. These are excellent for complex transformations, especially with large, complicated datasets. They provide more flexibility and advanced processing capabilities.
2. Data cloud transforms. Salesforce Data Cloud also has built-in transformation capabilities. These work great for simpler data transformation tasks, keeping everything within the same ecosystem and saving you time.

Phone numbers present another interesting standardization challenge. Data Cloud recommends using the E.164 format, which isn’t the default for many systems. This means you’ll likely need to create a transformation that generates a new column with correctly formatted phone numbers.

Pro tip: Perform data profiling on your historical data before setting up transformations. This helps you understand common patterns, spot outliers, and create more effective standardization rules.

For data profiling, you have several options:

• Use specialized libraries like Python’s Data Profiler
• Leverage cloud computing platforms
• Run analysis with SQL or Excel for simpler datasets

The ultimate goal is to create a unified, reliable view of your customer data that works across all your systems.

Keeping your data clean with strategic blacklisting

Not all customer data is created equal. Sometimes, you need to be strategic about what information gets processed in your Identity Resolution setup.

Consider a multichannel retail business with a complex backend ecosystem. In one of their unique scenarios, they sell certain clothing lines through specialized drop-shipping partners. These third-party shipping arrangements create interesting data complexities in the backend systems. When these orders are processed, the shipping and customer data appear differently compared to direct sales channels. The billing might reflect the end customer, while shipping details consistently show the drop-shipper’s information.

In this case, you probably don’t want to run those shipping customer details through your Identity Resolution process.

Pro tip: There’s a crucial distinction in Data Cloud mapping. You can only map one stream to profile Data Model Objects (DMOs) for Identity Resolution. This must be a stream of the “Profile” or “Other” type. But that doesn’t mean losing data. Here’s a smart workaround: Send your full order feed as an engagement stream, and create a separate stream for customer data. This lets you fine-tune the customer information specifically for Identity Resolution while preserving complete order details.

Another common challenge? Fake or placeholder data. Customers sometimes enter nonsense information like ‘example@gmail.com’ or phone numbers of all zeros. These entries can mess up your Identity Resolution rules.

The solution is blacklisting. This means strategically filtering out or nullifying these low-quality data points. But how do you know what to filter?

Data profiling is your friend here. By analyzing your historical data, you can:

• Identify common placeholder values
• Spot unusual patterns
• Create smart rules for filtering out bad data.

Important: Effective data preparation isn’t just about collecting information. It’s about collecting the right information.

Managing individual IDs: The silent unification rule

There’s a subtle but powerful rule in Salesforce Data Cloud that catches many implementers by surprise. If multiple records share the same Individual ID, they will automatically unite – even without explicit Identity Resolution ruleset configurations.

We learned this the hard way during our own implementation. While setting up our main data streams, our initial quality assurance revealed that records weren’t unifying as we expected. After digging into Salesforce documentation, we uncovered a critical insight about Individual ID behavior.

To add to the topic of Individual IDs, many beginners make a common mistake: assuming the same identifier should work across all marketing channels. But that’s not the case. Each data stream might require a unique approach to identifying individuals:

• Email Marketing: Use email as the primary identifier
• SMS Marketing: Leverage phone numbers
• Web Tracking: Utilize cookie-based customer IDs.

The goal is to choose IDs that most authentically represent your customer in each specific system. This nuanced approach ensures more accurate profile unification and richer customer insights.

Pro tip: Carefully map out your Individual ID strategy before implementing Data Cloud. The right approach can save you hours of troubleshooting later.

The importance of mapping to contact point DMOs

You might be tempted to take a shortcut. “Why map data to multiple DMOs?” you’ll ask. “Can’t I just map everything to the Individual DMO, especially if my data setup isn’t complex?” Here’s the reality: No, you can’t.

When you start creating Identity Resolution rules, you’ll hit unexpected roadblocks. Mapping only to the Individual DMO creates significant limitations. Data Cloud wants stricter rules and won’t allow you to create rules using just the Individual DMO.

Below is an example. Data Cloud asks for more match criteria. The issue is not that you need more than 6 criteria. You’ll notice that once you add other DMOs to the rules, you will overcome a lot of such red error messages.

