prescriptive analytics saving the store



Retail continues its transformation − as consumers deepen their demand to buy anywhere, anytime, and through any touchpoint − whether in-store, at desk-top, or mobile and to buy on-line: pick-up in-store (BOPUS), buy in-store: ship-to-home or to any other destination. New commerce and fulfillment models continue to emerge, many with high degrees of customer engagement. This customer-driven on-demand purchasing behavior is escalating the need for better integration of retailers’ digital and physical spaces. While 70% of retail transactions still take place in physical store locations, the rapid escalation of e-commerce has stripped constrained capital and technology resources away from stores, as many companies play catch up to digital demand. Despite historically higher profitability of brick-and-mortar, most retailers have focused their recent infrastructure investments on building a stronger online presence, with smaller portions of the technology spending outlay going to the physical store. Some well intended omni-channel approaches−designed to fulfill e-commerce demand from the store−have actually deteriorated store productivity, as associates shift attention away from customer service, visual merchandising, re-stocking and selling, towards distribution functions. In this paper, we will explore how the power of prescriptive analytics is helping physical store retailers deliver “anywhere, anytime, any way” service and compete more aggressively in today’s competitive retail marketplace, by using a technology that levels the playing field with e-commerce.

The Evolution of Analytics: From Descriptive, Diagnostic, and Predictive . . . to Prescriptive

First, a quick history lesson: Years ago the field of analytics began with descriptive analytics, which helped retailers understand what had happened in their stores. This approach was followed by diagnostic analytics, which told them why it happened. These became the core of most retailers Business Intelligence (BI) process. More recently, predictive analytics has made major advances by showing retailers what might happen. Predictive analytic tools are becoming a critical part of leading e-commerce marketing organizations tool kit. Today, brick-and-mortar retailers can leapfrog competitors with a further advancement in analytics, one which can bring real-time actions to the store: prescriptive analytics. This revolution in analytics advises retailers what should be done, using automated, machine-learning algorithms that identify opportunities for resolving in-store issues that might prevent a sale or reduce productivity.

“For many years I have advocated for more sophisticated types of analytics,” stated Tom Davenport, an independent senior advisor to Deloitte Analytics, in his May 2016 post on (Deloitte University Press)1. “My goal has typically been to encourage companies to move from descriptive analytics (also known as reporting or business intelligence) to predictive (not surprisingly, analytics that allow predictions about the future) and prescriptive (analytics that make recommendations for human action). Think of prescriptive analytics as recommendations — analytical models that decide the best course of action . . . and then inform a human about it.”

Are retailers ready to leverage powerful data algorithms and impactful recommendations that are
received via a phone or tablet app, or email or text, which inform store associates of the immediate best
course of action − in order to save the stores?

Real-time, Real-world Examples for Stores

Digital retail is both data and analytics rich. For best of class on-line retailers, it is also dynamic and machine-driven, allowing real-time decisions on inventory availability, pricing, shipping, promotions, personalization and customer insights. These real-time analytics tools were designed to enhance the productivity of a commerce model which has high customer acquisition cost, high traffic, but low conversion and typically lower market basket value than a physical store.

In contrast, retail stores which have the capability to convert a significantly higher percentage of their traffic at a higher value are generally ill-equipped to understand in real-time the dynamics effecting sales performance. Virtually all reporting and performance dash-boards are based on trailing data… and are therefore not actionable at the speed of shopping.

But this is changing. Imagine having the real-time, app-based capability to respond proactively − leveraging the power of prescriptive analytics − to everyday store situations with actionable insights to associates. Grasp all the real-time data available through typical retail systems (point-of-sale and traffic counters) and turn it into actionable information − just like Amazon and other pure-play e-commerce winners are doing − to improve profitability and save the stores. Examples:

