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In industries as diverse as ecommerce, events, edtech, retail and hospitality, businesses struggle to manage complex B2C processes and intelligence in parallel with increasingly important B2B processes and intelligence.
In edtech, that means selling SaaS products to teachers while engaging in a long sales cycle with the school district. In retail and ecommerce, the problem is to manage one set of processes for individual consumers who purchase products for their homes – and another for businesses that purchase products in bulk. In hospitality, the problem manifests itself as ensuring an outstanding customer acquisition process and visit experience for individual business travelers as well as company events.
Regardless of industry, the B2C challenge usually arrives first because of the larger pool of potential customers and shorter sales cycles. As companies grow and mature, the value and importance of enterprise clients represent a larger potential market with more consistent renewal rates, along with the internal resources available to invest in longer sales cycles.
As B2B grows in importance, a common set of questions arises for these mixed B2C/B2B companies:
- How do we acquire and monitor individual vs. enterprise leads through our sales funnel?
- What are the strongest indicators of individual vs. enterprise lead conversion?
- What are the pivotal points in the customer journey for individual and enterprise customers?
- What are the drivers of individual and enterprise customer renewal, retention, and repeat purchase?
- How do we develop internal processes that support both client types successfully without increasing overhead?
- How do we structure our data models to support internal processes with the information business owners need to grow revenue while increasing margins?
Obviously, processes for the sales, engagement and retention of individual and enterprise customers must be approached differently. This leads to difficulty structuring operational databases, CRMs, and ERPs that can manage both business lines.
| Functional Analysis | Individual | Enterprise |
|---|---|---|
| Marketing Funnel | Assessing Demand | Assessing Influence on Budget and Decision |
| Sales Funnel | Low-touch, Minimize Friction | High-touch, Remove Barriers |
| Customer Segmentation | Demographic and Location-based | Industry Vertical and Role-based |
| Retention Drivers | Personal Delight | Criteria Determined by Decision-maker |
| Churn Risk Indicators | Lack of Personal Engagement; Bad Experience | Lack of Team Usage; Lack of Clear ROI |
Shuffling cards and coasters
In order for processes and intelligence to evolve, separate sets of customers require different data models with their own evolutionary paths. As discussed above, the enterprise-client relationship is structured, analyzed, understood, and served differently than an individual relationship. Separate processes for each group require separate data structures to drive those processes. And the data structures for each target market are constantly changing as business needs grow and customer understanding develops.
As the organization scales, more processes must be automated, resulting in operational databases becoming increasingly critical to customer interactions and transactions. But changing data structures leave the organization with operational databases and source-of-truth systems that have to hit two or more moving targets – one for each customer group. That tug-of-war multiplies the requirements for these systems beyond the capacity of database, CRM, and ERP administrators to maintain.
This added complexity could be tackled by hiring more developers to build complex data structures and workflows into these business-critical systems, but that approach increases overhead and maintenance burden, reduces system reliability, and does not meet the increasing intelligence needs of the business. Operational databases, CRMs, and ERPs should not be used to manage this complexity because:
- Lead and customer understanding, as well as methods of analysis, are constantly evolving through an iterative, R&D-like process. This means that the modeling and analysis under development must be consistently revised, while the outputs are generally used to answer a consistent set of questions and trigger a consistent set of actions from your business systems.
- CRMs and ERPs, as well as operational databases, have varying performance with expanding, customized data models. This is because many of the things you like about your internal systems, boilerplate tools and out-of-the-box functionality, rely on your system knowing exactly how its data is structured. Once you decide to customize, these systems are asked to respond to interaction patterns they haven’t seen before.
- These systems are not designed for this analytics workload. Your company will always be spending excess resources swimming against the tide of your technology systems, systems that are designed to focus on driving action rather than crunching numbers.
The solution is simple -- migrate your operational data from your company’s business systems into a platform designed for constantly evolving data models and huge analytics workloads: the cloud data warehouse. Cloud Data Warehouses like BigQuery, Snowflake, Redshift, and Azure Synapse have been explicitly designed to enable analytics professionals to iterate efficiently on their modeling and analysis. After your analytics team has analyzed your data and determined the answers to the questions above, then you can ship the answers back into your business systems, where the action is being taken by your marketing, sales, and customer success teams.
Unfortunately, the data migration portion of this path is not as simple as it seems. There are “gotchas” in every custom data pipeline development, and a first-time, custom implementation is destined to require substantial upfront investment and ongoing maintenance costs.
Don’t reinvent the wheel
The complexity of migrating data from operational databases, CRMs, and ERPs is too great to invest in a custom implementation.
In several instances, CorrDyn has been brought in to repair a broken custom ETL pipeline. One involved a national sports franchise that struggled to maintain reliable feeds of primary and secondary ticketing market data housed in an upstream database. The pipeline broke down multiple times per month because of schema changes from the vendor’s source database. Eventually, the pipeline required a complete rebuild.
In another instance, a building equipment company developed a custom ETL pipeline for their customer equipment monitoring application. When CorrDyn arrived, the company spent days debugging each pipeline error, mostly the result of inadequate data validation and data integrity controls.
Even with a strong team of data engineers, custom data pipelines from your operational databases and third-party platforms involve the following challenges:
- Circumventing API limitations for data export which could require rounds of testing and experimentation
- Implementing change data capture on an ad hoc basis, which requires a robust approach to limiting the amount of data migrated on an ongoing basis, providing comprehensive data to end users, and meeting latency requirements
- Handling schema changes from the source system in a way that maintains data integrity, allows historical access, and doesn’t break downstream systems
- Managing expanding requirements for data access and usage across the organization, if your company starts by scoping the data migration narrowly
With a data migration partner like Fivetran, you can reduce time-to-value, increase the reliability of data migration, and rely on your partner to stay in sync with API updates on behalf of thousands of companies. You also get a smart, out-of-the-box, analytics-enabled schema definition that empowers your team to hit the ground running when putting that data to work in the cloud data warehouse.
This architecture pattern has an increasingly widespread descriptive term: The Modern Data Stack. Connectors like Fivetran migrate data from your source systems into the cloud data warehouse, which can connect to data science and business intelligence tools, and can also be used as the source from which processed data is pushed back into your business systems (using a reverse ETL).
By automating and outsourcing the first two steps of the migration process (Extract and Load), your data engineers and business intelligence analysts can concentrate on projects that drive value for business stakeholders rather than the prerequisite plumbing. As a result, business leaders can realize value faster and build momentum around analytics projects that help their teams increase sales, repeat sales, retention, and customer satisfaction.
With Fivetran and the Modern Data Stack, your people can concentrate on generating insight from your data – insight that can then be pushed back into operational databases, CRMs, and ERPs without disrupting complex business processes or asking these systems to do work they weren’t designed to do. For mixed B2B and B2C companies, the questions being asked of both enterprise and individual customers require this approach to efficiently drive value from difficult questions asked of complex data structures.
If you want to generate value from your data more quickly and more efficiently – or just lack an internal data team that can tackle all the paths to value your company needs to pursue, an implementation partner like CorrDyn can accelerate your pursuit.
Want to continue the conversation? Get in touch with us to see how we can help you assemble a modern data stack and make sense of both individual and enterprise customers.
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