Having data available across different DMOs – Contact Point Address, Contact Point Phone, Contact Point Email, and Party Identification – makes rule creation much more flexible and straightforward. Think of these DMOs like different puzzle pieces that help you build a complete customer profile.

Ensuring data uniqueness through primary keys and deduplication

In Data Cloud, uniqueness matters more than you might think. Individual and Contact Point IDs must be unique within each data source. If you have duplicate primary keys, Data Cloud will discard extra records (it’s expectable overall that primary keys must be unique).

Imagine this scenario: You’re tracking address information, using Address Line 1 as your primary key. But here’s the catch – that single address might actually represent completely different people or locations. Without careful key generation, you’ll lose valuable data.

To prevent losing important information, get creative with your key generation. For addresses, consider creating a more specific identifier by combining multiple data points. A smart approach might be concatenating:

• Full Name
• Full Address.

Below is a visual showing what’s happening to the mapping of data from the same data source to the Contact Point Address primary key.

Important: Duplicate primary keys are a silent data killer.

By creating a more granular primary key, you ensure that each record remains distinct and no critical information gets discarded during the data ingestion process.

Always think beyond the obvious when creating unique identifiers. The more specific your key, the more data you preserve.

Understanding web tracking with behavior and profile streams

When implementing Web SDK tracking in Data Cloud, there’s a subtle but important distinction in how customer interactions are captured.

All web interactions are collected in the behavior stream, but only identified users make it into the individual Web profile stream. This might sound confusing at first, but there’s solid logic behind this approach.

During our implementation, we did a thorough quality assurance check comparing identified customers across streams. What we found confirmed the system’s design: every record in the profile Web stream was present in the broader web stream.

Important: Anonymous users (those with only a cookie ID) aren’t automatically added to the profile stream.

This makes practical sense. It’s challenging to meaningfully connect with customers when you only have an anonymous tracking ID. As users become identified over time – by logging in, making a purchase, or providing contact details – they’ll start appearing in the profile stream.

There’s an added business benefit, too. Many companies are billed based on the number of unified customer profiles. This approach helps prevent unnecessary billing for unidentified, anonymous web visitors.

Quality assurance tools: Query Editor and Tableau

It’s important to QA your data setup, identity resolution included. One of the built-in tools I like using is Query Editor, where with the help of SQL you can explore your DLOs and DMOs.

While powerful, this approach has limitations: it may present a steep learning curve for team members less familiar with SQL and lacks strong visualization capabilities.

An effective alternative is to connect Data Cloud to a dedicated BI tool. Though SFDC includes native BI capabilities, we’ve found external tools often provide greater flexibility. Tableau stands out as a particularly good match, as both Tableau and Data Cloud are part of the Salesforce ecosystem, ensuring good integration.

With Tableau, you can connect directly to your Data Cloud objects, create visualizations that reveal identity resolution insights, and build detailed tables showing unified individual IDs alongside their raw input attributes. This visual approach makes it much easier to spot areas of concern and quickly diagnose problematic profile aggregations, helping you understand what’s causing issues with certain profiles.

You can make a custom dashboard addressing your needs, or you can use an accelerator from Salesforce. ‘Accelerators’ is the term used by them for dashboard templates.

Salesforce has an accelerator tailored specifically for Identity Resolution. You can access it here.

Conclusion

Effective identity resolution is the cornerstone of truly understanding your customers. With Salesforce Data Cloud, your business can turn fragmented data into unified, actionable insights, enabling more meaningful connections and smarter decisions.

Remember, every implementation has its unique challenges and opportunities. By carefully preparing your data, strategically managing identifiers, and continuously adapting your approach, you’ll set the foundation for more accurate insights and better business decisions.

Need support implementing or enhancing identity resolution in Salesforce Data Cloud? Our certified specialists are here to help. Get in touch today and let’s make your Data Cloud project a success.

The post A Practical Guide to Identity Resolution in Salesforce Data Cloud appeared first on scandiweb.

Salesforce Data Cloud—a Customer Data Platform (CDP)—helps build a complete 360-degree view of your customers.

It brings together different data sources that track customer interactions across your business, creating one complete, unified customer profile to deliver better experiences and insights. You can also add extra information, like store details (location, type, region etc.), to make your customer profiles even more complete and useful for deeper segmentation and analysis.

