Create value and drive profit by leveraging technology and artificial intelligence

By Sam Fayez

Few words have been used and overused more than “data.” What emerged from a series of zeros and ones has evolved into a critical pillar of every business. Data drives all aspects of a company, from research, buying behaviors, stock/inventory levels, assortment plan, replenishment, website design, store merchandising, staffing and finance to product lifecycle and customer lifetime value — and everything in between. Data has been supporting businesses since the invention of the computer. The only thing holding back data has been human intervention. Computers are only as smart as the most intelligent people operating and evaluating them. Enter artificial intelligence.

With machine learning, data can now be independently managed and served to make more accurate, quicker and predictive decisions that drive intelligent insights, directed actions and value.

The New Retail L.I.S.T. Defined

Technology fueled by data is a critical pillar of retail today. As the industry continues to change, the one constant is how a well-informed decision is the best decision. Retailers must rely on four critical components to thrive in the new economy: Logistics/supply chain transparency, Inventory management, organizational Structure supporting a unified commerce platform, and Technology and AI. And data has become the foundation upon which these pillars stand.

While much attention has been given to supply chain lately, building enterprise capabilities needs to vie for its share of resources. The new race in retail isn’t just product speed to market and ultimately to consumer, it’s the data race. The ability to acquire, apply and assess data as an ongoing cycle is propelling transformation and a fundamental need of organizations to create value, grow sales and become more profitable.

Path to Monetization Through Building a Robust Enterprise Capability

Building enterprise capabilities isn’t a one-and-done corporate initiative; instead, it needs to be treated like a business process across divisions and disciplines. While data intelligence and analytics teams shoulder the responsibility to champion this pillar within an organization, it needs to be prioritized from the top down and across functions in order to fully realize data’s monetized value. Unlike other company initiatives, enterprise development and data utilization are not linear deliverables; they’re circular. Indeed, there’s no end to data acquisition. More data equals more insights and more insights equals smarter decisions, and smarter decisions equal greater value. The faster a company can circle these “laps,” the better its performance will be.

If enterprise building and enhancement is a cycle, where exactly is the starting point and how do retailers begin their own race? There are essentially five points on the data race track:

Related story: The Responsible Retailer’s Guide to Building a Walled Garden

  1. Building a data asset
  2. Accessing data via a data platform
  3. Applying data science for insights
  4. Creating governance for consistency and clarity/alignment of data
  5. Understanding the data and its applications

And since there’s no real end to this circular track, every lap a business makes in cycling through these capabilities enables it to mature the capabilities and create more value and new data monetization opportunities  

Value Creation Cycle Explained

It may feel exhausting to enter a race that never ends, but the truth is that strong enterprise capabilities can create value (aka money). And who doesn’t want to have a healthier, more successful business? But before you can reap the benefits of data, you have to make sure that you have the right people, process and technology in place to optimize the value returns. There’s a difference between data analytics and science and reporting; just like there’s a difference in using Tableau over Excel. Instilling a thirst for data acquisition and an appreciation for leveraging data findings is a critical component to the value equation.

Educating an organization on the benefits of data is the first step. Many organizations only ask for data when they need it — it becomes a push/pull operation when, in turn, employees should see data as an essential tool to doing their job faster, smarter, better. Imagine putting forth the same effort (maybe less), investing the same money, using the same resources and yielding much better returns. That’s the value of data.

The second step in yielding the benefits of data is to make sure the organizational structure and talent/teams can support the evolving enterprise capabilities. Don’t expect business intelligence/reporting to become data scientists and analysts because you want them to. Similarly, end users of the capabilities, whether it be marketing, business development or other functions, won’t simply apply data-driven insights and AI learnings without having the skill sets to appreciate the predictive nature of the solutions.

Lastly, building a technology-enabled organization that has the right tools is essential to leveraging and monetizing data. This is where retailers may need to take a hard look at their current platforms and assess whether they have the best planning, merchandising, allocation, inventory, digital, supply chain, financial planning and CRM tools in their data/tech stacks. In the simplest terms, having data but not having access and visibility to the data won’t enable value.

The value equation is: Data +Modeling/Analysis + Insights/AI + Actions=Value

Further explained, collecting data and centralizing data is only one variable in the equation. Companies need to align fragmented data sources for a unified data feed and universal data warehouse. The next variable in the equation requires creating visibility and access to the data with end-to-end tracking and tracing. Layering on transactional/behavioral and customer segmented models along with applied machine learning allows for faster, more accurate and more predictive data discovery. All of these variables are critical pieces to the value equation, but none more so than the actual application of the data — knowing, using, doing. If you don’t change or enhance business practices as a result of data, you won’t create incremental value.

2 Ways to Monetize Data (MIT Defined)

The true value of obtaining and using data is to actually monetize it. How do you take information and learnings to drive more sales and/or reduce costs? This can be obtained by doing the same functions with less investments; acquiring new customers; retaining and increasing performance from existing customers; mitigating business risks and/or minimizing unproductive behaviors/purchases.

