Companies can now make decisions in a timely manner to increase sales and productivity, but that’s just the beginning of big data analysis. When it comes to market impact, big data is now being talked about in bold language. It creates an omnipresence for executives, and with information being logged and harnessed faster than ever.

With the proper solution, retailers will be able to access all the available information and data that they need; send it to the right person at the right time; more importantly recommend best practice actions, based on the culture and policies of the company; and monitor and track the actions. All of this should be able to be done automatically, easily, and of course, quickly.

Even more intriguing is the blend of the data scientist and detective.  Only these next generation data AP professionals have an eye on margin, not just malicious behavior.  It was interesting to hear the ways data AP teams are cross correlating information and using analysis, rather than “gut feelings” to identify root causes impacting the financial health of the retailer.

With technology that can bridge the gap between information, and execution.  Data AP professionals utilize algorithms coupled with business logic to make cost reduction but more important top-line growth a new tool in their arsenal. A key component of this sophisticated tool is to not only bring forth opportunities, but also resolutions of questions and issues that may have otherwise never been asked or brought to the retailers attention.  Data AP teams can utilize big data and predictive analytics to draw correlations that will be key to implementing effective process change and ultimately increasing sales and margin.

The growth of the new Data AP professional is allowing asset protection to become a profit hub and not just a capital expenditure.  Retailers will be able to rely on them for information and not just criminal deterrence, making them invaluable, and not just the “cost of doing business”.

PROFIT is part of the margin calculation and we wouldn’t be implementing anything that would knowingly decrease our PROFITS (although there was a company in the early 1970’s that discovered if you price your products to ‘give them away’ that people would actually ‘come and take them’) but I’ve never heard anyone proclaim ‘we’re going to do this purely for PROFIT growth’.

When we set our PROFIT margin we do not realistically expect to realize all of it and have created a number of ‘buckets’ into which we calculate/assume where the unrealized PROFIT is going.  Among these buckets are malicious events (fraud, theft, collusion, etc.) which accounts for about 30% of the unrealized value and these events are both internal and external in cause.  Retailers fund this side of unrealized PROFIT margin to the tune of BILLIONS OF DOLLARS ANNUALLY in an effort to control exposure.  Based on available studies and surveys; we’ve reached a stalemate.  With the investment in cameras, honesty testing, background checks, investigative teams, visual analytics, alarms, security tagging, etc., it is a staggering investment.  I can make the point that the needle of control on this side of unrealized PROFIT is barely moving ‘year to year’ so additional investment struggles to be justified.

And, we have the non-malicious events (lost sales, failed processes, compliance lapses, mistakes, poor buying decisions, damage, waste, failed credits, unrealized returns, out of season, date compliance lapses, excessive markdowns, etc.) which accounts for the other 70% of unrealized PROFIT.  The pursuit of controlling, recovering, managing these lost profit opportunities is segmented along the functional areas of the business including Store Operations, Finance, Audit, Warehouse Management, Inventory Control, and so forth.  Each of these areas has their own ‘turf’ and appropriate tools and staff BUT no one owns the whole picture of PROFIT on this side of the discussion.  This has been referred to a ‘multiple versions of the truth’ since each area has its own view, message, and analyze their own data.

If we were undertaking a project purely focused on PROFIT, we would devote assets to these two areas and while historically the ‘malicious’ side of unrealized PROFIT has garnered billions of dollars annually in staff, services and products—-the investment on the ‘non-malicious’ side has had little investment.  Largely, this area is allocated to the ‘cost of doing business’ bucket when honestly it should be placed in the ‘cost of not knowing what is going on in the business’ bucket!!

Significant PROFIT that goes unrealized on the non-malicious side of the discussion and to date I cannot name one single retail company (worldwide) where one individual is charged with owning what happens to PROFITS in this area.  Is the reason is that PROFIT is not really the focus but the byproduct?  Or perhaps because we are functionally ‘silo’d’ and no one wants to take on the turf wars with these powerful elements of the organization (Finance, Asset Protection, Audit, Warehouse/Logistics Management, Store Operations)?  So the duplication of efforts, processes, reports teams of analysts are chasing a part of the opportunity in a purely dis-functional way meaning that MILLIONS OF DOLLARS ANNUALLY are lost for lack of a unified, focused effort.

All because NO ONE owns the responsibility for minimizing non-malicious unrealized PROFIT!  If this is the reality, then millions in every retail company, every year are lost in unrealized PROFIT margins.

