Revenue AI Portfolio Architecture

Portfolio Architecture

What is it and why does it matter?

Brands have products with different prices designed to reach certain customer segments. That’s a simple definition of portfolio architecture and it’s incredibly important in a competitive market. Your brand and products are competing with other brands and products for market share. The goal of optimizing your portfolio architecture is to identify your ideal product and pricing mix to beat your competition, tactically, and strategically. You may gain a tactical advantage by adjusting prices or strategic advantage by introducing new products.

Even organizations with mature strategies for managing their portfolio architecture struggle to get these activities right. By the time they finish analyzing their products using traditional tools and processes, the market has already changed. Revenue AI provides reporting and data analysis on-demand so you can gain insights, make and execute decisions in real-time. The Revenue AI Portfolio Architecture Module provides you with six components enabling on-demand reporting, analysis, and simulations. Descriptions of the components in our list follow below.

  1. Brand View Perception
  2. Brand Portfolio Pricing
  3. Product Portfolio Enhancement
  4. Product Potential
  5. Store-based Assortments
  6. Non-Productive Inventory

Brand Value Perception – Get to know your customers, how they drive product categories, and better understand how they perceive your brand. Product-market fit is hugely important and it requires companies to have a detailed understanding and definitions of their customers and customer segments. Moreover, with today’s social media, social consciousness, and viral velocity, small issues can quickly evolve into big ones. Alongside highly detailed demographical data, Revenue AI has the capability to tie into consumer surveys and other data to show how you stack up against your competitors.

Brand Portfolio Pricing in Revenue AI utilizes a multi-level pricing approach while actively factoring brand-level price elasticity and competitor-based targeted pricing. Our Digital Assistant provides you with near real-time reporting and analysis so you can see your current position in product categories, by segment and channel. Your brand’s price elasticity helps predict gains/losses in sales volume from increasing or decreasing product prices. Armed with this understanding, you can determine optimal retail prices for each of your brand’s products and devise a strategy to get there.

Product Portfolio Enhancement focuses on finding and mapping “White Space” opportunities or products your customers would love to buy – but are not available on store shelves. In a very simple scenario, a beverage company may sell single cans and two-liter bottles but could be losing out not offering two-liter two-packs. Here you are calling upon market data for your product categories to compare your products against consumer demand and your competitor’s products. This includes a comparison of pack sizes and price tiers across different regions down to the individual retailer level.

Product Potential  gives you the capacity to conduct rapid simulations on how introducing new products or changing a given product’s price will impact your entire portfolio. The beverage company above likely offers a combination of single-container bottles and cans of different sizes, six-packs, twelve-packs, and cases. Introducing a two-liter two-pack will impact sales of other products, but for better or worse? Revenue AI will provide you details about the size of the prize and growth rate so you can evaluate the attractiveness of that opportunity, and many others.

Non-Productive Inventory Identification helps you identify and decide how to get rid of all Non-Productive Inventory across your organization. Non-productive inventory may include entire brands, products, or pack sizes. If they aren’t directly causing your organization to actually lose revenue, the overhead managing them comes at the expense of more time for better-performing products. Non-productive inventory is attributable to a wide variety of reasons to include products on the verge of being phased out or your release of better, newer products. In some cases, you’ll be able to identify locations where they are performing so you can productively channel your remaining inventory on a more selective basis.

Store-Based Assortments helps synthesize the insights you’ve gathered to include SKU’s with high-levels of customer loyalty while offering the right mix of new and niche products. This also extends to formulating Occasion Brand Package Price Chanel Architecture (OBPPC). But where every store is different, RAI can help you better define your store clusters or even define strategies on a per retailer basis. With the demographic, point of sale, customer loyalty, and other metrics in your data catalog they can be aligned to more relevant performance indicators and revenue opportunities.

This largely consumer-driven data combined with RAI’s predictive analytics help you create precise tailor-made assortments for each store’s customers, custom-fitted for allotted shelf space. With RAI handling nearly all of your reporting and data analysis, your team picks up an enormous amount of time to more efficiently find insights and act upon them. The ability to optimized store-based assortments on a per-retailer basis presents a huge opportunity for revenue growth – and it’s just one of many “next generation” RM capabilities offered by Revenue AI.

Portfolio management and exponential data

More data enables more informed decisions. Organizations better able to make use of data have a distinct advantage over others. If that wasn’t the case, Amazon, Google, Facebook, Walmart, and other giants wouldn’t be where they are today. Each of these giants has a data-focused strategy.

The greatest difficulties involved in effectively managing a product portfolio boil down to volume, velocity, and variety of data. Everyone has heard of Big Data these days, but it’s almost a quaint way of talking about Exponential Data. Portfolio management concerns many data points. Think of all of the data associated with the activities above. We can start with SKU-level data, local store demographics, shelf assortments – and then figuring out the impact of new products or price changes. It can take weeks or months for an RM team to conduct an analysis. During that time the market will have changed, to some extent at least. Competitors are shifting their prices and conducting promotions, too.

The sheer amount of data circulating in today’s market is more than what humans can process, even with the assistance of traditional RM tools.

Data, data everywhere…

Where’s all of this data coming from? In addition to what is publicly available, you are collecting some of it internally. Even more data is available on the market, many organizations buy and sell their data directly and through data exchanges in what is presently a $50 billion market.

In 2010, 2 zettabytes of data were produced worldwide. Today, we’re producing nearly 50 zettabytes with projections for generating over 175 zettabytes of data by 2025. This extends from computers, PoS devices, and the proliferation of mobile devices. Expectations are that by 2025, there’ll be 75 billion Internet of Things (IoT) devices tracking…everything:

  • Wearables
  • Automotive IoT & Connected Cars
  • Smart Homes
  • Industrial IoT (IIoT)
  • Internet of Medical Things (IoMT)
  • Smart Retail
  • Smart Supply Chains
  • Smart Cities & Grids
  • Smart Agriculture

All of these devices aren’t just sending data into a vacuum, but to other devices, triggering alerts to inform organizations of, among other things, revenue opportunities.

Revenue AI’s Digital Assistant

Our “Digital Assistant” provides organizations across the CPG, Retail and Life Sciences Industries with powerful real-time RM capabilities. Portfolio Architecture is the first of our four modules based on our AI chatbot that provides you with data analysis on-demand. Our other three modules cover Precision Pricing, Tailored Promotions, and building a Capable Organization.

It’s easy to use. All you need to do is ask a question, and the Digital Assistant provides you with an answer in plain English. It also shows you the corresponding visual (chart or graph) that any stakeholder can easily understand. You can continue to ask it questions, mouse-click on closely related questions, or simply browse at your leisure. Has your team been spending a lot of time analyzing data? Now nearly all of that time can be directed to gathering real-time insights and executing timely, informed decisions.

Revenue AI is a “Software as a Service” (SaaS) product. You avoid the otherwise massive upfront investment in time, money, and effort to become RM capable. You can rapidly integrate our product into your operations via the Cloud. We handle security for you with encryption and two-layer authentication. We take this responsibility very seriously.

The Portfolio Architecture module provides you data analysis and reporting on-demand for:

  • Brand Value Perception – track how customers feel about your vs your competitor’s products.
  • Product pricing and elasticities to rapidly evaluate changes via simulations.
  • Find opportunities for new or niche products to meet consumer demands.
  • Explore the best options for removing Non-Productive Inventory organization-wide.
  • Gain a more granular view of per store assortments to apply a more competitive mix.
  • Democratize your data – break down knowledge silos as the Digital Assistant keeps all stakeholders informed, focused on the same objectives, and reduces the training time to onboard new members of your RM Team.