As noted in my previous post around Revenue AI, we’re building a Real-Time, AI-driven Revenue Growth Management (RGM) Platform. We are releasing it as a Software as a Service (SaaS) product that can function as a Digital Assistant for your entire RGM team, a simulation tool, an information portal, and a real-time pricing engine. One of our main goals with Revenue AI is making it super-easy for everyone to interact with our Digital Assistant, RAI. We made sure that we enable our platform to self-service as our objectives were to massively accelerate Deployment and Adoption of RGM initiatives with Self-Service. In the first case, self-service is fully dedicated to enabling any stakeholder in your business to interact with RAI to find the information they need with Google-like search capabilities. RAI is enhanced to provide “next question” suggestions. Additionally, users have an easy-to-use dashboard to navigate the information portal on their own. In the second, self-service provides your analysts and data scientists the ability to fine-tune and add data sets with Low-code tools like Rapidminer – our primary tool of choice. Super-users can create their own custom code, use third-party API’s, and other data science platforms.
Self-Service Analytics for Business UsersAs it was already well communicated by Gartner, Self-Service Analytics (SSA), “Makes it easier and more cost-effective than ever before for non-specialists to perform effective analysis and better inform their decision making.” This holds true for Revenue AI, being incredibly easy-to-use, surprisingly affordable while empowering users with next-generation Augmented Analytics. In our case, RAI is a “chatbot” that business users with a very simple interface to an incredibly powerful tool. Users can interact with RAI in three ways to find the information they need:
- Conduct a search just like they were using Google – they type their question in the search field and hit enter.
- Point and click on relevant frequently asked questions, or
- Browse the navigation menu at their leisure.
Self-Service for Analysts and Data ScientistsOur developers and partners have made “DIY dataset management” very easy even for non-coders with Low-Code tools. We’ve partnered with RapidMiner, the highest-rated, easiest to use predictive analytics software according to G2 Crowd users, with accolades from Forrester and Gartner. Even non-coding experts will enjoy how easy it is to use a graphical interface to “drag ‘n drop” new data sets into our Platform. For Analysts and Data Scientists, we’ve created specific channels so you can fine-tune your data with custom coding, third-party API’s, as well as your own Python, R, and SQL libraries. You can link or upload your PoS, Competitor, Internal, Financial, Market, Household Panel, and other data directly to the AI Datastore. If you use another Data Science Platform other than RapidMiner, you can connect with it instead. We’re compatible with Azure, Amazon Web Services, and Google Cloud Platform. Further, data sets can be added and customized in real-time, without downtime.
Self-Service Analytics BenefitsCompeting effectively in today’s global market requires continuous organizational development in rapidly gathering, making sense of, and acting upon market shifts. Most organizations are still just starting on their RGM journey – basing decisions upon year on year comparisons or moving-day averages. Even organizations with mature RGM programs relying upon traditional tools are left acting upon the analysis of last month’s data. Revenue AI’s proposition allows organizations at any stage of their journey to leapfrog to the front of RGM capability – easily and with surprising affordability. Our SSA and Augmented Analytics “data analysis on-demand” capabilities alone provides numerous benefits:
- Users save huge amounts of time with AI-automated, real-time data analysis.
- Time saved means organization-wide ability to gain more real-time market insights.
- Increased decision-efficiency increases competitive advantage by allowing actions to be taken when data is most valuable.
- Democratization of data counters silos and increases team cross-functionality.
- Onboard new team members faster and offset knowledge-loss via turnover.
- Organizations reduce their reliance on data scientists with highly specific subject matter expertise – often in high demand.
- Stop Action alerts help safeguard users from making decisions counterproductive to organizational goals and revenue growth.