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4 Posts authored by: Aaron Kerzner

In our recent articles we explored the history of data integration and what it takes for an automated integration platform to deliver for business users.


Next, we’ll look at the top SugarCRM customer use cases for breaking the status quo of data integration using Bedrock Data. The goal is to help SugarCRM customers reap the most value from Sugar as their unified operating system across marketing, sales, and customer success.  


An underlying theme throughout these use cases is avoiding the Which system has the right data? conundrum. Aligning your data across systems helps ensure customers are being engaged most effectively,  avoiding confusion and wasted time from mismatched data between systems. 

Use Case #1 - Connect all lead sources to Sugar

You have leads coming from many different sources: your website, webinars, events, and email, just to name a few. An integration platform allows you to connect all of those lead sources to your CRM with real-time delivery which means:

  • You never need to upload another spreadsheet again
  • Your sales team can be more effective with timely lead follow-up
  • You can enforce consistency with how incoming data is tagged in your system (e.g. lead sources, lead source details)...which helps you be complete and accurate with your analytics on these leads. 

Use Case #2 - Provide visibility to reps in the CRM they are using every day

There are key interactions collected through marketing systems that provide insight to sales reps to help them have more contextualized, effective conversations with prospects.  


What was the last content on the website they engaged with? What was the last piece of content they downloaded? What was the last webinar they attended?  What event sessions did they attend?


These data points can be mapped to custom fields in Sugar, providing sales with visibility in the interface they are using every single day. And with your reps, that’s probably the difference between them seeing the information ahead of a sales call, or not.


Use Case #3 - Closed Loop Reporting through Sugar

Bringing historical marketing interactions into Sugar custom fields, allows you to leverage Sugar for closed loop reporting, connecting marketing interactions to sales results.


This type of reporting can traditionally get very complex. To combat that complexity, a great way to get the right reports is to  tag certain field such as:

  • Original lead source (where did this lead originate?)
  • Original lead source details
  • MQL source (what made the lead an MQL?)
  • MQL source details


Then, use Sugar to summarize the correlation between these values, and pipeline and closed won business. Bedrock Data’s connection of your marketing systems to Sugar provides you with the right feeds of data to make this happen.

Use Case #4 - Manage a de-duplicated database of prospects, contacts & customers

All of these use cases rely on this one - if you have duplicates in your data, you run the risk of making the wrong conclusions from your data, or communicating to someone incorrectly. If you have a record tagged as a prospect, but they are really a customer, you’re going to deliver the wrong message. If you have multiple records and someone updates their preferences, you’re not going to miscommunicate to them.


Deduplication has a couple parts to it. If you have duplicates in your CRM data, you want a way to rapidly review those duplicates, and clean and consolidate the data to a unique set of records. Then, on an ongoing basis, as contacts engage with you across multiple systems, you need to ensure the records are aligned across all systems, without duplication.  


Use Case #5 - Feed customer database to marketing systems for customer upsell programs

Pushing customer data from Sugar out to the various systems you’re using to engage customers (in a way that is easily segmented by account level data) helps you drive effective customer upsell programs. Most marketing automation systems can be used to score customers based on different upsell offerings, as a way to feed intelligence back to your account management team.


Use Case #6 - Align support & sales teams on support status for the customer

Syncing support tickets from a third party support system back to Sugar, provides sales and account management teams with visibility to help them prioritize and tailor sales conversations most effectively. Support data can also be leveraged to identify high potential customer advocates to target for customer advocacy programs via marketing and sales.


These are all things you can achieve with Bedrock Data, without writing a line of code.


Take full advantage of your Sugar instance. Let's have a discussion to align your marketing, sales, and customer success systems.


If you're an ISV looking to expand your app's integration ecosystem, reach out to me directly at

In the previous post in this series, we shared highlights of the history of data integration, to set the stage for the breakthrough of automated integrations for marketing, sales, and support business users.


Marketing, sales, and support teams need to quickly adjust their processes to adapt to the continuously evolving preferences of their buyers. Their system integrations therefore need to be easy, flexible and self-managed by these departmental users. This allows them to react to ever evolving business conditions and ensure their connected systems are delivering.