1. Sales are down because a particular size or flavor is not on the shelf, and the inventory control system indicates that the product is in the store. Prescriptive analytics tools alert the store associate via a mobile device that there is sales demand but the item is out of stock on the sales floor and immediately must be replenished from the back room.
2. Using sales by style, size, color and market attribute, prescriptive analytics identifies regional preferences within the chain, allowing improved product allocation to better satisfy customer desires, down to the SKU per store. In the past, this level of information was typically buried in static, weekly or monthly aggregated reports that summarized data at a higher level (merchandise style) or required broad store “clustering” to address allocation variances; therefore, missing granular customer preferences. After implementing a new planning and allocation solution paired with real-time monitoring, retailers can fine-tune product allocations without having to wait for an end-of-period reporting.
3. Products being shipped to stores, include not only advanced shipping notification to the store managers, but prescriptive action to be taken in the store including: a) ensuring all products received are moved to the sales floor by end-of-day of store receipt; b) checking the scheduling of stocking labor, and c) sending retraining information to stocking team on efficient processing of shipments.
4. Real-time customer, merchandise and trac data that feeds from point-of-sale (POS) tells the store at noon that it is 40% behind plan. Prescriptive analytics rolls that data into real-time, app-based tactical coaching information: “Trac is down X%, as is transaction size, so basket size must be increased by $3 per transaction to be back on plan.”
5. Select items suddenly are selling rapidly. The prescriptive analytics solution contacts the store manager to suggest placing these items near the cash wrap for increased impulse purchasing, thereby improving upsell and increasing revenue. Imagine the improvement of the sale of umbrellas on a rainy day, if 100% of stores complied, based on a mobile reminder.
6. Payroll cuts have reduced on-floor department supervisors across the chain. Those who remain now supervise two- to three-times the number of associates. A mobile prescriptive analytics tool puts actionable data and “here’s what to do” recommendations directly into the hands of every customer-facing associate − using real-time data trends, not historical reports.
7. Think far beyond “loss prevention” application of point-of-sale data and exception reporting. Imagine a tool that gathers and analyzes store data then identifies opportunities that allows a store to compete like an e-commerce giant.

These and other powerful “do it now” action steps are delivered to the palm of the hand with today’s prescriptive analytics delivery systems: Why not leverage the data already being collecting to increase the efficiency of in-store teams, better serve customers, improve inventory management, reduce shrink − and ultimately save the stores?

The Store: Where Retailers Can Differentiate

The physical store remains the place where retailers can bring brands to life with high-touch, personalized experiences − producing higher sales per transaction and increased margins. The store provides the best opportunity for powerful new customer-centric technologies to create a Seamless Circular Commerce environment, which unifies retailers’ rich depositories of on-line and off-line data; delivering more satisfying face-to-face engagements to digitally-savvy store shoppers; and to generate impressive results.

In fact, the store is the primary place where retailers can differentiate themselves and thrive as a vital part of the new data-centric, digitally-enabled retail ecosystem. The move by online giants, such as Amazon and Alibaba, to open physical stores is the single greatest example of the importance of the store to the consumer purchase decision.

A Forbes article from February 20162 posed the question, “Why Would Amazon Open Physical Stores?” The article stated: “Although Amazon is an established player in the global retail e-commerce market, which is expected to reach $2.5 trillion by 2018, e-commerce still would represent less than 10% of the global retail market. We believe that by opening physical stores, Amazon might be looking to provide a more personal shopping experience to its consumers, reduce shipping costs by providing a store pick-up facility, and integrate the online and offline shopping experience for its consumers in addition to creating a strong brand image.”

To best leverage store opportunities like these, retailers must be armed with mobile solutions designed specifically to enhance store productivity, ensure inventory availability, build associate knowledge and create exceptional consumer experiences. Powerful analytics tools and actionable recommendations, delivered by prescriptive analytics, bring retailers full circle, back to the basics of Retailing 101: Quality of the sale and transaction, conversion, market basket size, inventory optimization and personalized service. Tools which provide people in the field with the right actions versus reports− will help save the stores by re-inventing shopkeeping.

The move by online giants such as Amazon and Alibaba to open physical stores is the single greatest example of the importance of the store to the consumer purchase decision.