So, while ‘all of our projects are to improve profit margin’……are they really?  Or are we overlooking the biggest PROFIT opportunity right under our noses because PROFIT isn’t a focus; it’s just a byproduct of everything else we’re doing to stay competitive?

A recent example of an unknown, unknown involved a store’s loyalty program. We came across a situation where a retailer built their own loyalty card program.  Customers would receive points for every dollar spent, and these points could then be used towards purchasing other goods within the store chain. However, there was a gap in terms of how they would account for layaway purchases, and when payments are made to purchase something such as a ‘big ticket’ item.

Take for example, a customer would buy a dining room table for $1000 retail and they would put down $500 as the layaway. As the program stood, the customer would receive 500 loyalty points at that time. But when the table is shipped to the house after paying the final $500, they would receive the full 1000 loyalty points at that time, bringing the total to 1500 points for a $1000 purchase. It was also discovered that customers were able to keep loyalty points from returned products. In effect, the extra points equate to “free money” for the customers to use in the stores at the expense of profits for the retailer.

Pattern technology was able to align customers’ loyalty points with their purchases.  The unequal correlation between the two were identified immediately. The retailer was able to readjust the program to properly account for layaway and returned purchases, saving them from giving away points, and therefore free money, which was an unknown unknown system opportunity.

These unknown/unknowns are always eye openers for retailers when found, and they do exist in any complex organization and more common than most retailers realize. Using accumulated data streams by crawling through the data at the lowest level available, highlights the unknown/unknowns through pattern detections and statistics.  Bringing these descriptive insights and guided actions to the right person in a timely manner to act quick and deliver the maximum value eliminates the unknowns.  As the expression goes, knowing is half the battle…

One simple example of this occurrence comes from perpetual inventory, or PI adjustments. Let’s take a retailer that runs based on retail accounting and therefore calculates shrink based on retail price. Now let’s assume a store manager has 1,000 units of a product in their system at $50 (retail) a unit, and therefore they have $50,000 of inventory at “retail”. After an inventory count is performed, the on hand perpetual inventory shows 800 units, meaning only $40,000 of inventory. With this PI adjustment, the store’s inventory value shrunk by $10,000, which is counted as shrink for that store, if calculated at “retail”.

However, since PI adjustments can typically be done whenever the store manager decides to perform them, he may wait until this high-priced item is marked down by headquarters, to lets say $25. With 1,000 units in the system, the inventory count at this time is believed to be $25,000, and that loss of $25,000 ($50,000-$25,000) is not reflected upon the store manager; it is the headquarters P&L, because it was a headquarter markdown in price from their level. If the manager performs a PI adjustment now, the current 800 units are now worth $20,000. So the store manager’s shrink number was reduced from $10,000 to $5,000, simply because they chose to count their inventory at a different time.

It is very easy for a store manager to discover this power to “lower” the store’s shrink. Once this is realized, it is fairly simple to manipulate the numbers even more. What is even more worrisome is that this practice could be considered legal (however unethical), since the store manager is not responsible to count inventory every day. Managers are often informed in advance about markdowns or cost reduction (for retailers doing cost accounting), and can plan PI adjustments accordingly, for their own benefit. The incentive is high to manipulate these counts, especially since bonuses are often calculated with these shrink numbers in mind.

Retailer are aware the PI adjustments are being made, so the problem is known, but it looks like the loss is stemming from corporate, as a result of poor markdown strategies and merchandising. However, the real cause is related to missing inventory in individual stores. The same amount of money is being lost, but these “wooden dollars” are simply being shifted around, moving the blame from the store manager up to corporate. Therefore hitting the product P&L side, and not the shrink side, of the equation.

The retailer knows something is happening, and the perception is that its happening in the field. But they cannot perform the complex analysis of the reports needed to find out what is actually happening. Pattern seeking technology is able to identify a pattern of PI adjustments (inflated or deflated) over time, and pinpoint the root cause, which is at the store level. The problem will be identified at the store, by sku, instead of at HQ, stopping the manipulation by the manager before it begins to negatively affect those individuals who may be blamed at the corporate level.  As a result, retailers can move quickly to prevent profit leakage at all levels of the organization, ultimately improving business processes and the top and bottom line.

There are 3 categories of discoveries that a solution should provide you. These are “known/knowns,” “known/unknowns,” and “unknowns/unknowns.” These categories are determined based off of whether or not your organization knows there is an opportunity. More importantly, it is how these opportunities are used to generate value, by finding and acting on opportunities correctly within your systems, processes, or personnel.