These users don’t have time to invent solutions - they need to be pre-built. They need to amplify the key features of those systems to help you get more out of both systems, together. And, with more and more systems in play between these departments, the integration platforms need to do this for an ever expanding set of systems.


With that in mind, breaking the status quo of integration requires these breakthroughs:

Breakthrough #1 - A wide breadth of pre-built connectors

There are a growing number of systems that marketing, sales, and support teams use to engage customers. A data integration platform tailored for business users requires pre-built connectors to these systems such that integrations can be activated without requiring any code.


Automated data integration platforms need to span a wide range of systems including CRM,  marketing automation, webinars, events, data enrichment, power dialing, e-commerce, subscription management, and support.


Breakthrough #2 - Deep integrations to enable key features of the connected systems

Automated data integration platforms need to enable the key use cases of the connecting systems. Data syncs limited to only contact data are usually not sufficient to meet the use cases.


A key data object for marketing and sales integrations is the opportunity object, as opportunity data answers the question, “What sales results have we driven?” This is key for connecting sales and marketing activities to business outcomes.


Breakthrough #3 - Multi-directional integrations enabling continuous data updates

Connecting multiple sales and marketing systems requires multi-directional syncs. As customers engage across multiple touchpoints, data integration can’t simply be a handoff from one system to another. There is continuous interaction with data gathered and updated every step of the way. One day a customer is on the website, the next day they attend a webinar, the next day they speak to sales rep, etc. - and any data updates collected through these interactions need to be kept in sync across multiple connected systems.


An important technical breakthrough here is having a mechanism for managing conflicts. What do you do when the data from one system doesn’t match a connected system? An automated data integration platform allows customers to configure a system of record on a per field basis, to determine the winner for such conflicts.


Breakthrough #4 - Easy to use, self-guided interface

For an automated data integration platform to cater for business users, it needs to simplify work that has traditionally has been complex. Just a few of the things a user needs to be able to do cleanly and intuitively are:

  • Field Mappings: Mapping fields across two or more connected systems
  • Data Workflow Triggers: Managing rules for when data should sync - for example a marketing system should house your marketing database - without cluttering up a CRM system - and only sync leads over to the CRM system once they are marketing qualified
  • Error Notifications & Management: Provide error notifications to users and provide them a means to quickly review and resolve those errors. An example of an error is a field mismatch - if you have a field in one system that is a numeric field, for example, the field in the connecting system needs to usually match that field type. Another example is matching values to picklists between systems. If one system requires a set of values for a field, then data passed into that system will need to also match those values to avoid causing errors.


Breakthrough #5 - “Just works” behind the scenes

Providing an integration platform to business users means “under the hood” operations that used to be managed by IT teams, now need to “just work” so that the business teams can rely on the systems having up-to-date data.


A key aspect of this is system performance, including rapid sync times and handling of peak volumes. When sales reps are following up on leads from a marketing system into your CRM, every minute matters.


These are the key breakthroughs that allows Bedrock Data to deliver maximum value for Sugar users when connecting various systems to Sugar.


In our next post in this Breaking the Status Quo of Data Integration series, we will explore the top use cases for SugarCRM integrations with Bedrock Data that these breakthroughs make possible.


Data integration has come a long way over the last couple decades. In this three-part series, we'll provide a brief recap of the history, a perspective on how data integrations are being disrupted today, and how businesses can best take advantage of the disruption to drive effective sales and marketing operations through Sugar.

Part 1 - A Brief History of Data Integration

Data integration is a powerful concept which, believe it or not, pre-dates the Internet. It’s a history full of acronyms and with far too many protocols and platforms to thoroughly document here, so our intent is to give you a good sampling of its evolution over the past few decades.


We’re in the midst of a true breaking of the status quo of data integration. As all of the evolution over the past decades has been about “building a better mousetrap,” but all the while focussed on IT as the gatekeeper for data integration.  


Our history builds to that today, for the first time, integrations can be managed by business users. We’re breaking through the “IT as the gatekeeper” paradigm.