Finally, the Advantages of E-commerce Come to the Store

Today’s online retailers have some clear advantages: They instantly know customer demand, and with sophistication, can use consumer shopping and purchase data to improve the current and future transactions. They know who their customers are, what they are looking for and whether those items are available − before purchases are made. E-commerce also provides immediate feedback on inventory availability and shipment information to avoid lost sales. Most provide immediate acknowledgement of purchase, shipment and delivery… and close the customer loop with requests for feedback on service, offer incentives for future purchase and even solicit packaging comments.

While these metrics exist today for some physical store transactions, there is a wide gap between how the information is tracked, monitored and available within a store: Most retailers rely on in-store reports, based on trailing data that lack insights or real-time actions. In fact, until now, almost every analytics tool in the brick-and-mortar environment has concentrated on what happened yesterday, last week, last month − or even last year.

Until now, almost every analytics tool in the brick-and-mortar environment has concentrated on what happened yesterday, last week, last month – or even last year.

But armed with advanced prescriptive analytics tools, physical store retailers can level the playing field
with their on-line counterparts: The same fast, flexible, real-time data, complete with prescribed
recommendations of the actions to take right now, mobilizes and propels the retail store opportunity.
With prescriptive analytics, brick-and-mortar retailers finally can leap from analyzing what happened yesterday and guessing what will happen tomorrow, to knowing what is happening right now and what to do immediately to enhance in-store performance and customer experiences. Now, powerful prescriptive analytics solutions apply machine-learning algorithms to billions of data points to instantly detect patterns and deliver actionable recommendations retailers can implement to impact results in the moment.

Compete Like Amazon Where “Data is Power”

As pure-play online retailers open their brick-and-mortar stores, they will bring along with them the prescriptive analytics tools that are part of their success stories. For example, at the store level, Amazon will continue to track everything its customers do, combine this online data with its online data repositories, then, in an excellent example of Seamless Circular Commerce, apply that customer-centric data to create the personalized shopping experiences for which Amazon is known. Amazon will in fact be totally channel agnostic.

“‘Data is Power’ is the success mantra at Amazon,” according to a post on dzone.com3, the Big Data Zone for developers. “Just look at your Amazon homepage, it is never the same. Amazon tracks everything you do… to collect as much data as it can… With various sections on the homepage like ‘Inspired by Your Wish List,’ ‘Recommendations for You,’ ‘Inspired by Your Browsing History,’ ‘Related to Items You Have Viewed,’ ‘Customers Who Bought This Item Also Bought,’ Amazon is continuously tracking what their customers do − to provide them a personalized preeminent shopping experience.”

Retailers can compete as aggressively as Amazon, and save the stores, and powerful prescriptive analytics capabilities will make this possible. Using vital, machine-learning prescriptive analytics, traditional retail can join the ranks of most all pure-play e-commerce digital giants who understand the power of turning real-time data into actionable information. Retail leaders know that this customer-centric data is the only currency that can buy success in today’s increasingly digital paradigm.

The New Law of Selling: Get the Right Information to the Right Person at the Right Time

Retail leaders are moving from historical evaluations to acting on real-time data as the primary driver of their overall business strategies. They know who shops and for what, and ensure products are available before consumers arrive. They also know when products aren’t available in-store but can be delivered directly to customers, and communicate this before shoppers leave.

The key to these capabilities is providing actionable information to every associate. That’s the new law of selling: You must get the right information, which drives the right action, to the right person at the right time. This is the antithesis of those unwieldy data reports delivered to store managers’ offices, where the data sits without being viewed or leveraged.

Your store operations group also knows the challenges created by heavy payroll cuts, which have eliminated many on-floor department supervisors, along with other key store management personnel. With prescriptive analytics that puts actionable data directly into associates’ hands, retailers can leverage personnel and better manage labor costs. The need for numerous department supervisors becomes surprisingly obsolete: They’re no longer needed to monitor and control routine behaviors, such as poking an employee to replenish a shelf. Associates will know what to do because the app tells them. Today, even though on-floor staff has been reduced, those who remain − particularly the crucial customer-facing employees − have a technology solution that makes them more knowledgeable and self-sufficient, and therefore better able to deliver stellar and profitable customer experiences.