In the first of this series we will start with the known/knowns. These are opportunities that you are usually aware of within your organization. Retailers are able to find them, but it takes time and resources to do so. In most cases, the retailer is not being efficient or effective in identifying these opportunities, and this is where improvements can be made in the “known/known” category.

Improving efficiency means the retailer does not need to waste time searching for the known opportunity, sifting through multiple systems and reports to try and identify its source. It also means that you will have everything in one place, and the opportunity will be pushed to the right user rather than a user trying to find opportunities.

Pattern recognition software boosts efficiency by accumulating occurrences, automatically alerting the retailer to a problem. If you compare pattern recognition to legacy EBR (Exception Business Reporting) methods you find that there is a significant increase in the quantity of alerts created by EBR. Additionally, many of the alerts end up being false positives and the retailer is forced to waste time and resources tracking them down. Real efficiency and effectiveness is having all the necessary data in a single, easy to use system that will provide “true positive” opportunities.

To better understand the “known/known” category, we can look at an example of an opportunity identified through pattern recognition. The retailer had a very lenient return policy, which they were proud of because it helped stress their focus on customer service. In the past, they only focussed on their high return vs. sales rates. The average return rate was about 7% of sales for all products. Whereas it was identified that one product was being returned at a rate of 18% across multiple stores within a region. A pattern was created to analyze returns vs. sales, correlated with returns vs. damages, to minimize false positives and properly direct associates.

The pattern allowed the retailer to quickly discover that the entire product batch was damaged. Since the high damage rate was across multiple stores, it was determined the issue stemmed from the manufacturer. This entire issue was found and resolved within a week of go-live with the retailer’s data. The quick response allowed the remaining product to be pulled from the shelves before they were sold and returned, assuring increased customer satisfaction by reducing the returns that would have occurred. Moreover a new batch of the product was expedited and sales were in-line with expectation as well.

• Data sources were relatively small and structured, and came from internal sources;
• Data had to be stored in enterprise warehouses or marts before analysis;
• The great majority of analytical activity was descriptive analytics, or reporting;
• Creating analytical models was a “batch” process often requiring several months;
• Quantitative analysts were segregated from business people and decisions in “back rooms”;
• Very few organizations “competed on analytics”—for most, analytics were marginal to their strategy.

It was in 2010 that the world began to take notice of “big data,” and we’ll have to call that the beginning of Analytics 2.0. Big data analytics were quite different from the 1.0 era in many ways. Data was often externally-sourced, and as the big data term suggests, was either very large or unstructured. The fast flow of data meant that it had to be stored and processed rapidly, often with parallel servers running Hadoop. The overall speed of analysis was much faster. Visual analytics—a form of descriptive analytics—still crowded out predictive and prescriptive techniques. The new generation of quantitative analysts was called “data scientists,” and many were not content with working in the back room. Big data and analytics not only informed internal decisions, but also formed the basis for customer-facing products and processes.

Big data, of course, is still a popular concept, and one might think that we’re still in the 2.0 period. However, there is considerable evidence that organizations are entering theAnalytics 3.0 world. It’s an environment that combines the best of 1.0 and 2.0—a blend of big data and traditional analytics that yields insights and offerings with speed and impact. Although it’s early days for this new model, the traits of Analytics 3.0 are already apparent:

• Organizations are combining large and small volumes of data, internal and external sources, and structured and unstructured formats to yield new insights in predictive and prescriptive models;
• Analytics are supporting both internal decisions and data-based products and services for customers;
• The Hadoopalooza continues, but often as a way to provide fast and cheap warehousing or persistence and structuring of data before analysis—we’re entering a post-warehousing world;
• Faster technologies such as in-database and in-memory analytics are being coupled with “agile” analytical methods and machine learning techniques that produce insights at a much faster rate;
• Many analytical models are being embedded into operational and decision processes, dramatically increasing their speed and impact;
• Data scientists, who excel at extracting and structuring data, are working with conventional quantitative analysts who excel at modeling it—the combined teams are doing whatever is necessary to get the analytical job done;
• Companies are beginning to create “Chief Analytics Officer” roles or equivalent titles to oversee the building of analytical capabilities;
• Tools that support particular decisions are being pushed to the point of decision-making in highly targeted and mobile “analytical apps;”
• Analytics are now central to many organizations’ strategies; a survey I recently worked on with Deloitte found that 44% of executives feel that analytics are strongly supporting or driving their companies’ strategies.