Our history starts with:


EDI (Electronic Data Interchange)

Data integration pre-dates the Internet as a mechanism for the structured exchange of data between two computers. The origins of EDI are attributed to developments in military logistics. A key moment was the 1948 Berlin airlift where large volumes of data and information about transported goods required transfer over a baud teletype modem.

EDI exchanges remain in place today, typically for highly secure, high-volume data exchange between business partners. They tend to support larger enterprises and require ongoing management from IT teams to oversee data exchange and uptime.


ETL (Extract, Transform, Load)

Early data integration methods, dating back to the 1970s, used the extract-transform-load process for data integration. These are heavily structured data integration processes with three discrete, sequential steps:

  1. Data extraction - bringing in data from multiple data sources
  2. Data transformation - adjusting the format and structure of the data to prepare it to be queried and analyzed
  3. Data loading - moving the data into a final target database, such as an operational data store, data mart, or data warehouse  

ETL is an IT-intensive process with powerful but complex tools from vendors such as Oracle, IBM, SAP, SAS, Cognos, and many more.  

Because of the rigid sequence required to get data into an accessible format, ETL systems face challenges around scalability and real-time data access. There are also strong dependencies on system and process design, as improperly designed ETL systems run into significant issues.


SOAP (Simple Object Access Protocol)

SOAP became a standard protocol for web services, leveraging XML to integrate systems in the early 2000s.

SOAP was originally designed by a team at Microsoft lead by Dave Winer in 1998, and version 1.2 of the specification became a W3C recommendation in 2003.

SOAP became a common method for developers to write data connections between systems using a common XML data format. Generally speaking, SOAP is effective for building a one-to-one bespoke integration (e.g. inter-bank communications) but is not optimal for scalability or speed of implementation.


REST (Representational State Transfer)

Computer scientist Roy Fielding introduced the term representational state transfer in 2000 as part of his doctoral dissertation at the University of California, Irvine. In between 2008 and 2010, REST reached a tipping point in popularity where it exceeded SOAP and has dominated SOAP in popularity since.

REST has become the standard for most publicly available APIs due to its scalability and speed to implement. REST web services are stateless, which delivers performance, reliability, and scalability. REST services re-use components that can be managed and updated independent of the greater system, even while it is running - a significant advancement from the rigidity of ETL.

That said, the stateless nature of REST also represents a challenge for how it is implemented in connecting systems. REST has no memory, so it relies on the connecting systems to manage audit trails and data history.


iPaaS (Integration Platform as a Service)

Over the past five years, iPaaS emerged with dozens of vendors providing integration tools to IT teams to develop, execute, and manage integration flows between multiple disparate applications. The technical breakthrough for iPaaS was allowing IT teams to leverage standard cloud services so that they don’t require hardware or middleware to manage their data integrations.

iPaaS represented an advancement in the data integration space, although it’s one whose benefits were largely directly at mid-sized to large IT teams who gain efficiencies through using iPaaS technology instead of developing their own customer integrations through the aforementioned protocols or many others.

Integration Platform for Business Users - Automated Integrations for Marketing, Sales & Support

The alphabet soup of acronyms leading up to this point represent an evolution in the capabilities around integrating data, but they remain in the old paradigm of requiring IT resources to manage data integrations.


Empowering business users to manage their data integrations breaks - dare we say, shatters - the status quo of data integrations.


Marketing, sales, and support users prefer not to be reliant on IT to create and update integrations for the systems they use every day.


Is there a new system feeding leads for the SDR/BDR team? Or a new data field that you want to give sales visibility to for context around leads? How about a new data field tracking the effectiveness of marketing programs through to sales results?


These are all adjustments these business teams want to make themselves. Businesses are moving too fast to be stuck in IT development queues for updating their data integrations.


In our next article on Breaking the Status Quo of Data Integration, we’ll share our perspective on what it takes for an automated data integration to work for business users today.

In a great post on the SugarCRM blog last month, Andrew Staples highlighted five ways to drive revenue from your CRM. He stated that only 17% of customer relationship managers believe their CRM is generating revenue. I co-sign Andrew’s premise that CRMs should absolutely be helping you drive revenue growth, and want to take it one step further. As the central hub of your revenue teams, your CRM needs to be supporting your revenue growth. A critical step to making this happen is connecting your marketing, sales and support systems with the CRM as the central platform.