The latest tools − sophisticated on the back-end, yet easy-to-deploy, powered by best-in-class machine learning algorithms, help retailers operate with a customer-centric approach that puts the focus on serving the customer, not a task list. Regardless of the number of locations, vendors and distribution complexity, retailers can put productivity-enhancing capabilities and action recommendations in the hands of every front-line associate across the chain.

These recommendations allow store associates to maximize performance by:

  • Assuring products are available when and where the customers want them
  • Monitoring internal and external fraud
  • Ensuring employee and vendor compliance
  • Decreasing shrink, waste, and damage
  • Improving other operational or compliance issues that may arise

Everyone’s Collecting Data – How You Can Best Leverage Its Implications

Beyond the development of the data dashboard, there’s been little major change in the way retailers use and generate business intelligence and reports for stores. BI departments and financial reporting continue to focus on often-aggregated historical data that deliver less-than-finite information, and lack action plans for store operators. Meanwhile, data proliferates with every new product, transaction, RFID reading, promotion, sale and fulfillment activity. Most retailers utilize BI analysts to summarize multitudes of activity report findings, based on trailing data, to recommend the next action steps. They also use exception based reporting (EBR), and while few have prediction capabilities, until now, virtually none have had highly efficient, machine-learning engines which prescribe the exact actions that will have an immediate impact on a retailer’s ability to service each customer appropriately.

Pattern detection and machine learning prescriptive analytics identify behaviors and trends within data without users having to analyze it. Rather than scour reports − which may only identify the top five product categories or departments with high inventory − today’s store operators can rely on prescriptive tools that interrogate the same data, but also identify and recommend actionable findings.

“For digital winners in the retail space, [the number and kinds of questions arising from new data capabilities] have evolved from their historical status as purely tactical and executional to being primary drivers of the overall business strategy.”
Ryan McManus, EVRYTHNG

“When you look at performance data across industries, including retail, companies that lead in the digital space are winning in the marketplace with the consumer as well as at the financial and shareholder performance levels,” stated Ryan McManus, founder of Accenture’s Digital Business Strategy business and currently SVP at EVRYTHNG, a cloud-based IoT data management platform that manages billions of online identities. “One major differentiator of leading digital companies is their data strategy. When new data capabilities come to market and retailers engage with them, the challenge for the retail executive, who historically has not had the opportunity to work with this kind of information and context, is the number of questions that arise. For example, how do we work with all of this new data? How should we think about combining this new data with existing systems? And in a broader sense, how do we use this data to drive growth? What are the overall implications for our business, our challenges and our strategy, given the importance of digital leadership in terms of overall business performance, competitive positioning, and meeting evolving customer expectations? For digital winners in the retail space, these sorts of questions have evolved from their historical status as purely tactical and executional to being primary drivers of the overall business strategy.”

Evolution and Next Frontier: Analytics That Tell You What to Do

Years ago, descriptive analytics tools helped retailers determine what happened in their stores, while diagnostic analytics told them why it happened. These strategies were displaced by predictive analytics, which delivered the foresight from which retailers could evaluate what might happen; smart users who understood the output of these predictive tools leveraged these insights to determine how to use their marketing spend to encourage other people of like mind. While these insights are important to a retailer’s knowledge base, predictive analytics can’t drive retail performance by addressing what action to take in-store at the moment of a sales-producing activity, such as notifying a store that sales of a particular product are trending down for that time period and a restock is necessary.

Today, brick-and-mortar retailers can move past their counterparts with prescriptive analytics, which advises what should be done, using automated, machine-learning algorithms that identify opportunities to help resolve issues that might prevent a sale or reduce productivity − then measure the value of subsequent actions and monitor their completion. Prescriptive analytics focus on how to encourage real behaviors, in real time, to impact what is going on inside the transaction right now. With today’s prescriptive analytics tools, retailers have the comprehensive insight to completely optimize the transaction.

As early as 2012, as shown in the following graph from Gartner4, Gartner noted the eventual progression to prescriptive analytics − and here in 2016, that moment has arrived.