Even though it hasn’t been long since the advent of Big Data, I believe these attributes add up to a new era. It is clear from my research that organizations—at least the big companies—are not keeping traditional analytics and big data separate, but are combining them to form a new synthesis. Some aspects of Analytics 3.0 will no doubt continue to emerge, but organizations need to begin transitioning now to the new model. There is little doubt that analytics can transform organizations, and the firms that lead the 3.0 charge (like Procter & Gamble , which I wrote about last week) will seize the most value.

About Thomas Davenport
Thomas H. Davenport is an academic and author specializing in analytics, business process innovation and knowledge management. He is currently the President’s Distinguished Professor in Information Technology and Management at Babson College, Director of Research at the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.

Davenport has written, coauthored, or edited fourteen books, including the first books on analytical competition, business process reengineering and achieving value from enterprise systems, and the best seller, Working Knowledge, on knowledge management. He has written more than one hundred articles for such publications as Harvard Business Review, MIT Sloan Management Review, California Management Review, the Financial Times, and many other publications. Davenport has also been a columnist for CIO, InformationWeek, and Darwin magazines.

Wouldn’t it be nice to have answers instead of questions? With the proper solution, you should be able to access and analyze all the available information and data that you need; send it to the right person, at the right time; recommend best practice actions based on your own culture; and monitor and track the actions. All of this being done automatically, easily, and quickly. The real value of a solution comes from the actions that are being taken, not necessarily the opportunities that are being identified. So what really makes an opportunity actionable? It must be timely, detailed, and guided.

1. Timely: The opportunity needs to be created and communicated in a timely fashion. It needs to be sent as close as possible to the time it occurred and/or was identified. Opportunities need to be evaluated every day. If you wait a month or a quarter to find the opportunity, you will miss the chance to take an action and to have an actual impact. Most large issues that are identified are only large because of the time that passed and the value that was accumulated from not taking action. If only known about sooner, and acted on, the issue could have been dealt with and prevented (with less resources and effort) while still a small issue.
2. Detailed: The opportunity needs to have as much detail as possible; store, sku, day or week level, as this will allow you to focus your actions on the right entities. By having detailed opportunities, your store visits will be much more valuable. When you visit a store, you are able to focus on the areas that require attention, instead of aimlessly visiting all the areas of all stores. We refer to these efficient and effective store visits as “value visits”.
3. Guided: By guided, we mean two basic things. First, the opportunity needs to be sent to the right person that will be able to take the actions. The last thing you want is for everyone to look at everything and try to figure out what is theirs and what is not. Secondly, the solution needs to contain recommended best practices, so when the assigned person receives it, they will be guided on what actions to take and how to perform them.

We now know that actionable opportunities need to be timely, detailed, and guided; but how do we make this a sustainable process to ensure we are continuously improving and bringing value to the business? The automated process of identifying new opportunities, based on the patterns that are already set or known, is referred to as pattern matching. We know what we are looking for, so we let the system do it for us automatically. However, it is very important to continuously look for and recognize patterns in new areas, based on your business knowledge, and then add them to the process of pattern matching. This is referred to as pattern recognition. So in the world of the profit amplification solution, the job of the analyst is not to analyze the same thing over and over again every day; their job is to look for new patterns, new opportunities, and automate them. This is the continuous improvement that creates operational excellence.

Assuming the employee has the specialized talent necessary to correctly understand the report; then what? There are many different possible ways to harvest the identified opportunity. She can try to solve the issue herself, or assign it to another person, maybe a group of people. This means that you not only need to rely on the employee’s initial understanding of the report, but also how she understands your culture.  The employee must have the proper business acumen to act on it in a way that makes sense for your organization and their role or the ability to instruct others. Even further, whatever action is taken, you need to monitor the opportunity to ensure it gets done and the value is realized.

Even with the best BI system, there are several ways this can go wrong.  These include, but are not limited to the following:

1. Relying on employees to understand the report’s insights;
2. Having employees take the right action, based on your culture and their roles and responsibilities;
3. Whatever action is undertaken, ensuring that it is completed correctly will build on the opportunity and generate real dollars from the insights.

Because you cannot guarantee that everyone will interpret the insights the same way and act on them in the right way, you can not guarantee that the company is performing in the most efficient and effective manner.  This can limit the potential dollars generated from correcting the identified opportunity.

So what needs to be done to fix this issue, or opportunity? You need a system that can identify the story behind the opportunity and translate it to an automated task that can monitor the completion. These tasks are outlined in clear, simple language.