Data Cloud’s key advantage is data harmonization. It focuses on bringing different data sources together and organizing them in a way that makes sense for your business.

Note: Salesforce Data Cloud isn’t designed to replace your data warehouse. Not every dataset belongs here, and specialized platforms exist for complex storage, transformations, and third-party integrations. Be selective about what you ingest—unnecessary data and jobs create system load, slow down processing, and potentially increase costs depending on your contract terms.

Let’s explore the key concepts and practical steps required to successfully bring your data into Data Cloud. In this guide, we’ll cover the essentials you need to know for effective data ingestion.

Key terms and concepts

Before we proceed, let’s clarify the key terminology and theoretical concepts you’ll encounter when working with Data Cloud.

The data in Salesforce Data Cloud follows a specific path:

Data Stream Objects (DSO) → Data Lake Objects (DLO) → Data Model Objects (DMO)

Most data you import begins as a DSO. However, some data sources skip the DSO stage and appear directly as DLOs in the data lake.

Think of this like rivers and lakes in nature: streams (DSOs) can flow into a lake (DLO). And just as some natural lakes have no rivers feeding them but are filled by underground springs or rainfall, some DLOs exist without corresponding DSOs.

Data Model Objects (DMOs) are the business-ready versions of your data that have been processed and structured for specific use cases, You get them available after you map DLOs to DMOs.

You’ll also see the term feed throughout this article. While it’s not official Salesforce terminology, it’s commonly used to describe regularly updated data flows. We’ll use this term to refer to files that continuously move into your system rather than one-time uploads. For example, daily order exports, customer activity logs, or campaign performance data that continually update your Data Cloud environment.

Choosing and setting up your data source connector

When creating a new data stream, start by clicking “New” on the data stream page:

Next, you’ll need to select your data source. Data Cloud offers numerous connector options (the image below shows just a preview of the available choices):

The most commonly used connectors include:

• Other Salesforce systems (Marketing Cloud, CRM)
• Cloud data storage platforms (AWS S3, etc.)
• SFTP servers

If you need to connect different Salesforce systems or streamline data management across platforms, our Salesforce services help you set up, optimize, and scale your Salesforce ecosystem.

While file uploads work for smaller datasets, it’s generally better to use a central data storage system like S3 or Redshift as your connector. This approach prevents data fragmentation and promotes better data accessibility across your organization.

To set up these connections:

1. Navigate to Setup → Platform tools → Data Cloud → Other Connectors
2. Select your desired connector (in this example, S3):

3. Enter the necessary access details and use the “Test Connection” button to verify that Data Cloud can access your data

Note: When setting up an S3 connector, your configuration will be limited to a single bucket. If you need to access multiple buckets, you’ll need to create separate connector configurations.

Returning to the stream creation process, select your configured connector:

With your data source now accessible, let’s examine the core ingestion process in the next section.

The essentials of configuring your data stream

After selecting your connector, you’ll need to specify the data format. The two most common options are CSV and Parquet.

CSV is widely used and human-readable. However, Data Cloud currently offers limited options to customize CSV parsing details, which can sometimes lead to data issues.

Parquet, while not easily readable by humans, tends to be more error-resistant during Data Cloud processing, reducing the likelihood of column integrity problems.

Next, specify the directory and file name for your data source.

Data Cloud supports the asterisk (*) wildcard character, which is a powerful feature. In data contexts, the asterisk represents “any character, any number of times.”

For example, if you have daily order files in an S3 bucket with names like “DailyOrderFeed-20042025.csv” and “DailyOrderFeed-21042025.csv,” you can use:

• DailyOrderFeed-*.csv

This pattern will capture all files that start with “DailyOrderFeed-” and end with “.csv”. You can also use just “*” as the filename to include all files in the specified directory.

On the next screen, you’ll name your Data Lake object. The API name generates automatically but can be modified if needed.

The category selection is critical and cannot be changed after creation:

• Profile – For customer attribute data (name, email, phone, country, etc.). Cannot map to Engagement DMOs.
• Engagement – For event data (purchases, email opens, website visits). Must include a date field. Cannot map to Profile DMOs.
• Other – The most flexible category for data that fits neither Profile nor Engagement. Can initially map to any DMO category, but once mapped to either Engagement or Profile DMO, cannot switch to the other type.

