- The problem with function-specific siloed tech stacks is that they don’t talk to each other
- Instead of vertical tech stacks, the Revenue Engine Tech Stack Model is built in horizontal layers across channels
- This new paradigm comprises four layers: data, analytics, orchestration and delivery
“There is a big paradigm shift happening in terms of how operations teams should organize their tech stack,” said John Donlon during today’s TechX keynote session “Built for Growth: The Revenue Engine Tech Stack” — which he co-presented with Gil Canare. “We’re trying to move away from the martech stack vs. the sales stack — function-specific, siloed tech stacks that don’t talk to each other.”
Gil and John used this session to detail the Revenue Engine Tech Stack Model — SiriusDecisions’ answer to this shift. “This model is a reflection of some forward thinking on our part, but we’ve also pressure tested it in the market over the last year. So it also reflects what people are starting to do, and a not-so-distant North Star they’re starting to work toward,” said John.
Instead of building vertical tech stacks that exist in functional silos, the revenue engine tech stack model is built in horizontal layers that encompass multiple tactic types or delivery channels. The result: “We get more cohesiveness, which drives two things — more efficient internal operations and a better experience for the audience of buyers and customers. That’s the advantage of this shift,” said John. This model has four layers:
- Data. “Before you can connect the dots, you have to collect the dots,” said John. In the data layer, all relevant raw information about audiences and their activities should come together as the input. That information gets standardized, formatted and joined to relevant data sets. What comes out the other side is intelligence, which is the input to the analytics layer. This layer will typically take the form of one unified platform such as a data warehouse or customer data platform.
- Analytics. In the analytics layer, the intelligence produced by the data layer is analyzed. This layer involves drawing conclusions and reading between the lines. The results are meaningful insights about the organization’s markets and audiences. Those insights are only as good as the organization’s ability to act on them. The analytics layer should comprise technologies that enable historical reporting and deep statistical analysis and leverage embedded AI in operational systems.
- Orchestration. “In this layer, we come up with a coordinated plan for the next set of interactions we’re hoping to drive,” said Gil. This plan is coordinated across functions and channels and at the individual, buying group and account levels. The output of this layer is the action plan/playbook, which is the input to the delivery layer. Orchestration technologies may be used for the decision engine that dictates the next set of actions and for facilitating internal content and resources.
- Delivery. “This layer is where we execute the action plan — send the emails and tweets, invite contacts to an event,” said Gil. These actions drive more interactions, which create more raw information, which adds to the data layer. Although many of the delivery technologies organizations use today will remain, the intelligence embedded in them will move into the orchestration layer.
The revenue engine tech stack is the foundational model for many of this year’s TechX presentations, which explain in detail how each of the layers of the tech stack can be optimized. Check back for more TechX coverage in subsequent blog posts.