With marketing and sales efficiency plummeting by more than a third, software companies need to get more for their marketing and sales investment dollars. Enter machine learning, which provides answers to crucial consumer questions and predicts potential future transactions. This form of Artificial Intelligence gives companies a competitive advantage in the quest for competitive differentiation, resulting in a “winner-takes-most” model.
Slow and steady will not be enough to win the race when it comes to implementing data science and machine learning to accelerate growth. In order to turbo charge profitable growth, companies implement these five leading practices to streamline the transition, increase impact and reduce time to results:
1. Identify a cross-functional core team with representation from key go-to-market functions and from your data science team and foster a collaborative environment. Data science team leaders with personal line management experience provide a results-focused approach to data science analyses, while field leaders with a passion for data science optimize the business leverage of the information that machine learning yields. With an iterative approach, team members learn from each another and create a mutual beneficial relationship.
2. Adopt a personalized agile approach to analytics. In a “winner-take-most” environment, companies benefit from learning by doing and also reduce the risk of projects gone awry when applying machine learning. With a focus on a specific area of decision-making, businesses improve performance with quick marketing and sales evaluations and rapid implementation.
3. Build appreciation through demonstration. By understanding how analytics can improve everyday performance, teams start to embrace machine learning models by enjoying tangible quick wins. The process evolves into informed decision-making on a larger scale and helps teams to exceed their goals.
4. Leverage on-hand data and collect additional data to fill the gaps. Data accumulates exponentially, but machine learning tools can process new inputs at previously impossible speed. Application programming interfaces (APIs) collect additional data to improve predictive models for revenue forecasting and marketing ROI.
5. Transition from small wins to broader changes as opportunities arise. While individual leaders can execute minor changes to the current go-to-market approach, a broader change management program will be needed over time to capture the full benefits of machine learning.
Leading businesses have already begun to accelerate growth through machine learning models and effective decision-making. Only by integrating data science and developing a vital go-to-market strategy will companies remain competitive in the global marketplace. Those slow to transition risk to be hopelessly left behind more aggressive competitors. Given the exponential learning curve, machine learning creates an unprecedented opportunity to leap frog slower competitors. So get on the machine learning train and quickly get up to speed!
For more information about how data science and machine learning can drive tangible results across marketing, sales and costumers success, download the full report on KPMG’s study, Data-Driven Growth