The primary key selection is crucial for data integrity. This field helps Data Cloud identify unique records. For example, using an order number as a primary key keeps only one record if two orders have the same number.

Remember: primary keys must be unique! If you choose a non-unique identifier, you risk losing records. For engagement events like email interactions, creating a composite key (combining values like customer email + event datetime + event name) can solve uniqueness issues.

The Record Modified field determines which version of duplicate records to keep. If two records share the same primary key, Data Cloud will retain the record with the more recent Record Modified timestamp.

For Engagement streams, you must also define an Event Time Field.

Organization unit settings are only needed if you’re working with multiple organization units.

When defining data types for your fields, review each column carefully. Data Cloud attempts to determine types automatically, but it isn’t perfect—it might incorrectly classify ZIP codes as numbers when they should be text. You cannot modify column types after stream creation.

You can also create formula fields using supported functions. Common use cases include creating composite primary keys or, for example, combining first and last names into full names.

The final configuration screen appears before deployment:

You’ll need to choose between two update modes:

• Upsert – Updates existing records if the primary key matches, or adds new ones if it doesn’t; old records stay, even if the source file disappears.
• Full Refresh – If records disappear from source files, they’ll also be removed from the stream.

You can modify refresh modes after stream creation.

Finally, set your update frequency schedule. Without this, your stream won’t update even if new files appear in the source location.

Once you’ve reviewed all settings, it’s time to bring your data stream to life. Hit the “Deploy” button to start moving your data into Salesforce Data Cloud!

How data cloud handles file updates and refreshes

When working with data streams in Data Cloud, we discovered an important behavior.

Data Cloud tracks which files it has already processed by their filenames. If you update the contents of a file without changing its name, Data Cloud may not detect or process these changes in either refresh mode.

This becomes particularly noticeable when you expect a Full Refresh to completely update your data. Despite selecting this option, if your updated source file retains the same filename as the previously processed version, your changes might not appear in the stream.

For reliable updates, consider implementing a filename versioning system (like adding timestamps or version numbers) to ensure Data Cloud recognizes and processes your updated files.

Updating your stream settings after deployment

You can adjust certain stream configurations after initial setup. Adding formula fields remains possible, but it’s important to understand that these changes only apply to new data going forward.

For instance, if you create a ‘Full Name’ formula field that combines ‘First Name’ and ‘Last Name’, this field will only appear in records processed after you added the formula. Later, if you modify this formula to also include ‘Middle Name’, that updated version will only apply to records processed from that point on.

When your source data evolves to include additional columns, Data Cloud allows you to incorporate these new fields into your stream. Click ‘Add Source Fields’, and Data Cloud will display any new fields it has detected in your source. You can also manually add columns through this interface if needed.

Problem records DLOs

You may notice DLOs with names starting with ‘PR_…’ in your Data Cloud environment. These are Problem Records—a special category created automatically when Data Cloud encounters issues processing certain records from your data streams.

Problem Records contain data that couldn’t be properly ingested due to various issues like data type mismatches, missing required fields, or formatting problems. Data Cloud separates these problematic records instead of failing the entire batch, allowing successful records to continue processing while isolating the problematic ones for your review.

Checking and validating your ingested data

After successful data ingestion, you’ll want to verify the data quality and ensure everything imported correctly. Data Cloud offers two main tools for this purpose.

The simplest approach is to use Data Explorer, accessible from the Data Cloud menu. This tool provides a quick preview of your data and supports basic filtering capabilities, making it ideal for initial data validation.

For more comprehensive data validation, the Query Editor offers advanced capabilities. Also found in the Data Cloud menu, this tool incorporates Data Explorer functionality but adds powerful SQL-based analysis options. Here you can create complex queries with aggregations, advanced functions, and sophisticated filtering to thoroughly examine your data integrity and quality.

If your company has a strong data system already with data stored and transformed in cloud, it’s best to do the first quality checks there before sending data to Data Cloud. This helps catch and fix issues earlier in the data pipeline.

Still, even with clean source data, you should check how Data Cloud processes it. Data type conversions, formula fields, or connector-specific behaviors can sometimes alter data in unexpected ways. In these cases, Data Cloud’s native tools are ideal to test how your data is actually being processed within the platform.