Gartner defines prescriptive analytics5 as follows: “Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question ‘What should be done?’ or ‘What can we do to make _____ happen?’, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.” Some have characterized the move in analytics from static to action orientation as the natural progression of making data a true business currency. In a LinkedIn post from April 20165, Maneesh Bhandari, a VP at Accenture, asserted, “Prescriptive analytics is the ‘cool’ thing and the endeavor of analytics professionals across the spectrum is to use more of prescriptive analytics in their solutions. If we look at the general work breakdown of most analytics organizations/teams about 60% – 75% of the work is descriptive analytics, 20% – 30% is predictive analytics and rest, which is a small proportion, is prescriptive analytics. There are exceptions but those are far and few. So why is the gold rush to move more to prescriptive analytics? Part of the reason for this gold rush is that generally an organization’s or team’s maturity in analytics is measured by what % of the work they do is prescriptive analytics. Teams with higher proportion of prescriptive analytics work are considered to be more evolved.”


Deloitte: “Data is the New Oil. Where are the Refineries?”

A February 2016 post on dupress.com6 (Deloitte University Press), claimed that “Data is the new oil. Where are the refineries?” The writers went on to say: “Many data efforts are descriptive and diagnostic in nature, focusing primarily on what happened and why things happened the way they did. These are important points, but they only tell part of the story. Predictive analytics broadens the approach by answering the next obvious question—“what is going to happen?” Likewise, prescriptive analytics takes it one step further by helping decision makers answer the ultimate strategic question: “What should I do?” Business executives have been trained to limit their expectations to the “descriptive” analytics realm. Education and engagement may help inspire these leaders to think more boldly about data and their potential. It could lead them to embrace the advanced visualization tools and cognitive techniques they’ll need to identify useful patterns, relationships, and insights within data. Most importantly, it could help them identify how those insights can be used to drive impact and real business outcomes.



Prescriptive Analytics: Actionable Data Is The Currency That Helps Save the Store

The O Alliance utilizes a trademarked technique in helping retail organizations address the fundamental changes created by the growth of digital retail and to unlease the power of data abundance to enhance profitability. This new organizational work process, called The O MethodTM 7, measures the lost of potential value creation within a retail organization, when decisions and best organization actions are blocked by a disruption to the flow of information. Much of the friction at retail which pushes customers increasingly to the on-line channel is a direct result of lack of visibility to data in real time at the store. Store Productivity Analytics is one of the integral parts of creating Seamless Circular Commerce. By replacing the typical linear and channel specific transactional model with a fully integrated eco-system, the capabilities of real-time prescriptive analytics enables customer interaction at the store to be equal to the best of online experiences. It is estimated that store four-wall productivity can be enhanced by up to 15% when utilizing mobile enabled prescriptive analytic tools.

Do Not Fall Behind: What Do Other Experts Say?

A November 2015 report from IDC Research8 offered the company’s top 10 predictions affecting Big Data analytics initiatives in 2016. And prescriptive capability was near the top of their list.

Prediction #2 “By 2020, 50% of all business analytics software will incorporate prescriptive
analytics built on cognitive computing functionality.”

In the report’s abstract10, Dan Vesset, IDC’s program VP for business analytics and big data, said that big data and analytics solutions “present a potential for significant business value. Organizations that are able to take advantage of the most important trends will be prepared to reap new benefits and overcome challenges provided by big data and analytics solutions.”

A January 2016 article posted on DataInformed11, a media website dedicated to big data analytics, Editor Scott Etkin shared the top few trends data experts were seeing as 2016 rolled in. According to the article, Scott Zoldi, chief analytics officer for FICO, noted the growing importance of acting on data as it happens: “From the Internet of Things to healthcare to cyber terrorism, it’s no longer just about gathering and analyzing data. It’s about gathering, analyzing, and acting on data as it happens… It is now economically feasible to squeeze even more value out of data in real time. A cornerstone of prescriptive analytics, streaming analytics will come of age in 2016.”