Opportunities should be connected to the “best practice” solution bank, removing the need to rely on “talent” to correctly interpret the actionable opportunities. Even if there are multiple ‘correct’ actions that can be taken, they should be ‘spelled out’ within the task management, including which personnel should be associated with each possible solution. Once these automated best practices are acted on, the system can track the actions until they are completed, allowing the retailer to minimize personnel interpretation and therefore diminished return. With this in mind, retailers can realize the full dollar return from every opportunity identified by the patterns crawling the big data schema.

Then there are the ERPs (which has become code acronym for we want to control your entire technology infrastructure) and, perhaps worse, the point solution providers who fancy themselves Big Data big shots.

Consider the last time you sat through a software product demo that didn’t either lead or conclude with a sweet-looking dashboard full of pretty pie charts and line graphs that promised to tell you everything you didn’t realize you needed to know. You can’t remember what a software sale felt like before dashboards, can you?

In all my discussions with analytics vendors, I’ve learned a couple of things that I’d like to pass off to you in the form of free advice.

1. 1. If the BI/analytics package doesn’t have workflow tools, don’t buy it. When I say any dashboard package should have workflow tools built in, I don’t mean you should be able to right click on a particularly alarming pie chart, open up a new message in Outlook, drop it into the attachment field, then sit at your desk thinking for 20 minutes about who to send it to and what to tell them to do about it. I mean you should be able to share the data with a set of stakeholders, predetermined by role, within the application. In turn, the response of those stakeholders should be transparent within the application. And, if at all possible, integration with the very systems you’re measuring and analyzing should suggest — if not automate — responsive action to the data.
2. 2. If you get bored within an hour of playing with it (and you will if it doesn’t have predictive modeling), don’t buy it. Unlike infants, we’d rather play with the toy than the packaging it came in. If static data presented in pretty colors can hold your attention for a little while, an interactive predictive modeling layer based on real data will enthrall you for days. More importantly, when you can slide the scale around and drop in your own KPIs and goals to see their impact on sales, inventory, labor, finance, etc., you come away with more than glassy eyes and drool on your chin (common symptoms of dashboard addiction). You come away with creative, fact-based ideas and the inspiration to initiate them.

I’ve shared these two simple suggestions with you because I feel strongly about them. I’ve shared them for free because I feel so strongly about them, they now seem obvious to me. And it just wouldn’t feel right to expect remuneration for something I think you should already know. Workflow tools and predictive analytics — two fine points that separate BI vendors from BS vendors.

About Matt Pillar

Matt Pillar is editor-in-chief of Integrated Solutions For Retailers and President of Matt Pillar Communications.

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I told him that this was not my viewpoint but it did bother me a bit and made me question whether I have a better handle on this than he does. This is all the more true because I’ve heard the same sentiment echoed by others who I hold in high esteem and who have had the chance to observe our industry closely.

Their basic premise is that our industry under-contributes to their organizations because we address only one small segment of losses – those caused by malicious actors, whether that be shop thieves, dishonest employees, ORC gangs, or burglars and robbers. They don’t see Loss Prevention groups focusing on process issues or paying attention to issues such as damaged goods, returns to vendor, supply chain errors, inventory integrity, excessive markdowns, and other large causes of profit degradation.

Are they right? Perhaps one way we could assess this is to look at the agendas of major loss prevention conferences and see what they reflect as the primary issues we face and address as a group? How often are there sessions on non-malicious types of loss? How often do we hear success stories from organizations that have saved millions of dollars by fixing an operational process, instituting a new inventory control system, or changing packaging to reduce theft and damages?

Even if they are right, is that a problem? After all, one of our core responsibilities is to address theft, physical security, and investigations. I cannot imagine that many people would argue that those are not important functions for our industry. These are areas where we have expertise that no one else in our company does. So, this focus alone is not a problem.

But, are we myopic? Are we ignoring other areas where we should be engaging? Should we take the lead in fixing process losses and systemic issues that affect profit?

Clearly, there are examples of many loss prevention departments taking responsibility for important issues other than theft. Safety, crisis management, disaster preparation, and business continuity are all areas where I could rattle off a number of retailers who have tasked their LP/AP group with a lead role.

I also know of several departments that are highly engaged on systemic issues, inventory integrity and accuracy, and other line items that have significant impact on the profitability of their company. But, is this the norm?

What are your thoughts? Is this an area where we should be engaging more often? Do you have success stories from your organization that you can share? Should industry conferences have more emphasis on these aspects of our business?