The Query Editor is particularly valuable when you need to verify relationships between datasets, check for missing values, or validate business rules across your ingested data. Its SQL interface gives you the flexibility to construct exactly the verification checks your use case requires.

How to safely delete streams and data assets

When removing data assets from Data Cloud, understanding dependencies is essential. You cannot delete a DSO or DLO that has active connections to other components.

Streams mapped to data models must typically be unmapped before deletion. This becomes particularly challenging if the asset is used in identity resolution or calculated insights. Remember that dependencies flow downstream—a single DLO might feed into multiple models, segments, insights, and activation targets.

In certain situations, you can delete a mapped stream directly, specifically when the mapping exists but isn’t being used in any active downstream processes. However, this behavior isn’t consistent and shouldn’t be relied upon. In complex cases, you might need to delete an identity resolution ruleset first, then remap and reingest your data.

Plan your data architecture carefully from the start to avoid these complications. A clear understanding of your asset relationships will make future modifications much simpler.

Key takeaways

Setting up data ingestion in Salesforce Data Cloud takes careful planning and attention to detail. From choosing the right connectors and file formats to managing problem records and understanding dependencies, each step builds the foundation for reliable, scalable data operations.

Taking the time to get it right early will save you a lot of time, trouble, and costs down the road.

Need help setting up or optimizing your Salesforce Data Cloud? Our team is here to help. Get in touch to make your Data Cloud project a success.

The post The Complete Guide to Data Ingestion in Salesforce Data Cloud appeared first on scandiweb.

В соответствии с 10 статьей закона “предотвращении последствий кризиса, вызванного распространением Covid-19”:

10. pants. Atļauts realizēt akcīzes preces, izmantojot distances līgumu, izņemot tabakas izstrādājumus un elektroniskajās cigaretēs izmantojamos šķidrumus. Aizliegts realizēt alkoholiskos dzērienus personām, kuras jaunākas par 18 gadiem, un laikā no pulksten 22.00 līdz 8.00.

Это открывает возможность продажи алкоголя через интернет с доставкой.

Ниже перечислены пункты которым следует уделить особое внимание чтобы успешно создать интернет магазин нацеленный на продажу алкоголя.

Бизнес стратегия 

Для любого бизнеса важно определить основные преимущества, которыми он обладает по сравнению со своими конкурентами. Необходимо спланировать каждый шаг цепочки продажи алкоголя от оптового приобретения и хранении до доставки к покупателю. Именно на этом шаге должны выявляться основные конкурентные преимущества бизнеса и его отличительные черты. В этой статье мы постараемся разобрать ключевые моменты необходимые для успешного запуска онлайн магазина по продаже  алкоголя в Латвии. Важно ответственно подойти к каждому пункту, которые будут описаны далее. 

Scandiweb со своей стороны готовы оказать помощь и предоставить консультации по всем важнейшим вопросам.

Лицензия

Лицензия является важнейшим фактором создания онлайн магазина с возможностью доставки алкоголя. Лицензия (или разрешение для коммерческой деятельности) позволяет предприятию легально реализовывать акцизную (в данном случае, алкогольную) продукцию на территории Латвии. 

Оформление лицензии на продажу алкоголя для микропредприятий обходится в € 42. Так же необходимо предоставить перечень документов и заполнить необходимые формы доступные на сайте СГД.

В случае возникновения проблем с оформлением лицензии Scandiweb может взять на себя все сопутствующие обязанности и оказать юридическую поддержку.

Вебсайт

Вебсайт магазина является основной бизнеса, обеспечивающей взаимодействие между покупателем и продавцом. Сайт, как визитная карточка компании должен привлекать клиентов и обеспечивать весь необходимый функционал магазина. Разработка сайта является наиболее затратным процессом, поскольку размещение интернет магазина на бесплатных платформах с минимальной оптимизацией может привести к отсутствию пользователей и прибыли.