“The rising popularity of automated business decisions, especially against the backdrop of an evolving on-demand consumer economy, is expected to expand the addressable market for prescriptive analytics.”
Global Industry Analysts Inc. 12

In April 2016, Global Industry Analysts, a worldwide market research leader, released an intensive study of the prescriptive analytics market12. The company found that the global market for prescriptive analytics “is projected to reach $1.6 billion by 2022, driven by the radical disruption underway in the field of business intelligence (BI) and analytics. Analytical disruption is defined as the disruptive use of data that creates value in the practice of business intelligence. The modern insight-driven enterprises are no longer running their businesses based on historical data. Rear view business intelligence is increasingly falling out of favor and emerging over the horizon are new analytical capabilities such as those epitomized by prescriptive analytics. The rising popularity of automated business decisions, especially against the backdrop of an evolving on-demand consumer economy, is expected to expand the addressable market for prescriptive analytics. North America represents the largest market worldwide. The dominance of the region is supported by the growing commitment of companies to big data projects and the ensuing focus on prescriptive analytics as the most desirable tool for big data synthesis.”

As part of that research, Global Industry Analysts shared a presentation, titled “Analytic Disruption Fuels the Emergence of the Era of Prescriptive Analytics12,” on


The year 2016 is now. The prescriptive analytics market has taken off; don’t let it fly right past you.


Retail Leaders Embrace a Simple Approach to Complex Business Problems

A wide range of retail leaders in various sectors, from grocery to drug and apparel − including Abercrombie & Fitch, DSW, Ahold-Delhaize (Giant Food and Stop & Shop), Hollister, King Kullen, Lowes Foods, Sally Beauty Supply, TOPS Markets, Ulta Beauty, Weis Markets and others − are already distinguishing themselves by implementing powerful, easy-to-use prescriptive solutions. These retailers are leveraging automated “do-it-now actions” based on real-time recommendations primarily delivered in-store via phone or tablet, through a text or email.

Prescriptive analytics helps these and other organizations “reduce the risk of decisions,” stated Jim Hare, a research director in Gartner’s technology & service provider group. In his March 2016 article published in Forbes, titled “Use Prescriptive Analytics to Reduce the Risk of Decisions13,” Hare said: “The practice of advanced analytics is “less about data and more about reducing the risk of decisions. Although many organizations are still moving from basic reporting to predictive analytics, the next wave of investments will be in prescriptive analytics to improve decision making. However, analytics and simulation alone will not be enough — the decision process will also have to change, with people learning new skills and new ways to make decisions. The transformation must be organizational, as well as technological, and the change will have to come from the top.”

Prescriptive solutions can be used by every level of the retail organization because of the simplicity of the messages they deliver, the specific guided actions that are included and prescribed recommendations can be accessed from anywhere, anytime.

“The practice of advanced analytics is less about data and more about reducing the risk of decisions…Although many organizations are still moving from basic reporting to predictive analytics, the next wave of investments will be in prescriptive analytics to improve decision making.”
Jim Hare, Gartner

The Profitect Prescription

In the previously mentioned presentation from Global Industry Analysts, titled “Analytic Disruption Fuels the Emergence of the Era of Prescriptive Analytics12,” the company identified a handful of aggressive leaders in the prescriptive analytics arena. One of them is Profitect14, a Boston-based technology solution provider, which has earned its position as a leader in advanced analytics for the retail industry, with endorsements from many high-profile and successful retail winners. The company’s prescriptive analytics technologies focus on helping retailers increase profitability through operational efficiency and organizational synergy across the entire value chain. Comprehensive, amalgamated retail data is analyzed through pattern recognition algorithms and machine learning. Profitect improves or replaces retailers’ current BI and exception-based reporting tools with powerful prescriptive technologies that automatically deliver prescribed actions, in real-time and in plain language to the right person, which can improve in-store performance and profitability, and are easily tracked and monitored.


As illustrated in the graphic above, Profitect’s prescriptive analytics platform connects what’s happening right now in-store to the behaviors store personnel can and should take. Retailers instantly receive insights as they occur, along with the prescribed actions they should take, to solve real-time, real-life opportunities.

