All teams benefit from having ready access to a single and central source of customer data. Marketing teams can use a centralized database to identify which leads and campaigns drive ROI. Executive leadership would harness the centralized database to discover which investments make new and existing customers more successful. Sales would keep tabs on how they can close better deals (rather than just more of them).
Aside from the benefits of account-based roll-ups, less has been said about how centralized data helps customer service teams — the collective of account managers tasked with onboarding, implementation, success, and support — to improve the customer experience (CX). In this article we’ll delve into why a centralized database is so important for companies of any size, industry, or vertical. Then we’ll demystify how centralized data elevates the quality of customer service, so you can bring yours to the next level.
Great Companies Build Brand Loyalty
If the common goal of business is to foster strong brand loyalty, every fast-growth company tends to share three means for achieving the same end:
- They don’t settle for satisfied customers; they delight customers with repeated remarkable experiences and do so at scale and every stage of the business.
- They hold all teams — not just customer success — accountable in the delivery of these remarkable customer experiences, whose universal expectation of mutual ownership manifests both as cultural dogma and measurable outcomes (e.g. OKRs, KPIs, etc.).
- They monitor customer needs, pain points, and values all the time and across a diverse set of touch points — to simulate the customer journey, test hypotheses about buyer personas, and remove ego bias by way of scientific observation.
Collecting Customer Data & Feedback
While all teams strive to increase retention, customer service teams play arguably the biggest role in minimizing churn. In the worst case scenario, customer service teams can sully the brand. Best case, onboarding and support teams impress customers enough to create upsell opportunities and stoke a network of influencers into a frenzy of such wild devotion that they (i.e. the influencers) advocate on the brand’s behalf by word-of-mouth.
Customer service teams should thus care less about blasé fulfilment metrics like Customer Satisfaction (CSAT) and more about nuanced metrics such as Net Promoter Score (NPS), which segments customers into detractors, passives, and promoters, to measure how willing a customer is to recommend the company’s products or services to others.
Customer service teams should also view such feedback in the larger context of the customer lifecycle. And as studies have shown, rather than imagine this perspective, experiencing another’s viewpoint oneself helps us to:
- Eliminate egocentric bias
- Avoid misleading predictions
- And understand others’ preferences more deeply than if these same stories, problems, and complaints were heard secondhand.
Here is where most companies get in trouble. They host webinars. Deploy NPS surveys. Record customers who’ve “gone dark” during onboarding, with detailed reports on why they’ve disengaged by account owner and stage. And some even get users drunk just to test their product. Then what happens?
Either they squander their limited time by constantly updating spreadsheets to report statuses which aren’t a holistic version of the truth, diverting resources away from helping customers. Or all this valuable customer data stays where it was captured — GoToWebinar, HubSpot, Salesforce, HelpScout, and other SaaS applications — locked away in silos.
Centralized Customer Data
What if customer service teams could combine such data pipelines, while preserving the relationships between major objects like Companies and Contacts? What if customer service could collect forge meaning, statistical harmony, out of the white noise of text fields, interviews, forms, financial transactions, conversations, POC status notes, stories, and other customer data sources?
Support could go from a reactive mindset to a more proactive approach when resolving bugs. Customer success would find unhappy customers early, understand their pain more quickly, and invent new ways to get them more engaged with certain product features or correct behavior so they use features as their design intended. Account managers could monitor at-risk clients by how much the closed deal was worth and triage their outreach by ticket severity. With all their customer data centralized in a single database, customer service teams can message clients who are struggling, saying: “Hey, I noticed a few errors in your implementation. Have a look at this help article and let me know if you have any questions.” Or: “I saw you recently talked to our marketing team on our website and asked about X, Y, Z. Great idea! I’d be happy to submit these as product feature requests for our engineering team.”
In short, centralized data has great potential to turn satisfied customers at risk of churning into customers who tout your product without your asking them to. And in 2018, you don’t need IT to building an ETL stack that accounts for multiple data models and API endpoints that can jeopardize data quality, accuracy, and latency. Tools like Fusion automatically collect, combine, and clean customer data from SaaS applications, storing all this information in a single database for SQL analysis or visualization.
Customized Analysis for Multiple Data Sources
As a MySQL database, Fusion serves as a master data source for support and onboarding teams. By combining a range of data sources from NetSuite and HubSpot to Shopify and Zuora, Fusion gives a 360-degree view of the customer. And since Fusion refreshes data automatically, you needn’t worry about updating .csv’s or running a bulk extract every time you want to run a simple report. With a standardized data schema of object relationships, Fusion simplifies the task of multi-dimensional data queries and provides standard SQL keys to access the warehouse via reporting, dashboard and analytics tools.
Centralizing data in a single warehouse also frees you from using just the canned customer health metrics of your success tools. If you use a support tool like Freshdesk or Zendesk, you can unleash activity metrics without code or data prep. Fields are de-duped, formats standardized, and conflicts resolved — all at the push of a button.
With a MySQL warehouse connected to your analytics tool of choice, dashboards load faster and you don’t hit your API limits. Customer service teams can use any reporting tool — Tableau, Looker, you name it — that integrates natively with MySQL. So if you want to make infographic charts in Yellowfin or visualize data in Power BI, Fusion synergizes your customer activity and support data so you can analyze feature usage, user adoption, and other relevant health metrics in real-time.