If I get enough responses, I’ll write a follow-up and give an update on what you have to say on this topic. Email me at wpalmer@PCGsolutions.com.

About Walter Palmer

Walter Palmer is Chief Executive Officer & President, PCG Solutions.

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Retailers are always searching for any way to amplify profit, and management teams must continue to look for ways to lower SG&A expenses in order to improve overall efficiency and productivity. Addressing the individual areas that make up these expenses and focusing on efficiency through technology will allow retailers to achieve their 2013 goals. Recent analyst suggestions include investments in technologies which enable more efficient business processes and can allow for sustained long term reduction in SG&A expenses.

Historically the focus of cost reduction is usually on store labor. However, it’s not always about reducing the number of man-hours used, but improving the efficiency of the store personnel by reducing the number of tasks they perform (tasking out), thereby allowing them to spend more time with the customer. Retailers will never be capable of completely “tasking out,” but they can get close by ensuring that the right tasks are sent to the right person at the right time. In some cases, the issue can be found upstream reducing any need to involve the store. Retailers who provide current personnel with fewer, more focused tasks, will enable stores to improve customer service, combat showrooming, increase same store sales, and improve other critical Key Performance Indicators (KPI’s).

It’s no surprise that a common problem in retail involves the time and money spent on non-selling activities. Leveraging solutions that improve business processes will greatly decrease these costs, and allow employees to focus their time on more important items such as customer service and increasing same store sales.

Another relatively simple solution to increase retail-wide efficiency that often goes unrealized involves sharing business processes and services. By consolidating services and sharing best practices with all stores in the company, retailers can ensure that each store is working as efficient as possible. The emergence of cloud computing technologies have made this much easier, allowing retail-wide sharing and collaboration of information.

Focusing on leveraging SG&A expenses should provide retailers with the opportunity to continue to grow and be profitable.

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Properly managing inventory levels is critical to the overall retail sales cycle: ordering the right products, distributing to the right stores so that your customers can purchase the products they want, when they want it and at the price you planned.

Choosing the right inventory management strategy is one of the more difficult strategic decisions for a retailer to make.  While there are multiple approaches one can take when it comes to organizing and managing inventory, the decision on which one to use becomes even more important during times of high sales volume, like the holiday season, where the wrong decision could lead to decreased sales or too much waste or damage.

Is Being Lean Too Risky?
Should retailers focus on “lean inventory”, concentrating on creating a very wide breadth of assortment to satisfy multiple consumers’ preferences, while minimizing depth in order to cut down on their post-holiday promotions and inventory dumping?  Or is the optimum strategy the opposite approach, focusing on creating a very deep selection of products in order to account for the expected increase in sales, to ensure customer satisfaction and loyalty when they are able to find the size, style, or color?

With both approaches there exists risks and rewards. With a leaner inventory, retailers run the risk of not satisfying some customers because they may not have the exact style, size, or model they desire. Decreases in customer satisfaction can lead to declining customer loyalty and repurchasing activity, turning ‘your customers’ back into “up-for-grabs” consumers. If the retailer wants to prevent this by having a deep assortment, they run the risk of leftover inventory, which will need to be discounted, increasing labor costs to clear the product.

So It’s Really About On Shelf Availability and Accuracy.
In the end, it’s not about whether there is too much or too little inventory, but it’s about having the right inventory at the right time.  The best way for retailers to manage this is by accurately measuring and controlling on shelf availability (OSA), so that customers are able to buy what they want, when they want it.  Improving same store sales can only be achieved when the right products are on the shelves at the right time.

With big data and predictive analytics retailers can identify patterns impacting OSA and ultimately affect the bottom line.  Pointed in the right direction, retail personnel can identify and resolve inventory management, resulting in increased same store sales.  The key using your data to ensure you have products in the right place to prevent the out of stock and overstocked discounts.

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1. 1. Omnichannel retail maturity will move from foundation to convergence, and from precision to immersion;
2. 2. Retailers omnichannel objectives will require platform & architecture investments;
3. 3. Retailers pivot merchandising and marketing on customer analytics to drive revenue and profit; relevance and reciprocity being the watchwords;
4. 4. Retailers will invest in customer analytics, merchandising, and marketing technologies to curate commerce and contextualize communications;
5. 5. The time is right to break down marketing silos;
6. 6. Marketing processes and infrastructures will align with the omnichannel business;
7. 7. Retailers will remove barriers and instead encourage the “stop start shopper;
8. 8. The convergence of web-based customer experience touch points to unify the customer journey;
9. 9. Retailers will optimize omnichannel customer service and cost by enabling trustworthy, efficient and effective supply chains; and
10. 10. Retailers will invest in technologies that enable visibility, visualization and virtualization.