Сайт должен отвечать следующим критериям:

1. Иметь запоминающееся доменное имя.
2. Иметь весь необходимый функционал. Клиенты должны иметь возможность сделать и оплатить заказ, все страницы и категории должны быть доступны, а в случае возникновения вопросов, ответы на них должно быть легко найти.
3. Сайт должен быть оптимизирован. Медленно работающий сайт с плохим дизайном будет хуже восприниматься клиентами и приведет к меньшей востребованности и прибыли чем возможно. Более того, поисковые системы не будут размещать плохо оптимизированный сайт на первой странице поисковых запросов; это означает что клиентов возможно будет привлекать только через платную рекламу, что дополнительно увеличит расходы компании. 
4. Сайт должен быть удобным для пользователя. Интуитивно понятный сайт уменьшает вероятность того что клиент покинет сайт, не осуществив покупку. 
5. Сайт должен быть дешевым. Конечно можно потратить десятки тысяч евро на создание вебсайта с нуля командой разработчиков с недостаточным уровнем опыта, но в этом нет никакого смысла. Продажа алкоголя в интернете это абсолютно новый рынок в Латвии и скорость в данной ситуации играет наиболее важную роль.

ScandiPWA является уже готовой платформой с постоянной поддержкой разработчиков. Сайт может быть создан за считанные часы, при этом весь функционал заточен на максимально продуктивную работу и удобство как для клиентов, так и для поисковых систем (с демо-версией готового магазина, созданного на основе ScandiPWA, можете ознакомиться, перейдя по этой ссылке). Даже бесплатные платформы, такие как WordPress и Shopify требуют больше вложений времени и приносят меньше отдачи в связи с системными ограничениями. ScandiPWA, в свою очередь, будет настроен в течении 24 часов после заключения контракта. Более того, Scandiweb поможет вам выбрать лучшее доменное имя в случае если у вас его еще нет.

Доставка клиентам

После приобретения товара на сайте необходимо организовать удобную и быструю доставку клиенту. В нынешней ситуации работа курьеров особенно высоко ценится и важно правильно ее организовать, чтобы избежать долгой доставки. Исследования показывают, что люди готовы ждать доставку 40-60 минут. В условиях высокого ожидаемого спроса это может быть труднореализуемым своими силами, а также может значительно увеличить расходы компании. 

Закон запрещает продажу алкоголя людям до 18 лет, что легко контролировать в физическом магазине, но практически невыполнимо в интернете (если вы не хотите просить людей прислать вам копии паспорта).

Scandiweb предоставляет помощь в планировании доставки и ее непосредственной реализации, а также предоставляет свое решение “возрастного” вопроса.

Привлечение клиентов на сайт  

Мы прогнозируем высокую конкуренцию на рынке онлайн продажи алкоголя. Закон вступил в силу менее недели назад, а уже крупные игроки, такие как Rimi, заявили об организации доставки алкоголя клиентам. В таком случае правильная маркетинговая кампания является залогом успешного привлечения клиентов. 

Для начала стоит оптимизировать сайт под наилучшее взаимодействие с поисковыми системами, чтобы обеспечить привлечение клиентов без затрат на рекламу. Для этого используются техники SEO, в числе которых – 1) Написание оптимизированного контента под конкретные ключевые слова 2) Размещение ссылок на сторонних сайтах для привлечения клиентов и увеличения рейтинга самого сайта 3) Техническая настройка сайта для соответствия всем стандартам поисковых систем.

Параллельно с настройкой SEO можно запустить рекламную кампанию в интернете (PPC) и ретаргетинговую кампанию. Это позволит привлекать дополнительных пользователей и продвигать проект во время SEO оптимизации, а также возвращать потенциальных клиентов обратно на сайт.  Однако неправильно проводимая кампания может увеличить затраты и никак не повлиять на прибыль. Поэтому правильная оптимизация рекламных кампаний должна доверяться профессионалам. 

Дальнейшее улучшения сайта и маркетинга

Даже после реализации всех предыдущих пунктов необходимо непрерывно мониторить ситуацию на рынке и следовать трендам, постоянно улучшая бизнес процессы. Е-коммерция непрерывно развивается и правила могут измениться в любой момент, поэтому только внимательное наблюдение за трендами и своевременное адаптирование к ним может принести успех вашему бизнесу.

Как открыть свой онлайн-магазин по продаже алкоголя всего за пару часов? Всю необходимую информацию Вы можете найти на этой странице. Также, не забудьте изучить рабочую демо-версию готового магазина!

The post Шаги для успешного открытия онлайн-магазина по продаже алкоголя appeared first on scandiweb.