“As the leader in prescriptive analytics, Profitect is working to educate the market on the importance of advanced analytics and how retailers can use it to improve productivity and eciency in any sales channel, including brick and mortar and e-commerce,” stated Guy Yehiav, Profitect’s CEO and chairman of the board. “Though swift implementation our prescriptive analytics tools can provide value from day one and help retailers quickly generate more than 300% ROI.”

Using Profitect’s prescriptive analytics solution, customer-centric retail leaders are creating a new one-store vision to drive performance and create seamless customer experiences that can:

  • Boost sales, items per transaction
  • Increase inventory accuracy and integrity
  • Optimize margins
  • Reduce costs
  • Boost conversion
  • Decrease shrink, waste and damage
  • Increase customer satisfaction and loyalty
  • Minimize IT involvement
  • Optimize allocation
  • Improve vendor quality and compliance
  • Shift store focus from reports to the sales floor

“The most interesting product I found [at the NRF Big Show, January 2016] was from a company called Profitect, which provides a prescriptive analytic solution for retailers,” wrote Dan Gilmore, editor-in-chief of Supply Chain Digest, in his NRF recap15. “The term ‘prescriptive analytics’ gets thrown around a lot, rarely with much in the way of detail other than that such capability is coming, but Profitect appears to have put some real meat on the prescriptive analytics bone. It has defined a large number of events or scenarios that indicate something is amiss (you can also of course create your own scenarios). A very simple example: sales are occurring at a store for a SKU which the inventory system says is out of stock. The Profitect solution not only automatically identifies this anomaly, but then sends an alert to the appropriate person(s) as to what needs to be done in response. The company has a large library of such event-action combos (turns out most retailers would react to a given issue in the same way), but you can easily craft your own best practice. Very cool. There is no question that increasingly the computers will tell us all what to do, and here is an early example.”

“The term ‘prescriptive analytics’ gets thrown around a lot, rarely with much in the way of detail other than that such capability is coming, but Profitect appears to have put some real meat on the prescriptive analytics bone… Very cool. There is no question that increasingly the computers will tell us all what to do, and here is an early example.”
Dan Gilmore, Supply Chain Digest

The organizational data that the Profitect tool analyzes and pushes into recommended action steps includes but is not limited to:

  • Inventory Movement: receipts, RTV, recall, cycle count adjustments, physical inventory, markdowns, damages and waste, etc.
  • Point of Sale Activities: sales, returns, exchanges, price changes, trac, loyalty, gift card sales, etc.
  • Delivery and Receiving: shipping notices, seals, drivers, delivery methods, etc.
  • Logistics and Warehouse: picks, drops, deleted labels, scanned and not scanned, damages, etc.
  • Planning and Buying: plan, forecast, adjustments, allocation, sales, on-hand inventory, etc.
  • Marketing: customer and household spending behavior, basket size, coupon use, customer demographics, etc.
  • Circular Commerce: channel sale, fulfillment location, order and sale, etc.

“Retailers want improved technologies and are building excitement around automation, usability and specifically prescriptive analytics, since it identifies a solution and the right person at the organization to solve the problem,” said Yehiav. “It’s not surprising that analysts predict the prescriptive analytics market to exceed $1 billion in just a few years.”

About Profitect

Since 2012, Profitect has helped companies leverage existing big data investments to identify, resolve, and measure opportunities for improvement by delivering actionable prescriptive analytics to the right person, at the right time. If you’re ready to learn more about how prescriptive analysts can catapult you past competitors, and ultimately save your stores, please contact Francis Clark (

About The O Alliance

The O Alliance is a transformational consultancy which is guiding retail organizations to compete more profitably in today’s new digital paradigm. We serve as:

  • Advisors to the Board of Directors;
  • Partners with the C-suite;
  • Agents of change; and
  • Hands-on implementation teammates that help execute the fine points of Seamless Circular Commerce.

For more information, please contact us at:
The O Alliance
115 E. 23rd Street
Suite 315