As organizations are collecting more and more data with their apps and tools, they are immensely interested in gaining insights from this data to improve business outcomes and customer experiences and to bring efficiencies. A Forrester Consulting study commissioned by Microsoft found that 85% of surveyed business leaders see analytics as central to driving business growth, more than 90% said that data and insights have an indispensable role in their day-to-day work activities, and 86% said analytics are key to driving business innovation.
Despite this alignment on the benefits of analytics, only one third of organizations are analyzing the data they collect. At Microsoft, we have been improving our analytics and injecting machine learning (ML) and artificial intelligence (AI) in various decision-making tools for sales and marketing. Through this journey, we learned a few strategies that would increase the speed of analytics projects and adoption.
Interoperable technology provides quality data
Collecting quality data is requisite for doing deep analysis and learning. Interoperable technology is the foundation for collecting interconnected data where customer activities can be traversed and linked. So, organizations must focus on interoperable and integrated technology to ensure they are collecting good quality data. At Microsoft, we are continuously upgrading and replacing our marketing technology to reduce manual processes, eliminate disconnected systems, and bring in good data.
Transitioning to the cloud helps in securing data and bringing applications together. The next important step in analytics adoption is data visualization. Several tools like Power BI are available for intuitive and deeper visualization that would entice user’s curiosity and show complex data in simple views. Organizations should modernize their data visualization tools.
Setting a vision and driving to action
More business leaders are recognizing the value of data science and, per this Forrester study, 86% of surveyed business leaders are investing in analytics. Organizations should set a clear and bold vision for the Data Science team and align all projects to the vision and drive to action. Without a vision and clear actions to end-users, analytics projects may receive accolades, but will not be adopted by users. The same Forrester study commissioned by Microsoft found that only 32% of the organizations are analyzing marketing engagement and demographics data. This is even lower for other datasets including preferences, purchase history, loyalty programs, and more.
At Microsoft, we have a vision to create a combined marketing and sales customer journey where we know precisely where and when to involve sellers in the marketing process and vice versa. Today, our sellers focus on marketing highly engaged accounts derived from several ML models based on business outcomes, customers’ engagement, and demographics. We provide highly engaged accounts with no pipeline, uncommitted pipe, or low pipe to sellers, to take action and increase the pipeline. In addition, our Marketing data science provides next best actions with target accounts and contacts that contribute to increase in deal size and improve win rate.
Data insights need to drive a future action and cannot be just insights from the past.
Up-levelling data scientists’/analysts’ skills
After realizing its value, many organizations are creating or expanding Data Science organizations. The demand for these skills is high in the market and they are hard to recruit for.
In Microsoft field marketing, we took a two-pronged approach to skill up our Data Science teams.
- Our current analysts are a great asset to Microsoft, and we created a phased training program to train them in AI and ML. This program is helping our members to skill up and keep their skills up-to-date.
- We hired a few experienced data scientists to drive impact and coach our existing team in real-time. With this approach, our data developers are learning where they like the most—while developing. These approaches are boosting our employee satisfaction and boosting productivity.
Most business leaders agree that analytics can improve their business outcomes and that AI will play a major role in digital transformation. To enable data analytics, organizations must build interoperable technology, modernize data visualizations, and build a bold and aligned data science strategy, action-driven insights and up-level their existing teams while hiring experienced data scientists.