It’s easy to agree with these predictions, especially when it comes to omnichannel and the goal of streamlined consumer-centricity efforts.  However, with the impending fiscal cliff and its aftermath on cost-conscious consumers, only the retailers that deliver a valuable shopping experience will rise above in 2013.  For retailers, the foundation of this effort is grounded in the ability to minimize the manual effort necessary to identify the controllable factors and provide actionable opportunities that when acted upon will have the greatest impact on future performance.  This ability to identify and improve the efficiency and effectiveness of your personnel, processes and systems is now available to you.

The retail business is increasingly competitive, this means placing even more importance on making the right decisions about things that will appeal to customers. In effect, the only way to deliver the desired shopping experience is by using predictive analytics that are linked to operational efficiency and effectiveness, produce actionable opportunities, and can handle a variety of data streams.  Thus allowing the retailers to see what needs to be done and know the most efficient and effective way to perform corrective actions.

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Retail is an example of an industry where things are always changing. Technological advancements are being implemented in every area of the retail business, and more solutions are being developed every day.

Data is available at every touchpoint with a consumer or vendor.  Retailers are now exploring different ways of utilizing this abundance of data to bring value to their customers. To utilize the power of big data, CIO’s must invest in new forms of analytical software, like predictive analytics or pattern seeking technology.

Using these technologies, patterns will emerge from the data, shedding light on the spending habits of target customers. Having extensive knowledge of the company’s target market is of huge importance to help mold a business model and product offering in a way that appeals to their tastes and preferences. These analytical capabilities enable the user to better-predict the future of their business, and be able to take preventative measures to make sure it is running as efficiently as possible.

In retail it is possible to analyze sales data to optimize the merchandising strategy to maximize the profitability potential of buying operations; automatically detect transactions at the POS with suspicious performance, showing additional training is necessary; and view inventory data to reduce out of stock and improve on shelf availability, to name a few capabilities. Having an entrepreneurial CIO who steps outside the traditional way of thinking has allowed many of today’s retailers to advance their operational efficiency to a new level throughout their value chain. However, for those who have yet to take a stab at this new way of thinking, if they do not engage soon they may find themselves falling by the wayside.

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The EBR re-engineering of cases is much like a police officer at an auto accident. It’s the starting point of both exercises. From the ‘accident’ or in EBR terms – the ‘result’; we work backwards to collect evidence and develop causes. The auto accident case development might be worn tires, driver history, auto reliability, or evidence at the scene. The retail re-engineering will involve finding previous incidents that point to what EBR found which can include CCTV evidence, manager notes, customer complaints, previous transaction details, etc.

The EBR re-engineering will require on average about 46 hours of time per case (Jack L. Hayes International “Annual Retail Theft Survey”) and relies heavily on analyst and investigator expertise; not to mention access to necessary data.

Suppose you didn’t start at the ‘auto accident’ but at the ‘clue level’ that leads to the accident, even before the accident occurs?

This is quite a different methodology and will require a new approach with different tools.
Pattern seeking technology is a way to identify what could cause an accident prior to the accident happening. When these clues are identified, they are scored as to the risk level contributing to a possible future accident. These could be interesting voids, coupon penetration out of range, lower basket price to peers and dozens of other clues. It doesn’t take too many of these clues with their risk score to accumulate and become noticeable and therefore actionable. The accident has yet to happen but the pattern has been established.

What is the value of the pattern seeking technology approach? Well for starters, you don’t experience the ‘accident’, the associated financial loss (which is never recovered), nor the investment of time and resources required to re-engineer the case. For perpetrators, you’ve successfully tilted the ‘risk vs. reward’ scale to the risk side, which acts as a deterrent to future culprits. Lastly, this technology is NOT just for fraud but for all forms of loss and operational inefficiency, meaning that you and your department can now show clear profit contribution to the company’s bottom line quicker than ever before.

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So, how does a retailer find the invisible sweetheart?

Most often free-bagging investigations are initiated as a result of a “hunch” or “tip”.  This hunch is then validated using CCTV footage and employee interrogations.  In the absence of a hunch or tip, retailers often write complicated exception queries analyzing time between transactions.  The results of these queries can lead to a high rate of false positives and a large amount of wasted time.  Some retailers have even invested in high tech computer vision analysis that requires an outsourced team to view and determine if the video movement is a credible free-bagging scenario.  All of this costs time and money and often leads the retailer on a wild goose chase seeking false positives.

