Revenue AI Capable Organization

Capable Organization

What RM Capability Is and Why It Is Needed 

Revenue Management (RM) Capability measures the cross-functionality of Sales, Marketing, Accounting, and other teams, to predict consumer behavior to maximize revenue growth. Many variables are in play, in example, with a brand’s portfolio architecture, pricing, marketing mix, and promotion strategies. RGM Capability factors how accurately and rapidly these variables can be synthesized in response to challenges and shifts in the market. The ideal state is the ability to respond in real-time, on a per-retailer basis.

Until recently, this real-time, ultra-granular capability has proven an insurmountable obstacle for organizations relying upon traditional RM tools. While even early-stage RM efforts have proven reasonably profitable for early adopters, most organizations (95%) have only started their RM journey. Everyone recognizes the potential of Big Data, but few are able to effectively harness it. As humans, it is beyond our ability to derive meaningful insights from 4-5 gigabytes of raw data in real-time. Large, global organizations often have terabytes of data to analyze.

Days or even weeks of human-driven data analysis can be performed almost instantaneously with the help of Artificial Intelligence (AI). Revenue AI and our Digital Assistant excel in breaking Big Data down into Small Data that “people” can access, understand, and act upon – in real-time, while it is still relevant. Having the data without the ability to act upon in a timely manner has little value.

A Continuous Development Process

Improving RM Capability is a continuous development process. The digital devices and applications we use are becoming more powerful and their rate of development is accelerating. Digital devices, from wearables to smart home devices, are exponentially increasing the volume, velocity, and variety of data available.

You always want to be making use of the best tools available and incorporating new channels of market-relevant data. If this wasn’t the case, we’d all still be using typewriters. The productivity increase that can be associated with incorporating AI into data analytics is at least as great.

Companies best able to make use of available data have a huge, advantage over others. Companies like Amazon, Google, AirBnB, Uber, and others, dominate their markets owing in large part to their focus on data strategy. Many of the early RM adopters have made massive financial investments over many years to achieve an advantage. Revenue AI makes it surprisingly affordable to jump ahead of the pack.

Leapfrogging Technologies

Until recently, nearly every organization deciding to develop an RM Strategy needed to step through the physical processes to build it. As new software and technologies are developed, they can be adopted immediately. If you have iPhone 7, you don’t have to purchase iPhones 8, 9 and 10, to begin enjoying iPhone 11. Similarly, level 1 and 2 organizations can immediately begin reaping the benefits of Revenue AI and dramatically accelerate their RM Strategy.

The Intelligent Assistant – RAI “Chatbot” Experience

Introducing RAI, our intelligent assistant …

Revenue AI provides a very simple User Interface that anyone can use to get the information they need, now – “data analysis on demand.” Using it is as simple as typing in a question and hitting enter. The chatbot queries the AI and instantly responds with an answer in plain English and an easy-to-understand visual representation. The chatbot also provides three related questions that can be selected as follow-up questions with a mouse-click. Users are also free to navigate each module on their own if they desire.

Stop Doing and Preventive Actions

Another characteristic of a capable organization is being able to identify and halt actions that are harmful to revenue growth. A good example involves promotions that cannibalize loyal customer spending without generating a greater uptick in casual users, non-users, and other brand users. Another is including Non-Performing Inventory in assortment strategies, which can be managed on a per-retailer basis. Of course, everyone needs to be aware of what is taking place in order to take corrective action. Revenue AI’s “Digital Assistant” is right there to provide your team with intelligent alerts to keep everyone focused on growing revenue.

Simulation Control Tower

See the likely impact of the changes you make, as you make them. Whether you’re adding new products, product prices, store assortments, or any related RM activity, you’re shown “the ripple effects” and their degree of probability. Revenue AI provides the ability to swiftly set up, alter, and remember your scenarios to help you optimize changes across your entire brand and product portfolio. When you find the combination that you like it can be immediately pushed into the decision-making process.

Real-Time Insights Wherever You Go

The power of Revenue AI is available 24/7/365. You can check in any time to see how recent changes you’ve made are performing, as well as receive and respond to any recent alerts. The full capabilities of Revenue AI’s Digital Assistant are accessible wherever you go, available on any device – PC, iOS, Android. API’s are available so you can connect Revenue AI to any system. Additionally, you are able to receive and generate system-wide newsletters to share insights with your fellow team members.

Leveraging Big Data

The amount of data CPG organizations need to engage in RM is massive. Their datasets may range from several Gigabytes for a typical regional organization to many Terabytes for a large global one. Not all of this data is necessary for every query, but must be extracted from the central database.

Using traditional RM tools to analyze this data can take entire an RM team days or weeks to perform. Even then, the results of the data analysis will lack in its granularity. Every store is different, thus an effective RM strategy depends upon finding the differences and optimizing them.

Let’s take a “simple” example. The sales director of an ice cream company wants to sponsor an event 1-2 months in advance. How many data points might be involved in her decision? She’d likely want to know:

  1. expected supply situation
  2. customer segments likely to attend
  3. purchasing power of the customer segments
  4. likely weather conditions
  5. channels best able to reach the customer segments
  6. other participating vendors and if any are direct competitors
  7. how her brand performs relative to any direct competitors
  8. other events taking place on the same day that may suppress turnout

You can probably come up with a few more pretty easily. The weather alone will have a huge impact on the event’s success.

Scale this up nationally or globally and the sheer amount of data involved to achieve precision or the ideal assortment becomes insane. It’s more data than any human team can be expected to tackle. Achieving precision RM involves much more than year-on-year comparisons or moving day averages. Often by the time one completes an analysis using traditional revenue management tools, the market has already changed.