Connecting Sales, Marketing & Support Systems Drives Growth 

Here are some of the ways connecting data to your CRM helps you driving revenue growth.


Visibility helps sales reps be more effective

The bar for a quality sales rep conversation has become increasingly higher. Prospects need to get value from sales reps in the context of the most important challenges they are facing, for prospects to give up some of their time and engage with a rep.

The key to this engagement is relevance, and sales reps stand the best chance to be relevant by having the right visibility on how a prospect has engaged with their company.

How did they discover you? What topics did they engage with on your website? What content did they look at? This type of engagement has a direct impact on conversion rates for leads to opportunities, which in turn impacts pipeline and revenue.


Closed loop reporting helps managers optimize the end-to-end sales & marketing process

Another key to driving revenue is having a pulse on what sales and marketing activities are leading to pipeline growth. By identifying underlying growth drivers, you can double down on those things to accelerate more growth. You want to answer questions like:

  • What are the top sources of business that result in pipeline and revenue?
  • Is this being driven through specific partners channels, through the website or through outbound marketing programs?
  • To what degree is the website contributing to pipeline growth, and which areas of the website are contributing?
  • What marketing program investments are yielding growth?  


Aligning data across departments builds more effective acquisition, up-sell & retention programs

Sales, marketing and support are three legs of the stool supporting your revenue growth. The more you can align data across the three groups, you will have a stronger your integrated view of the business. Some of the revenue driving initiatives that will now be possible include:

  • Target new customers based on what your most successful customers are doing with the product
  • Identify successful customers to be part of customer advocate programs
  • Identify successful customers for a certain product line who are strong candidates for upsell to a second product line - and then engage them through marketing programs

Five Keys to Getting Your Data Right

There is one fundamental key to executing this, which is getting your data right in your CRM.  Here are five tips for getting this right. We call them our CRM DQ (Data Quality) tips.

CRM DQ Tip #1 - Create a standard mechanism for tagging your marketing lead sources

Your closed loop marketing reporting is going to require consistent data that is clean to roll up and analyze. Look at all the ways data is getting into your CRM and apply standard definitions for the tagging of this data, for both automated and ‘human’ processes.

CRM DQ Tip #2 - Avoid manual data uploads

To make point #1 more feasible, connect your data automatically to your CRM wherever possible. Phase out manual spreadsheet uploads. Phase in automated data connectors whereby you can map data once between your CRM and data sources such as marketing automation systems, websites, event systems or webinar systems.


CRM DQ Tip #3 - Take a “less is more” approach to making data visible to sales

When identifying which data you want to make visible to your sales team, take a “less is more” approach. Salespeople will take a just in time approach for talking to their leads. So the data that gives context to sales calls needs to be easily absorbed.


CRM DQ Tip #4 - Be diligent around processes to ensure your data is de-duplicated

The rubber often hits the road for the accuracy of your reporting and the use cases we discussed above with the question of - is your data de-duplicated?

Use cases start to fall apart when there are multiple records of a customer, or a prospect, or a company.

Throw closed loop reporting out the window. Forget about drawing insights by connecting support to sales to marketing. This is why it’s so important to invest in having the right process to review and cleanse duplicates and ensure you have that true unified data set for your commercial team.


CRM DQ Tip #5 - Leverage your CRM reporting infrastructure

Your CRM system gives you the reporting infrastructure to drive the revenue growth use cases we’ve discussed in this article. Rather than reinvent the wheel, configuring the reports in your CRM gives you a trusted, “single source of truth” for your teams to align around and leverage to drive revenue.


This will get the job done provided it’s supported by the right data feeds and data quality processes.

Key takeaways

  • To support your revenue growth, your CRM needs to be your connected platform across marketing, sales & support
  • Sales visibility, closed loop reporting & creating programs with aligning data across teams are three specific steps you can take
  • Data quality is a critical underpinning, and we share five tips to ensure you have clean and consistent CRM data supporting your growth initiatives