The Profit Amplification Suite employs pattern seeking technology which is able to look at data, correlate that data in conjunction with other statistical measures, resulting in a true, valuable event that requires action.  The goal of pattern seeking technology is to evaluate and establish a loss value for each event to quantify its validity.  In this way, users of the product are able to spend their time resolving sweethearting losses and spend less time searching and validating the fraudulent activity.

Profit Amplification Finds Sweethearting In Grocery Chain

Recently a grocer, using Profitect’s Profit Amplification Suite, detected a correlation among a sales, scan rate and the unit values.  The pattern resulted in identification of collusion between a cashier and another third party.  As a result, not only did the customer prosecute the cashier involved, the pattern has been successfully applied across all data immediately to give them visibility to what was previously invisible.

Looking at different data and their relationship to each other to identify patterns is one way that Profit Amplification differentiates itself from the typical EBR and other business intelligence solutions.  Data alone is just numbers.  Profit Amplification finds and tells the story with the data that you couldn’t find with standard analytical tools.  So the question raised in the LPI blog is an important one — how much is this problem worth to you and your company?  Are you willing to spend a significant amount on a technology that may only lead you on a wild goose chase? Or will you implement a solution that uncovers and tells you the story, regardless whether or not you are looking for it.

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With BI, as ‘smart’ as it may be, there is still plenty of interpreting required to attain the knowledge you desire. With this approach, retailers must amass the data and search for a ‘conclusion’, then think backwards to identify the origin of the issue. With more data becoming available, this will increase the amount of work required to analyze the data using BI.

There exists a positive relationship between the amount of data available and the amount of work and resources required by a retailer when using BI. Many retailers use an ‘ETL’ process (Extract data from the operational system, Transform into relational data, Load into data warehouse), which takes even longer the more data you are extracting.

Luckily, there exists a technology that flips this relationship on its head. With pattern-seeking technology, the more data, the better. With more data points, the patterns, and therefore anomalies, are easier to spot. Pattern-seeking technology analyzes the data to identify ways in which the retailer can apply more profitable operations. The profit amplification solution, employing the pattern-seeking technology, and also provides dashboard management and data visualization in one easy-to-use suite.

Compared to BI, where you must search for a solution for something that may be wrong after it has already occurred, pattern-seeking technology identifies anomalies at the very early origins of profit loss activity, allowing employees to take preemptive action to stop the error before it has a true detrimental effect. Instead of searching for solutions, profit amplification also provides a correlated list of best practice opportunities for resolution of the issues. Where business intelligence falls short, pattern-seeking technology picks up the slack and the profits.

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Taxation is an important topic when it comes to showrooming and online retail.  In many states no sales tax is charged when purchased online.  This loophole not only means lost revenue for the retailer, but also lost revenue to the state, which can negatively affect the economy and jobs.

Retailers are also using pricing as a way to combat showrooming.  Making head-to-head price comparisons provides consumers with a satisfying shopping experience. Many retailers are now offering a low price guarantee and will match online prices to remain competitive.  Another obvious advantage of making a purchase in store is that the consumer can take ownership of the item immediately.

Exclusive merchandise is another way retailers can reduce showrooming.  If a product can only be purchased at a particular retailer, the consumer will have to go to the traditional brick-and-mortar location to get the product.  Target is banking on this concept to boost their holiday sales.

Consumer loyalty can be achieved inspite of showrooming.  If a retailer provides exceptional customer service they can change the mind of a consumer who initially came into the store for the showrooming aspect.  Face-to-face interaction can often have a larger impact than a small price differential.  Retailers must build on a foundation of relevant and high quality assortment, as well as supply reliable inventory levels ensuring the right product is on the right shelf at the right time.

These strategies will allow retailers to turn showrooming into positive traction.  Helping to convert consumers to buyers through better showrooming, efficiency, assortment and on shelf availability rather than fighting it.  This can all be achieved through profit amplification which employs pattern-seeking technology to constantly analyze business processes to make sure they are as efficient as possible.

This will assure profit margins do not decline due to the negative effects of pricing transparency, and will even provide retailers with opportunities to reduce costs and increase revenue, therefore increasing profitability at the bottom line. Analyzing data throughout the retail supply chain, pricing and product performance can be achieved from the store down to the SKU level thereby offsetting the negative impact that showrooming can have on profitability.

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