MIT (Massachusetts Institute of Technology) summarizes how data can be monetized by doing one or more of the following:

  1. Improving: reducing bottom line costs, increasing top-line revenue
  2. Wrapping: bundle core products with analytics experiences
  3. Selling: convert the data to revenue

[Columbus Consulting recommends focusing on the first two options as the third, selling, is controversial and extremely limited given current and forthcoming regulations.]

Improving may be the most familiar of the options. Using data in order to drive sales or reduce costs is very tangible. Models like Next Product to Sell allow for retailers to assign correlations that indicate customer behaviors based on their prior behaviors, most likely behaviors or similar buyer behaviors — thus driving more conversion and higher average order values. Customer Churn Models can be used to identify risk of attrition or higher value customers to drive retention and more frequency of purchase. AI models can even apply predictive measures that trigger customer demand before they know they have it. Using data through machine learning can identify a combination of high-potential customers and conversion proclivity.

Retail marketers can design customer relationship campaigns that drive a purchase journey — e.g., those used in prenatal to pregnancy and pre-mover to home owner cycles. Data provides essential information that shows what consumers are likely to purchase and at what times they’re likely to buy them. If a customer is buying prenatal vitamins, she is a high potential diaper purchaser in nine months. Similarly, shoppers buying dorm room supplies are prime segments to target apartment furniture in three years to four years.

Fashion retailers can also benefit from “improving” data value applications. Fifty percent to 60 percent of fashion inventory isn’t productive, meaning color, size, style, general assortment isn’t selling well. If retailers are able to use real-time machine learning at the beginning of a season (buy breadth not depth), then forecast the highest performing products within an assortment, they can quickly respond by shifting investments from unproductive to productive inventories. The value? Higher sell-thrus, more full-price sales, less clearance, less discounts. More profit.

Wrapping is also a way to monetize data. A bit less obvious to most, data wrapping can be a powerful application to cultivate intrinsic value from both products and experiences. Wrapping is a result of the Internet of Things (IoT) where data acquisition and analytics features are wrapped around the core product or service to enhance the product/service usability and experience. RFID technology, for instance, is a more common exposure to IOT. Here we have products tagged with information or tracking that enables both the retailer and consumer to engage with items on a deeper level. End users can trace the origins of a product and manufacturers can track its supply chain from end-to-end (anticipating delivery risks and responding in time to mitigate them).

RFID is now being used to provide consumers with product attributes, brand history, sustainability stories and the like, all of which enhance perceived or actual value of a product. IoT is being used in vehicles that transmit driver and car data back to dealers and brands. Appliances, too, are joining the IoT universe by enabling products with smart technology to read when your groceries need replenishment (refrigerators) or when your dishwasher needs more detergent.

Not only is this technology gathering more data, but it’s transporting it back to the source which, in turn, is processing, analyzing and re-applying the learnings. Thus is the data race/data cycle. Vending machines, open source/on-demand services (e.g., Uber, GrubHub), wrist trackers (like amusement park bands or fitness watches), and especially smartphones are all part of providing value through data knowledge. How can you monetize this value? The simple answer is to command higher price points and grow gross revenue. If customers see more value in a product, they’ll pay more for it.

Another way to harness monetized value with wrapping is to take action against the learnings. If technology enables a retailer to anticipate when a consumer is running low on a product, it can capture the sale and keep the customer engaged and loyal. It can also utilize suggestive selling/may we recommend “like” products to grow units per transaction (UPT) and AOV — all of which result in higher sales. Consumption patterns can be modeled and subscription/replenishment orders can be enabled to drive faster conversion and more frequency. Again, all driving sales.

3-Dimensional Data and Beyond

The value creation cycle relies on data, analysis/modeling, insights and actions. With AI, machine learning can cycle through this process at speeds we’ve never seen before. What began as zeros and ones has evolved into the seamless IoT that enhances a customer’s purchase or experience and creates value for the source of that product or experience. Data collected through solicitation, behaviors, predictive patterns or like segments can all be centrally consolidated and leveraged to create sales and drive profit. But what’s ahead for the data cycle?

Imagine for a moment what will happen in the metaverse? As brands and consumers migrate into a 3D digital world, new data will be made available. It may be challenging to envision the monetized value of that type of data today, but as we see more driverless cars on the road, digital currency in circulation, and drone technology in the air, it may be easier now than you thought.

How much is your data worth now?

With No End to Data, Where Do You Start?

While the data race does have competition, it’s more important to focus on your own progress. Wherever your organization is in its data journey, where you start and what you do next all depends on where you are today. Building a mature and consistent path to monetize data is a process, not a destination. As articulated before, the ability to acquire, apply and assess data as an ongoing cycle is propelling transformation and a fundamental need of organizations to create value, grow sales and become more profitable.

Building the asset, developing a platform, applying science and governance, and finally understanding findings and implementing actions differs across industries, brands, and even seasons and economies. It’s not a linear initiative; it’s a business practice. Driving performance through the value creation cycle becomes less complicated with every “lap” taken. Once you accept the cyclical nature of data monetization, you can create your own business stride, not to win tomorrow, but to innovate, transform and scale for the long haul.

Sam Fayez is a senior consultant at Columbus Consulting International with 20 years of dual experience and value delivery in supply chain and data science.

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