Data driven is more than just a buzzword. Nearly every company today wants to reposition their resources so they can capture and analyze information on nearly every aspect of their business. The concept is so popular that a poll by Forrester found that 74% of firms strive to be data driven, and they are backing up that claim with real investments. Gartner projects the global revenue in business intelligence and analytics software will reach $18.3 billion by year’s end — 7.3% more than the 2016 market.
But buying up analytics products doesn’t automatically transform a business into a data-driven company. The firm has to have a culture that enables analytics insights to proliferate through business practices, something few firms can claim. That same Forrester report shows only 29% of companies say they are good at connecting analytics to action. How can organizations reverse course so they not only get some benefits from their analytics but actually thrive by becoming data driven?
Data-Driven is not new
Throughout history, companies have been fundamentally data driven. Whether it’s preparing information for an initial public offering or simply balancing the books, ledgers filled with customer and accounting data are nothing new at companies.
But today, enterprises must track new metrics like engagement, revenue per user and the overall customer journey, just to name a few, a process that involves blending a complex web of data streams into a comprehensive marketing and sales funnel. And then sales teams must be metrics- and data-driven in their approach to close the deal.
From GE to Microsoft, Oracle to SAP, companies are integrating digital technologies into their sales process to create a data-first methodology, a switch from decades of simply having to listen to individual feedback from employees and blend gut instinct with experience. However, many organizations are struggling to take this same line of thinking throughout their organizations — not just creating data-driven metrics for sales, but pushing this mindset out to core development and product analytics.
While sales expenditures are significant and measuring the ROI on this spend is a must, how do we measure the impact of spend on engineering development efforts on customer experience? The engineering versus sales spend in the Software as a Service industry varies a good amount, with engineering at the low end representing about 10% of overall investments, while other companies have it as high as 40%. The key to creating value across the board at companies is measuring the end-to-end customer experience. Using data to understand that buyer journey will drive product and engineering value by intimately understanding each step, its impact and the return on engineering investment.
To truly understand the impact data can have on product development and motivate internal teams around this common cause, companies must parse through the customer journey, clearly defining key performance indicators that map back to the overall goals and objectives of the business.
Understanding your customer journey
Which customers intend now to renew their contract? Who just waited three seconds for your web page to load? Who just filed a support ticket? Why is someone loading up items into their virtual shopping cart and then bouncing?
Businesses can’t understand what they don’t measure, and the customer journey is a complex road filled with potential interactions on a multitude of channels. Thankfully, there are now many vendors and tools out there that can assist companies with tracking these data points and understand their metrics, so they can evaluate the big picture from a series of smaller interactions, like clicks, time spent reviewing a product page, what gets bought and what almost gets bought.
Create a 'metrics first' approach
To gain insights from these actions, companies must understand that every metric is influenced by another metric. A customer journey has a series of triggers, automated by certain KPIs and again this flow must map back to company goals.
When clearly mapped out, these KPIs and related goals will look a lot like an organizational chart, each item, identified by organizational or business unit, flowing up to more items and powering the highest order business goals. Mapping your organization's metrics and coupling with the tools to measure will enable a “metrics first” conversation.
This metrics first approach will provide a strong foundation to build on when the company is deciding to make a change, so decisions don’t get implemented without understanding their impact. If a company isolates down to its decisions, it can pinpoint how each and every change or code push alters each of these metrics, the customer journey and, therefore, the business.
Monitor, monitor, monitor
Ultimately, creating a data-driven culture means making a permanent change in how a business acts and reacts in its market space, meaning that monitoring these KPIs and the customer journey must be an ongoing activity. This metrics-first concept needs to live at the executive level, so everyone in the C-suite understands how product and engineering decisions filter down through the sale funnel. The company should hold weekly reviews and highlight which decisions are enabling new leads and which areas need improvement.
When teams across an entire business understand how their data points translate to real-word customer results, it reshapes how an enterprise imagines its products, processes and goals, giving the business confidence that it will drive ROI and keep customers coming back for more.
Trevor Stuart is the president and co-founder of Split Software. He brings experience across the spectrum of operations, product, and investing, in both startup and large enterprise settings. Prior to founding Split, Trevor oversaw product simplification efforts at RelateIQ (acquired by Salesforce). While there, he worked closely with Split co-founders Pato and Adil to increase product delivery cadence while maintaining stability and safety. Prior to RelateIQ, Trevor was an Associate at Technology Crossover Ventures and an analyst in the Technology Investment Banking Group at Morgan Stanley in New York City and London. He is passionate about data-driven innovation and enabling product and engineering teams to scale quickly with data-informed decisions. Trevor holds a B.A. in Economics from Boston College.